geoplanning vol 2, no 1, 2015, 30-37 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning | 30 open access evaluasi citra worldview-2 untuk pendugaan kedalaman perairan dangkal pulau kelapa-harapan menggunakan algoritma rasio band t.subarnoa, v.p.siregarb, s.b.agusc a program studi tekonologi kelautan_institut pertanian bogor, indonesia, email: allanawani@gmail.com b program studi tekonologi kelautan_institut pertanian bogor, indonesia, email: vingar56@yahoo.com c program studi tekonologi kelautan_institut pertanian bogor, indonesia, email: mycacul@gmail.com abstract: remote sensing technology is so advanced that recently produced satellite sensors with the capability to provide imagery options with very high spatial resolutions. one of the options is worldview-2 that has 1.84 meter of spatial resolution. besides, worldview-2 also has at least five bands on visible rays. the capability of remote sensing for underwater detection through specific depths and the availability of its bands on visible rays give more appropriate options to apply logarithm bands ratio on shallow water depth estimation. this research is aimed at analyzing the capability of worldview-2 imagery to estimate shallow water depth of kelapa-harapan islands by using bands ratio algorithm. there were six bands combination used in applying band 1 through band 4 of worldview-2 imagery. the results have shown that the best combination of bands to estimate the shallow water depth in the study area is the ratio between band 1 and band 3 with the r2 value of 0.067 and the average bias of 0.66 m. the ratio between band 1 and band 4 gave the value of r2 as big as 0.55 of its regression to the field depth samples. meanwhile, the other four bands combination ratios have shown very low correlations to the water depth in the field. © 2015 gjgp undip. all rights reserved. abstrak: perkembangan teknologi penginderaan jauh saat ini telah menghasilkan sensor satelit dengan kemampuan untuk menyediakan pilihan citra satelit dengan resolusi spasial sangat tinggi, diantaranya adalah citra worldview-2 dengan resolusi spasial 1,84 m. selain memiliki resolusi spasial sangat tinggi, citra worldview-2 juga memiliki setidaknya 5 band pada sinar tampak. kemampuan penginderaan jauh untuk mendeteksi kolom air hingga kedalaman tertentu dan tersedianya pilihan band pada sinar tampak memberikan cukup banyak pilihan untuk mengaplikasikan algoritma rasio band dalam menduga kedalaman suatu perairan. penelitian ini bermaksud mengkaji kemampuan citra worldview-2 untuk menduga kedalaman pada perairan pulau kelapa-harapan dengan menggunakan algoritma rasio band. sebanyak 6 kombinasi band digunakan dengan memanfaatkan band 1 sampai band 4 citra worldview-2. hasil kajian menunjukkan kombinasi band terbaik untuk menduga kedalaman perairan adalah rasio antara band 1 dan band 3 dengan nilai r 2 sebesar 0,67, dan rata-rata bias sebesar 0,66. rasio antara band 1 dan band 4 memberikan nilai r 2 sebesar 0,55 dari hasil regresi terhadap sampel kedalaman lapangan. sedangkan 4 kombinasi rasio band lainnya menunjukkan korelasi yang sangat rendah terhadap kedalaman lapangan. © 2015 gjgp undip. all rights reserved. 1. pendahuluan kemampuan cahaya untuk menembus kolom air hingga kedalaman tertentu menjadi keunggulan penginderaan jauh untuk melakukan studi pada kolom hingga dasar perairan dangkal. perkembangan tekonologi penginderaan jauh saat ini telah menghasilkan banyak jenis sensor satelit dengan kemampuan yang baik untuk mengkaji permukaan air dan mendeteksi kolom air hingga dasar perairan dangkal. beberapa jenis sensor telah tersedia dengan kemampuan membedakan objek secara spasial (resolusi info artikel; diterima: 30 maret 2015 hasil revisi : 10 april 2015 disetujui: 25 april 2015 publikasi on-line: 30 april 2015 kata kunci: citra wordview-2, rasio band, kedalaman, perairan dangkal article info; received: 30 march 2015 in revised form: 10 april 2015 accepted: 25 april 2015 available online: 30 april 2015 keywords: worldview-2 imagery, water depth, shallow water mailto:allanawani@gmail.com mailto:vingar56@yahoo.com mailto:mycacul@gmail.com geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 31 spasial) yang sangat tinggi, diantaranya adalah sensor satelit wordview-2 dengan resolusi spasial 1,85 m untuk citra multispektral dan 0,5 m untuk pankromatik. aplikasi teknologi penginderaan jauh di wilayah pesisir dan laut saat ini merupakan salah satu elemen kunci yang digunakan baik untuk keperluan penelitian maupun pengelolaan sumber daya dan lingkungan (kay et al. 2009; kuffner et al. 2007; friendlanders 2007). diantara aplikasi penginderaan jauh di wilayah perairan adalah untuk memetakan batimetri suatu perairan dangkal karena kemampuan citra satelit untuk mendeteksi kolom air. beberapa algoritma telah dikembangkan untuk mengestimasi kedalaman perairan dari citra satelit. stumpf et al. (2003) adalah salah satunya yang mengembangkan algoritma pendugaan kedalaman perairan dengan memanfaatkan rasio 2 buah band. pengembangan algoritma menggunakan rasio band tersebut dilakukan karena adanya perbedaan respon spketral kolom air dan dasar perairan terhadap gelombang elektromagnetik (gem) pada panjang gelombang yang berbeda (stumpf et al. 2003; loomis 2009). jenis dan jumlah material dalam kolom air berperan penting dalam penyerapan dan pemantulan gem yang mencapai dasar perairan dangkal. selain itu, jenis substrat pada dasar perairan dangkal juga turut andil dalam proses penyerapan dan pemantulan gem. penggunaan rasio band untuk menduga kedalaman diharapkan akan memberikan hasil dugaan kedalaman yang lebih akurat karena sifat perairan yang memberikan respon berbeda terhadap penetrasi gelombang elektromagnetik (gem) di dalam kolom air. sensor satelit memiliki band pada gelombang sinar tampak dengan rentang panjang gelombang yang berbeda-beda. sensor satelit worldview-2 memiliki 5 buah band pada rentang panjang gelombang sinar tampak, yaitu band coastal (1), blue (2), green (3), yellow (4), dan red (5). dari 5 band pada gelombang sinar tampak ini, setidaknya terdapat 10 pasang band yang berpotensi untuk digunakan menduga kedalaman dari citra worldview-2 menggunakan algoritma rasio 2 buah band. adanya sifat respon spektral yang berbeda-beda pada masing-masing band sensor satelit pada suatu perairan, memungkinkan untuk mencari kombinasi band terbaik dalam mengestimasi kedalaman dan memetakan batimetri pada perairan pulau kelapa dan sekitarnya. perairan kepulau seribu secara umum memiliki nilai kecerahan 9,5 m (susilo 2007). dengan nilai kecerahan ini, berarti bahwa gem sangat sulit untuk menembus kolom air lebih dalam, terutama pada panjang gelombang merah. jika band merah tidak digunakan karena keterbatasan tersebut, masih terdapat empat band worldview-2 (coastal, blue, green, dan yellow) dengan 6 pasangan kombinasi rasio band yang dapat digunakan untuk menduga kedalaman yaitu coastal/blue, coastal/green, coastal/yellow, blue/green, blue/yellow, dan green/yellow. penelitian ini bermaksud mengkaji potensi dari 4 buah band tersebut dan menentukan pasangan band terbaik untuk menduga kedalaman pada perairan pulau kelapa dengan menggunakan algoritma rasio band. 2. data dan metode gambar 1. lokasi kajian pada wilayah perairan pulau kelapa-harapan (citra satelit, 2014) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 32 lokasi kajian yaitu perairan pulau kelapa dan harapan, kedua pulau ini masuk dalam kawasan taman nasional laut kepulauan seribu dan termasuk dalam wilayah administrasi kecamatan kepulauan seribu utara, kabupaten kepulauan seribu. data yang digunakan dalam kajian ini adalah citra worldview-2, data hasil sounding di lapangan, dan data pasang surut. citra worldview-2 yang digunakan diperoleh dari digitalglobe dengan akuisisi tanggal 5 oktober 2013. sensor worldview-2 dilengkapi 9 band pankromatik (632, 2 nm), multispectral coastal (427,3nm), blue (477,9 nm), green (546,2 nm), yellow (607,8 nm), red (658,8 nm), red edge (723,7 nm), dan inframerah dekat nir 1 (831,3 nm) dan nir 2 (908,0 nm). data lapangan berupa data sounding yang telah dikoreksi pasang surut dan kemudian digunakan sebagai sampel regresi terhadap hasil rasio band untuk dikalibrasi ke kedalaman aktual yang diestimasi dari citra. citra worldview-2 yang digunakan sebelum dilakukan proses lebih lanjut terlebih dahulu dilakukan masking untuk menutup wilayah daratan dan koreksi radiometrik untuk mengurangi distorsi radiometrik pada citra. masking dilakukan dengan memanfaatkan band 8 (nir-2), pemilihan band ini dikarenakan respon spektral antara darat dan laut pada band nir-2 sangat kontras sehingga cukup baik digunakan untuk membedakan antara darat dan laut. citra worldview-2 yang telah dimasking dan dikoreksi radiometrik selanjutnya dikonversi dari informasi nilai digital/digital number (dn) pada setiap piksel menjadi nilai reflektansi. konversi nilai digital citra menjadi nilai reflektansi dilakukan melalui 2 tahap (loomis, 2009; madden, 2011). tahap pertama yaitu mengkonversi dn menjadi radiansi (spectral radiance) dengan persamaan (digital globe, 2010) : = ........................... (1) dimana adalah nilai radiansi piksel citra (w-m-2 –sr-1 -µm-1) kband adalah nilai absolut faktor kalibrasi masing-masing band (w-m-2–sr-1-count-1), qpixel adalah nilai digital piksel pada masing-masing band, dan ∆ band lebar efektif masing-masing band. nilai radiansi selanjutnya dikonversi menjadi nilai reflektansi dengan persamaan (digital globe, 2010) : = ...................... (2) dimana adalah nilai rata-rata reflektansi masing-masing band, adalah nilai radiansi piksel citra, des 2 adalah jarak bumi-matahari pada saat perekaman citra, adalah nilai solar irradiance masing-masing band, dan θ adalah solar zenith angle. selanjutnya dilakukan estimasi kedalaman perairan melalui citra worldview-2 dengan menggunakan algoritma yang dikembangkan oleh stumpf et al. (2003), yang ditulis : z = m1 ............................................... (3) dimana z = kedalaman estimasi, m1 = koefisien kalibrasi masing-masing band, rw( = nilai reflektansi piksel pada setiap band, m0 = faktor koreksi untuk kedalaman 0, dan n = konstanta untuk menjaga rasio tetap positif. koefisien m1 dan m0 masing-masing diperoleh dari hasil regresi rasio band terhadap kedalaman lapangan. dengan demikian untuk memperoleh kedalaman duga dari hasil regresi rasio nilai-nilai reflektansi pada masing-masing band yang digunakan, persamaan (3) dapat ditulis ulang berdasarkan persamaan regresi linear menjadi (madden 2011): y = ax + b ...................................................................... (4) dimana nilai slope (a) mewakili koefisien m1, nilai intercept (b) mewakili koefisien m0, dan nilai x mewakili hasil rasio nilai reflektansi pada band yang digunakan. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 33 3. hasil dan pembahasan 3.1. estimasi kedalaman sampel data kedalaman lapangan yang digunakan dalam mengestimasi kedalaman perairan wilayah kajian melalui citra worldview-2 adalah sebanyak 130 titik yang tersebar mewakili setiap tingkat kedalaman. data lapangan ini diperoleh melalui hasil sounding yang dilakukan pada tanggal 1819 maret 2015. data kedalaman lapangan yang digunakan telah dikoreksi pasang surut menggunakan data pasut hasil pengukuran pada stasiun pulau panggang (wilayah kepulauan seribu bagian selatan). algoritma rasio band memerlukan masukan nilai-nilai piksel pada setiap band dalam bentuk nilai reflektansi. setelah pengolahan awal citra, pada kajian ini nilai-nilai digital setiap piksel dikonversi terlebih dahulu menjadi nilai reflektansi mengikuti prosedur dan menggunakan algoritma yang disediakan oleh digitalglobe (2010). perhitungan nilai rasio band dilakukan dengan menjadikan band dengan panjang gelombang lebih pendek sebagai pembilang dan band dengan panjang gelombang lebih panjang sebagai penyebut (stumpf et al 2003; loomis 2009). dari hasil pengujian 6 kombinasi band yang digunakan dalam kajian ini melalui regresi dengan sampel data lapangan, diperoleh hasil yang berbeda-beda (lihat pada gambar 2). gambar 2. hasil regresi rasio band terhadap sampel kedalaman lapangan, a) rasio b1/b2; b) rasio b1/b3; c) rasio b1/b4; d) rasio b2/b3; e) rasio b2/b4; dan f) rasio b3/b4 (analisis, 2014) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 34 hasil regresi 6 kombinasi rasio nilai reflektansi band terhadap data kedalaman lapangan terlihat bahwa hanya ada dua kombinasi band yang memiliki korelasi cukup baik dengan sampel kedalaman yang digunakan yakni rasio antara band 1 dengan band 3 (dengan nilai koefisien determinasi r2 = 0,67) dan rasio antara band 1 dengan band 4 (r2 = 0,55) sebagaimana terlihat pada gambar 2b dan c. adapun 4 kombinasi band lainnya memberikan korelasi yang sangat lemah terhadap data lapangan. hal ini diduga disebabkan oleh sampel data kedalaman lapangan yang diambil kurang mewakili sebaran secara spasial dari wilayah kajian. walaupun sampel kedalaman lapangan mewakili sebaran kedalaman, mungkin akan memberikan hasil yang lebih baik jika sebarannya mewakili seluruh wilayah kajian. hal ini agar sifat perairan yang memberikan respon yang berbeda terhadap gem dapat terwakili dari seluruh wilayah kajian. secara umum, kemampuan penetrasi gem pada panjang gelombang lebih panjang, lebih rendah seiring bertambahnya kedalaman dibanding pada panjang gelombang lebih pendek, hal ini akan menghasilkan nilai rasio antara reflektansi pada band-band yang dirasiokan akan semakin meningkat seiring bertambahnya kedalaman. hal menarik dari hasil regresi rasio band terhadap data lapangan adalah nilai rasio band berbanding terbalik dengan kedalaman lapangan. hal ini menunjukkan bahwa besarnya reflektansi pada masing-masing band di lokasi kajian berdasarkan pertambahan kedalaman tidak memberikan pola yang teratur seiring bertambahnya kedalaman. berbeda dengan hasil regresi yang dilakukan oleh loomis (2009) pada rasio band citra quickbird yang menunjukkan perbandingan lurus dengan sampel data kedalaman lapangan. hal ini disebabkan oleh perbedaan jumlah dan jenis material dalam kolom air, serta jenis substrat yang dominan pada dasar perairan sehingga memberikan respon yang berbeda-beda pada setiap panjang gelombang. pada gambar 3 disajikan pola reflektansi pada 4 band yang digunakan dalam kajian ini berdasarkan garis sampling yang diambil tegak lurus ke arah perairan yang lebih dalam (a ke b). pada gambar 3b terlihat reflektansi band 2 dan band 3 pada perairan yang lebih dangkal memiliki nilai yang lebih tinggi dari band 1 dan band 4. band 1 dan band 4 lebih banyak terserap pada wilayah dangkal, substrat pasir yang dominan menutupi dasar perairan yang lebih dangkal menghasilkan nilai reflektasni yang lebih besar pada band 2 dan band 3. akan tetapi band 1 mengalamai penyerapan yang lebih lambat dengan bertambahnya kedalaman. hal ini terlihat dari pola nilai reflektansi pada 4 band (gambar 3b) dimana pada band 2 dan 3 cenderung lebih cepat menurun dibandingkan pada band 1. kemampuan gem pada panjang gelombang lebih pendek untuk melakukan penetrasi pada kolom air yang lebih dalam adalah alasan mengapa reflektansi pada band 1 di wilayah kajian ini cenderung lebih lambat menurun seiring bertambahnya kedalaman dibandingkan band 2 dan band 3. gambar 3. pola reflektansi spektral 4 band dari daerah dangkal ke daerah lebih dalam (analisis, 2014) titik sampling reflektan si geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 35 3.2. akurasi hasil estimasi merujuk pada nilai r2 dari hasil regresi yang dilakukan pada kombinasi band terhadap data lapangan, persamaan yang digunakan untuk mengkalibrasi hasil rasio band menjadi kedalaman estimasi adalah persamaan yang dihasilkan dari rasio band 1 dengan band 3 dan rasio antara band 1 dengan band 4. untuk mengetahui besarnya bias yang terjadi antara kedalaman estimasi melalui citra dan kedalaman lapangan, dilakukan pengujian terhadap citra kedalaman. sebanyak 40 titik sampel kedalaman lapangan digunakan untuk mengekstraksi nilai-nilai piksel pada citra worldview-2 hasil transformasi kedalalaman. selisih antara kedalaman duga dan kedalaman lapangan bervariasi mulai dari nilai terkecil 0,0029 m pada hasil transformasi menggunakan rasio band1/band3 dan 0,0059 m pada hasil transformasi band1/band4, sedangkan untuk selisih nilai tertinggi yaitu 4,687 m pada rasio band1/band3 dan 8,739 m pada hasil transformasi rasi band1/band4 (selengkapnya dapat dilihat pada tabel 1). tabel 1. sampel kedalaman lapangan dan kedalaman estimasi serta selisihnya (analisis, 2014) no kedalaman (m) bias (m) no kedalaman (m) bias (m) lap. b1/b3 b1/b4 b1/b3 b1/b4 lap. b1/b3 b1/b4 b1/b3 b1/b4 1 1.6 1.4347 1.5406 0.16534 0.05938 21 6.5 5.6924 8.761 0.80756 2.26098 2 2 2.0029 4.8363 0.00292 2.83632 22 7 5.0817 7.7539 1.91832 0.75391 3 2.1 2.5381 2.5967 0.43813 0.49671 23 7.1 5.0817 7.7539 2.01832 0.65391 4 2.2 2.0029 4.8363 0.19708 2.63632 24 7.2 7.7748 11.345 0.57475 4.1447 5 2.3 2.5381 2.5967 0.23813 0.29671 25 7.3 6.1806 0.8287 1.11944 6.47131 6 2.5 2.5381 2.5967 0.03813 0.09671 26 7.4 7.1024 7.5516 0.29765 0.15159 7 2.6 1.9118 0.6947 0.68821 1.90534 27 7.5 7.8854 11.218 0.38542 3.7183 8 3.1 3.2516 1.4135 0.15155 1.68655 28 8 7.2472 10.59 0.7528 2.5896 9 3.4 1.8982 5.345 1.50181 1.94502 29 9 8.7126 11.804 0.28739 2.8037 10 3.7 3.303 8.3949 0.39698 4.69488 30 10.6 5.913 9.6581 4.68697 0.94189 11 3.8 3.0967 2.1476 0.70326 1.65241 31 11.3 11.2 10.542 0.1003 0.7578 12 4.1 4.8103 4.3482 0.71026 0.24815 32 13.6 13.52 12.128 0.08 1.4717 13 4.2 5.6924 8.761 1.49244 4.56098 33 14 11.369 10.221 2.631 3.7788 14 4.6 5.3632 6.1943 0.76317 1.59432 34 14.8 16.203 12.947 1.4029 1.8534 15 4.7 4.771 7.07 0.07095 2.36998 35 16.8 16.892 17.628 0.092 0.8284 16 5.1 5.6924 8.761 0.59244 3.66098 36 18.6 15.042 14.986 3.5583 3.6137 17 5.2 4.8817 11.991 0.3183 6.7913 37 19.5 16.066 12.268 3.4339 7.232 18 5.7 7.4429 6.886 1.74286 1.18601 38 20.4 17.559 18.131 2.8409 2.2693 19 6 8.1734 9.1973 2.1734 3.19734 39 22.8 19.311 15.715 3.4889 7.0855 20 6.4 6.8334 9.5211 0.43339 3.12113 40 23.2 19.592 14.46 3.6082 8.7398 rata-rata bias (m) 0.64094 2.25183 dari tabel 1 terlihat rata-rata bias (selisih) antara kedalaman hasil transformasi citra dan kedalaman lapangan untuk rasio band1/band3 cukup kecil yaitu hanya 0,6049 m, dan bias yang banyak terjadi yaitu pada kedalaman diatas 13 m. hal ini menunjukkan bahwa kombinasi band 1/band3 pada perairan ini cukup baik digunakan hanya untuk kedalaman dibawah 13 m. pada gambar 3b terlihat bahwa pola reflektansi dari band 1 dan band 3 cukup kontras pada perairan yang lebih dangkal, namun cenderung memiliki nilai reflektansi hampir sama pada perairan yang lebih dalam. band 1 (coastal) pada citra worldview 2 berada pada rentang panjang gelombang yang lebih pendek sehingga diharapkan memiliki kemampuan penetrasi kedalaman yang lebih besar dari band-band lainnya (digitalglobe 2010). pada kajian ini terlihat bahwa band 1 justru memiliki nilai reflektansi yang lebih rendah dari band 2 dan band 3 baik pada perairan dangkal maupun perairan yang lebih dalam, akan geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 36 tetapi nilai reflektansi band 2 dan band 3 cenderung lebih cepat menurun seiring bertambahnya kedalaman (gambar 3b). substrat pasir yang dominan pada perairan dangkal diduga menjadi faktor yang sangat berpengaruh pada reflektansi dari kolom air sehingga memberikan nilai yang lebih tinggi pada band 2 dan band 3 pada perairan yang lebih dangkal. sifat respon spektral yang berbeda pada jenis substrat yang berbeda diduga menjadi penyebab utama rendahnya korelasi antara rasio band terhadap kedalaman lapangan sehingga menyebabkan besarnya bias antara kedalaman duga dan kedalaman lapangan pada kajian ini. dasar perairan yang didominasi oleh pasir akan memberikan nilai reflektansi yang lebih besar dari dasar perairan yang banyak terdapat karang dan lamun. algoritma rasio band memanfaatkan kombinasi 2 buah band yang berbeda dengan harapan akan memberikan hasil estimasi kedalaman yang lebih akurat hingga pada perairan yang memiliki beragam jenis substrat (stumpf et al 2003), asumsi ini dibangun berdasarkan sifat respon spektral yang berbeda pada masing-masing band berdasarkan jenis substrat dasar. sehingga dengan memanfaatkan rasio band, diharapkan akan mewakili variasi respon spektral pada masing-masing tipe substrat dasar. loomis (2009) mampu meningkatkan akurasi hasil pendugaan kedalaman perairan menggunakan algoritma rasio band berdasarkan pemisahan tipe substrat dasar perairan. kajian yang dilakukan olah siregar dan selamat (2010) dalam mengevaluasi citra quickbird untuk memetakan batimetri pada gobah karang lebar dan pulau panggang menggunakan algoritma berdasarkan zona penetrasi kedalaman (zpk) menemukan bahwa algoritma tersebut tidak konsisten memberikan hasil pendugaan yang akurat pada wilayah kajian. adanya perbedaan kualitas dan tipe perairan menjadi penyebab ketidak konsistenan algoritma tersebut. penyebab lain besarnya bias dan rendahnya korelasi antara rasio band pada citra worldview-2 terhadap kedalaman lapangan yaitu diduga adanya distorsi dari gps pada perangkat sounding yang digunakan di lapangan. tingginya resolusi spasial pada citra worldview-2 menjadikan beragamnya informasi yang terdapat pada suatu luasan sempit berdasarkan nilai reflektansi pada masing-masing piksel pada luasan tersebut. untuk meningkatkan akurasi pendugaan kedalaman, diperlukan proses lebih lanjut untuk mencocokkan posisi antara gps yang digunakan pada suatu titik di lapangan terhadap piksel yang mewakili titik tersebut. pada gambar 4 terlihat daerah yang banyak terdapat bias dalam estimasi kedalaman pada citra yaitu pada bagian selatan perairan, baik pada rasio band 1 dan band 3 maupun band 1 dan band 4. hal ini kemungkinan disebabkan kurangnya sampel data kedalaman lapangan yang diambil mewakili wilayah ini pada saat melakukan regresi terhadap hasil rasio band. gambar 4. citra kedalaman perairan lokasi studi, a) hasil transformasi rasio band1/band 3; dan b) hasil transformasi rasio band 1/band 4 (analisis, 2014) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 30-37 subarno et al. | 37 4. kesimpulan beberapa hal yang dapat disimpulkan berdasarkan hasil penelitian ini yaitu, 1) dari 6 kombinasi band yang digunakan untuk mengestimasi kedalaman pada perairan dangkal pulau kelapa-harapan, kombinasi band terbaik dengan nilai rata-rata error terkecil adalah rasio antara band 1 dan band 3. 2) kedalaman maksimum yang dapat diestimasi dengan baik dari citra worldview-2 menggunakan algoritma rasio band adalah 13 m. 3) selisih antara kedalaman duga dan kedalaman lapangan kemungkinan dapat diperkecil dengan memisahkan proses estimasi kedalaman berdasarkan jenis substrat dasar. 5. daftar pustaka digital globe. (2010). radiometric use of worldview-2 imagery. technical note. prepared by : todd updike, chris comp. doxani g, papadopulou m, lafaxani p, tsakiri-strati m. (2012). shallow-water bathymetry over variable bottom types using multispectral worldview-2 image. international archives of the photogrammetry, remote sensing and spatial information sciences, 39(8). friedlander am., brown ek., and monaco me. (2007). coupling ecology and gis to evaluate efficacy of marine protected areas in hawaii. ecological applications, 17(3):715-730. kay s., hedley jd. and lavender s. (2009). sun glint correction of high and low spatial resolution images of aquatec scenes : a review of method for visible and near-infrared wavelengths. remote sens, 1:697730. kuffner ib., brock jc., grober-dunsmore r., bonito be., hickey td., and wright cw. (2007). relationship between reef fish communities and remotely sensed rugosity measurements in biscayne national park, florida, usa. environ biol fish, vol. 78:71-78. loomis mj. (2009). depth derivation from the worldview-2 satellite using hyperspectral imagery. naval postgraduate school. madden ck. contributions to remote sensing of shallow water depth with the worldview-2 yellow band. naval postgraduate school. siregar vp, selamat mb. (2010). evaluasi citra quickbird untuk pemetaan batimetri gobah dengan menggunakan data perum: studi kasus gobah karang lebar dan pulau panggang. ilmu kelautan, undip stumpf rp, holderied k, and sinclair m. (2003). determination of water depth with high-resolution satellite imagery over variable bottom types. applied optics, vol. 28(8):547-556. susilo sb. (2007). analisis keberlanjutan pembangunan pulau-pulau kecil: pendekatan model ekologiekonomi. jurnal ilmu-ilmu perairan dan perikanan indonesia, vol. 14(1):29-35. sdb using random forest | 117 geoplanning vol 3, no. 2, 2016, 117-126 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.2.117-126 satellite-derived bathymetry using random forest algorithm and worldview-2 imagery m. d. m. manessa a, b, a. kanno a, m. sekine a, m. haidar c, k. yamamoto a, t. imai a, t. higuchi a a graduate school of science and engineering, yamaguchi university, ube, japan b center for remote sensing and ocean science (cresos), udayana university, indonesia c center for thematic mapping and integration, geospatial information agency, indonesia abstract: in empirical approach, the satellite-derived bathymetry (sdb) is usually derived from a linear regression. however, the depth variable in surface reflectance has a more complex relation. in this paper, a methodology was introduced using a nonlinear regression of random forest (rf) algorithm for sdb in shallow coral reef water. worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in rf and then compared with conventional methods of multiple linear regression (mlr) at ten times cross validation. moreover, the performance of each possible pair from six visible bands was also tested. then, the estimated depth from two methods and each possible pairs were evaluated in two sites in indonesia: gili mantra island and panggang island, using the measured bathymetry data. as a result, for the case of all bands used the rf in compared with mlr showed better fitting ensemble, -0.14 and -1.27m of rmse and 0.16 and 0.47 of r2 improvement for gili mantra islands and panggang island, respectively. therefore, the rf algorithm demonstrated better performance and accuracy compared with the conventional method. while for best pair identification, all bands pair wound did not give the best result. surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy. copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): manessa, m. d. m, et al. (2016). satellite-derived bathymetry using random forest algorithm and worldview-2 imagery. geoplanning: journal of geomatics and planning, 3(2), 117-126. doi:10.14710/geoplanning.3.2.117-126 1. introduction due to the limitation of costly and time-consuming bathymetry measurement, getting a dense or full coverage water depth data is difficult to achieve. satellite derived bathymetry (sdb) is useful to efficiently densify (depend on image spatial resolution) the information of water depth in shallow water areas. in the case of a multispectral image, there are simple and applicable sdb methods such as linear regression of reflectance logarithm (lyzenga, malinas, & tanis, 2006; lyzenga, 1978; paredes & spero, 1983), the linear ratio (stumpf, holderied, & sinclair, 2003), and depth of penetration zone (jupp, 1988). at the current state, the linear regression is the most common and widely used sdb (flener et al., 2012; kanno & tanaka, 2012; liceaga-correa & euan-avila, 2002; yuzugullu & aksoy, 2014). unfortunately, stumpf et al. (2003) showed a low accuracy estimation of sdb method using linear regression. the problem might be caused by the assumption of a linear relation between depth and water surface reflectance that sometimes does not hold. especially when the following conditions exist: noisy satellite image, the dark bottom object in shallow water or vice versa, and high water attenuation. nonlinear regression approach seems promising to improve the sdb accuracy. the non-linear regression of random forest (rf) algorithm shows a good performance in estimating a variable with non-linear condition (knudby et al., 2013). open access article info: received: 11 august 2016 in revised form: 18 september 2016 accepted: 19 september 2016 available online: 31 october 2016 keywords: satellite-derived bathymetry, worldview-2, random forest, multiple linear regression corresponding author: masita dwi mandini manessa graduate school of science and engineering, yamaguchi university, ube, japan email: masita@yucivil.onmicrosoft.com http://dx.doi.org/10.14710/geoplanning.3.2.117-126 http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 118 | the multispectral image used in this study is the high spatial resolution image of worldview-2 (wv-2) imagery. several studies, for instances kerr (2011), lee et al. (2011), doxani et al. (2012), and eugenio, marcello, & martin (2015), have been carried out on sdb using wv-2 imagery. since wv-2 imagery has six visible bands then choosing the right band combination for sdb was also important. kerr (2011) has revealed the best pair of wv-2 bands for the combination of linear ratio and mlr method. however, identifying the best pair for the linear reflectance and random forest algorithm for sbd was never done before. this study examined the usage of rf algorithm for sdb. as a comparison, the confessional sdb method using multiple linear regression (mlr) algorithms for multiple bands were analyzed. the accuracy change between both algorithms shows the performance of linear and nonlinear regression for sdb. additionally, 63 possible pairs from six visible bands of the wv-2 image was equally tested as an input for rf algorithm to identify the best pair band for sdb using rf algorithm. 2. data and methods 2.1. study sites as shown in figure 1, the study site was a shallow coral reef environment located in indonesia. the gili mantra islands is off the coast of lombok island, and the panggang island is at north part of jakarta coast. the gili matra islands are a marine natural park including three islands: gili trawangan, gili meno, and gili air. both sites are coral reef environment with clear water and good visibility. figure 1. a. panggang island worldview2 true-colour image (rgb 532); and b. gili mantra islands worldview2 true-colour image (rgb 532) (digital globe, 2012) 2.2. depth measurement data the bathymetry data of the gili islands were resulted from a collaborative effort between cresos (center for remote sensing and ocean science, udayana university—jaxa program), yamaguchi university, and the research institute for marine research and observation (ministry of marine affairs and fisheries republic of indonesia). while for panggang island, the data were measured by center for thematic mapping and integration, geospatial information agency of indonesia, indonesia. for both sites, the measurement was carried out using a single beam echo sounder and differential global positioning system (d-gps). since the depth measurement data was tide affected, the measurement depth was necessarily referred to mean sea levels (msl). the converter was done by subtracting the measurement depth with at time tide. moreover, the tidal data were collected from nearest tidal station. 2.3. image acquisition and processing in this study, the level 2 radiometric corrected of worldview-2 imagery with six visible bands and two near-infrared bands were utilized. the imagery passed three steps of image pre-processing. the first step was sensor calibration from digital numbers to the units of band-averaged spectral reflectance or toa (top of atmosphere) reflectance. the equations and calibration coefficients applied were based on the digital globe technical note about the radiometric use of worldview-2 imagery (digital globe, 2012). the physical a b http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 | 119 units of band-averaged spectral radiance are w∙m−2∙sr−1∙µm−1. secondly, the atmospheric and surface noise then toa reflectance were corrected by applying lyzenga et al.’s (2006) correction. then, the formula of lyzenga et al.’s (2006) atmospheric correction is written as: [1] where is the measured toa reflectance in nir 1 band, is that average over the deep water pixels, and is the slope of the simple regression line between the visible reflectance and nir 1 reflectance for the deep-water pixels. while is for nir 2. lastly, the relationship between radiance and depth was linearized to create the transformed reflectance ( ). based on lyzenga et al. (2006), the transformed reflectance ( ) is a linear value of reflectance and depth and written as: [2] where is the mean of surface reflectance deep water area for each band i. the for six visible bands are used as on input for mlr and rf regression model. 2.4. prediction models 2.4.1. random forest (rf) random forests for nonlinear regression are formed by growing trees depending on a random vector such that the tree predictor takes on numerical values as opposed to class labels (breiman, 2001). this nonlinear regression is a machine learning approach that belongs to the family of decision tree learning (breiman, 2001). the goal of decision tree learning is to create a model that predicts the value of a target variable based on several input variables (diesing et al., 2014). for estimating the water depth, the “random forest” function from random forest package of r software was used. 2.4.2. multiple linear regression (mlr) the multiple linear regression is (lyzenga et al., 2006). the mlr analysis was conducted to depth as the dependent variable and the as the independent variables. then depth estimation formula for worldview-2 imagery with six visible bands is as follows: [3] where β0 is offset, βi is determined by a linear regression analysis using a set of depths measured with the linearized surface reflectance and n is a number of the band. in the case of mlr analysis, the water depth estimation is predicted using the “lm” function from basic package in r software. 2.5. band pair or combination since wv2 have six visible bands, 63 different pairs (= 6 combinations one band + 15 combinations of two bands + 20 combinations of three bands + 15 combinations of four bands + 6 combinations of five bands + 1 combination six bands) could be used as an input for eq. 3. we tested the performance of each pair to estimate the water depth using rf formula (see figure 2). http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 120 | figure 2. worldview-2 image: a. panggang islands image before correction, b. panggang islands image after correction, c. gili mantra islands image before correction, d. gili mantra islands image after correction. (analysis, 2016) a b c d http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 | 121 2.6. cross validation in order to further test the efficiency of each model, a cross-validation experiment was performed. in each round, the depth measured points were randomly selected, with 10% used as training data and 90% as test data. this cross-validation procedure was repeated 100 times. for each run, the model prediction accuracy were evaluated using two statistical key that explain in subsection 2.7. 2.7. model validation the depth estimation accuracy of each model is measured by: [4] [5] where h is measurement depth, is estimated depth, is the mean of depth measurement value, and n is the number of input data. 3. results and discussion 3.1. image correction lyzenga et al.’s (2006) image correction was applied to the evaluated worldview-2 images of gili mantra islands and panggang island. the nir band was used to remove the noise from sea surface and atmospheric. since the worldview-2 have two nir bands, both of them bands was used in this study as shown in equation 1. figure 2 shows the image of before and after correction. besides correcting the image noise, this method also masks the depth and land areas, as shown in figure 2b for panggang island and figure 2d for gili mantra islands. figure 3. scatterplot of estimate depth versus actual depth for rf and mlr algorithm in gili mantra island and panggang island. the red line axis is line of y = x. (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 122 | figure 4. graph of statistic value of rmse and r2 for rf and mlr algorithm in gili mantra island and panggang island (analysis, 2016) 3.2. random forest (rf) algorithm for sdb figure 3 shows the result of the sdb performance for the rf compared with mlr algorithm in gili mantra islands and panggang islands. in general, the estimation of depth shows better fitting in the shallow area with depth less than ± 7 m then gradually increase following the increse water depth. the higher error in deeper depth mainly occurred because of high noise in the deeper water generated by the high absorption and scattering of light, and as explain by stumpf et al. (2003). in figure 4, a statistical evaluation was carried out to qualitatively measure the performance of rf in compared with mlr algorithm. as a result, the error created from rf algorithm is smaller than mlr algorithm for the both evaluated sites. the rf algorithm for depth estimation accuracy is more accurate with less error with -0.14 and -1.27 m of lower rmse and better fitting of 0.16 and 0.47 of r2 improvement for gili mantra islands and panggang island, respectively. this result has further strengthened our conviction that in some case the depth had a complex’s relation with reflectance. theoretically, the relation between depths and linearize surface reflectance should be linear but a noise could cause a non-linear condition (lyzenga, 1978). referring to the gili mantra islands and panggang islands site, the plotted value (figure 5) between depths and linearize surface reflectance shows unlinearity relations, where the relationship became scatter following the increases depth. the scatters relation in the deeper water areas because of reflectance or radiance received by multispectral satellite contain higher percentage of noise than bottom reflectance information due to high absorption and scattering. then, it has limited the maximum detected depth of multispectral sdb. this un-linear relation factor is potentially responsible for this different performance in both sites. moreover, in panggang island, the pixels in different depth had almost the same value that might be caused by dark object in shallow depth or bright object in deeper depth. this fact became the main reason of poor estimation in panggang island. http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 | 123 figure 5. the relation between depth and linearize surface reflectance for each band in gili mantra island and panggang island (analysis, 2016) 3.3. best combination bands of random forest (rf) algorithm for sdb figure 6 shows the performance of rf algorithm under 63 possible combinations of worldview-2 bands for both sites. as a result, the rank of the best pair is varied between gili mantra islands and panggang island. thus, this result needs to be interpreted with caution as a following. firstly, the sdb accuracy ranges between 0.746 – 1.779 m and 0.350 – 0.874 for rmse and r2, respectively, in the case of gili mantra island. while for panggang islands, the accuracy varies between 1.747 – 3.712 and 0.354 – 0.858 for rmse and r2, respectively. secondly, in contrast with the previous study (kerr, 2011), the best performance of rf was not given by the usage of all the visible bands. in the case of gili mantra island, the best accuracy was achieved when four bands of coral, green, yellow and red (14.cgyr) were used. while panggang island site shows the best accuracy using three bands of green, yellow and red (39.gyr). although six visible bands of wv-2 were expected to estimate the depth accurately, it was not predicted that the six bands would also give the best accuracy compared with less number of bands as an input. this is not particularly unexpected considering that some bands contain more noise or less bottom reflectance information than other, such as red edge bands having less bottom reflection information especially in the deeper depth due to high absorption value. interestingly, for both evaluated sites the usage of band green, yellow, and red shows better estimation accuracy. meanwhile, the other bands namely coastal, blue, and rededge had a tendency to give the adverse effect of poor estimation accuracy. as shown in figure 5, even though the short wavelength namely coastal and blue band are sensitive to water depth and penetrate into the deeper water, the high noise is also included. this problem is an issue of wv-2 coastal band that claims to be useful for shallowwater mapping. http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 124 | figure 6. graph of sdb estimation accuracy of rf algorithm of 63 combination bands. (left) gili mantra island and (right) panggang islands (analysis, 2016) the color bar is the rmsr in meter, and black dot is r2. example: 01.cbygrred is combination number 1 consisting of coral, blue, yellow, green, red, and red edge bands, for gili islands the rmsr is 0.8 m and r2 is 0.9. http://dx.doi.org/10.14710/geoplanning.3.2.117-126 manessa et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 117-126 doi: 10.14710/geoplanning.3.2.117-126 | 125 it is worth noting that this study has a few limitations. the first is a time gap between measurement and image recording dates; some morphological change might appear in the shallow water areas. the second is error created from measurement instrument; the single beam echo sounder generates error caused by inaccurate of average sound speed measurement, especially due to rapid movement of the boat or extreme morphological changes. then, the measurement data on deeper depth will tend to have a high error. thirdly, in the application of random forest algorithm, three hyper-parameters, i.e., mtry, sampling size, and node size should be optimized. moreover, the random forest function (random forest package of r software) used in this study has an auto-tuning capability for mtry, but it does not consistently work well. further works need to be performed to do the manual optimization. 4. conclusion in this study, non-linear model of random forest (rf) regression was tested to estimate the water depth of shallow coral reef. also, the linear model of multiple linear regression (mlr) was used for comparison. a cross-validation test comparing the accuracy of the both algorithms was performed for two coral reef sites of gili mantra islands and panggang island using worldview-2 (wv-2) imagery and corresponding insitu depth measurements. considering the six visible bands of wv-2 images, 63 possible pairs were evaluated to identify the best pair as an input in estimating the water depth using random forest algorithm. the result of this study indicates that the nonlinear regression (rf) performed better than linear regression (mlr) in estimating the water depth. the rf regression is suitable for multispectral-based sdb, especially, when the relation between depth and linearized reflectance was far from linear due to the noisy image. moreover, the best rf model for sdb was set when green, yellow, and red bands have been utilized. this study only tested two sites with water in clear visibility. subsequently, the rf performance for the different type of water might show a different result. further studies, which take more sites into account, are suggested. 5. acknowledgments the authors acknowledge support from the indonesia endowment fund for education (lpdp) by the ministry of finance. we would like to thank the geospatial information agency of indonesia for providing the gili mantra islands’ as well as panggang island’s bathymetry and tidal data. this research was partially supported by a research grant of kurita water and environment foundation (no. 15b013). 6. references breiman, l. 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(2014). generation of the bathymetry of a eutrophic shallow lake using worldview-2 imagery. journal of hydroinformatics, 16(1), 50. http://doi.org/10.2166/hydro.2013.133 http://dx.doi.org/10.14710/geoplanning.3.2.117-126 | 137 geoplanning vol 3, no. 2, 2016, 137-146 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.2.137-146 temporal vegetation dynamics in peat swamp area using modis time-series imagery: a monitoring approach of high-sensitive ecosystem in regional scale y. setiawan a, h. pawitan b, l. b. prasetyo c, m. parlindungan b, p. a. permatasari a a center for environmental research, bogor agricultural university, kampus ipb darmaga, bogor 16680, indonesia b department of geophysics and meteorology, bogor agricultural university, kampus ipb darmaga, bogor 16680, indonesia c department of forest conservation and ecotourism, bogor agricultural university, kampus ipb darmaga, bogor 16680, indonesia abstract: peat swamp area is an essential ecosystem due to high vulnerability of functions and services. as the change of forest cover in peat swamp area has increased considerably, many studies on peat swamp have focused on forest conversion or forest degradation. meanwhile, in the context of changes in the forestlands are the sum of several processes such as deforestation, reforestation/afforestation, regeneration of previously deforested areas, and the changing spatial location of the forest boundary. remote sensing technology seems to be a powerful tool to provide information required following that concerns. a comparison imagery taken at the different dates over the same locations for assessing those changes tends to be limited by the vegetation phenology and land-management practices. consequently, the simultaneous analysis seems to be a way to deal with the issues above, as a means for better understanding of the dynamics changes in peat swamp area. in this study, we examined the feasibility of using modis images during the last 14 years for detecting and monitoring the changes in peat swamp area. we identified several significant patterns that have been assigned as the specific peat swamp ecosystem. the results indicate that a different type of ecosystem and its response to the environmental changes can be portrayed well by the significant patterns. in understanding the complex situations of each pattern, several vegetation dynamics patterns were characterized by physical land characteristics, such as peat depth, land use, concessions and others. characterizing the pathways of dynamics change in peat swamp area will allow further identification for the range of proximate and underlying factors of the forest cover change that can help to develop useful policy interventions in peatland management copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): setiawan, y., et al. (2016). temporal vegetation dynamics in peat swamp area using modis time series imagery: a monitoring approach of highsensitive ecosystem in regional scale. geoplanning: journal of geomatics and planning, 3(2), 137-146. doi:10.14710/geoplanning.3.2.137-146 1. introduction information on ecosystem characteristics and its changes in a highly vulnerable land, such as peatlands, is the key to many aspects of global environmental change, environment adaptation and mitigation studies. peatland ecosystems are vulnerable to changes in quantity and quality of their water supply, and it is expected that climate change will have a pronounced effect on peatlands through alterations in hydrological regimes with climate variability (erwin, 2009). the importance of peatland in the tropical environment has been studied by many researchers, such as the estimation of co2 emission released by forest fire (page et al., 2011), the variation in its ecological function (achard, 2002), the loss of biodiversity through land conversion (myers et al., 2000) and the sustainable management of peatland (hooijer et al., 2006). moreover, as explained by wösten et al. (2008) that inundated condition of peatland is recommended as a regional ecosystem conservation program and article info: received: 11 august 2016 in revised form: 22 september 2016 accepted: 29 september 2016 available online: 31 october 2016 keywords: temporal vegetation dynamics, peat swamp area, modis corresponding author: yudi setiawan bogor agricultural university, kampus ipb darmaga, bogor 16680, indonesia email: setiawan.yudi@apps.ipb.ac.id open access http://dx.doi.org/10.14710/geoplanning.3.2.137-146 http://dx.doi.org/10.14710/geoplanning.3.2.137-146 mailto:setiawan.yudi@apps.ipb.ac.id setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 138 | to reduce co2 emissions from the lands. as the importance role of peatlands and to prevent fires in peatland areas, the indonesian government has a commitment to restore about two million hectares of peatland areas by 2020 due to the establishment of a peatland restoration agency (wardhana, 2016). in more complex tropical peatland areas in indonesia, the peatland characteristics and its change are the results of many, non-linear, interactions between socio-economic and cultural conditions, biophysical constraints and land use history (cole et al., 2015). the biophysical processes of peatlands ecosystem can be represented as interactions between different land use/cover types, biophysical conditions and spatial elements of the landscape (evans & moran, 2002). this study examines the feasibility of using time series satellite datasets to recognize the changes occurred in the peat swamp ecosystem that would be possible to consider the peatland characteristics. a monitoring approach to recognizing the peatland characteristics and its dynamic changes on a regional scale is due to simultaneous analysis of land surface attributes from long-term data sets and seasonal variation. monitoring of land surface continuously can provide information on the ecosystem characteristics, including the actual subtle nature of inter-annual change (setiawan & yoshino, 2014). this approach will provide information how the change occurred accurately as well as how big are these affected areas. moreover, based on this information, we can choose the priorities for restoration and further research. 2. data and methods 2.1. study site the study site is the main island of sumatra with an area of 473,481 km2. the island is administratively divided into 8 provinces, namely aceh, north sumatra, west sumatra, riau, jambi, bengkulu, south sumatra and lampung (figure 1). sumatra has a mean annual rainfall mostly less than 3,000 mm, below 1,500 mm on the northeast coast, and below 2,000 mm in several intermontane basins from aceh to bengkulu. over 4,000 mm fall on the off shore mentawai islands, on the sibolga and padang coasts, and around mount malintang and mount pantaicermin. on the mountain slopes of bukit amas and of northern bengkulu mean annual rainfall exceeds 5,000 mm. the eastern islands receive 2,000 to 3,000 mm/year (land resources department/bina program, 1990). figure 1. study site (authors, 2016) 2.2. satellite images we used moderate resolution imaging spectroradiometer (modis) vegetation index dataset derived from u.s. geological survey land processes distributed active archive center (usgs lp daac, 2009). the composited 16-day product of modis vegetation index which is embedded in mod13q product contains http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 | 139 various vegetation indexes (vi), one of them is enhanced vegetation index (evi). the evi is developed by the modis land discipline group (modland) using the equation in huete et al. (1999). we used the modis datasets acquired from january 2001 to december 2014. the datasets captured 322 time series data with an interval of 16 days. such kind of the high temporal datasets are required to determine the vegetation dynamics change of peat swamp areas. 2.3. maps datasets we used a map of land cover that provided by the ministry of forestry (2013) to describe the type of land cover in the sumatran peatlands. this land cover map was produced based on visual interpretation of landsat 5 tm/7 etm+. the ministry of forestry classified the type of land cover into 23 types as follows: (1) primary dry land forest (hp); (2) secondary dry land forest (hs); (3) primary swamp forest (hrp); (4) secondary swamp forest (hrs); (5) primary mangrove forest (hmp); (6) secondary mangrove forest (hms); (7). bush/slash (b); (8) swamp (rw); (9) swamp bush (br); (10) savannah (s), (11) industrial forest plantation (ht), (12). plantation (pk), (13). dry land agriculture (pt); (14) mixed dry land agriculture (pc); (15) rice field (sw); (16) fishpond (tm); (17) bare land (t); (18) transmigration (tr); (19) mining (tb); (20) airport area (bdr); (21) built-up area/housing (pm); (22) water body (a); and (23) cloud cover. the land cover map provided by the ministry of forestry is concerned primarily with natural vegetation categories. consequently, in the forest class, delineated polygons could be labeled with two codes, representing categories un-disturbed by human activities (primary type) and those disturbed (secondary type). moreover, some of thematic maps were used to characterize the complex situation of peat swamp ecosystem; such as: map of peat depth distribution, logging concession, wood-timber plantation concession (industrial forest plantation) and oil palm plantation. those thematic maps were provided by the global forest watch (2002). the peat depth was classified into 6 classes: (1) less than 50 cm; (2) 50–100 cm; (3) 100-200 cm; (4) 200-300 cm; (5) 300-400 cm; and (6) more than 400 cm. in this study, concession areas for oil palm and other timber plantations as well as conservation areas were also used to seek out any relationship with dynamic changes in the peatlands. 2.4. image processing although modis vi (mod13q1) data have some advantages in providing basic information related to vegetation pattern change, time-series of these data inevitably contain disturbances caused by atmospheric variability and aerosol scattering (xiao et al., 2003). therefore, noise reduction (de-noising) or fitting a model to observe data is necessary before vegetation dynamics pattern can be determined. in this work, we used the modis evi datasets filtered by an average moving window over 3 months (almost equal to 7 time series) data in order to smooth and reduce the residual noise of these images (figure 2). figure 2. average moving window over 7 time series of evi data an example for 2002 datasets (authors, 2016) http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 140 | 2.5. pattern change detection in this study, we recognized the change of vegetation dynamics using a distance of average evi values for two successive three months (january-march  april-june  july-september  oktober-december). all pixels in the consecutive study years during periods from 2001 to 2014 were computed by a function shown in the equation below (bouman, 2009): [1] where k and l are two successive 3 months, and dk,l is the distance between evi values of the two successive patterns of k and l three months data, nk,nl is the number of observations in k and l three months data, nnew is the number of observation of the two pattern of k and l three months data (nnew = nk + nl) and k, l is the mean of evi values in k and l three months data, new is the mean of evi values of the two pattern of k and l three months with following equation: [2] figure 3 illustrates the change detection based on vegetation pattern change thorugh a change of the distance of index value in two successive time series data. a complete image processing analysis for this change detection method was applied in the previous work (setiawan & yoshino, 2014). figure 3. illustration of the forest cover change detection system example change pattern at period july – september 2005 (setiawan & yoshino, 2014) http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 | 141 peatlands distribution (gwf, 2002) 2.6. change pattern thresholds we assumed that the difference in distance for those four periods in every year shown an approximately normal distribution. therefore, we identified the pixels that had the greatest change in distance of evi for each period. the three change thresholds (th) of 2.0, 3.0, and 4.0 were selected corresponding to a range of z-value probabilities which was the limit to define a change or no-change in the temporal pattern. based on the previous study (setiawan et al., 2013; setiawan et al., 2014), these value ranges were selected because they produced appropriate estimates of annual change values based on a previous change rate for the java area. some pixels of the change and no-change events were randomly selected to determine the threshold of change pattern probability. 2.7. accuracy assessment in this study, accuracy assessment is performed using a finer spatial resolution landsat tm and etm+ as well as ground checking at some selected areas. the hotspot datasets derived from noaa 8 were collected in the period of 2000 – 2015 from the asean specialised meteorological centre (asmc) (http://asmc.asean.org). the asmc was established in january 1993 as a regional collaboration programme among the national meteorological services (nmss) of asean member countries. 3. results and discussion characterizing the time series vegetation dynamics will provide information about the temporal vegetation pattern in the peatlands. the distribution map of the change patterns of peat swamp ecosystem in sumatra will be also provided. figure 4. temporal vegetation change in peatland areas detected by two successive 3 months (analysis, 2016) figure 4 shows the distribution of vegetation pattern change in the peatland areas which was detected by the approach. detail changed area in jambi and riau is given in figure 5. some significant causes of the change pattern in the peatlands can be identified systematically, either caused by natural forest fire or http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 142 | figure 6. significant change patterns of evi on selected sites as forest change detected by th-1 (analysis, 2016) figure 7. dynamics change patterns of evi on selected sites as forest change detected by th-3 (analysis, 2016) human activities factor. based on the identification of the pattern change, we found many significant patterns that indicated a change pattern in the peatland areas. figure 5. detail change distribution in riau (left) and jambi (right) detected detected by the change of temporal pattern two successive 3 months (analysis, 2016) the evi pattern was used to measure reliable spatial and temporal characteristics of vegetation dynamics of land cover, as a means for better understanding of land characterization (setiawan et al., 2013; verburg et al., 2009). the previous study (setiawan et al., 2016) explained that some typical evi patterns of the sumatran peat swamp indicates different type of ecosystem and/or different response of ecosystems to the changing environment. monitoring temporal vegetation patterns allowed the change in peatlands to be detected including some properties of that change such as the location, area, time and mechanism of the change. for instance, figure 6 shows several patterns of forest cover change detected in peatland areas; (1) forest conversion into barren land through burning (at the end of march, 2014), (2) forest area was converted into plantation with was firstly converted to barren land. meanwhile, figure 7 shows several patterns of forest cover changes affected by anthropogenic activities (timber forest management), where forest changed to open land because of the threes harvesting and the later replanting process. regarding to the change pattern of changed on may 2014 changed on december 2013 http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 | 143 evi, the selection of thresholds (th-1 and th-3) was significantly affected to the accuracy of change patterns detection system. we compared the result with the hotspot occurrences with up to 90 percent of confidence, there was a relationship between the pattern changes in peatlands and the land fires. the highest number of hotspots was happened in 2006. however, the majority of hotspots was relatively constant every year during the dry season (july september). it seems that the pattern changes was mainly occured in dry season when it was high in hotspot occurrences. this could be the case that fire is still being used both small-holders and large holders as the only tool for land clearing (stolle et al., 2003). moreover, the majority of pattern changes occurred in production forest zone, where forest and palm oil industrial estates established. in addition, the amount of changes was relatively constant every year, means that there have been activities in the production forest areas related to clearing for new plantation. however, based on limited ground checking activities, the changes of vegetation index did not wholly related to forest clearing; some of them were cleared from old and non-productive plantations to new plantations especially in oil palm companies. meanwhile, it also happened in the several big timber companies, the vegetation index were drop when they harvested the trees and planted new ones after that. compared to hotspot occurrences with up to 90 percent of confidence level, it was very unlikely that these big companies used fires for replanting their new plantations. it could be the case that logging companies control fire during their exploitation of concessions, logged-over forest and forest allocated to production. conservation areas (nature preserves, protected forest, national parks, and protected peat forest) have also experienced changes. this is due to several conservation areas such as berbak national park are located in peatland area which is vulnerable to forest fires especially in the border with other nonprotected areas. miettinen et al. (2012) studied peatland conversion and degradation processes in jambi between 1970 and 2009. they found that nearly-pristine forest cover have declined from 95 to 73 percent inside the berbak national park and outside the national park from 86 to 25 percent. outside the protected area, 66 percent of former nearly‐pristine forests turned into degraded forests or unmanaged deforested areas. large‐scale oil palm plantations accounted for 21 percent of the formerly nearly‐pristine areas and small‐holder agriculture for 8 percent. the production forest which is mainly occupied by logging companies, oil palm plantation, and industrial timber concessions is the most dynamic land use in sumatra. the peatland ecosystem in this area is constantly changing either by opening new cultivation area or from re-planting activities by the owners. however, based on this study which looks the changing based on vegetation activities, it is not clear that the land cover and land use changes from one type to another or within the same type but different age of vegetation. it needs more study by combining different satellite imageries with different resolutions in combination with more ground checking activities. the performance of our change detection approach was evaluated by 100 reference points, and the result revealed an overall accuracy of 80.10%. comparison of the accuracy in change detection among many land use types in peatlands reveals a variation. plantation area had the greatest overall accuracy (94.02%), followed by agricultural land (86.62%), and natural forest (81.97%). in the natural forest, 44.61% of the errors is due to omission errors, meaning that the change area in those classes was assigned incorrectly. the result also indicates that the change in plantations could be detected more accurately relative to other classes. overlaid the deforestation detected area of modis with the two different dates of landsat images in selected locations in sumatra are presented below. by the figure 8, the results indicate that the approach developed in this study offer great promise to detect deforestation events quickly (monthly) at large scale. furthermore, through these results, the understanding of environmental changes will be improved including carbon storage change and sequestration by terrestrial plants. characterization of typical peat ecosystem based on temporal pattern analysis would provide useful information regarding the change dynamics in the peatland ecosystems; consequently it should be possible to provide information of the inter-annual ecosystem change in sumatran peatlands. http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 144 | figure 8. comparison between the result of our approach with the two different dates of landsat images in selected locations in sumatran peatlands (analysis, 2016) 4. conclusion due to characterizing the long-term vegetation dynamics, the main types of change in peatlands were recognized. the first is the change in forest lands, either by forest fire or human factors which forest is converted into oil palm plantation as well as an open area. the second is the temporary change in oil palm plantation (replanting) as well as timber forest plantation (harvesting and replanting). the third is agricultural development, including some trajectories such as non-agricultural lands converted into intensive agricultural lands. each change category has specific change mechanisms or processes, which then reveals the specific spatial model for each type. additionally, the results indicate that on a regional scale, many of the change patterns are affected by temporary changes in land cover. a change in the temporal vegetation pattern is detected as a land use change even if that land use type had not really changed. it is highly inter-related with climate variability, http://dx.doi.org/10.14710/geoplanning.3.2.137-146 setiawan et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 137-146 doi: 10.14710/geoplanning.3.2.137-146 | 145 for example, the enso (el nino southern oscillation) that has significantly affected the water availability due to the low-intensity rainfall. also, some agricultural lands became barren in those periods because there was insufficient water for plant growth. distinguishing the actual type of change from temporary change by land cover dynamics provide sufficient, significant and useful information to understand the pattern of dynamic pattern change of peatlands in sumatra and their future roles. regional shifts in temporal vegetation dynamics, including the actual changes of land use and temporary changes of land cover in peat swamp areas, have numerous consequences relevant to the environment as well as changes in carbon and nitrogen storage, land degradation and loss of biodiversity. an understanding of temporal vegetation dynamics to explain the mechanisms and pathways of vegetation change pattern is important because of its relationship to ecosystem characteristics and socio-economic attributes of the peatland, and it will be discussed separately in further studies. 5. acknowledgments we would like to thank the center for environmental research, bogor agricultural university (pplh-ipb) for giving us opportunity to get the research funding. this research was funded by the indonesian directorate general of research and higher education (ristekdikti) for the fiscal year of 2016. 6. references achard, f. 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(2003). sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in northern asia. remote sensing of environment, 84(3), 385–392. http://doi.org/10.1016/s0034-4257(02)00129-3 http://dx.doi.org/10.14710/geoplanning.3.2.137-146 | 63 geoplanning vol 5, no. 1, 2018, 63-74 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.63-74 preliminary investigation of the robustness of maximally stable extremal regions (mser) model for the automatic registration of overlapping images o. g. ajayi a, i. j. nwadialor b, i. c. onuigbo b, o. a. kemiki b a department of surveying and geoinformatics, federal university of technology, minna, pmb 65, minna, nigeria, nigeria b federal university of technology, minna, nigeria abstract: various researchers in digital image processing have developedkeen interest in the automation of object detection, description and extraction process used for various applications, and this has led to the development of series of feature detection and extraction models, one of which is the maximally stable extremal regions features algorithm (mser). this paper investigates the robustness of mser algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. the robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth online image data repository and image pairs acquired from an unmanned aerial vehicle (uav) flight mission. sum of square difference (ssd) and bilinear interpolation models were used to establish the similarity measure between the registered images, resampling of the pixel-values and computation of non-integer coordinates respectively while random sampling consensus (ransac) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. the results obtained from this preliminary investigation shows that the processing speed of mser is quite high for automatic image registration with a relatively high accuracy. while an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. the obtained speed and accuracy shows that mser is a very robust model and as such, can be deployed as a feature detection and extraction model in the development of an automatic image registration scheme. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. ajayi, o.g et al. (2018). preliminary investigation of the robustness of maximally stable extremal regions (mser) model for the automatic registration of overlapping images. geoplanning: journal of geomatics and planning, 5(1), 63-74. doi: 10.14710/geoplanning.5.1.63-74 1. introduction recently, obtaining descriptors of features by analyzing the linear scale space of an image have proved to be robust and reliable in the recognition, extraction and matching of objects (sivic & zisserman, 2003) which is a very crucial stage in mosaic generation from overlapping images. one of the essential image processing operations in remote sensing is image registration. diverse applications such as change detection, image fusion, etc. are made possible by mosaicking overlapping image pairs differently acquired under different imaging conditions and circumstances, at different time epochs, covering the same imaging area (kumar, manjunath, & rao, 2003). the basic aim of image registration is to ensure that two overlapping image pairs are aligned and matched spatially such that analogous pixels in the overlapping image pairs will correspond to the exact imaged scene of interest (physical region). this alignment and matching is achieved by estimating scale, rotation and translation using a defined or selected appropriate transformation function. it is an unavoidable issue in various fields of application of digital image open access article info: received: 21 november 2017 in revised form: 5 march 2018 accepted: 30 march 2018 available online: 30 april 2018 keywords: mser, image registration, overlapping images, ransac, uav corresponding author: department of surveying and geoinformatics, federal university of technology, minna, pmb 65, minna, nigeria, nigeria email: ogbajayi@gmail.com https://doi.org/10.14710/geoplanning.5.1.63-74 https://doi.org/10.14710/geoplanning.5.1.63-74 mailto:ogbajayi@gmail.com ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 64 | processing, especially, those that involves pixel by pixel comparison of two or more images of the same scene (dai & khorram, 1999). the determination of a transformation function which best aligns features in the base image with the conjugate features in the observed image is the major task of image registration. according zitová & flusser (2003), automatic image registration can be classified into both area based method which directly aligns the intensity or pixels of the image pair holistically, and feature based method which extracts higher-level structures from the image pair, and find similar features to execute the registration task which makes this method more useful when the detection and extraction of conjugate features are reliably possible. nevertheless, low spatial resolution and the presence of blur and noise in the base and moving image pairs will negatively affect the accurate detection and extraction of corresponding or conjugate feature points (zitová & flusser, 2003). brown (1992) presented the four major components of image registration as feature space, search space, search strategy and similarity measure. the selection of each of these model components is determined by the nature and properties of the images (rao, rao, manjunath, & srinivas, 2004). various feature detection and extraction models have been implemented for accurate generation of mosaic from overlapping image pairs. some of these include speeded up robust feature (surf) algorithm, scale invariant feature transform (sift) algorithm, harris and stephen corner detector, etc (olaleye et al., 2015) (ajayi et al., 2014). though these models have proved to be relatively robust, current research focus still seeks to investigate the possibility of obtaining a more robust feature detection and extraction model, which necessitated the need for this study. the maximally stable extremal regions (mser) model is a robust feature extraction algorithm. mser extracts a number of co-variant regions from an image, and it is a blob-like, local and affine invariant feature detector which is invariant to illumination changes and image resolution. it scales well for both small and large objects (varah & grujić, 2013). an mser is a stable connected component of some gray-level sets of the image and it is based on the idea of extracting regions which stay nearly the same through a wide range of thresholds. while mser has been widely and successfully applied in different image processing applications (mikolajczyk et al., 2005) (fraundorfer & bischof, 2005) some of which include tracking and 3d segmentation (donoser & bischof, 2006), retrieval or restoration of images (nister & stewenius, 2006), matching of wide baselines (matas et al., 2004) and curvilinear structures (lemaitre et al., 2011), object recognition (obdrzalek & matas, 2002), real-time visual surveillance (salahat et al., 2015), field programmable gate arrayfpga (kristensen & maclean, 2007), cell detection and analysis (kaakinen et al., 2013), etc, research efforts aimed at implementing it for mosaic generation or automatic image registration is relatively unknown. this paper presents some preliminary findings of the investigation of the robustness of mser for the automatic registration of overlapping image pairs using images acquired from trimble ux-5 unmanned aerial vehicles (uav) and google earth online image data repository. 2. data and methods 2.1. mathematical formulation of the mser model obdrzalek & matas (2002) presented the formal definition of mser and mathematical annotation of its terms such as image region, region boundary, extremal region and maximally stable extremal regions. according to (kimmel, zhang, bronstein, & bronstein, 2011), the following is the mathematical formulation of mser model: let tr be the family of connected components representing an edge in the component tree. such regions are referred to as extremal by obdrzalek & matas (2002) since either: int int( ) or ( ) t t t ti r i r i r i r  which implies that the value of all the pixels within the regions are either completely darker or completely brighter than the pixel values along the boundary where the intensity is exactly equal to t . equation (1) gives the stability of a region tr : https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 | 65 1 ( ) ( ) ( ) t t t a r r d a r dt   , where ( )ta r = area tr ...................................................................................... (1) a region whose area remains slightly thesame with the change of the threshold t is considered stable while a region tr is termed maximally stable if 1( )tr (an affine – invariant property) has a local maximum at t since area ratio is retained under affine transformation. this suggests that for an affine transformation t of the domain x , the corresponding regions ' and r r detected in images i d and 1( )i t  , respectively will be related to 'tr r . these are the kind of regions that the mser model detects. briefly highlighted in figure 1 are the stages involved, techniques and the mathematical models used in the automatic registration of overlapping images. figures 1 and 2 present a flowchart of the methodology. it extracts the feature points by various filters or descriptors, maximally stable extremal regions (msers) feature detector algorithm was used for the feature detection and as the descriptor extraction method. builds the relationship between the feature point sets from the reference and floating images. sum of square difference (ssd) metric was used at a match threshold of 1.0 scalar vector. ssd establishes similarity measure between the images to be registered. the model estimation decides the types and parameters that are needed for the mapping function. the parameters are computed from the feature pairs of the correspondence built in feature matching the bilinear interpolation was used for the resampling of the pixelvalues and computation of non-integer coordinates. ransac was used to exclude the outliers and to compute the transformation matrix using affine transformation function figure 1: stages and techniques used for the automatic image registration detailed discussion of the mathematical models of ssd, ransac, bilinear interpolation and affine transformation functions used can be found in (ajayi et al., 2014). all the necessary computations and image registration were carried out using code scripts written in matlab r2014a environment. 2.2. data used for experimentation the model experimentation is subdivided into three (3) different image registration campaigns with overlapping image pairs showing different geographical areas used for the experimentation. pairs of overlapping images with image size of 700x1028 pixels each, showing part of the university of lagos, akokacampus, lagos nigeria, extracted from google earth online image data repository was used for the first image registration campaign while overlapping image pairs showing part of the federal university of technology, main campus, minna nigeria, also extracted from google earth was used for the second image registration campaign. the images are of the size 2745x4800 each. overlapping image pairs of part of the federal polytechnic ado-ekiti, acquired with the aid of trimble ux5 uav, was used for the model experimentation in the third image registration campaign. the image pairs for the first image registration campaign is presented as figures 3a and 3b while the image pairs for the second image registration campaign is presented as figures 4a and 4b. the image pairs used for the third registration campaign is presented as figures 5a and 5b and its size is 800x532x3 pixels each. feature detection feature matching model estimation resampling and transformation https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 66 | figure 3a: an image of part of unilag campus (the base image). figure 3b: an image of part of unilag campus (the moving image). figure 4a: image part of futminna, main campus (base image). https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 | 67 figure 4b: image part of futminna, main campus (moving image). figure 5a: image part of federal polytechnic ado, figure 5b: image part federal polytechnic, ado ekiti (base image) ekiti (moving image) affine transformation function was adopted for the computation of the model’s transformation matrix (equation 2). it has six unknown parameters and requires a minimum of three reference points.                         f e v u dc ba y x …………………………………………………………………………………………………………… (2) where:       dc ba are scalar quantities,       f e are translation parameters. α = rotation angle, tx = translation in x axis and ty = transformation on y axis where: ɑ = cos α, b = sin α (α is the rotation angle), c = tx and d = ty (translation in x and y axis respectively) https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 68 | feature extraction initial feature matching initial transformation (affine transform) and consistency check in image space estimation of transformation parameters (affine transform) image resampling registered image reference or base image moving or floating image feature search and detection input the images figure 2: flowchart of the step by step procedure of executing feature based auto-registration (adapted from ajayi et al., 2014) 2.3. accuracy assessment in order to ascertain the robustness of the registration model, an accuracy assessment method developed by (olaleye et al., 2015) was adopted. this method (equation 3) makes use of the total percentage of matched inliers, out of the total matched conjugate points (inliers and outliers) to determine how robust the feature extraction process is and in turn, define the degree of accuracy or accuracy measure of the entire image registration process since the success of the process depends largely on the robustness of the feature detection and extraction model and the model used for the exclusion of outliers. (100%) a x b  ............................................................................................................................... (3) where x  the overall accuracy (%), a  total matched inliers (excluding outliers), and b  total matched points (both inliers and outliers). https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 | 69 3. results and discussion the results obtained from the first automatic image registration campaign using mser as the feature detection and extraction model are as given in figures 6a, 6b and 6c. figure 6a shows the identified and extracted corresponding features, figure 6b presents the extracted corresponding points/features having excluded the outliers using ransac while figure 6c presents the mosaic generated by registering the two overlapping images. all units are measured in pixels. figure 6a: all matched and extracted corresponding points (inliers and outliers) for the first campaign (in pixels). figure 6b: extracted corresponding points used for the computation of transformation matrix (outliers excluded) for the first campaign (in pixels). https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 70 | figure 6c: the registered image or mosaic generated from the overlapping image pairs for campaign 1 (in pixels) using the developed model. with a registration accuracy of 61.44%, the computed transformation matrix of the first registration campaign is: 1 1.0171 0.0756 502.0217 0.0009 1.0169 49.8708 0 0 1.0000 t           the results obtained from the second image registration campaign are presented in figures 7a-7c. figure 7a presents all the matched corresponding features (inliers and outliers inclusive) while figure 7b presents the extracted corresponding points/features, having excluded the outliers using ransac. figure 7c presents the mosaic generated by registering the two overlapping images. all units are measured in pixels. figure 7a: all matched corresponding points (inliers and outliers inclusive) for campaign 2 (in pixels). figure 7b: matched inliers used for the image registration for campaign 2 (in pixels). https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 | 71 figure 7c: mosaic generated for campaign 2 (in pixels) using the developed model. with a registration accuracy of 52.02%, the computed transformation matrix of the second registration campaign is: 2 0.0010 0.0000 2.0287 0.0000 0.0010 0.0504 0 0 0.0010 t             the results obtained from the third registration campaign are presented in figures 8a-8c. while figure 8a presents all the matched corresponding features (inliers and outliers inclusive), figure 8b presents the extracted corresponding points/features, having excluded the outliers using ransac and the mosaic generated by registering the two overlapping images is presented in figure 8c. all units are measured in pixels. the accuracy obtained from the third registration campaign is 55.62% while computed transformation matrix of the registration for the third registration campaign is: 3 1.0051 0.0041 115.8039 -0.0014 1.0197 15.3436 0 0 1.0000 t           figure 8a: all matched corresponding points (camp. 3) figure 8b: matched inliers (campaign 3) in pixels https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 72 | figure 8c: mosaic generated from the uav acquired image pair (campaign 3) in pixels a total of 191, 662 and 27 corresponding points were automatically extracted from the overlapping images of the first, second and third image registration campaigns respectively. the number of matched corresponding points of the second image registration campaign was more than the matched corresponding features of the first and third campaigns because the image pairs used for the second registration campaign have a larger total area coverage or image size (approximately 2745x4800 pixels) compared to the image pairs used for the first and third registration campaigns which have image sizes of 700x1028 and 800x532 pixels respectively. also, the image pairs used for the second registration campaign have a higher overlapping percentage, and as such, the model has sufficient latitudes for the extraction of corresponding features. it was observed that the number of matched points using mser are very few which makes it an excellent model for applications where only small matches are needed such as the computation of epipolar geometry (mikolajczyk et al., 2005). this suggest that integrating mser with epipolar correlation in the development of an automatic image registration scheme will result into a highly robust model with respect to time and accuracy. approximately 61.44%, 52.02% and 55.62% of the matched corresponding points for the first, second and third registration campaigns respectively were used for the automatic image registration. these percentages were obtained after the exclusion of outliers (mis-matches) from all the matched points using random sampling consensus (ransac). this shows that 38.56%, 47.98% and 44.38% of all the matched points were outliers for the first, second and third image registration campaigns respectively. this imply that mser automatically extracted more outliers during the second image registration campaign which can be attributed to the quality of the spatial resolution of the used overlapping image pairs. they also depict the accuracy level or measure of reliability of the automatic registration model (olaleye et al., 2015). the time taken for the complete execution of the automatic image registration was approximately 11.92 seconds (speed of the developed registration scheme) for the first registration campaign, about 11.20 seconds was expended on the second campaign and 3.27 seconds was used for the third campaign. the processing time of each of the registration campaigns was determined automatically using the system run time generated by the matlab report. this is in tandem with the results obtained by (mikolajczyk et al., 2005) which attests to the speed of mser when compared to 5 other detectors. an improved computational time was also recorded by nistér & stewénius (2008) and (kaakinen et al., 2013) and this is as a results of mser’s negligible need for pre-optimization procedures which dramatically reduces its computation time (kaakinen et al., 2013). though mser is regarded as one of the best region detectors due to its robustness against view point, scale and lightning changes, this fair results obtained especially in registration campaign 2 can be attributed to https://doi.org/10.14710/geoplanning.5.1.63-74 ajayi et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 63-74 doi: 10.14710/geoplanning.5.1.63-74 | 73 the sensitivity of mser algorithm to image blurs (śluzek, 2016) and as such, the resolution of the image may be influential to the process of determining the efficiency of the successfully matched inliers and consequently, the registration result. this is evident in the fact that better accuracies were recorded in the first and third registration campaigns compared to the accuracy obtained from the second registration campaign because the image pairs used for the second registration campaign are quite blurry. 4. conclusion the result of the preliminary investigation of the robustness of msers feature identification, detection and extraction algorithm for the extraction of corresponding features used for the automatic registration of overlapping images is herein presented. all computation was done with code scripts written in matlab r2014a environment. though the percentage matched inliers used for the registration of the images were quite good (61.44%, 52.02% and 55.62% for the first, second and third registration campaigns respectively), registration campaign 2 produced the least satisfactory results of the three campaigns which is an evidence that mser is highly sensitive to image blurs (lemaitre et al., 2011), since the spatial resolution of the image pairs used for the second campaign is not as high as the resolution of the image pairs used for the first and third campaigns. it is however noted that mser is highly robust and can be a choice model for feature detection and extraction in an automatic image registration scheme. for further studies, attempt shall be made to investigate the possibility of pre-defining a minimum threshold (radius) as the acceptable region for smaller msers so as to avoid unnecessarily small regions. the robustness of edge-enhanced maximally stable extremal regions which should help the sensitivity of this algorithm to image blurs and noise will also be investigated. 5. references ajayi, o. g., 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[crossref] https://doi.org/10.14710/geoplanning.5.1.63-74 https://doi.org/10.1007/s11263-005-3848-x https://doi.org/10.1109/cvpr.2006.264 https://doi.org/10.1007/978-3-540-88688-4_14 https://doi.org/10.5244/c.16.9 https://doi.org/10.1109/iecon.2015.7392528 https://doi.org/10.1109/iccv.2003.1238663 https://doi.org/10.1016/s0262-8856(03)00137-9 | 87 geoplanning vol 7, no 2, 2020, 87-94 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.7.2.87-94 flood-reduction scenario based on land use in kedurus river basin using swat hydrology model s. purwitaningsiha* , a. pamungkasa , p. t. setyasaa, r. p. pamungkasa, a. r. alfiana, s. a. r. irawana a urban and regional planning department, sepuluh nopember institute of technology , indonesia abstract: the rapid growth population phenomenon has caused excessive land demand for residential and economic activity. moreover, rapid urbanization also increases the contribution of land constraints. land conversion from conservation to cultivation uses affects the surface runoff volume that leads to flooding. according to these problems, it is necessary to take steps to control floods in kedurus watershed. one of the proper urban development concepts is water sensitive city (wsc). the protection against floods in wsc can be accomplished using the land use arrangement to reduce the surface runoff. the aim of this research is to determine the proper land use scenario to reduce floods in kedurus watershed. in order to reach this aim, the writer uses sensitivity analysis to identify the proper land use scenario to be applied in the watershed and swat to select the best scenario. the efforts to reduce flood through the land use scenario (scenario 2) could reduce the flood volume by 44,320.32 m3 or 8.11% of the total volume of flood in the area. the average reduction of flood volume in each sub-basins is 12,92% and the highest number of reduction is 65,67% (sub-basin 22). copyright © 2020 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): purwitaningsih, s., pamungkas, a., setyasa, p., pamungkas, r., alfian, a., & irawan, s. (2020). flood-reduction scenario based on land use in kedurus river basin using swat hydrology model. geoplanning: journal of geomatics and planning, 7(2), 87-94. doi: 10.14710/geoplanning.7.2.87-94 1. introduction indonesia, as one of the developing countries, still faces big problems in the development of its cities. the urbanization that occurs in big cities has increased the need for housing, which is one of the basic human needs (machyus, 2006). it has also increased land constraints and land conversions from unbuilt areas into built areas (masitoh, ma’rif, & rudiarto, 2002). the impact of land conversion from the unbuilt area to be residential, especially in the catchment area, has increased the volume of surface runoff. the land conversion in the entire sub-basin has contributed to a 9.84% increase in water volume and a 4.13% increase in peak discharge volume. on the contrary, by doing conservation in the sub-basin, the water volume will be decreased by 10.7%, and the peak discharge rate will also decrease by 5.67% (aryanto, 2010). recent studies discuss disaster mitigation scenarios dan intervention reducing the disaster’s impact (garschagen, 2017; mertens et al., 2018; peters et al., 2019; rahmawati et al., 2016; schryen & wex, 2014; waghwala & agnihotri, 2019). floods are a common disaster in high-density cities caused by a combination of human activities and climate change. therefore, flooding is one of the problems in cities with a high level of urbanization in indonesia such as surabaya. surabaya, as the core urban area of gerbangkertosusila, has a high urbanization rate. based on the central bureau of statistics, in 2014, surabaya had a total population of 67,416, and it increased by 3.64% from the previous year. the high rate of urbanization and economic growth in surabaya has increased land demands. the additional land for residential from 2001 to 2015 was recorded at 4,556.16 ha (37.2%) (zulkarnain, 2016). article info: received: 2 august 2017 in revised form: january 2018 accepted: january 2019 available online: 1 november 2020 keywords: flood, land use scenario, swat, water sensitive city *corresponding author: s purwitaningsih urban and regional planning department, sepuluh nopember institute of technology, indonesia email: purwitasantika@gmail.com open access https://orcid.org/0000-0001-8363-6140 https://orcid.org/0000-0001-9251-1681 http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.7.2.87-94 mailto:purwitasantika@gmail.com purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 88 | kedurus river has upstream in gresik regency and downstream in surabaya city. the area around the kedurus river often experiences flood when the rainy season comes. in 2016, in driyorejo, the flood had reached 70 cm in height and be the worst flood in gresik regency (perdana, 2016). meanwhile, in surabaya, the worst floods have occurred in wiyung district. the flood has reached 1 m in height (sugiharto, 2016). the flood caused by kedurus river has inundated a more than 100 ha area (development planning agency of surabaya city, 2014). according to rtrw surabaya 2014-2034, medium density residential area will be developed in wiyung district. in addition, the urban settlement will also be developed in driyorejo district. with the current flood problems, while the city's growth continues, it is necessary to reduce flooding in kedurus river basin. one of the urban planning concepts that support flood controlling is water sensitive city. water sensitive city is a concept that prioritizes the sustainability of water resources in a city. the best urban water management and planning are considered capable of protecting and sustaining the benefits and services of water cycles heavily influenced by the community, including flood protection. in wsc, flood protection can be achieved with land use management that reduces surface runoff (wong & brown, 2009). therefore, this study aims to determine the most appropriate land use scenario in reducing floods in kedurus river basin. 2. data and methods in terms of methodology, we used swat model simulation to find out the boundary of the research area, including the hydrology model of kedurus river basin. the data required in this stage were slope, soil types, rainfall data, relative humidity, temperature, wind speed, solar radiation, and land use types (gibbs, 1987; meteorology climatology and geophysical agency, 2017; rahayu et al., 2009; triatmojo, 2014). then we used sensitivity analysis to build potential scenarios applicable in kedurus river basin and swat model simulation of the scenario that has been built. the data required in this research are the daily peak discharge rate of each sub-basins (wong & brown, 2009), and the volume of the flood of each sub-basins in kedurus river basin that has been obtained from the previous stage. 2.1. identify the hydrology model of kedurus river basin using swat hydrology model to identify the hydrology model of kedurus river basin, we used swat model simulation. the first stage was delineating the boundary of kedurus river basin, then we defined the hrus using the slope, soil, and land use data, then we generated the weather data, and the last we simulated the model for the year of 2016. more detailed explanations and research steps are published in previous journals. 2.2. building potential land use scenario to be applied in kedurus river basin scenarios are what may and/or can happen in the future presented in the form of descriptions of stories. scenarios can be used to identify future alternatives and identify the steps that may cause them to arise (ogilvy, 2015). we used sensitivity analysis to build the scenarios. sensitivity analysis is an analysis that can be used to identify sensitive variables or variables that have a high influence on a model (pannell, 1997). there were several steps taken to build the potential land use scenarios applicable in the kedurus river basin. the first stage was determining the parameters to identify the impacts of variable changes. in this research, the parameters used were the daily discharge peak rate in the flooded sub-basin. then conducted the sensitivity analysis by doing simulation to the hydrology model of kedurus river basin by changing the variable of land use by + 10% from its existing value to know the priorities of variable change that can be used to build the land use scenario. after the priority of land use change has been identified, then compared it with the existing regulation to find out the possible variable changes to be applied in kedurus river basin. the selected variable changes were then built into a scenario. purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 | 89 2.3. identify the best land use scenario to reduce flooding in kedurus river basin the scenarios arranged in the previous stage were then simulated using the swat model to find out how many discharges were successfully reduced by the implementation of the scenario. the result of the simulation was then compared by the result of the first target simulation (the discharge rate of swat simulation before the implementation of the scenario) to know the difference of discharge rate simulated by the model and the channel capacity to find out how much flood volume occurred after the implementation of the scenario. if the scenario simulation results can overcome the flood, it can draw conclusions about the effective scenario to reduce floods. if the scenario simulation results cannot overcome the flood in the entire flooded sub-basins yet, a new scenario was formulated with a modification of one or more variable changes from the previous scenario. 3. results and discussion kedurus river basin located in 112º33'40" le 112º43'30" le, and 7º16'35' sl 7º20'25" sl (figure 1a). based on swat simulation using dem ifsar 2013 map data input with cell size 3m x 3m, kedurus river basin has an area of 7.270,10 ha and is divided into 27 sub-basins (purwitaningsih & pamungkas, 2017). land use in kedurus river basin consists of residential (high density), industry, trade and services, public facilities, paddy fields, row crop agricultural, plantations, green open space and water bodies (blue open space) (figure 1b). figure 1. (a) research area and (b) land use of kedurus river basin (purwitaningsih & pamungkas, 2017) the swat hydrology model simulation was performed after combining the watershed hydrological network, hru data, and climate data together (neitsch et al., 2005). the results of the swat model simulation were daily flow data and hydrological data of the kedurus river basin during the simulation period within 1 year starting from january 1st 2016 until december 31st 2016. based on the simulation results, the highest daily discharge rate in kedurus river basin occurred in march 2016 and reached 15.59 m3/s, which in sub-basin 2. the daily average discharge was 0.41 m3/s (purwitaningsih & pamungkas, 2017) (table 1). the total flood volume in the kedurus river was 546,797.53 m3, which occurred in 12 sub-basins, they were sub-basin 1, 2, 3, 5, 6, 8, 12, 13, 15, 22, 24, and 27. the following data bellow is the channel capacity and flood volume of each sub-basins of kedurus river basin (purwitaningsih & pamungkas, 2017) (table 2). purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 90 | table 1. peak discharge rate in each sub-basins of kedurus river basin (purwitaningsih & pamungkas, 2017) sub-basin peak discharge rate (m3/s) sub-basin peak discharge rate (m3/s) 1 0.66 15 7.84 2 15.59 16 0.33 3 2.54 17 8.15 4 3.01 18 1.43 5 0.27 19 9.37 6 0.21 20 0.72 7 3.43 21 14.72 8 6.21 22 1.31 9 0.56 23 0.51 10 6.91 24 13.58 11 0.58 25 0.69 12 3.74 26 0.83 13 4.43 27 0.40 14 14.72 table 2. identification of kedurus flooded sub-basin (purwitaningsih & pamungkas, 2017) sub-basin peak discharge rate (m3/s) channel capacity (m3/s) flood discharge (m3/s) daily flood volume (m3) 1 0.66 0.20 0.45 6,515.71 2 15.59 12.44 3.15 45,406.28 3 2.54 0.34 2.20 31,707.19 4 3.01 16.51 0.00 0.00 5 0.27 0.07 0.19 2,804.69 6 0.21 0.12 0.09 1,346.46 7 3.43 11.63 0.00 0.00 8 6.21 0.25 5.96 85,779.18 9 0.56 18.96 0.00 0.00 10 6.91 24.37 0.00 0.00 11 0.58 3.92 0.00 0.00 12 3.74 1.34 2.40 34,630.30 13 4.43 0.52 3.92 56,393.81 14 14.72 40.68 0.00 0.00 15 7.84 1.40 6.43 92,621.18 16 0.33 18.78 0.00 0.00 17 8.15 16.86 0.00 0.00 18 1.43 23.10 0.00 0.00 19 9.73 15.11 0.00 0.00 20 0.72 1.44 0.00 0.00 21 14.72 16.35 0.00 0.00 22 1.31 1.16 0.14 2,061.24 23 0.51 4.88 0.00 0.00 24 13.58 0.85 12.73 183,246.57 25 0.69 23.10 0.00 0.00 26 0.83 31.14 0.00 0.00 27 0.40 0.10 0.30 4,284.92 total 546,797.53 red cell means flooded sub basin 3.1. building potential land use scenario to be applied in kedurus river basin in building a potential land use scenario to be applied in the kedurus river basin, the first step was identifying the variable changes that significantly affect the hydrology model of the kedurus river basin. from the sensitivity analysis conducted before, 11 variable changes became a priority in the preparation of land use scenarios to reduce flooding in the kedurus river basin (table 3). not all priorities were used in developing land use scenarios. choosing the appropriate variable changes was based on regulations in the study area (in this case were rtrw kota surabaya, rtrw kabupaten gresik, regulation of mayor of surabaya, and minister of public works regulation no. 41), and based on the number purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 | 91 of land use changes. after the justification, the selected variables were then organized into a scenario. this scenario then became in scenario 1. 1. changes in types of land uses it is a change of one land use type to other land uses • land type change 10% from the paddy field to green open space • land type change 10% from row crop agricultural to green open space • land type change 10% from row crop agricultural to blue open space • land type change 10% from the paddy field to blue open space • additional green open space 10% taken from the plantation or 14.6% change of plantation to green open space 2. changes the portion of built-up area/green space area in a parcel land changes in floor area ratio or green area ratio in built up areas. • additional green area ratio 10% in residential areas. the addition of gar is done by increasing the green open space in residential areas. • additional blue open space 10% in residential areas table 3. the priority of variable changes priority variable changes average of discharge rate reduction (%) 1 additional green area ratio 10% in the residential area 1.40 2 land type change 10% from the paddy field to green open space 0.95 3 land type change 10% from residential to blue open space 0.92 4 additional paddy fields 10% from row crop agricultural 0.85 5 additional paddy fields 10% from residential 0.65 6 land type change 10% from row crop agricultural to green open space 0.61 7 land type change 10% from row crop agricultural to blue open space 0.36 8 land type change 10% from paddy fields to green open space 0.22 9 additional green open space 10% from row crop agricultural 0.22 10 additional green open space 10% from plantations 0.19 11 land use type change 10% from plantations to green open space 0.13 3.2. determine the best land use scenario to reduce flooding the scenarios that had been established based on the sensitivity analysis were then tested to determine the appropriate land use scenarios in reducing flood seen from the decrease of flood volumes in the flooded sub-basin after scenario implementation. scenario testing was done using swat model simulation. table 4. the simulation result of scenario 1 sub-basin q0 (m3/s) q1 (m3/s) qs (m3/s) qb (m3/s) v0 (m3/day) v1 (m3/day) δv (%) 1 0.66 0.63 0.20 0.42 6,515.71 6,093.79 6.48 2 15.59 14.91 12.44 2.47 45,406.28 35,614.28 21.57 3 2.54 2.46 0.34 2.13 31,707.19 30,641.59 3.36 5 0.27 0.26 0.07 0.19 2,804.69 2,701.01 3.70 6 0.21 0.20 0.12 0.09 1,346.46 1,275.90 5.24 8 6.21 5.99 0.25 5.74 85,779.18 82,625.58 3.68 12 3.74 3.64 1.34 2.30 34,630.30 33,147.10 4.28 13 4.43 4.24 0.52 3.72 56,393.81 53,542.61 5.06 15 7.84 7.44 1.40 6.03 92,621.18 86,861.18 6.22 22 1.31 1.24 1.16 0.08 2,061.24 1,082.04 47.51 24 13.58 13.00 0.85 12.15 183,246.57 174,894.57 4.56 27 0.40 0.39 0.10 0.29 4,284.92 4,119.32 3.86 total 546,797.53 512,598.97 purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 92 | the simulation results show that the implementation of scenario 1 was still not able to overcome the flood. implementation of scenario 1 was only able to decrease the flood volume by 34,198.56 m3 (6.25%). the average decrease in flood volume of each flooded sub-basin is 9.63%, with the highest percentage of flood volume decrease was in sub-basin 22, (47.51%) (table 4). therefore, it is necessary to modify the scenario so that flooding problems in sub-catchments 1, 2, 3, 5, 6, 8, 12, 13, 15, 22, 24, and 27 can be solved. the modifications of the scenario were done by increasing the percentage of variable changes in the flooded sub-basin according to the existing land uses. 1. additional green area ratio 20% in residential area in sub-basin 1, 8, 22, and 24. 2. land type changes 20% from the paddy field to green open space in sub-basin 3, 6, 8, 12, 13, 15, and 27. 3. land type changes 20% from the paddy field to blue open space in sub-basin 3, 6, 8, 12, 13, 15, and 27. 4. land type changes 20% from row crop agricultural to green open space in sub-basin 3, 5, and 17. 5. land type changes 20% from row crop agricultural to green open space in sub-basin 3, 5, and 17. these scenario modifications then became scenario 2. table 5. the simulation result of scenario 2 sub-basin q0 (m3/s) q1 (m3/s) qs (m3/s) qb (m3/s) v0 (m3/day) v1 (m3/day) δv (%) 1 0.66 0.61 0.20 0.41 6,515.71 5,884.99 9.68 2 15.59 14.76 12.44 2.32 45,406.28 33,454.28 26.32 3 2.54 2.43 0.34 2.09 31,707.19 30,166.39 4.86 5 0.27 0.26 0.07 0.19 2,804.69 2,701.01 3.70 6 0.21 0.20 0.12 0.09 1,346.46 1,226.94 8.88 8 6.21 5.91 0.25 5.66 85,779.18 81,487.98 5.00 12 3.74 3.60 1.34 2.26 34,630.30 32,614.30 5.82 13 4.43 4.15 0.52 3.63 56,393.81 52,318.61 7.23 15 7.84 7.30 1.40 5.90 92,621.18 84,917.18 8.32 22 1.31 1.21 1.16 0.05 2,061.24 707.64 65.67 24 13.58 12.86 0.85 12.01 183,246.57 172,878.57 5.66 27 0.40 0.39 0.10 0.29 4,284.92 4,119.32 3.86 total 546,797.53 502,477.21 the result of the simulation of scenario 2 shows that scenario 2 also couldn't overcome the flood problem in kedurus river basin. scenario 2 was only able to reduce the flood volume by 44,320.32 m3 (8.11%). the average decrease in flood volume of each flooded sub-basin was 12.92%. with the highest percentage of flood volume decrease in sub-basin 22 (65.67%) (table 5). table 6. the comparison between the implementation of scenario 1 and scenario 2 sub-basin δv1 (%) δv2 (%) δv1.2 (%) 1 6.48 9.68 3.20 2 21.57 26.32 4.76 3 3.36 4.86 1.50 5 3.70 3.70 0.00 6 5.24 8.88 3.64 8 3.68 5.00 1.33 12 4.28 5.82 1.54 13 5.06 7.23 2.17 15 6.22 8.32 2.10 22 47.51 65.67 18.16 24 4.56 5.66 1.10 27 3.86 3.86 0.00 average 9.63 12.92 3.29 purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 | 93 the implementation of scenario 1 to scenario 2 still cannot solve the flood problem in the kedurus river basin. each scenario only contributes to the average decrease in flood volume in each sub-basin by 9.63% and 12.92% (table 6). of the 12 flooded sub-basins. sub-basin 22 experienced a significant increase in the percentage of the decrease of flood volume (18.16%). the implementation of scenarios that emphasize the addition of green area ratio in residential areas had a significant effect on flood reduction in sub-basin 22. this is caused by the land use in sub-basin 22 is dominated by high-density residential. as well as the large presence of existing green open space. thus the addition of gar and blue open space in residential areas had a significant impact. in addition. the channel capacity in sub-basin 22 does not have a high disparity with the simulated discharge rate. so the existing flood volume was also relatively low. in the sub-basin 1. after the implementation of scenario 2 that emphasized the addition of gar in the residential area was only able to increase the decrease of flood volume by 3.20%. the channel capacity at the measurement point at sub-basin 1 is relatively small and only had a channel capacity of 0.20 m3/s. while the simulated discharge rate is 0.66 m3/s. there’s no scenario modification implemented in sub-basin 2. but there was an increase in the decrease of flood volume by 4.76%. sub-basin 2 is located at the downstream of the river. therefore it can be seen that the flood occurred or the hydrological conditions presented in the sub-basin 2 were influenced by the upstream sub-basins. in sub-basin 3. which is dominated by paddy field and row crop agricultural land use type. the implementation of scenario 2 was only able to increase the decrease of flood volume by 1.50%. the reason behind this was the small channel capacity. which is only 0.34 m3/s. meanwhile. the simulated discharge in sub-basin 3 was 2.54 m3/s. the implementation of scenario 1 and scenario 2 in sub-basin 5 did not have any effect on the flood volume. therefore. more technical flood reduction efforts are required in sub-basin 5. in sub-basin 8. the implementation of scenario 2 was only able to increase the decrease of flood volume by 1.33%. the reason was the small channel capacity. which is only 0.25 m3/s. meanwhile. the simulated discharge in sub-basin 8 is 6.21 m3/s. in sub-basin 12. the implementation of scenario 2 was only able to increase the decrease of flood volume only by 1.54%. this is partly due to the small area of sub-basin so the implementation of land use scenarios also didn’t have an insignificant impact. similar to sub-basin 12. the implementation of scenario 2 in sub-basin 13 was only able to increase the decrease of flood volume percentage by 2.17%. the area of the sub-basin was small. and the channel capacity in sub-basin 13 was also small. the channel capacity was only 0.52 m3/s. meanwhile. the simulated discharge rate was 4.43 m3/s. in sub-catchment 15. the implementation of scenario 2 was only able to increase the decrease of flood volume percentage by 2.10%. this was due to the too small channel capacity in sub-basin 15. which had a discharge rate of 7.84 m3/s had a channel capacity of 1.40 m3/s. implementation of scenario 2 in sub-basin 24 was only able to increase the decrease in flood volume percentage by 1.10%. the channel capacity in sub-basin 24 was too small to accommodate the discharge rate of 13.58 m3/s (it’s only 0.85 m3/s). meanwhile. the implementation of scenario 1 and scenario 2 in sub-basin 27 did not have any effect on the flood volume. from the discussion. it can be seen that the flood reduction efforts through land use arrangements can effectively reduce flood volumes in river basin/sub-basin that have dominant land use in the form of highdensity residential. and have sufficient channel capacity. meanwhile. the implementation of the land use scenario has not been able to reduce flooding across the kedurus river basin. therefore. there should be more technical and management flood reduction efforts. 4. conclusion the addition of green area ratio in the built-up area has the highest impact compared to other land use changes in reducing flooding. therefore. the addition of gar is the key to land use arrangements. land use scenarios can be effective if applied in a river basin/sub-basin that has dominant land use in the form of highdensity residential. and also has sufficient channel capacity. in addition. we still need the other flood reducing efforts. which are technically relevant. in the future. it is hoped that there will be a collaboration between the implementation of land use scenarios to reduce flooding and other efforts so that the flood problem in kedurus river basin can be solved thoroughly. purwitaningsih et al. / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 87-94 doi: 10.14710/geoplanning.7.2.87-94 94 | 5. acknowledgments the authors gratefully acknowledge to ministry of research technology. and higher education republic of indonesia and australia-indonesia centre (sp3 infrastructure adaptation pathways) for the financial support. 6. references aryanto. a. f. 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(2016). pengaruh perubahan tutupan lahan terhadap perubahan suhu permukaan di kota surabaya. institut teknologi sepuluh nopember surabaya. https://doi.org/10.1016/j.landusepol.2018.01.028 https://doi.org/10.12962/j23373539.v6i2.24809 https://doi.org/10.1016/j.sbspro.2016.06.091 https://doi.org/10.4018/ijiscram.2014010102 https://doi.org/10.1016/j.ijdrr.2019.101155 https://doi.org/10.2166/wst.2009.436 | 25 geoplanning vol 7, no 1, 2020, 25-36 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.7.1.25-36 flood hazard mapping in residential area using hydrodynamic model hec-ras 5.0 m. b. al amina*, r. s. ilmiatya, a. marlinab a civil engineering department and planning, university of sriwijaya, indonesia b civil engineering department, university of tridinanti, indonesia abstract: the flood hazard rating is one of the essential variables in flood risk analysis. the identification of flood-prone areas urgently requires information about flood hazard zones. this research explains the method to develop flood hazard maps by using hydrodynamic modeling in residential areas. the hydrodynamic model used in this research is hec-ras 5.0, which can simulate the oneand two-dimensional flow regimes. the study area is bukit sejahtera and tanjung rawa residences located in palembang city with a total area of about 200 ha, where the lambidaro river was frequently overflowing caused flood inundation in the area. there are five indicators of flood hazard being analyzed, i.e., 1) flood depth, 2) flow velocity, 3) energy head, 4) flow force, which is the result of multiplication between flood depth and the square of flow velocity, and 5) intensity, which is the result of multiplication between flood depth and the flow velocity. the simulation results show that the flood hazard rating in the study area ranges from high to low level. the zones with a high flood hazard rating are dominated by the area around or near the river, whereas the further zones have a moderate and low flood hazard rating. the flood depth indicator has a more significant influence than the flow velocity on the flood hazard level in the study area. this research is expected can contribute to the development of flood maps and flood control methods in advance. copyright © 2020 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): al amin, m., ilmiaty, r., & marlina, a. (2020). flood hazard mapping in residential area using hydrodynamic model hec-ras 5.0. geoplanning: journal of geomatics and planning, 7(1), 25-36. doi: 10.14710/geoplanning.7.1.25-36 1. introduction flood control is the effort that must be made in order to minimize the impacts and losses due to floods. the formulation of a flood control strategy substantially requires a flood risk map that shows locations and areas with high, medium, and low flood risk levels. solin & skubincan (2013) stated that the level of flood risk in an area is a function of flood hazards, vulnerability, and adaptive capacity. if the levels of flood hazard and vulnerability are high, while the level of capacity is low, an area will have a high-risk level. conversely, if the level of capacity is high, even though the levels of hazard and vulnerability are high, the level of risk of flooding can be low. the standard methods and guidelines in the development and classification of flood hazard levels in indonesia are not currently available in detail. the available guidelines are still general, as stated in the regulation of the head of the national agency for natural disaster countermeasure, abbreviated as bnpb, is the indonesian board for natural disaster affairs, no. 2 the year 2012 concerning general guidelines for disaster risk assessment. even research related to this is also still minimal. several previous studies related to flood hazard analysis and risk are still limited to simple spatial methods (elkhrachy, 2015; oubennaceur et al., 2018; ovando et al., 2018; ozkan & tarhan, 2016; rahman & thakur, 2018; zhang et al., 2015). it causes many variables relating to the characteristics of floods, which are very important to be unavailable. this study fills this gap by using the hec-ras model which focuses on article info: received: 30 september 2019 in revised form: january 2019 accepted: march 2019 available online: 7 july 2020 keywords: flood hazard, flood risk, flood simulation, hec-ras, gis *corresponding author: m. b. al amin civil engineering department and planning, university of sriwijaya, ogan ilir, indonesia email: baitullah@unsri.ac.id open access http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.7.1.25-36 mailto:baitullah@unsri.ac.id al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 26 | hydraulic analysis. the determination of the flood hazard level is very closely related to the hydraulic parameters. wallingford (2005) explained that the flood hazard is a function of inundation depth, flow velocity, and debris flow factor. to obtain these parameters can only be done through a flood modeling and simulation using the hydrodynamic model. the flood inundation simulations based on hydrodynamic models are generally carried out using a one or two-dimensional flow regime and a combination of both (integrated one-dimensional and twodimensional). the advantage of the one-dimensional model compared to the two-dimensional is a fast and simple simulation, while the disadvantage is hydraulic variables such as flow velocity and flow time cannot be described in terms of time and spatial functions. the flood inundation map generated from the onedimensional flow model can only provide information about the distribution and depth of the inundation, while another parameter such as flow velocity cannot be described. the mapping of flood hazards using various hydraulic parameters can only be done with two-dimensional flow models (al amin & haki, 2017). this paper discusses the flood hazard mapping method using a oneand two-dimensional flow-based hydrodynamic model using hec-ras 5.0. the objective of this study is to illustrate how each hydraulic parameter affects the level of flood hazard. the study area took two residential areas, with a total area of 200 ha. it is intended to limit the area so that it only focuses on residential areas that are not too large, and also the results obtained can be more thorough (micro-scale). besides, to simulate a large area requires very high computer specifications and considerable time and effort. however, the same method also can be applied to a broader area. the hydraulic parameters being used as indicators and thresholds in the classification of flood hazard ratings in this study are inundation depth, flow velocity, energy head, intensity, and flow force. 2. data and methods this research was conducted at bukit sejahtera and tanjung rawa residential area located in palembang city, south sumatra, as shown in figure 1. the study area is a former swampland converted into a residential area since the beginning of 1990 (situngkir et al., 2014). in the west, both residential areas are directly adjacent to the lambidaro river, which is a tributary that flows into the musi river. because the study area is located in the lower reaches of the river, the flood discharge and river tides often cause inundation in both areas. the modeling and simulation of flood inundation were carried out through a series of hydrological and hydraulic analyses. the hydrological analysis aims to produce peak flood discharge (qp) with the principle of rainfall-runoff, while the hydraulic analysis aims to produce hydraulics flow parameters such as water surface elevation (wse), flow velocity, flow time, and other flow variables with the principle of a combination of oneand two-dimensional flow. the hydraulic simulation results are then integrated with geographic information systems to produce a flood map. figure 2 illustrates the general stages of flood modeling to produce a flood map. 2.1. flood hazard indicators several hydraulic indicators can be used to classify the level of flood hazards. ribeiro neto et al.(2016) recommended the flood hazards mapping by using indicators based on depth and flow velocity. there are five suggested indicators, namely flow depth, flow velocity, energy head, intensity, and flow force. the last three indicators are a combination of flow depth and velocity formulated as: energy head = d + v2/2g (m) (1) intensity = d.v (m2/s) (2) flow force = d.v2 (m3/s2) (3) where: d: flow depth (m) v: flow velocity (m/s) g: gravitational acceleration (m/s2) al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 | 27 table 1 below provides a range of flood hazard levels for each indicator. these levels are divided into three, namely low, medium, and high ratings. table 1. range of flood hazard indicators (adapted from ribeiro neto et al., 2016) indicators low medium high flow depth (m) 0 – 0.60 0.60 – 1.20 > 1.20 flow velocity (m/s) 0 – 0.60 0.60 – 1.20 > 1.20 energy head (m) 0 – 1.00 1.00 – 2.00 > 2.00 flow force (m3/s2) 0 – 1.00 1.00 – 2.00 > 2.00 intensity (m2/s) 0 – 0.36 0.36 – 1.50 > 1.50 table 2 shows the influence of flood hazard indicators on the damage caused. each indicator has a different effect on the structure of residential buildings, road structure, monetary losses of residential buildings, losses of road infrastructure, and disruption of social and economic activities. for example, the indicator of flow velocity has a strong impact on the structural damage to roads, while the flow depth indicator has a strong influence on the structural damage of residential buildings. table 2. the influence of hydraulic indicators on flood damages (adapted from kreibich et al., 2009 in ribeiro neto et al., 2016) impact indicators damage types structural damage of residential buildings structural damage to roads monetary losses to residential building monetary losses to road infrastructure business interruption and duration flow velocity no strong weak no no water depth strong medium medium no medium energy head strong medium medium no weak flow force weak strong weak no no intensity weak strong weak no weak 2.2. hydrodynamic model hec-ras 5.0 brunner et al. (2015) explained that the hydrologic engineering center's river analysis system abbreviated as hec-ras, is the most widely used river hydraulic model software in the world. initially, hecras was only able to simulate one-dimensional flow only. however, with the increasing need for oneand two-dimensional flow models, hec-ras 5.0 has currently been able to simulate oneand two-dimensional flow regimes. several previous studies that succeeded in simulating oneand two-dimensional flow using hec-ras 5.0 were among those conducted by quirogaa et al. (2016) and patel et al. (2017). the hec-ras software is a public domain that can be downloaded through its official website at http://www.hec.usace.army.mil/software/hec-ras/. the hec-ras 5.0 model uses the saint venant equation or wave diffusion for two-dimensional flow. in general, the wave diffusion equation provides a faster and more stable solution, while the saint venant equation is more applicable to a broader range of hydraulic problems. users are given the freedom to choose the equations they will use. in detail, these equations are given in the hec-ras hydraulic reference manual (brunner, 2016). http://www.hec.usace.army.mil/software/hec-ras/ al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 28 | figure 1. study area figure 2. a flowchart to produce a flood map (adapted from national research council, 2009) this paper only focuses on simulating the flood hazard rating generated for each hydraulic indicator mentioned earlier. therefore, an explanation of the hydrological and hydraulic analysis methods used will not be discussed in more detail here. the detail hydrological modeling and simulation used in this study have been described in the previous study conducted by al amin et al. (2015), while the hydraulic model and the simulation used in this study in detail can be found in al amin et al. (2018). the data used in the hydrological analysis include rainfall intensity, river discharge, topography, river network, land use/cover, and soil type, whereas the data used in the hydraulic analysis consists of channel cross-sections, tidal water level, land cover, base map, and digital elevation model (dem). al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 | 29 3. results and discussion 3.1. a framework to generate flood hazard maps the flood hazard mapping in this research is carried out in stages using an integrated framework. figure 3 below shows the flood hazard mapping framework developed and proposed in this study represented as a flowchart. the framework is the development of the flow chart described earlier in figure 2. the stages begin with hydrological modeling and simulation to produce flood hydrographs. besides, the tide level is generated using a tidal forecasting analysis based on a continuous water level measurement. the flood hydrographs and tide levels are then used as boundary conditions at upstream (as flow hydrograph) and downstream (as stage hydrograph), respectively, in the hydraulic modeling and simulation, as shown in figure 4a. the one-dimensional flow of hydraulic simulation produces a floodwater profile along the river reaches, which is then connected with a two-dimensional model, namely 2-d areas in hec-ras 5.0, using lateral structures. the combination of oneand two-dimensional flow simulation will generate flood inundation on a floodplain with various hydraulic parameters, as given in figure 4b. the hydraulic parameters generated from the simulation are then used as indicators or flood thresholds to produce flood hazard maps. figure 3. a proposed framework to produce flood hazard maps 3.2. model accuracy test the model accuracy test has been carried out and explained in detail in the previous study conducted by al amin et al., (2018) by comparing the simulated inundation depths with the observed one. the observed inundation depths in the field were obtained by interviewing residents and tracking flood trails observed in buildings, houses, and other objects. there were 41 observation points analyzed, as described al amin et al., (2018). figure 5 below shows the relationship of comparison between the simulated inundation depths and observed depths. the model test accuracy parameter used is the root mean square error (rmse), with the value obtained, which is 0.379. the resulted rmse value is quite small, which indicates that the inundation depths of the simulation and observation results have values that are similar or close to each other. thus, the accuracy of the flood inundation model can be said to be quite good, and the results can be trusted. al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 30 | figure 4. the geometry model for river reaches (1-d domain) and floodplain (2-d domain) in hec-ras (a), the simulation result shows flood inundation visualized in ras mapper (b) (adapted from al amin et al., 2018) 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 f lo o d d ep th ( m ) observed points observed depth simulated depth figure 5. the comparison of the simulated and observed depths (adapted fromal amin et al., 2018) 3.3. inundation depth the simulation results of hec-ras 5.0 show that the inundation depths in the study area are varied, of which for bukit sejahtera residence between 0 1.50 m and tanjung rawa residence 0 4.25 m. figure 6 shows the map of inundation depths and the flood hazard rating based on the threshold given in table 1 earlier. from the figure, it can be seen that the high inundation depths likely occur in the area near the river. it is because the topography of the area has a lower elevation compared to other areas. the flood hazard level based on inundation depth indicators in the study area shows a high value because most areas al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 | 31 have inundation depths of more than 1.20 m, especially in tanjung rawa residence. figure 11 shows the distribution of pixel values from raster data analyzed using a geographic information system for each flood hazard indicator. the average inundation depth in the study area is 1.916 m represented by a dashed line, as shown in figure 11. 3.4. flow velocity the flow velocity of the simulation results in the study area is given in figure 7. the high flow velocities likely occur around the tributary of the lambidaro river. it is because the smaller channel size tends to cause the flow velocity to be higher. the flow velocities in the study area range from 0 5.50 m/s. because most of the surface velocities are at low rates, the flood hazard level based on the flow velocity indicator in the study area is dominated by low levels. the average flow velocity is 0.621 m/s, as shown in figure 11. 3.5. energy head the energy head is a function of the sum of the hydraulic head and kinetic head. this energy head is also known as specific energy. figure 8 shows the energy head of the flood flow and flood hazard level in the study area. at a high depth, the flow velocity will be low, so the energy head is more influenced by the flow depth parameter. on the contrary, at a low depth, the influence of flow velocity is more dominant. based on figure 8, it is obtained that the energy head in the study area ranges from 0 – 4.50 m. the flood hazard level based on energy head indicators is in medium to high rating, especially in areas around the river. the average energy head in the study area is 1.912 m, as shown in figure 11. 3.6. intensity the flood intensity is the result of multiplication between inundation depth and flow velocity. figure 9 shows the map of flood intensities along with the flood hazard level. from the figure, it can be seen that the flood intensity in the study area ranges from 0 3.00 m. the level of flood hazard in the study area based on the intensity indicators is dominated by a low rating. the level of medium to high flood hazard likely occurs around rivers and areas with lower topography. it is due to the hydraulic factors, i.e., depths and flow velocities have the same impact on the value of flood intensities. the average flood intensity in the study area is 0.335 m2/s, as given in figure 11. 3.7. flow force unlike the intensity, the flow force is the multiplication of the inundation depth and the square of flow velocity. thus, the influence of the flow velocity is greater than the inundation depth. the flow force map and the flood hazard level for the study area are given in figure 10. from the figure, it can be seen that the flow force ranges from 0 – 5.50 m3/s2. the level of flood hazard in the study area based on flow force indicators is mostly low. the medium level to a high flood hazard likely occurs around the river. the average flow force in the study area is 0.586 m3/s2, as shown in figure 11. al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 32 | figure 6. the inundation depth map (left) and based flood hazard level (right). low hazard: depth = 0 – 0.60 m, medium hazard: depth = 0.60 – 1.20 m, and high hazard: depth > 1.20 m figure 7. the flow velocity map (left) and based flood hazard level (right). low hazard: velocity = 0 – 0.60 m/s, medium hazard: velocity = 0.60 – 1.20 m/s, and high hazard: velocity > 1.20 m/s al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 | 33 figure 8. the energy head map (left) and based flood hazard level (right). low hazard: energy head = 0 – 1.00 m, medium hazard: energy head (1.00 – 2.00 m), high hazard: energy head > 2.00 m figure 9. the flow intensity map (left) and based flood hazard level (right). low hazard: intensity = 0 – 0.36 m2/s), medium hazard: intensity = 0.36 – 1.50 m2/s, and high hazard: intensity > 1.50 m2/s) al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 34 | figure 10. the flow force map (left) and based flood hazard level (right). low level: flow force = 0 – 1.00 m3/s2, medium hazard: flow force = 1.00 – 2.00 m3/s2, and high hazard: flow force > 2.00 m3/s2 figure 11. the distribution of pixel values for each flood hazard indicator. average depth = 1.916 m, average flow velocity = 0.621 m/s, average energy head = 1.912 m, average intensity = 0.335 m2/s, and average flow force = 0.586 m3/s2 there are five flood hazard indicators analyzed in this study, i.e., inundation depth, flow velocity, energy head, intensity, and flow force. based on the results of the analysis, it was found that the level of flood hazard in the study area based on the depth indicator was dominated by a high rating, while based on the indicator, the flow velocity was dominated by a low rating. the other indicators are based on a combination of depth and flow velocity; namely, energy head generates flood hazard levels that vary from medium to high, while indicators of intensity and flow force both generate low flood hazard levels. thus, the inundation depth factor more influences the level of flood hazard in the study area than the flow velocity. it can occur due to the leading cause of the flooding is the high tide of the river, which is higher than the ground elevation in several study areas. the influence of flow velocity on flood hazard level is only dominant in the rivers and surrounding banks. al amin, ilmiaty, and marlina / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 25-36 doi: 10.14710/geoplanning.7.1.25-36 | 35 based on table 2, which classifies the impact of flood hazard indicators on the level of flood damages, it can be stated that the inundation depth indicator, whose influence is more dominant in the study area, has the potential to cause more damage to the structure of house buildings. besides, the impact can also be at a medium level to cause road damage, monetary losses of the residential building, and business interruption and duration. the recommended flood control priorities for the study area are the construction of flood dikes and sluice gates to control the tide level of the river. also, retention ponds and swamps should also be maintained and optimized as reservoirs for the surface runoff. 4. conclusion the flood hazard maps can be generated through hydraulic modeling using hec-ras 5.0 integrated with geographic information systems. the five flood hazard indicators that can be used are inundation depth, flow velocity, energy head, intensity, and flow force. the resulted flood hazard map shows that the study area has a high to low hazard level. the areas with high to medium hazard levels are located around rivers and surrounding areas with elevations lower than floodwaters. the inundation depth indicator has a more significant influence than the flow velocity in determining the level of flood hazard in the study area. this study has succeeded in developing the flood hazard maps based on the hydrodynamic model using hecras 5.0. thus, the results of this study are expected to be useful in the development of subsequent flood control methods. 5. acknowledgments this article’s publication is supported by the united states agency for international development (usaid) through the sustainable higher education research alliance (shera) program for universitas indonesia’s scientific modeling, application, research, and training for city-centered innovation and technology (smart city) project, grant #aid-497-a-1600004, sub grant #iie-00000078-ui-1. the authors would like to thank the university of sriwijaya for funding this research. also, the authors expressed their gratitude and appreciation to the civil engineering students under the supervise of authors who had assisted in field surveying and measuring work. 6. references al amin, m. b., & haki, h. 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[crossref] https://doi.org/10.1016/j.ijdrr.2018.09.007 https://doi.org/10.1016/j.jag.2018.02.013 https://doi.org/10.1016/j.protcy.2016.01.026 https://doi.org/10.1007/s11069-017-2956-6 https://doi.org/10.1016/j.riba.2015.12.001 https://doi.org/10.1016/j.ejrs.2017.10.002 https://doi.org/10.21168/rbrh.v21n2.p377-390 https://doi.org/10.1016/j.wse.2015.05.002 115 geoplanning journal of geomatics and planning vol. 8, no. 2, 2021 original research prospective mapping of land cover and land use in the classified forest of the upper alibori based on satellite imagery dramane issiako 1,2*, ousséni arouna 1,2, karimou soufiyanou 1, ismaila t. imorou 1, brice tente 3 1. laboratory of cartography (lacarto), university of abomey-calavi (uac), cotonou, benin 2. geosciences, environment and applications laboratory (lagea), national university of sciences, technologies, engineering and mathematics (unstim), abomey, benin 3. laboratory of biogeography and environmental expertise (labee), university of abomeycalavi, benin doi: 10.14710/geoplanning.8.2.115-126 abstract the dynamics of land cover and land use in the classified forest of the upper alibori (fcas) in relation to the disturbance of agro-pastoral activities is a major issue in the rational management of forest resources. the objective of this research is to simulate the evolutionary trend of land cover and land use in the fcas by 2069 based on satellite images. landsat images from 2009, 2014 and 2019 obtained from the earthexplorer-usgs archive were used. the methods used are diachronic mapping and spatial forecasting based on senarii. the molusce module available under qgis remote sensing 2.18.2 is used to simulate the future evolution of land cover and land use in the fcas. the land cover and use in the year 2069 is simulated using cellular automata based on the scenarios. the results show that natural land cover units have decreased while anthropogenic formations have increased between 2009 and 2014 and between 2014 and 2019. under the "absence multi-criteria zoning (mzm)" scenario over a 50-year interval, land cover and use will be dominated by crop-fallow mosaics (88%). on the other hand, the scenario "implementation of a multicriteria zoning (mze)", was issued with the aim of reversing the regressive trend of vegetation types by making a rational and sustainable management of resources. copyright © 2021 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction africa had the highest net loss of forest area over the period 2010-2020, at 3.94 million hectares per year (fao et pnue, 2020). forests managed with the support of forestry projects are not spared (gbedahi et al., 2019). sustainable forest management requires the ability to characterise and spatialise the resource within a forest massif (munoz et al., 2015). land cover and its evolution over time is a good indicator of these interactions, as it reflects the impacts of land cover and climate change on natural environments (monier, 2010). furthermore, modelling and projecting land cover changes is becoming a relevant tool for decision support (thierry et al., 2018). it allows territorial planning policies to be analysed in order to assess and anticipate their environmental impacts (samie et al., 2017). exploring the future in a quantitative way is a scientific challenge. taking into account the spatial dimension (in the quantitative sense) in foresight is relatively recent and remains delicate, calling on various skills in geomatics and remote sensing for the reconstruction of past trajectories, but also in modelling (houet, 2015) e-issn: 2355-6544 received: 10 august 2021; accepted: 10 december 2021; published: 30 december 2021. keywords: land use, land cover, spatial prospective, trend, classified forest, upper alibori. *corresponding author(s) email: dramaneissiako@gmail.com https://doi.org/10.14710/geoplanning.8.2.115-126 mailto:dramaneissiako@gmail.com issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 116 in benin, forests are undergoing deforestation or degradation processes of varying severity, with negative impacts on ecosystems and the livelihoods of local populations in particular (moumouni et al., 2019). the human activities directly responsible for forest destruction are commercial timber exploitation, the establishment of crops and plantations, the use of firewood and intensive livestock farming (overgrazing) (sinsin et al., 2010). nevertheless, deforestation, which was estimated at 150,000 ha/year between 1960 and 1980, fell from 70,000 ha/year between 1990 and 2000 to 50,000 ha/year from 2000. this regression is a testimony to the efforts of the beninese state to curb the degradation of vegetation cover (fao, 2015). in relation to the national forest cover, the north of benin concentrates nearly 92.5% of natural resources thanks to the presence of a series of protected areas (classified forests, hunting zones and national parks) (mama et al., 2020). but with high population growth correlated with unsustainable land cover and land use, these reforestation efforts are being challenged. furthermore, the increasing lack of fertile land, the inadequacy of grazing areas and the search for water in the lands bordering the fcas are invoked to justify the unsustainable exploitation of the natural resources available in this area by the indigenous population (mama et al., 2020). agropastoral exploitation observed in the fcas in 2000 by (akindélé, 2000), in 2002 has continued and been reinforced (issiako & arouna, 2018; mama et al., 2020; seidou et al., 2017). deforestation and forest degradation continue in the facs despite the management plan developed with the participation of various stakeholders. the practice of agro-pastoral activities in the fcas and forest dynamics are intimately linked, leading objectively to a restructuring of forest areas. the understanding and monitoring of land cover dynamics as well as the representation of changes affecting the territory are thus political, economic and social issues in the fcas. what is the evolutionary trend of land cover in the fcas in the current context of intense agropastoral practices? the objective of this paper is to simulate the evolutionary trend of land cover and land use in the fcas by 2069 on the basis of scenarios. this research is based on the hypothesis that the evolutionary trends of the vegetation cover by 2069 will vary according to the scenarios put in place. 2. material and methods 2.1. study area the fcas straddles the provinces of atacora, borgou and alibori. it is located between 10°14 and 11°40 north latitude and between 1°54' and 2°55' east longitude. it is located in ecological zones 1 and 2 which include the districts of gogounou, kandi, banikoara, kèrou, ouassa-péhunco and sinendé, which are known to be major producers of cotton, maize and yams. this state forest estate is globally subject to degradation factors including agriculture, hunting, livestock, logging and various forms of encroachment related to the installation of housing and other infrastructure (issiako & arouna, 2018). figure 1 shows the geographical location of the fcas. 2.2. rationale for the choice of dates the participatory management plan for the fcas was developed for the period 2010 2019. thus, the use of spatial imagery before the plan (2009), during the implementation of the plan (2014) and after the implementation of the plan (2019). https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 117 data source: benin general map, national geographic institute (ign) of benin, 2018 figure 1. geographical location of the upper alibori classified forest 2.3. methodological flow chart this research used methodology that can be seen at figure 2. legend: azm: absence of multi-criteria zoning; mze: implementation of zoning with effectiveness source: inspired by (hakim et al., 2019) figure 2. flow chart of the research method https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 118 2.4. planimetric data used the planimetric data used are topographic maps, at 1:50,000 scale, sheets of bagou, alibori forest, goumori, kérou, péhunco, sinendé and sonsoro produced by the institut géographique national (ign) of benin in 2018; landsat 7 etm+ multi-spectral images in geotiff format from 22 december 2009: path192 and row 52; with a spatial resolution of 30 m; landsat oli-tirs (landsat 8) images in geotiff format from 25 december 2014: path192 and row 52; with a spatial resolution of 30 m; and landsat oli-tirs (landsat 8) images in geotiff format, of 20 december 2019; path192 and row 52;with a spatial resolution of 30 m. these images have been downloaded from www.earthexplorer-usgs.gov/usa. these images were radiometrically corrected before digital processing. 2.5. digital processing of landsat images the mapping of spatio-temporal land cover and land use changes started with the digital processing of satellite images using qgis2.18.2 software, in particular the train radom forest image classifier module contained in the orfeo toolkit. the "randomforest" algorithm has already been used in previous studies on satellite image classification (rodriguez-galiano et al., 2012; shao et al., 2016). this digital processing includes: importing landsat images into qgis software, clipping the area of interest, calculating the image pyramid, colour composition, choosing training areas and supervised classification by maximum likelihood. importing the images into qgis. the different image scenes were imported into the qgis software. mosaicing a mosaic of two (02) scenes was made to cover the entire fcas (figure 3). data source: landsat 7 etm+, december 2009, p.192 and r.52, 30 m figure 3. mosaic of two (02) scenes covering the fcas a. image from 2009 before mosaic b. image from 2009 after mosaic https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 119 correction of the 2009 image the 2009 image has been corrected (fill gab); the strips have been filled in to allow further work. figure 4 shows the 2009 image in the study area before and after treatment. data source: landsat 7 etm+, december 2009, p.192 and r.52, 30 m figure 4. landsat 7 etm+ image from 2009 before and after correction creation of rois (region of interest) after a colour composite, the land-use units were identified and coded on the different scenes. for each land-use unit, training areas (rois) were delineated away from the transition zones to avoid including mixed pixels that could be classified in two distinct classes. creation of the classification model in order to classify under random forest, a model was created to run the classification using the rois created previously. the train radom forest image classifier module of the orfeo toolbox was used to create the model. once the model was validated through the value "global performance", for each image (olouloi et al., 2006; toko mouhamadou, 2014), the classification was done using the image classifier module contained in the orfeo toolbox. create image classification this application performs a classification of the input image, based on the model file created with the train random image image classifier algorithm. the supervised classification was then performed. the training plots were used to establish a key numerical feature that could best describe the spectral attributes for each class type. in this case, the parametric algorithm chosen is maximum likelihood (toko mouhamadou, 2014). in supervised classification, the image analyst supervises the pixel categorisation process by specifying to the computer algorithm numerical descriptors of various land cover types present in the scene. thus, representative samples of known land cover sites (training plots) were used. a. 2009 image before correction b. 2009 image after correction https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 120 classification evaluation the control points were geolocalised using a garmin 62s gps receiver at all homogeneous units across the fcas. two validation visits were carried out to confirm and reclassify the results of the satellite image interpretation. of two hundred (195) sampled control points, 190 were found to be correctly classified, i.e. a proportion of 97 %. table i shows the distribution of the ground control points by land cover unit. table i. distribution of fieldwork points by land cover/landuse unit units of land cover traded points validated points percentage (%) field mosaic and fallow 45 44 23 woodlands 14 13 7 gallery forest / riparian formation 40 38 19 plantation 1 1 1 tree and shrub savannahs 95 94 48 vectorisation and development of the transition matrix the classified images were transformed into a shapefile in order to determine the areas of each land cover unit and to establish the transition matrix. the transition matrix is in the form of a square matrix and consists of x rows and y columns. the number of rows in the matrix indicates the number of land-use units at time t 0; the number y of columns in the matrix is the number of converted units at time t 1 and the diagonal contains the areas of the units that remain unchanged. the transformations are done from rows to columns. 2.6. detecting changes average annual rates of spatial expansion (t) the annual average rate of spatial expansion expresses the proportion of each land cover unit that changes annually (zakari et al., 2018). t = (𝑙𝑛𝑆2−𝑙𝑛𝑆1) (𝑡2−𝑡1) x 100 with t: average annual rate of spatial expansion; s1 and s2: area of a land-use unit at dates t1 and t2 respectively; t2 t1: number of years of evolution; ln: natural logarithm; e: base of natural logarithm; (e = 2.71828 invariant coefficient). conversion rate of land cover units the conversion rate of a land cover class is the degree to which the land cover class has changed by converting to other classes. tc = 𝑆𝑖𝑡−𝑆𝑖𝑠 𝑆𝑖𝑡 x 100 with tc: conversion rate; sit : area of unit i at initial date t; sis: area of the same unit remaining stable at date t1. [eq :1] [eq :2] https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 121 2.7. forward-looking land cover mapping method the 2014 and 2019 land cover maps were used to simulate the 2069 land cover. to estimate the reliability and predictive capacity of the simulation to 2069, the 2019 land cover was simulated from the transition of the land cover dynamics observed between 2009 and 2014 as a test. the reference map of 2019 and the one simulated in the same year were compared. therefore, if the validated result reaches an acceptable accuracy (50%), then the simulation for 2069 will be valid. however, if the result is less accurate, the simulation will not be valid. the simulated map in 2019 was produced using qgis remote sensing 2.18.2 software including the molusce (model for land cover change evaluation) module (mienmany, 2018). several factors influencing land cover change were incorporated into this model. these are distance to settlements, distance to fields, distance to roads and population density. cramer's v index was calculated for each explanatory factor and used to select those that best contribute to land cover dynamics. this is the cramer's v coefficient, which is a correlation coefficient that varies from 0.0 (no correlation) to 1.0 (perfect correlation). two scenarios were developed to project the future of land cover in order to facilitate decision-making. these are: scenario 1 absence of multi-criteria zoning (azm), trend (2014-2019) maintained, the azm scenario is a trend scenario that assumes no new forest management policies; and scenario 2 implementation of zoning with effectiveness sustainable management 2069, the main objective of the "sustainable management" scenario is to manage the remaining forest resource through the implementation of an integrated forest management plan. 3. results 3.1. land cover dynamics of the fcas land cover in 2009, 2014, 2019 in the fcas can be seen in figure 5. data source : landsat 7 etm+, landsat 8 oli-tirs, p.192 and r.52; resolution : 30 m ; method supervised classicification : train radom forest of the orfeo figure 5. land cover in 2009, 2014 and 2019 in the fcas https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 122 these three units have respectively -28%, -9% and -7% spatial expansion rates between 2009 and 2014. between 2014 and 2019, units such as dense dry forest (98%), tree and shrub savannahs (64%), gallery forest (49%) and open forest and wooded savannahs (38%) experienced a high conversion rate with a negative average annual spatial expansion rate. 3.2. probability of change of land-use units in scenario 1 the transition probabilities provide information on the likelihood of conversion of units to other landuse units between 2019 and 2069 (scenario 1). scenario 1 transition probability matrix (azm) table ii. transition probability matrix of land cover and land use units class name 2019 class name 2069 total gallery forest / riparian formation woodlands tree and shrub savannahs field mosaic and fallow plantation rocky / uncovered areas water body habitation gallery forest / riparian formation 0.7209 0 0 0.2061 0.0691 0 0 0 1 woodlands 0 0.2646 0.1503 0.5766 0 0 0 0.01 1 tree and shrub savannahs 0 0 0.12 0.8796 0 0 0 0 1 field mosaic and fallow 0 0 0 0.9981 0 0 0 0 1 plantation 0 0 0 0 0.8185 0 0 0.18 1 rocky / uncovered areas 0 0 0 0 0 0.0111 0 0.99 1 water body 0 0 0 0 0 0 1 0 1 habitation 0 0 0 0 0 0 0 1 1 data source : landsat 8 oli-tirs, lclu, 2019, cell transmission rules, simulation sotfware molusce (qgis remote sensing) based on table ii, it can be deduced that by 2069, open forests and wooded savannahs and tree and shrub savannahs will no longer exist and the other vegetation formations if the evolutionary trends observed between 2014 and 2019 are maintained. scenario 2 transition probability matrix (mze) table iii. transition probability matrix from anthropogenic to natural formations class name 2019 class name 2069 total gallery forest / riparian formation dense forest woodlands tree and shrub savannahs field mosaic and fallow plantation rocky / uncovered areas water body habitation gallery forest / riparian formation 0.9959 0 0 0 0 0.0041 0 0 0 1 dense forest 0 1 0 0 0 0 0 0 0 1 woodlands 0 0.0402 0.9551 0 0 0.0048 0 0 0 1 tree and shrub savannahs 0 0.1326 0.3106 0.5531 0.0014 0.0022 0 0 0 1 field mosaic and fallow 0.0267 0.0227 0.0791 0.8671 0.0037 0.0008 0 0 0 1 plantation 0.0073 0.0045 0.0139 0.106 0.0071 0.8607 0 0 0.0005 1 rocky / uncovered areas 0 0 0.3939 0.6061 0 0 0 0 0 1 water body 0.8718 0 0 0 0 0 0 0.1282 0 1 habitation 0 0.001 0.0036 0.9894 0 0 0 0 0.0059 1 data source: landsat 8 oli-tirs, lclu, 2019, cell transmission rules, simulation sotfware molusce (qgis remote sensing) https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 123 based on table iii with the implementation of multi-criteria zoning, there will be more anarchic installation of fields; the probability of reconstitution and stability of natural vegetation by 2069 will be high. 3.3. prospective states of land cover units by 2069 land use by 2069 based on the "absence multi-criteria zoning (azm)" and "implementation multi-criteria zoning" scenarios can be seen in figure 6. data source : landsat 8 oli-tirs, lclu, 2019, cell transmission rules, simulation sotfware molusce (qgis remote sensing) figure 6. land use by 2069 based on the "absence multi-criteria zoning (azm)" and "implementation multi-criteria zoning" scenarios changes observed with"absence multi-criteria zoning (azm)" scenario data source: landsat 8 oli-tirs, lclu, 2019, cell transmission rules, simulation sotfware molusce (qgis remote sensing) figure 7. changes observed between 2019 and 2069 in the absence multi-criteria zoning scenario indeed, in this scenario, gallery forests, dense dry forests, woodlands, tree and shrub savannahs and water bodies will lose 4%, 31% and 63% respectively by 2069 compared to the base year 2019 (figure 7). the area of fields and fallow land will increase by 88%. -100 -50 0 50 100 gallery forest / riparian formation dense forest woodlands tree and shrub savannahs field mosaic and fallow plantation rocky / uncovered areas water body habitation percentage c la ss n am e a) loss and gain in 2019 and 2069 (azm) loss -80 -60 -40 -20 0 20 40 60 80 100 120 gallery forest / riparian formation dense forest woodlands tree and shrub savannahs field mosaic and fallow plantation rocky / uncovered areas water body habitation percentage c la ss n am e b) net change in 2019 and 2069 (azm) loss gain https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 124 changes observed with "implementing multi-criteria zoning with efficiency (mze)" scenario data source: landsat 8 oli-tirs, lclu, 2019, cell transmission rules, simulation sotfware molusce (qgis remote sensing) figure 8. observed changes between 2019 and implementation multi-criteria zoning" scenarios the exam of the figure 8 shows that this loss is likely to be in favour of gallery, dense forest, woodlands and plantations, which will gain 2%, 5%, 13% and 79% respectively in area, or 207,359 ha (figure 8a). this indicates that the establishment of fields in the fcas will be regulated by a new forest management policy in the period 2019-2069. in contrast, gallery, dense forest, woodlands, plantations will experience a net positive change of 23.25% (figure 8b). 4. discussion the diachronic analysis with the combination of the transition matrix allowed to highlight the different forms of conversion that the land cover units in the forêt classée de l'alibori supérieur underwent between 2009, 2014 and 2019. this concerns the regression of natural vegetation formations in favour of anthropogenic formations. anthropogenic pressures on natural resources through these activities have favoured the transformation of natural formations (issiako & arouna, 2018; mama et al., 2020). knowledge of recent dynamics is essential to understand future evolution and its modelling (paegelow et al., 2004). the molusce method is used for the spatial survey in 2069. this method is implemented on the quantum gis software in which there is a modules for land cover change simulations (molusce) plugin (hakim et al., 2019). the land cover change was predicted using a molusce analysis method based on the cellular automata method by mirici et al. (2018) and subiyanto & suprayogi (2019). the multi-layer perceptron (mlp) cellular automata simulator tool simulates the land cover data for the period 2019 and the actual referenced 2019 land cover map obtained from the satellite image in 2019 was used to validate the model and the performance of the model. the overall accuracy indices of the land cover survey in 2069 are 87% (azm) and 85% (mze). this shows that the land cover survey based on the scenarios can be continued as the overall precision value is high. the results of the trend scenario1 (azm), show that the most likely evolutionary trend will be the conversion of gallery forest, dense dry forest, woodlands and shrub and tree savannah into field and fallow mosaic. thus, the areas of crop-fallow mosaics, settlements and plantations will increase by 2069. this increase could be explained by population growth and the lack of arable land in village areas. deforestation due to selective logging will lead to the loss of plant biodiversity. faced with these anthropogenic disturbances in these protected natural landscapes, which impart a change in composition and spatial configuration, biodiversity is in permanent danger in benin and africa (mama et al., 2020). this is in line with the work of thierry et al. (2018) and seko et al. (2018) who conducted their research in the north-west and north-east of benin respectively and who explain the high demand for cultivated land by the increase in population. in the framework of the implementation of the multicriteria efficiency zoning (mze) which is nothing else than the implementation of an integrated forest management plan that can combine both environmental conservation and agropastoral activities. the results obtained show that with the implementation of a new management policy, there will be no more anarchic installation of fields inside the forest. the probability of -100 -50 0 50 100 gallery forest / riparian formation dense forest woodlands tree and shrub savannahs field mosaic and fallow plantation rocky / uncovered areas water body habitation percentage c la ss n am e a) loss and gain in 2019 and 2069 (mze) loss gain -100 -50 0 50 100 gallery forest / riparian formation dense forest woodlands tree and shrub savannahs field mosaic and fallow plantation rocky / uncovered areas water body habitation percentage c la ss n am e b) net change in 2019 and 2069 (mze) loss gain https://doi.org/10.14710/geoplanning.8.2.115-126 issiako et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 115-126 doi: 10.14710/geoplanning.8.2.115-126 125 reconstitution and stability of natural vegetation formations by 2069 will be very high. the hypothesis that the evolutionary trends of the vegetation cover by 2069 vary according to the scenarios implemented is verified. 5. conclusion at the end of the spatial and temporal evaluation of the fcas land use and land cover units, it appears that natural formations have regressed in favour of anthropogenic formations between 2009 and 2014 and between 2014 and 2019, despite the status of protected area with a management plan for this geographical space. thus, the ecological balance of natural vegetation types has been severely disrupted. the projection of current land cover trends using cellular automata has made it possible to assess the dynamics of land cover and land use for the period 2019 2069. during this period, the cultivated area will increase significantly by almost 88% compared to the total forest area if the current trend is maintained with little implementation of the management plan. on the other hand, the implementation of an effective zoning system will allow the control of anthropic pressures, which will in turn allow the restoration of degraded areas. the maps translate into forward-looking trend scenarios and will allow the identification of degraded management areas on the one hand and favourable areas for the conversion of forest resources on the other. this research can provide decision-makers with the necessary data for the elaboration of a future spatial management plan for the conservation and rational exploitation of forest resources. therefore, the prospective study of potential areas for plant biodiversity conservation deserves to be done to further inform decision makers. 6. acknowledgements thank you to all those who have helped in this research. 7. references akindélé, g. 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[crossref] https://doi.org/10.14710/geoplanning.8.2.115-126 https://doi.org/10.19044/esj.2018.v14n15p450 11 geoplanning journal of geomatics and planning vol. 10, no. 1, 2023 original research spatio-temporal dynamics of land use and land cover in the alibori basin in northern benin republic (west africa) abraham babatounde alamou1*, ousséni arouna1, and joseph oloukoi2 1. laboratory of geosciences, environment and applications (lagea), national school of public works (enstp), national university of sciences, technologies, engineering and mathematics (unstim), benin 2. african regional institute for geospatial information science and technology, obafemi awolowo university campus, nigeria doi: 10.14710/geoplanning.10.1.11-22 abstract forest ecosystems of the alibori basin are subject to multiple anthropogenic pressures witch therefore modify their land use and their land cover. this research aims at analyzing the spatio-temporal dynamics of land use and land cover in the alibori basin in northern benin. the methodological approach used is based on the diachronic analysis of land cover from landsat 2, 7, and 8 satellite images acquired respectively in 1980, 2000, and 2020, and the evaluation of land cover change parameters (conversion rate, level of deforestation, intensity and speed of change of land cover units). the results obtained reveal that the number of classes has increased from 8 to 9 with the appearance of plantations between 1980 and 2000. between 1980 and 2020 the basin recorded a degradation of forest formations and an anthrogenization of savannah formations. the intensity and speed of loss of area are quite rapid in dense dry forests, open forests, and wooded savannahs between 1980 and 2020. the average rate of deforestation decreased from 1.27% annually between 1980 and 2000 to 1.26% annually between 2000 and 2020. copyright © 2023 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction the dynamics of land use and land cover evolution in recent decades, have been characterized by a considerable decline in the area of natural vegetation formations to benefit anthropogenic formations (diouf et al., 2019; folahan et al., 2018; imorou et al., 2017). according to fao (2018), the main causes of deforestation globally in the world are agriculture (80%) and infrastructure construction (20%). multiple studies have shown that commercial and subsistence agriculture are the main proximate in africa, subsistence agriculture and production for local markets are more important (curtis et al., 2018; de sy et al., 2019; gibbs et al., 2010; kissinger et al., 2012; rudel, 2013). the growing world population's demand for food, feed, and fiber creates the challenge of enhancing the global agricultural supply without compromising environmental sustainability (henders et al., 2015). tropical deforestation causes loss of biodiversity and other ecosystem services, soil degradation, and the disruption of hydrological cycles (henders et al., 2015). because of its location in the dahomey gap, benin has a small dense forest cover (akoègninou et al., 2006). like other developing countries, the anthrogenization of forest ecosystems has become a major environmental concern that impacts biodiversity in benin (biaou et al., 2019) and this in a worrying way (agbanou et al., 2018). the results of studies and research on the evolution of vegetation types have revealed that the dynamics of vegetation formations (forest ecosystems) in benin have a regressive trend in time and space (benin, 2006; boko, 2012; mama et al., 2013; ousséni et al., 2011). e-issn: 2355-6544 received: 08 may 2023; accepted: 27 october 2023; published: 31 october 2023. keywords: land use and land cover, landsat image, diachronic analysis, deforestation, alibori basin. *corresponding author(s) email: abramsalamou@gmail.com https://doi.org/10.14710/geoplanning.10.1.11-22 mailto:abramsalamou@gmail.com sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 12 the alibori watershed is a semi-arid area where protected areas and the cotton basin are juxtaposed. it is currently the site of a permanent « conflict » between environmental protection and the economic interests of the population (boko, 2012). the growing demand for agricultural land in the alibori basin is highlighted by increasing the cotton area in cultivation, which is one of the main factors not only for the degradation of forest resources but also for hydroclimatic changes (badou, 2016). for issiaka et al. (2016), the evaluation of the physiognomic changes recorded in these natural routes of 2000 to 2013 reveals a regression of forest formations in favor of savannah and anthropogenic formations in banikoara and karimama. the quickest change rate has been recorded at the level of open forests and wooded savannahs. the extent of the loss of forest cover could have an impact on climate regulation, the surface flow and the socio-economic conditions of the rural population that directly depends on it (vissin, 2007). despite extensive research (ousséni arouna et al., 2016; bogaert et al., 2011; kouta & imorou, 2019; mama et al., 2013) carried out in this area to alert decision-makers and attract the attention of various actors, nothing seems to slow down the environmental dynamics in progress. it is therefore appropriate to map the spatiotemporal changes in land use in this environment from satellite imagery. in view of the regressive trend that benin in general and the alibori sub-basins are experiencing, it is appropriate that the overall spatio-temporal dynamics of the alibori basin should be properly assessed. therefore, the aim of this paper is to evaluate the spatio-temporal dynamics of land use and land cover in the alibori basin. 2. data and methods 2.1. study area the geographical framework of this study is the alibori basin. it is located between 10°04'20'' and 12°10'24'' north latitude and between 1°52'17'' and 3°21'27'' east longitude (figure 1). the area is 13,866.24 km². the alibori basin has a population estimated in 2022 at 504,262 inhabitants with an average growth rate of 4.8%. the climate is sudanian with a rainy and a dry season (badou, 2016; boko, 2012; le barbé et al., 1993). figure 1. location of alibori basin https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 13 2.2. data collection two categories of data were used. the first category of data consists of landsat satellite images covering the alibori basin (table 1). these are landsat images of 1980, 2000, and 2020 downloaded from the usgs website (https://earthexplorer.usgs.gov/). 2.3. reasons for periods choice in the case of this study, a step of 20 years was preferred for deforestation measures between 1980 and 2020. the reasons underlying this choice are first: deforestation is not perceptible over a short period hence the impacts of ecosystem degradation of ecosystems. then the year 1980 was chosen because, before this year, the alibori basin did not have quality image scenes covering the entire sector in one year. finally in benin, the 2000s marked the era of transition of decentralized governance with the birth of municipalities and agricultural mechanization. table 1. characteristics of landsat images used satellites sensors reference dates path/rows spatial resolution landsat 2 mss february, 2nd 1980 206/052 et 053 60 m landsat 7 etm february, 2nd 2000 192/ 052 et 053 30m landsat 8 oli-tirs february, 2nd 2020 192/052 et 053 30m the second category of data, known as "complementary" data, consists of gps data (field control points) and the topographic base of the national geographical institute (2018). 2.4. data processing methods the digital processing of the satellite images was carried out in two essential stages: pre-processing and processing. 2.4.1. pre-processing the downloaded landsat data have already been geo-referenced. however, verification and geometric correction were done by superimposing the landsat images with the topographic map of the alibori basin. the images were classified using the maximum likelihood classification technique with envi 5.0 software to identify the land use and the land cover units. there is a correspondence between the planimetric elements (waterways, road network, and protected area) of the topographic map and the images. the images covering only the alibori basin were extracted to facilitate the processing on the screen. 2.4.2. digital and statistical processing the digital processing was done in two steps: the choice of the training sites and the classification method, while the statistical processing consisted of the calculation of the rates from the automatically generated transition matrix. a. selection of training sites the training sites represent the digital characteristics of the classes that allow the definition of the spectral signatures of each vegetation type. according to arouna (2012), training sites are delineated away from transition zones to avoid including mixed pixels, i.e., pixels that could be classified into two distinct classes. in the images, the training sites are plotted to the nearest pixel. they are scattered throughout the study area, representative of the diversity of each vegetation class or other land use unit. b. classification method it is a pixel-by-pixel classification based on the assumption that the spectral signature of each pixel is representative of the class of vegetation in which it is located. according to arouna (2012), the adoption of this classification method is indicated in the case of images of different resolution by considering their spatial resolution which supposes that the different details present in the perimeter of a pixel combine to form a https://doi.org/10.14710/geoplanning.10.1.11-22 https://earthexplorer.usgs.gov/ sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 14 relatively unique and homogeneous for this class of vegetation. the method of processing images of different resolutions adopted in the context of this research is the resampling operation which consisted in sampling the pixels of 30 m resolution through the raster tools of the arcgis software. the supervised maximum likelihood classification consisted of assigning to each group of pixels the most plausible class based on the spectral similarity between the pixels and the class signature. the set of pixels in each satellite image was classified according to the maximum likelihood algorithm extrapolating the spectral characteristics of the training areas to the rest of the image. the ground truthing consisted of verifying the pixel classes resulting from the classification. for this purpose, a sample of 15 classes of training areas per unit of land use and land cover was randomly selected. the accuracy assessment of the image classification was based on a confusion matrix. this matrix was automatically generated in envi 5.0 software. it has allowed us to evaluate the errors of omission, commission, map validity indices, class purity, and overall classification accuracy. c. statistical analysis of changes • transition matrix it is synthetic table that summarizes the different transformations in the state of land use and land cover units in protected areas and village lands in the alibori basin between 1980 and 2000 and between 2000 and 2020. • deforestation rate it is calculated from the following formula: 𝑻𝒅𝒆𝒇 = 𝑫𝒆𝒇 (𝒃,𝒏) 𝑺 𝒙 𝟏𝟎𝟎 ……………...eq. (1) where, def (b,n) brut or net deforestation and s natural formation area of forest at years t. the pontius matrix41 program was used to generate two graphs showing the intensities of land use and land cover unit changes based on the transition matrices between 1980 and 2000 on the one hand, and between 2000 and 2020 on the other. the program ''intensity analysis03.xlms'', has been used to generate statistics for the transitions between each land use and land cover category and the others, according to the time intervals, based on the transition matrices. the same is true for the losses and gains that occurred during transitions between land use and cover units in the alibori basin. 3. result and discussion the results of this research are presented under three headings: confusion matrix of land use and land cover units, land use and land cover dynamics; analysis of the intensity of changes between 1980 and 2020. 3.1 confusion matrix of land use units the accuracy of the maps derived from the interpretation of satellite images was based on a confusion matrix. tables 2, 3, and 4 present the confusion matrices for 1980, 2000, and 2020 respectively. an examination of table 2 shows eight (08) land use and land cover, 90.05% global precision, and weak mutation between the class units. tables 3 and 4 present nine (09) land use and land cover with the appearance of the plantation. the global precision is respectively 82.4% and 93.07%. in 2020, the high mutation can be noticed in farmlands and follows, woodland, and dense and dry forest. the year 2020 image has the highest global precision while the year 1980 image has the lowest. https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 15 table 2. confusion matrix for 1980 classes ff fg wb sss wws ddf sr ag total pgc (%) ff 57 0 0 0 0 0 2 3 62 90,05 fg 0 37 0 0 1 2 0 0 40 wb 0 0 25 1 0 0 0 0 26 sss 0 0 0 166 0 0 0 0 166 wws 0 0 0 0 41 10 0 0 51 ddf 0 21 0 1 4 38 0 0 64 sr 0 0 0 0 0 0 16 2 18 ag 10 0 0 0 0 0 5 70 85 total 67 58 25 168 46 50 23 75 512 *)luc: land use and land cover unit; fg: forest gallery; ddf: dense dry forest; wws: woodland; sss: tree and shrub savannas; pl: plantation; ff: farmlands and fallow land; sr: rocky surface; wb: water body; ag: agglomeration. table 1. confusion matrix for 2000 luc classes ff fg wb sss wws ddf pl sr ag total pgc (%) ff 403 0 0 0 0 0 0 0 0 403 82,4 fg 0 115 0 0 0 0 0 0 0 115 wb 0 0 66 0 0 0 0 0 0 66 sss 0 0 0 2018 0 0 0 1 0 2020 wws 0 0 0 0 112 0 0 0 0 112 ddf 0 0 0 9 0 174 0 0 0 183 pl 0 1 0 2 0 0 87 0 0 90 sr 0 0 0 2 0 0 0 72 0 74 ag 0 0 0 0 0 0 0 0 317 317 total 403 116 66 2031 112 174 87 73 317 3379 *)luc: land use and land cover unit; fg: forest gallery; ddf: dense dry forest; wws: woodland; sss: tree and shrub savannas; pl: plantation; ff: farmlands and fallow land; sr: rocky surface; wb: water body; ag: agglomeration. table 2. confusion matrix for 2020 luc classes ff fg wb sss wws ddf pl sr ag total pgc (%) ff 3065,55 0,00 0,00 808,94 64,82 3,93 64,16 0,00 60,24 4067,64 93.07% fg 5,72 7,06 0,00 0,00 0,00 0,00 0,65 0,00 0,00 13,43 wb 0,00 0,00 1,26 0,00 0,00 0,00 0,00 0,00 0,00 1,26 sss 2478,86 0,00 0,00 4832,36 72,37 4,26 13,75 0,00 5,89 7407,49 wws 488,01 0,00 0,00 442,97 490,34 3,60 10,48 0,00 0,00 1435,40 ddf 347,47 0,00 0,00 252,01 13,75 218,52 0,65 0,00 0,00 832,40 pl 0,32 0,00 0,00 0,00 0,00 0,00 0,33 0,00 0,00 0,65 sr 0,00 0,00 0,00 0,00 0,00 0,00 0,00 66,79 0,00 66,79 ag 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 31,1 31,11 total 6385,93 7,06 1,26 6336,28 641,28 230,31 90,02 66,79 97,24 13856,2 *)luc: land use and land cover unit; fg: forest gallery; ddf: dense dry forest; wws: woodland; sss: tree and shrub savannas; pl: plantation; ff: farmlands and fallow land; sr: rocky surface; wb: water body; ag: agglomeration. https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 16 3.1.1. land use and land cover dynamics from 1980 to 2020 figures 2 illustrate the states of the land use and land cover units in 1980, 2000, and 2020 respectively. the physiognomy of the 1980 land use and cover map (figure 2a) shows the dominance of three land use and covers: dense dry forest, woodland, and tree and shrub savannah. the dense dry forest is more represented in the south of the basin, the woodland, and wooded savannah are more localized from the center to the north, while the sss are found throughout the basin. as for the 2000 survey (figure 2b), the physiognomy in order of dominance is as follows: farmlands and follows, woodland, and tree and shrub savannah. the farmlands and fallows are mainly present in village territories and on the periphery of protected areas. the woodland and tree and shrub savannah are found in the protected areas of the basin. finally, the trends in order of dominance on the 2020 map (figure 2c) are as follows: farmland and fallows and tree and shrub savannah. these two-land use and land covers are more common in village lands and within protected areas, except for park w. (a) land use and land cover in 1980 (b) land use and land cover in 2000 (c) land use and land cover in 2020 figure 2. dynamics of land use and land cover from 1980 to 2020 https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 17 3.1.2. transition matrix of land use and land cover units between 1980 and 2000 table 5 presents the transition matrix of land use and land cover units between 1980 and 2000. an examination of table 5 shows that eight (8) land use and land cover classes were observed in 1980 and nine (9) classes in 2000 with the appearance of plantations class: proof of land use and land cover modification in degradation way. naturals formations as tree and shrub savannah, woodlands, and wooded savannah have known the important loosed area respectively: 2 910.71 km2. while the anthropogenic formations as agglomerations, farmlands, and fallows have gained area respectively: 8.19 km2 and 3 244.44 km2. table 5. land use land cover transition matrix between 1980 and 2000 2000 1980 fg ddf wws sss pl sr ff wb ag areas 1980 (km²) fg 13,43 0,00 0,00 0,00 0,00 0,00 2,29 0,00 0,00 15,72 ddf 0,00 162,05 278,27 282,85 0,00 0,00 93,30 0,00 0,00 816,47 wws 0,00 4,58 636,42 664,24 0,00 0,00 469,45 0,00 0,33 1775,02 sss 0,00 69,08 1625,73 5726,51 0,65 0,00 2904,82 0,00 5,24 10332,03 sr 0,00 0,00 0,00 0,00 0,00 66,79 0,00 0,00 0,00 66,79 ff 0,00 5,24 75,95 144,37 0,00 0,00 597,78 0,00 2,62 825,96 wb 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,26 0,00 1,26 ag 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 22,92 22,92 areas of 2000 (km²) 13,43 240,95 2616,37 6817,97 0,65 66,79 4067,64 1,26 31,11 13866,24 km2 *)luc: land use and land cover unit; fg: forest gallery; ddf: dense dry forest; wws: woodland; sss: tree and shrub savannas; pl: plantation; ff: farmlands and fallow land; sr: rocky surface; wb: water body; ag: agglomeration. 3.1.3. transition matrix of land use and land cover units between 2000 and 2020 table 6 presents the transition matrix of land use and land units between 2000 and 2020. table 6. land use land cover transition matrix between 2000 and 2020 2020 2000 fg ddf wws sss pl sr ff wb ag areas of 2000 fg 7,06 0,00 0,00 0,00 0,65 0,00 5,72 0,00 0,00 13,43 ddf 0,00 66,79 13,75 112,29 0,65 0,00 47,47 0,00 0,00 240,95 wws 0,00 3,60 295,30 1164,46 10,48 0,00 1142,53 0,00 0,00 2616,37 sss 0,00 4,26 272,37 4042,84 13,75 0,00 2478,86 0,00 5,89 6817,97 pl 0,00 0,00 0,00 0,00 0,33 0,00 0,32 0,00 0,00 0,65 sr 0,00 0,00 0,00 0,00 0,00 66,79 0,00 0,00 0,00 66,79 ff 0,00 3,93 64,82 808,94 64,16 0,00 3065,55 0,00 60,24 4067,64 wb 0,00 0,00 0,00 0,00 0,00 0,00 0,00 1,26 0,00 1,26 ag 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00 31,11 31,11 areas 2020 7,06 78,58 646,24 6128,53 90,02 66,79 6740,45 1,26 97,24 13856,17 *)luc: land use and land cover unit; fg: forest gallery; ddf: dense dry forest; wws: woodland; sss: tree and shrub savannas; pl: plantation; ff: farmlands and fallow land; sr: rocky surface; wb: water body; ag: agglomeration. an examination of table 6 shows that nine (9) land use and land cover classes were observed. all of the classes continued to record the mutations. naturals formations as tree and shrub savannah, woodlands and wooded savannah have known the important loosed area respectively: 2 498.5 km2 and 4 067.64 km2. while https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 18 the anthropogenic formations as agglomerations, farmlands and fallows have gained area respectively: 66.13 km2 and 2 672.81 km2. in general, the natural formations of the alibori basin have regressed by undergoing two modes of conversion: savannization and anthrogenization. 3.1.4. evolution of deforestation in the alibori watershed between 1980 and 2020 table 7 shows the deforestation rate in the whole forest units between 1980 and 2000 and between 2000 and 2020 on the other. table 7. deforestation rate of the alibori basin between 1980 and 2020 forests units areas 1980 (km2) areas 2000 (km2) areas 2020 (km2) tdef (%) 1980-2000 tdef (%) 2000-2020 tdef (%) 19802020 fg ddf wws 15,72 13,43 7,06 1.27 1.26 1.265 816,47 240,95 78,58 1775,02 2616,37 646,24 sss 10332.03 6817.97 6128.53 *)fg: forest gallery; ddf: dense dry forest and sss: tree and shrub savannas the analysis of table 7 shows that between 1980 and 2000, the alibori basin has a deforestation rate estimated at 1.27% per year. during the period, the global change rate has estimated to 25.46% of basin area then 16 549.35 hectares lost per year between 1980 and 2000. for the period 2000 to 2020, there was a continuous loss of forest formations with a rate estimated at 1.26% per year (a slight decline). during 2000 to 2020, the global change rate has estimated to 25.27% of basin area, then 12 241.30 hectares lost per year. globaly alibori basin has 1.265% as deforest rate between 1980 to 2020. with this average of deforestation rate, the total area of forest lost annually is estimated to 14 395 hectares between 1980 and 2020. 3.1.5. intensity analysis of changes between 1980 and 2020 figure 3 illustrates the intensities of change of the lucs between 1980-2000 and 2000-2020. figure 3. intensity of change in land use and land cover units between 1980 and 2000 examination of figures 3a show that the tree and shrub savannah (sss) have experienced more change with 33% of loss, 41% of stability, and 8% of profit over 82% of the study area between 1980-2000 while during 2000-2020 the same unit have experienced more changes with 20% of loss, 29% of stability, and 15% of profit over 64% of the study area. during 1980-2000, changes were also intense with wws and ff with respectively 8% and 0.5% of loss, 5% and 4% of stability for 14% and 25% of profit while in period 2000-2020 ff and wws, https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 19 with respectively 7% and 17% of loss, 22% and 2% of stability and 27% and 3% of profit. finally in 1980-2000, the dense dry forests (ddf) with 5% of loss, 1% of stability and no profit while in 2000-2020 ddf were found to have 0.5% of loss, no stability and no profit. 3.1.6. intensity and speed of land use change between 1980 and 2020 figure 4 illustrates the speed of change of lucs between 1980-2000 and 2000-2020. figure 4. intensity and speed of changes in land use and land cover units between 1980 and 2000 the observation in figure 4 shows globally that between 1980-2000 the changes were rapid in four units (ddf, wws, pl, and ff) while during the period of 2000-2020, the changes have been rapid in six units (forest gallery, dense dry forest, woodlands and wooded savannah, plantations, farmlands and fallows, and agglomerations) out of the nine-land use and land covers in the study area, because their rates of changes are greater than 45.32%. it is therefore perceptible that the natural vegetation types (fg, ddf, wws, sss) experienced more rapid or active losses between 1980-2000 with rates of 15%, 33%, 76% and 45% respectively, whereas the anthropogenic units (pl, ff and ag) experienced rapid or active profit with rates of 85%, 85% and 26% respectively. for 2000-2020, is therefore noticeable that the natural vegetation types (forest gallery, dense dry forest, woodlands and wooded savannah and tree and shrub savannah) experienced more rapid or active losses between 2000 and 2020 with respective rates of 47%, 72%, 89%, and 41%, whereas the anthropogenic units (plantations, farmlands and fallows, and agglomerations) experienced rapid or active profit (gain) with rates of 100%, 55%, and 68% respectively. these intensities and rates of change inevitably lead to areas of deforestation in the alibori basin. 3.2 discussion the comparisons of the 1980, 2000, and 2020 land use maps have allowed the assessment of land use and land cover dynamics and deforestation in the alibori basin with the global accuracy of image interpretation between 80% and 92%, which reflects the validity of the classification. the result is in accordance with the research of issiako et al. (2021) who obtained a global accuracy index above 90% for the classification of the images of the forest of upper alibori (fcas). the high rates of speeds and intensities of land cover changes between 1980 to 2020 in alibori basin reflected by rapid gains within anthropogenic formations (plantations, farmlands and fallows and agglomerations) and active losses within natural formations indicate an ongoing deforestation process. these regressive dynamics are explained by the increase in cultivated space and population growth then protected areas are new lands of conquest for farmers because of their fertility. these results are in harmony with those of boko (2012) who concludes that there is a marked decline in natural vegetation types in favour of an increase in the area of farmlands and fallows in the alibori basin, and with the results of issiako et al. (2021) who concludes that there is a 60.21% decrease in natural vegetation types in the fcas in favour of anthropogenic formations between 2009 and 2020. https://doi.org/10.14710/geoplanning.10.1.11-22 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 11-22 doi: 10.14710/geoplanning.10.1.11-22 20 the deforestation rate observed in alibori basin between 1980 and 2020 is 1.265% per year then global change estimated to 25.37% of forests areas. with this average of deforestation rate, the area of forest lost annually is estimated to 14 395 hectares between 1980 and 2020. these results are lower than those of fao (2010) which estimated that from 1978 to 2010, benin lost nearly 85% of its dry dense forests and more than 30% of its vegetation cover, and around 50,000 ha of forest are destroyed each year. other recent studies report a decline in national forest cover, which fell from 31.6% in 1990 to 30.6% in 2015 (biaou et al., 2019; fao, 2018). for djaouga et al. (2021) between 2005 and 2015, the deforestation rate for the entire alibori department was 1.83% per year, including 0.70% in protected areas. thus, 14.96% of the total area of the department is affected by deforestation. the conclusion of this study corroborates the results of the present research. the forests rates approximately equal. but the appreciation of forests areas destroyed in alibori basin per period showed that the lost area between 1980 2000 (16 549 hectares annually) is 1.35% important than 2000-2020 (12 241 hectares annually). this rate is less than the rate of kouta & imorou (2019)whose found that the proportion of the area of the forest landscape in the cotton basin of northern benin experienced a regression of 2.52 times between the periods 2000-2016 and 1986-2000. according to the results of imorou et al. (2019)the deforestation rate for the whole cotton basin between 2000 and 2015 is estimated at 2.94%. to ahononga et al. (2020) the deforestation rate is estimated at 2.94% in the sudanian zone between 2005 and 2015. other similar studies in part of the basin, or in the region have shown that the regression of vegetation types is in favour of anthropogenic formations such as farms and fallows, bare soil, and settlements. based on diachronic studies (avakoudjo et al., 2014; djaouga et al., 2021; issiaka et al., 2016; issiako & arouna, 2018; kouta & imorou, 2019) respectively in benin's park w, karimama district, sudano-guinean zone, alibori upper basin and cotton, there is a correlation between the economic activities of the study area and vegetation regression. 4. conclusion the mapping of the dynamics of land use and land cover revealed that the basin is increasingly undergoing spatio-temporal changes both at the level of village lands and protected areas. the vegetation types in the alibori basin are undergoing a regressive dynamic in favour of anthropogenic formations which therefore has led to deforestation. this research has shown that the alibori basin is experiencing strong deforestation at the profit of anthropogenic formations. the anthropization of the protected areas is brought about by progressive colonization from village lands and peripheries (between 1980 and 2000) to the interior (between 2000 and 2020). today, these protected areas are heavily anthropized. the results of this analysis call on the communal and central authorities to develop or implement an inclusive land-use planning policy for all the alibori basin municipalities. 5. references agbanou, t. b., abdoulaye, d., bogo, g. a. s. o., paegelow, m., & tente, b. 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(2007). impact de la variabilité climatique et de la dynamique des états de surface sur les écoulements du bassin béninois du fleuve niger. université de bourgogne. https://doi.org/10.14710/geoplanning.10.1.11-22 89 geoplanning journal of geomatics and planning vol. 9, no. 2, 2022 original research the correlation between urban development and land surface temperature change in palembang city nadiya t. utami1, bitta pigawati1* 1. diponegoro university, indonesia doi: 10.14710/geoplanning.9.2.89-102 abstract palembang city has experienced an increase in its population. population growth results in an increase in activities which enlarge the built-up areas. the increase of built-up areas is one of the indicators of urban growth. the increase in built-up areas is inversely proportional to the vegetation area. reduced vegetation area might cause an increase in land surface temperature. the aim of the study was to analyze the correlation between urban growth and changes in land surface temperature in palembang city using descriptive quantitative method and spatial analysis on the data obtained from remote sensing images. the result shows that in 1998-2018, palembang city has developed to the north (sukarami district) and to the west (ilir barat i district). there has been an increase in the temperature, documented as 2.12°c. there is a correlation between urban growth and changes in land surface temperature in palembang city. copyright © 2022 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction urban areas are experiencing continuous population growth. urbanization has an impact on high population growth and an increase in the number of urban population to 68% by 2050 (united nations, 2018). complete facilities and job opportunities are attractive factors for population movement (rana & parves, 2011). there is a particular type of relationship between the expansion of the residential area and the distance from the cbd that is affected by the availability of land and the location of facilities (pigawati et al., 2019). high number of population results in a larger need for space, which implies increasing built-up areas (sakti, 2016). limited urban land causes land expansion (inostroza et al., 2010; son et al., 2020). increasing built-up area is an indicator of urban expansion. the large number of physical developments resulted in increasing built-up area of the city (radhinal & ariyanto, 2017). limited land availability in urban areas has led to the development of population activities towards suburban areas (barros, 2004; parés-ramos et al., 2013; pigawati et al., 2017). the activities of urban residents are mostly carried out on built-up land, so they tend to reduce the vegetated areas. the rapid development of bulit-up area is inversely proportional to the vegetated land. an increase of built-up areas results in a decrease of vegetated area. the reduction of vegetation may causes several negative impacts such as increasing land surface temperature (el-hattab et al., 2018; mathew et al., 2018; ullah et al., 2019), initiating urban heat island (the temperature in city center is higher than the temperature in suburbs) (choudhury et al., 2019; ramachandra, 2012), and climate changes (ullah et al., 2019). climate change can worsen environmental conditions, increasing the risk of drought, flooding and extreme temperatures. intergovernmental panel on climate change (ipcc) reports that the impact of climate change is getting worse and requires serious handling (watts, 2018). countries in the world, have formed the united nations framework convention on climate change (unfccc) resulted paris agreement in 2015 e-issn: 2355-6544 received: 31 january 2021; accepted: 22 november 2022; published: 08 december 2022. keywords: urban development, built-up area, land cover, land surface temperatute *corresponding author(s) email: bitta.pigawati@pwk.undip.ac.id https://doi.org/10.14710/geoplanning.9.2.89-102 mailto:bitta.pigawati@pwk.undip.ac.id utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 90 aimed at limiting the rise of temperature surface between 1.5 ° c 2 ° c. indonesia as a member of the unfccc has implemented the agreement stated in the law of the republic of indonesia number 16 of 2016. indonesia is a developing country, so the characteristics of cities in indonesia tend to continue experiencing population increases, changes in land use and land cover. population growth is closely related to changes in land use and land cover (land use and land cover change/lulcc). the phenomenon of increasing the built-up area in an urban area shows that the city is experiencing development. changes in land use and land cover can encourage urban development (belal & moghanm, 2011; hegazy & kaloop, 2015). changes in vegetated land to built-up land can cause changes in temperature. based on the results of research conducted by wang et al., (2019) in the pearl river delta area, there has been an increase in temperature in areas that have changed vegetation land cover into developed land. palembang city has experienced a change in the distribution of land surface temperatures in 2001-2010 (fajar, 2010). this study aimed to analyze the correlation between urban development and land surface temperature change in palembang city. the research has used the quantitative descriptive method and spatial analysis using the geographic information system and remote sensing technology. remote sensing methods can be used to analyze urban development (banzhaf et al., 2009; belal & moghanm, 2011). figure 1 shows study area location, source: sas planet, 2020 figure 1. study area palembang city 2. data and methods this research aimed to analyze correlation between urban develpoment and land surface temperature change in palembang city. using images as its main data, which were obtained from landsat 5 tm for the data https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 91 of 1998 and 2008, and landsat 8 oli for the data of 2018. before conducting spatial analysis, images were processed for radiometric correction, geometric collection, cropping and cloud masking. the steps of the analysis were as follows: a). analysis of land use change in the study area for twenty-year period; b). analysis of urban develppment palembang city for twenty-year period; c). analysis of land cover change in the study area for twenty-year period; d). analysis of land surface temperature change for twenty-year period; e). analysis of correlation between urban develpoment and land surface temperature change in the study area. figure 2 shows a flow chart relating to the methods and analysis used, source: analysis, 2022 figure 2. research analysis flow chart land-use changes were identified by interpreting verified images which were obtained from google earth, such as landsat/copernicus images (in 1998 and 2008) and cnes/airbus images (in 2018). image interpretation was conducted by using the following parameters, namely, tone and color, texture, shape, size, pattern, location, shadow, and association (iryadi et al., 2017). interpretation on images was adjusted to modified land-use classifications proposed by anderson et al. (1976) which consist of industrial, settlement, commercials and services, and non-built-up area. the development of palembang city and its direction can be determined based on the expansion of the built up area in 1998-2018. land-cover changes analysis can be done by utilizing multi-temporal images to identify changes in 1998, 2008, 2018. land cover can be identified directly using images which are verified using images from google https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 92 earth, that are landsat/copernicus images (data of 1998 an0d 2008) and cnes/airbus images (data of 2018). training sample based on campbell’s standard (danoedoro, 2012) which requires a minimum of 100 samples which are categorized as land coverage based on the modification of national standardization agency of indonesia which consists of agriculture, water bodies, vegetation, and open space. classification of land cover was conducted by guided classification technique using gaussian mixture method (gmm) (sejati et al., 2019). the analized land surface temperature change in palembang city can be done by processing the temperature data of 1998 and 2008 based on images from landsat 5 tm (band 6), while the temperature data 2018 were analyzed based on images from landsat 8 oli (band 10). the analysis is carried out by calculating the thermal brightness which is the temperature recorded by the sensor without regard to other factors. the analysis process included conversion of digital number to toa radiance, conversion of spectral radiance to brightness temperatures and conversion of land surface temperatures to celsius scale. the correlation between urban develpoment and land surface temperature change in palembang city was analyzed using simple linear regression method. this statistical method aims to examine the causal relationship between the independent variable (x) and the dependent variable (y). in this study, the variable y (the dependent variable) is the impact caused by the variable x (the independent variable). average land surface temperature is the y factor (the dependent variable) while the built-up area in 1998, 2008, and 2018 were the x factors or the independent variables. spatial modeling validation method in this study is the kapa index. this is done by comparing the results of the model with real conditions in the field. of the 100 samples taken, 70% -80% are valid, so the model can be used as material for analysis. 3. result and discussion 3.1. land-use changes land-use in palembang city consists of the area of settlement, commercials and services, industrial, and non-built-up area. the settlement area takes up most of palembang city. the settlement area in palembang city in 2018 was 13857.49 ha (34.59%). the settlement area is concentrated in the center of palembang city along musi river. settlement area in palembang started to sprawl to nonbuilt-up area in 2018, as observed in sukarami district. the area of commercials and services was initially observed along musi river which is known as the origin of trade center in palembang, then expands along the arterial and collector roads as observed in jakabaring district. the industrial area was initially located on the banks of the musi river, which is accessible by water transportation. the industrial area then developed in kertapati and gandus districts, which are located along the musi river but far from residential area. the biggest land-use change in palembang city observed in 1998-2018 was the settlement area, recorded at 7754.12 ha. there was an increase of settlement area to 127.05% in 1998-2018 where the biggest change was located in sukarami district, recorded at 1714.20 ha (343.74%). the percentage of land-use changes in palembang city in 1998-2018 according to the category is presented in table 1. sukarami district is the area with the largest change to settlement area in palembang city. sukarami district is passed by arterial road and here is the location of sultan mahmud badaruddin ii airport as a means of air transportation for palembang city. the an airport has an influence on changes in land use in the surrounding area (kusumawati et al., 2016). alang-alang lebar and kertapati districts are the districts with the biggest land-use change for industrial area in palembang city, supported by good accessibility. alang-alang lebar district is located in northern palembang, passed by arterial road and the location of alang-alang lebar type a bus station, and closed to sultan mahmud badaruddin ii international airport. kertapati district is located in southern palembang, passed https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 93 by arterial road, and the location of kertapati type a train station, as well as located at the banks of musi river. figure 3 shows spatial distribution of land-use changes in palembang city in 1998-2018. table 1. land-use changes in palembang city in 1998-2018 no. district settlement 1998-2018 trade and services 1998-2018 industry 1998-2018 non-built up 1998-2018 ha % ha % ha % ha % 1. alang-alang lebar 547.62 124.22 87.24 42.50 125.74 173.20 -760.60 -47.68 2. bukit kecil 25.73 22.53 0.00 0.00 0.00 0.00 -25.73 -69.51 3. gandus 796.99 251.51 0.00 0.00 14.17 16.35 -811.16 -10.46 4. ilir barat i 1300.90 164.19 151.36 107.68 28.83 51.17 -1481.09 -32.21 5. ilir barat ii 83.95 30.83 0.00 0.00 0.00 0.00 -83.95 -61.89 6. ilir timur i 13.34 4.73 19.82 11.32 0.00 0.00 -33.16 -69.29 7. ilir timur ii 120.72 27.82 28.45 40.73 70.00 77.39 -219.17 -39.58 8. ilir timur iii 68.00 19.72 114.87 161.99 0.00 0.00 -182.87 -62.48 9. jakabaring 243.84 106.41 78.68 115.57 31.54 106.02 -354.06 -45.16 10. kalidoni 744.57 210.69 14.05 13.83 1.80 4.04 -760.42 -29.97 11. kemuning 108.95 27.60 32.54 33.74 0.00 0.00 -141.49 -73.00 12. kertapati 339.93 94.32 10.33 202.15 208.63 158.85 -558.89 -14.67 13. plaju 191.15 79.64 0.00 0.00 100.00 61.50 -291.15 -30.99 14. sako 507.10 137.20 0.00 0.00 0.00 0.00 -507.10 -40.32 15. seberang ulu i 117.32 47.34 13.87 24.79 0.00 0.00 -131.19 -48.73 16. seberang ulu ii 347.00 118.56 0.00 0.00 1.30 32.91 -348.30 -57.48 17. sematang borang 482.83 403.76 0.00 0.00 0.00 0.00 -482.83 -19.00 18. sukarami 1714.20 343.76 347.74 98.76 15.00 32.38 -2076.94 -56.64 palembang city 7754.12 127.05 898.95 58.46 597.01 74.33 -9250.08 -29.26 source: analysis, 2022 source: analysis, 2022 figure 3. land-use changes in palembang in 1998-2018 https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 94 3.2. urban development of palembang the development of palembang city and its direction can be determined based on the expansion of the built-up area in 1998-2018. the built-up area in palembang city has been increasing in the period of observed years, documented at 8444.28 ha (21.08%) in 1998, 12671.18 ha (31.63%) in 2008, and 17694.35 ha (44.17%) in 2018. sukarami district is the district with the biggest built-up area, documented at 2974.03 ha (16.81%). the growth has begun to occur in the suburban area because of the limited area and expensive land prices in urban area (prihatin, 2016). spatially, built-up area in palembang in 1998 was mostly observed in the center of palembang city, along musi river, in the following districts: ilir timur i, bukit kecil, ilir timur ii, ilir timur iii, and seberang ulu i. boom baru port is located in ilir timur ii district as the gate of distributions of goods and means of transportations in palembang, and has a significant impact on the development of its surrounding area. in 2018, the built-up area had developed to the northern (sukarami district) and western (ilir barat i district) suburbs of palembang city. figure 4. presents the built-up area in palembang city in 1998, 2008, and 2018. source: analysis, 2022 figure 4. built-up area in palembang city in 1998, 2008, and 2018 the built-up area in palembang city in 1998-2018 was documented at 4202.34 ha (10.49% of the area of palembang city). the built-up area continued to increase to 35.51% in 1998-2018 which mostly located in sukarami district, recorded at 1091.85 ha (64.07%). the percentage of changes in built-up area is presented in table 2, the area with the largest changes to built-up areas was located in the northern palembang (sukarami district, which borders banyuasin regency). the changes in built-up area were a result of initial built-up area. in some areas, a sprawling was observed in the surrounding area of initial built-up area (in 1998), like in sematang borang district. changes in built-up areas is irregular and unplanned. the result of overlaying builtup areas in 1998 and 2018 confirms the development of built-up areas on non-built-up areas. it shows that the development of built-up areas goes in line with the decrease of non-built-up areas (subasinghe et al., 2016). figure 5 shows changes in built-up area in palembang in 1998-2018. https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 95 table 2. built-up area and non-built-up area in palembang city in 1998-2018 no. district built up change non-built up change 1998 2018 ha % 1998 2018 ha % 1 alang-alang lebar 718.72 1479.32 760.60 105.83 1595.28 834.68 -760.60 -47.68 2 bukit kecil 184.99 210.71 25.73 13.91 37.01 11.29 -25.73 -69.51 3 gandus 405.60 1216.76 811.16 199.99 7751.40 6940.24 -811.16 -10.46 4 ilir barat i 989.24 2470.32 1481.09 149.72 4597.77 3116.68 -1481.09 -32.21 5 ilir barat ii 276.35 360.30 83.95 30.38 135.65 51.70 -83.95 -61.89 6 ilir timur i 457.15 490.31 33.16 7.25 47.85 14.70 -33.16 -69.29 7 ilir timur ii 594.28 813.45 219.17 36.88 553.72 334.55 -219.17 -39.58 8 ilir timur iii 425.32 608.19 182.88 43.00 292.68 109.81 -182.88 -62.48 9 jakabaring 326.98 681.04 354.06 108.28 784.02 429.96 -354.06 -45.16 10 kalidoni 499.50 1259.91 760.42 152.24 2537.51 1777.09 -760.42 -29.97 11 kemuning 497.18 638.67 141.49 28.46 193.82 52.34 -141.49 -73.00 12 kertapati 496.84 1055.73 558.89 112.49 3809.16 3250.27 -558.89 -14.67 13 plaju 453.41 744.56 291.15 64.21 939.59 648.44 -291.15 -30.99 14 sako 444.24 951.34 507.10 114.15 1257.76 750.66 -507.10 -40.32 15 seberang ulu i 303.80 435.00 131.19 43.18 269.20 138.01 -131.19 -48.73 16 seberang ulu ii 354.02 702.31 348.30 98.39 605.99 257.69 -348.30 -57.48 17 sematang borang 119.58 602.41 482.83 403.76 2541.42 2058.59 -482.83 -19.00 18 sukarami 897.09 2974.03 2076.94 231.52 3666.91 1589.97 -2076.94 -56.64 palembang city 8444.28 17694.35 9250.07 109.54 31616.72 22366.65 -9250.07 -29.26 source: analysis, 2022 source: analysis, 2022 figure 5. changes in built-up area in palembang in 1998-2018 https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 96 the growth of palembang city can be measured from the size of built-up areas and the areas that experiencing an increase in built-up areas. all areas have experienced an increase in their built-up areas but the district with the largest change was observed in sukarami district 2076.94 ha (231.52%) and ilir barat i district 1481.09 ha (149.72%). while the district experiencing the most rapid growth is sematang borang, where the size of built-up area was documented as 482.83 ha (403.76%). sukarami district is located in northern palembang, direct border with banyuasin regency. the district has good accessibility, passed by arterial and collector road, location of sultan mahmud badaruddin ii type a airport, and close to type a bus station in alang-alang lebar district. all those factors support the growth of built-up areas in sukarami district. furthermore, ilir barat i district has extensive growth because its strategic location, close to the center of activities in palembang city that are ilir timur i and bukit kecil districts. the usage of built-up areas in the city center causing the extension of activities to its surrounding areas, ilir barat i district. next, the growth of sematang borang district is influenced by collector roads that increase the accessibility in the area. based on the table of built-up area development, it can be concluded that palembang city has developed to many directions, yet the areas having the biggest growth are the north and the west of palembang. figure 6 shows the direction of urban development in palembang in 1998-2018. source: analysis, 2022 figure 6. the direction of urban development of palembang in 1998-2018 3.3. land-cover changes in palembang land-cover classification system consists of agricultural areas, water bodies, built-up areas, vegetation, and open spaces. in 1998, land cover in palembang was dominated by agricultural areas. in 2008 and 2018, it was dominated by built-up areas. built-up area in palembang was recorded at 17,694.35 ha (44.17%) in 2018. most of the area was located in sukarami district that is 2,974.03 ha (16.61%). https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 97 palembang city has experienced significant land-cover changes. during 1998-2018, built-up areas and water bodies were increasing, while agricultural areas, vegetation, and open spaces were decreasing. built-up area covered up to 9,250.07 ha (109.54%), whereas vegetation decreased to 4,014.67 ha (34.97%). the biggest change in built-up area was observed in sukarami district which was recorded at 2,076.94 ha (231.52%), whereas the area having the least change in built-up area was bukit kecil district, 25.73 ha (13.91%). the area with the largest decrease in vegetation was gandus district, 1,724.11 ha (51.01%), while the area with the least decrease in vegetation was ilir timur i district 1.80 ha (53.63%). table 3 presents percentage of land-use change in palembang city. table 3. land cover changes in palembang in 1998-2018 no. district agriculture 1998-2018 water body 1998-2018 built up 1998-2018 vegetation 1998-2018 open space 1998-2018 ha % ha % ha % ha % ha % 1. alangalang lebar -406.66 -66.32 1.70 1700.00 760.60 105.83 -330.94 -47.46 -24.70 -8.68 2. bukit kecil -3.33 -90.23 0.00 0.00 25.73 13.91 -3.10 -79.35 -19.30 -94.70 3. gandus 946.54 35.55 0.00 0.00 811.16 199.99 -1724.11 -51.01 -33.59 -2.62 4. ilir barat i 1324.96 -68.46 0.00 0.00 1481.09 149.72 -124.54 -5.80 -31.59 -6.19 5. ilir barat ii -38.81 -82.10 0.00 0.00 83.95 30.38 -26.51 -92.63 -18.62 -61.44 6. ilir timur i -3.72 -80.64 0.00 0.00 33.16 7.25 -2.30 -67.25 -27.14 -95.46 7. ilir timur ii -88.82 -73.49 0.00 0.00 219.17 36.88 -89.99 -56.82 -40.34 -65.28 8. ilir timur iii -71.05 -67.87 0.94 12.70 182.88 43.00 -53.11 -61.80 -59.65 -63.02 9. jakabaring -271.20 -59.98 0.00 0.00 354.06 108.28 -20.13 -14.67 -62.73 -46.65 10. kalidoni -201.28 -19.20 0.00 0.00 760.42 152.24 -327.16 -39.60 -231.98 -66.17 11. kemuning -57.78 -73.92 0.90 200.00 141.49 28.46 -41.08 -78.25 -43.52 -69.41 12. kertapati -135.19 -6.12 0.00 0.00 558.89 112.49 -328.74 -31.15 -94.95 -40.99 13. plaju -277.80 -48.29 31.62 18.93 291.15 64.21 -13.39 -21.79 -31.58 -23.25 14. sako -250.52 -54.12 0.18 100.00 507.10 114.15 -118.30 -25.97 -138.46 -40.83 15. seberang ulu i -109.86 -70.38 0.00 0.00 131.19 43.18 -12.49 -47.21 -8.85 -23.27 16. seberang ulu ii -214.63 -65.24 7.80 8.60 348.30 98.39 -117.98 -80.64 -23.49 -58.82 17. sematang borang -385.43 -31.63 0.32 1270.19 482.83 403.76 -67.67 -5.70 -30.04 -22.25 18. sukarami -936.68 -54.00 2.97 471.43 2076.94 231.52 -613.11 -59.81 -530.13 -58.47 palembang city -3831.18 -27.85 46.43 2.74 9250.07 109.54 -4014.67 -34.97 -1450.65 -30.95 source: analysis, 2022 the image of land cover in 1998 which was overlayed with land cover in 2018 shows that there is a noticeable increase of built-up areas in palembang city. land cover which was recorded as agricultural areas, vegetation, and open spaces in 1998, has changed into built-up areas in 2018. it indicates that land-cover changes occurred in palembang city in1998-2018. figure 7 shows spatial distribution of land-cover changes in palembang cities in 1998-2018. https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 98 source: analysis, 2022 figure 7. changes in built-up area in palembang in 1998-2018 3.4. land surface temperature change in palembang city land surface temperature is categorized into four groups, namely <22°c, 22-24°c, 25-27°c, and >27°c. in 1998, the largest distribution of land surface temperature was reported in the category of 22-24°c, documented as 26,760.08 ha (66.80%). while in 2008 and 2018, the largest distribution was reported in the category of 25-27°c, documented as 22,977.80 ha (57.36%), which mostly located in gandus district, documented as 3,502.47 ha (15.24%). in the category of highest temperature, >27°c, the area was documented as 7,160.67 ha (17.87%) which mostly located in ilir barat i district, documented as 795.95 ha (11.12%). land surface temperature distribution in palembang city had changed during 1998-2018. the noticeable decrease in land surface temperature distribution was in the following categories: <22°c and 22-24°c. the largest decrease in land surface temperature distribution was in the category of 22-24°c, documented as 21,580.30 ha (80.64%) which mostly located in in ilir barat i district, documented as 3,485.20 ha (75.90%). source: analysis, 2022 figure 8. changes in land surface temperature in palembang in 1998-2018 https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 99 land surface temperature categories that had noticeable increase were 25-27°c and >27°c. the largest increase was in the category of 25-27°c, documented as 16,346.98 ha (246.53%) which mostly located in sematang borang district, 3,428.11 ha (1668.42%). during 1998-2018, in the category of >27°c, an increase of 5,478.90 ha (325.78%) was documented and mostly located in sukarami district, 701.30 ha (1508.17 %). figure 8 shows spatial distribution of land surface temperature in palembang city in 1998-2018. the average land surface temperature increased during the observation period in 1998-2018. the average land surface temperature in 1998 was 23.29°c while in 2018, the average was recorded at 25.41°c, confirming an increase at 2.12°c. according to howard (1820), average city temperature in 1797-1816 was considered normal at 15-20°c, which means that land surface temperature in palembang is 5°c higher than normal temperature in cities in 1797-1816. temperature increase, which was documented at 2.12°c, is higher than allowed temperature increase based on paris agreement that is 1.5-2°c. an intervention on temperature increase is needed to reduce the impact of climate changes in palembang city. table 4 shows the average land surface temperature in palembang city in 1998, 2008, and 2018. table 4. average land surface temperature in palembang city in 1998, 2008, and 2018 (°c) no. district the average of land surface temperature (°c) 1998 2008 2018 1. alang-alang lebar 23.85 25.45 26.19 2. bukit kecil 28.43 27.78 28.99 3. gandus 21.29 22.39 23.01 4. ilir barat i 23.60 25.31 23.95 5. ilir barat ii 26.20 25.39 28.39 6. ilir timur i 28.65 27.94 29.22 7. ilir timur ii 24.74 25.21 27.45 8. ilir timur iii 26.06 26.55 28.15 9. jakabaring 23.64 24.24 26.88 10. kalidoni 23.12 24.52 25.79 11. kemuning 26.86 27.36 28.88 12. kertapati 22.92 21.89 25.88 13. plaju 23.59 25.19 26.82 14. sako 24.02 25.55 26.20 15. seberang ulu i 25.15 24.50 27.91 16. seberang ulu ii 24.06 24.78 27.72 17. sematang borang 23.01 23.93 25.74 18. sukarami 23.57 25.28 25.98 palembang city 23.29 24.26 25.41 source: analysis, 2022 3.5. the correlation between urban development and land surface temperature change in palembang city the analysis on correlation between urban development and land surface temperature was aimed to observe the impact of urban development to the changes in land surface temperature in palembang city in the period of 1998-2018. data obtained for the analysis were the built-up area as the independent variable and land surface temperature as the dependent variable. the data were documented in 1998-2018. the data were analyzed using simple linear regression on spss to determine the correlation between urban development and land surface temperature in palembang city in 1998-2018. the average land surface temperature and built-up area in palembang city had increased steadily in 1998, 2008, and 2018. the districts which experienced consistent increase of average land surface temperature and built-up area were alang-alang lebar, gandus, jakabaring, kalidoni, sako, seberang ulu i, seberang ulu ii, sematang borang, and sukarami. sukarami district were the district with the highest average land surface https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 100 temperature, which were recorded at 29.22°c in 2018, while gandus district was the lowest, recorded at 23.01°c in 2018. table 5 presents the average land surface temperature and built-up area in palembang city. table 5. average land surface temperature and built-up area in palembang city in 1998, 2008, and 2018 the average of land surface temperature (°c) built up area (ha) 1998 2008 2018 1998 2008 2018 23.29 24.26 25.41 11833.43 14079.76 16035.77 source: analysis, 2022 data obtained for analysis were considered as abnormal, so that data pre-processing step was conducted by data transformation. data transformation is conducted so that the data achieve normality prior to regression (priguno & hadiprajitno, 2013). the hypotheses were: h0 : there are no correlations between urban development and land surface temperature changes in palembang city in 1998, 2008, dan 2018 h1 : there is a correlation between urban development and land surface temperature changes in palembang city in 1998, 2008, dan 2018 the analysis using simple linear regression method resulted in an equation where y= 4.343+0.005 x. the analysis resulted in positive value with significance level 0.029 or less than 0.05 (probability), indicating a correlation between urban development and land surface temperature changes. r value at 0.999 shows that the correlation is strong. r-square value or coefficient of determination at 0.998 or 99.8% shows that the variable of built-up area contributes 99.8% to the variable of average land surface temperature while 0.02% is determined by other factors. it can be established that urban development correlates with land surface temperature. figure 9 presents the result of linear regression analysis on the correlation between urban development and land surface temperature changes in palembang city. source: analysis, 2022 figure 9. result of simple linear regression on the correlation between urban development and land surface temperature in palembang city in 1998, 2008, and 2018 the results of this study are in accordance with land surface temperature studies that have been conducted at the locations of semarang metropoliran region, asansol-durgapur development region and san salvador. (choudhury et al., 2019; sejati et al., 2019; son et al., 2020) so that the results of this study are able to show the correlation between urban development and land surface temperature in palembang city. 4. conclusion palembang city is expanding in all directions. the largest increase in built up areas occurred in sukarami district and ilir barat i district. during period of twenty-years (1998-2018) the development of palembang city towards the north and west (sukarami district and ilir barat i district). the decrease in the area of vegetated https://doi.org/10.14710/geoplanning.9.2.89-102 utami and pigawati/ geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 89-102 doi: 10.14710/geoplanning.9.2.89-102 101 land is significant with the reduced built-up area. in palembang, there was an increase in land surface temperature of 2.12°c in a twenty-years (1998-2018). this indicates that palembang city has exceeded the threshold for land surface temperature increase based on the terms of the 2015 paris agreement (1.5-2°c). the results of this study indicate that there is a correlation between urban development and land surface temperature change in palembang city. the correlation is positive with a significance of 0.029, r is 0.999 and the coefficient of determination is 0.998 or 99.8%. this research was conducted on a regional scale, it would be better if further research could be analyzed with a detailed scope 5. acknowledgments thanks to all staff in palembang regional planning agency (bappeda), badan pusat statistik (bps) as the institution of data providers and department of urban and regional planning of diponegoro university for providing us opportunity to conduct research. 6. references anderson, j. r., hardy, e. e., roach, j. t., & witmer, r. e. 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(2018). we have 12 years to limit climate change catastrophe, warns un. https://www.theguardian.com/environment/2018/oct/08/global-warming-must-not-exceed-15c-warns-landmarkun-report https://doi.org/10.14710/geoplanning.9.2.89-102 https://doi.org/10.24193/jssp.2019.2.03 https://doi.org/10.22212/aspirasi.v6i2.507 https://doi.org/10.24252/planomadani.6.1.9 https://doi.org/https:/doi.org/10.1007/s10668-010-9258-4 https://doi.org/10.1016/j.scs.2019.101432 https://doi.org/10.1016/j.uclim.2020.100617 https://doi.org/10.3390/ijgi5110197 https://doi.org/10.1016/j.jenvman.2019.05.063 https://doi.org/10.1016/j.uclim.2019.100455 165 geoplanning: journal of geomatics and planning, vol. 11, no. 2, 2024, 165-176 original research statistical analysis of short-term shoreline change behavior along the southern cilacap coasts of indonesia bachtiar w. mutaqin1,2*, ariko v. munandar2, jatmiko2, rika harini2, ig.l. setyawan purnama2 1. coastal and watershed research group, faculty of geography, universitas gadjah mada, yogyakarta 55281 indonesia 2. environmental science study program, graduate school of universitas gadjah mada, yogyakarta 55284 indonesia doi: 10.14710/geoplanning.11.2.165-176 abstract there is a threat of extreme waves and a moderate risk level of coastal erosion in bunton village. based on the preliminary assessment, there is huge erosion of the shoreline and visible changes in the shoreline temporally. however, there is no statistical data on short-term shoreline change behavior in this area. hence, this research aims to analyze statistically the short-term shoreline change behavior to understand the conditions and phenomena that occur on the coast of bunton village. landsat images spanning the years 2002 to 2022, with recording intervals of 5 years each, were used to identify the shoreline data, which was later analyzed using the digital shoreline analysis system (dsas). statistical analyses of shortterm shoreline change behavior were obtained using the end point rate (epr) and net shoreline movement (nsm) approaches. over a 20-year period, the bunton coastal area experiences dynamic changes that are primarily due to erosion, with an average distance change of -255.5 meters and an average speed of -14.6 meters per year (very high erosion). the existence of the electric steam power plant (espp) in adipala, which built a breakwater in 2012, has been proven to increase the erosion process. shoreline change in this area can affect various landuses and tourism activities as well as trigger environmental problems in the bunton coastal area. copyright © 2024 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction coastal areas serve as an intermediary region between the land and the water, making them susceptible to influences and forces from both environments (bird, 2008; marfai et al., 2020). human activities on land, such as establishing communities and building infrastructure, engaging in agriculture, and exploiting natural resources, can lead to alterations in the shape and structure of coastal areas (mutaqin, 2017; marfai et al., 2022; widantara & mutaqin, 2024). in coastal environments, tides, waves, and human activities like the use of marine resources can all affect shorelines (rangel-buitrago & neal, 2018; mutaqin et al., 2021a; ningsih & mutaqin, 2024).pinto (2015) states that tides and waves exert influence on the marine occurrence referred to as coastal erosion, which in turn affects coastal regions. furthermore, both human activity and natural processes have an impact on the configuration of beaches in coastal areas (bird, 2008; rangel-buitrago & neal, 2018; marfai et al., 2022). coastal erosion refers to the wearing away of land in coastal regions, a process that is impacted by climate change factors such as increasing sea levels (ningsih & mutaqin, 2024; widantara & mutaqin, 2024). this leads to the shrinking of coasts and has a detrimental effect (mutaqin, 2017; rangel-buitrago & neal, 2018). climate e-issn: 2355-6544 received: 01 january 2024; revised: 22 july 2024; accepted: 13 september 2024; available online: 30 november 2024; published: 04 december 2024. keywords: coastal dynamics, shoreline change rate, erosion, accretion, cilacap *corresponding author(s) email: mutaqin@ugm.ac.id https://doi.org/10.14710/geoplanning.11.2.165-176 mailto:mutaqin@ugm.ac.id mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 166 change, which encompasses sea level rise, alterations in sea water temperature, heightened wind intensity, and more frequent storm occurrences, has the potential to impact both the frequency and intensity of coastal erosion (gornitz, 1991; appelquist & balstrøm, 2015; micallef et al., 2018; ningsih & mutaqin, 2024; widantara & mutaqin, 2024). high population growth in coastal areas is also a serious problem. the increase in population causes an increase in land requirements, especially for settlements (arjasakusuma et al., 2021; alwi et al., 2023). the development of human settlements can reduce the size of the buffer zone on the coast, making this area more vulnerable to erosion (widantara & mutaqin, 2024). the impact of coastal erosion is very significant, both from an environmental and economic perspective. damage to the coastal environment impacts the lives of coastal communities that rely on coastal resources for their livelihoods (mutaqin, 2017). apart from physical impacts such as loss of land and habitat, coastal erosion can also disrupt the social and economic life of the community, including in indonesia. the following authors have documented these changes in the indonesian coastal area, i.e., east java province, north of bali, karimunjawa islands, southern part of yogyakarta, and denpasar city of bali: arjasakusuma et al. (2021), marfai et al. (2022), alwi et al. (2023), ningsih & mutaqin (2024), and widantara & mutaqin (2024). one of indonesia's coastal areas is cilacap regency, central java province, which is located in the south of java island so that it directly faces the indian ocean, which has the characteristic characteristics of large waves, high water salinity, and diverse sediment substrate composition (budiadi, 2020). this position makes cilacap regency have the potential for floods, landslides, tsunamis, and coastal erosion disasters in coastal areas. based on the 2017–2022 regional medium term development plan of cilacap regency, the target for disasterresilient villages in cilacap regency has only reached 7.04% of the total villages. the threat of coastal erosion in cilacap regency occurs in five sub-districts, one of which is adipala district. it is further specified that there is a threat of extreme waves and a moderate risk level of coastal erosion in 23 villages, one of which is bunton village. based on a preliminary visit and an interpretation of the satellite imagery in google earth, it can be seen that there is huge erosion of the shoreline and visible changes in the shoreline temporally (figure 1). (a) (b) source: bachtiar mutaqin, 2023 figure 1. a) the traditional breakwaters and hard-structures that are broken due to erosion; and b) a broken jogging track following the erosion therefore, it is important to carry out studies regarding the statistical analysis of short-term shoreline change behavior to understand the conditions and phenomena that occur on the coast of bunton village. the use of remote sensing technology can provide precise information spatially and temporally to monitor changes in shorelines and coastal ecosystems more efficiently. the acquired results can serve as valuable information for stakeholders in making informed decisions regarding the most suitable management approach to mitigate the adverse effects of coastal erosion threats in the future. https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 167 2. data and methods bunton village is located in the southernmost part of adipala district, directly bordering the indian ocean, and flanked by the adiraja river (east) and the serayu river (west) (figure 2). these two rivers are the natural boundaries of the bunton coast, with various unique physical and biotic characteristics. this position is enough to make bunton village vulnerable to disasters that occur on the coast. located on the southern highway (jjls), bunton can be accessed by buses on the yogyakarta-cilacap-jakarta route, while the distance between the village hall and the edge of bunton beach is 1.6 kilometers. the road that stretches from north to south from the beach to the adipala terminal is approximately four kilometers long. this route is the main evacuation route for the people of bunton village when a marine disaster occurs, such as a tsunami or tidal wave. figure 2. study area in the bunton coastal area, cilacap regency quantitative description is employed as the methodology, utilizing remote sensing techniques and geographic information systems. descriptive research is a method that attempts to describe the object or subject being studied according to the real condition, with the aim of systematically describing the facts and characteristics of the object being studied accurately (sukardi, 2008). the main dataset utilized for input consists of landsat images spanning the years 2002 to 2022, with recording intervals of 5 years each, specifically 2002, 2007, 2012, 2017, and 2022. the year 2012 was selected as the midpoint between the preand post-development of the electric steam power plant in adipala. furthermore, the selection of the image depicting the year 2022 was made in order to acquire the most recent data sources, which are readily available at no cost and exhibit little cloud cover (pribadi et al., 2020; septiangga & mutaqin, 2021; alwi et al., 2023). https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 168 the visual interpretation of the five satellite images was thereafter conducted in order to ascertain the presence of the shoreline. in addition, the process of manual digitization was conducted by utilizing the editing feature within the qgis software in order to get shoreline data. the shoreline data acquired was subsequently subjected to analysis utilizing the digital shoreline analysis system (dsas). prior to conducting any additional analysis, it is necessary to record the five shoreline vectors in a database as a single feature class file (himmelstoss et al., 2021). additionally, the feature class was enhanced by incorporating a new information column through the utilization of the attribute automator function. this additional column includes information about the time at which data sources were acquired and the values of uncertainty. the values of uncertainty serve as a means to ascertain the precise location of the intersection point between the shoreline and the transect by providing information on the distance surrounding the shoreline (arjasakusuma et al., 2021; himmelstoss et al., 2021; alwi et al., 2023). furthermore, it is imperative to establish a feature class baseline, which will serve as the initial reference for delineating the transect line (ningsih & mutaqin, 2024; widantara & mutaqin, 2024). the process of establishing the baseline might be facilitated by utilizing the buffer function inside the 2002 shoreline data, which is considered the oldest shoreline (mutaqin, 2017; marfai et al., 2022; alwi et al., 2023). it is also important to provide a new column in the feature class baseline that includes id, group, and search information. the baseline is grouped using id and group information, while the length of the transect line is determined using search information as a reference value (himmelstoss et al., 2021). subsequently, it became imperative to allocate various options within the default parameter function based on the data stored in shoreline and baseline attributes. after that, the compute rates function was used to find the shoreline change distances after the cast transects function was run (marfai et al., 2022; alwi et al., 2023; ningsih & mutaqin, 2024; widantara & mutaqin, 2024). statistical analyses of short-term shoreline change behavior were obtained using the end point rate (epr) and net shoreline movement (nsm) approaches. the nsm approach is employed for quantifying the extent of alteration in shoreline displacement between the most ancient and the most recent shorelines (mutaqin, 2017; marfai et al., 2022; alwi et al., 2023). the epr technique is employed for the computation of the shoreline rate of change, wherein the temporal dimension is divided by the distance between the oldest shorelines and the most recent shorelines (mutaqin, 2017; arjasakusuma et al., 2021). the dsas statistical data is utilized to determine the distance between the oldest shoreline, specifically 2002, and the most recent shoreline, specifically 2022 (during a span of 20 years). a positive value (+) denotes accretion, whereas a negative value (-) signifies erosion. in order to assess land use and environmental issues, an examination of field conditions is conducted through the utilization of cross profiles (cross sections) and visual observations. in the research area, crosssectional profiling is modified to align with topographic maps and digital elevation model (dem) data. 3. result and discussion based on satellite images from 2002, 2007, 2012, 2017, and 2022, it is evident that coastal dynamics have significantly altered the shoreline in bunton over the past 20 years (figure 3a). coastal erosion, one kind of coastal dynamics, is a disaster that is very detrimental to people's lives, especially those on the coast (mutaqin, 2017; arjasakusuma et al., 2021; marfai et al., 2022). coastal erosion is a natural phenomenon related to changes in sea level rise, climate, and ecosystems that are largely influenced by destructive human activities and result in many problems in coastal areas (ningsih & mutaqin, 2024; widantara & mutaqin, 2024). based on digital shoreline analysis system (dsas) analysis with the nsm approach, 90 transects were identified with shoreline changes caused by erosion ranging from -5 meters (transect 47) to -657 meters (transect 11) with an average change in distance of -255.5 meters, while the shoreline changes caused by accretion range from +4 meters (transect 41) to +19 meters (transect 48) with an average of +12.8 meters (figure 3b). based on epr analysis (figure 3c), changes in shorelines caused by erosion have an average speed of 14.6 meters per year, which is categorized as very high erosion according to nassar et al. (2018). meanwhile, changes in shorelines caused by accretion have an average speed of +0.6 meters/year, which is categorized as https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 169 moderate accretion (nassar et al., 2018; alwi et al., 2023). the erosion process that occurs shows that closer to the electric steam power plant (espp) in adipala, the erosion rate decreases. compared to other indonesian coastal areas that also have espps, such as pandeglang (banten) and buleleng (bali), the closer to the espp, the erosion rate is also decreased, and somehow there is an accretion (mutaqin et al., 2021b; marfai et al., 2022). (a) (b) (c) figure 3. a) coastal dynamics have significantly altered the shoreline in bunton over the past 20 years; b) nsm results in 90 transects; and c) epr results along bunton coastal area shoreline changes can occur naturally and be accelerated by human activities (luijendijk et al., 2018; vasconcelos et al., 2024). based on observations in the field, bunton is dominated by beach landforms with black sand materials. this material will more easily experience coastal erosion since most of the materials that make up the beach have not experienced compaction (mutaqin, 2017; arjasakusuma et al., 2021; alwi et al., 2023; ningsih & mutaqin, 2024; widantara & mutaqin, 2024). coastal erosion is the inability of coastal materials to reduce energy, including wave action (bird, 2008). the wave energy that hits bunton is a large wave that may trigger erosion. the wave propagates in a straight line without experiencing significant changes in its shape or speed over a fairly long distance and over a relatively long period of time until it hits the beach. furthermore, the waters south of java, including bunton, generally have characteristics of a combination of strong local winds, especially during the east monsoon, as well as wave-tide dominated coasts (mutaqin, 2017; mutaqin & ningsih, 2023). human or anthropogenic activities, like sand mining (luijendijk et al., 2018), carried out by private companies and the community up until 2018, could potentially contribute to erosion in coastal areas. in addition, the presence of a breakwater from the espp may impact the coastal dynamics in this area (luijendijk et al., 2018; vasconcelos et al., 2024). the construction of the adipala espp located in bunton began in early 2012 and began operating at the end of 2015 until now. to determine the effect of the espp and its breakwater construction, we compare the distance and rate of change in the shoreline before and after the espp construction. the comparison graph of distance and change rate demonstrates that there was a difference in epr and nsm before and after the construction of the breakwater by adipala espp (figure 4). https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 170 (a) (b) figure 4. comparison between preand post-construction of espp in adipala for: a) nsm results in 90 transects; and b) epr results along bunton coastal area before the adipala espp was established, the average erosion distance was -106.34 meters in 10 years (2002–2012), with an average rate of -11.06 meters per year. after the construction of the adipala espp, in 2012 there was an increase in the average erosion distance to -189.59 in 10 years (2012–2022) meters with an average rate of -20.03 meters/year. meanwhile, accretion from pre-construction had an average accretion distance of +44.75 meters in 10 years, with an average rate of +3.94 meters per year. after the espp was constructed, it reached an average accretion distance of +89.69 meters in 10 years, with an average rate of +8.97 meters per year. the presence of a breakwater from espp may influence the movement of the dominant hydrooceanographic parameters. hydro-oceanographic parameters play an important role, namely as a medium for transporting sediment and erosion agents in coastal areas (luijendijk et al., 2018; nassar et al., 2018; marfai et al., 2022; vasconcelos et al., 2024). greater erosion occurred in the western part of the espp based on dsas calculations after the construction of the espp, with an average rate of -29.8 meters per year compared to the eastern part, which reached -12.72 meters per year. strong local winds and wave energy may impact this situation, especially during the east monsoon. strong eastern waves have strong energy, which may cause the western part of the espp, due to wave deformation, to receive energy that is strong enough to cause more dominant erosion (mutaqin, 2017). wave breakers and wave deformation cause the eastern part of the espp to receive less wave energy. furthermore, apart from wave energy, other factors, such as current patterns, may also influence erosion. wave breakers have the potential to change the direction of longshore currents moving from east to west. due to the slowing down of currents and the diversion of their direction by coastal structures, longshore currents slow down or become low in speed (triatmodjo, 2012). an analysis of shoreline changes in the bunton coastal area from 2002 to 2022 can demonstrate the impact that erosion processes have on the environmental conditions near the bunton coast in relation to landuse. we conducted field observations using a 100-meter cross-sectional profile analysis at six locations experiencing massive erosion to understand the potential impact of coastal erosion on land use, both currently and in the future (figure 5). cross-profile 1 (figure 5a) is located to the west of the adipala espp and is the final point of the western observation location. based on the results of dsas calculations as a whole for 20 years in the 2002– 2022 period, cross-profile 1 is associated with transect 11, which experienced changes in the shoreline in the https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 171 form of erosion. the erosion process occurs at a rate of -32.9 meters per year, with a change distance of -657.92 meters. at this location, there are 57 meters of undeveloped, unplanted land from the coast to the river. at high tide, seawater inundates the beach at this location (figure 6a), potentially leading to environmental issues such as marine debris (isnain & mutaqin, 2023; wahid & mutaqin, 2024; hibatullah & mutaqin, 2024). conditions near river estuaries cause changes in shorelines (both erosion and accretion) that are very dynamic because they depend on the amount of river water supply, transported sediment material, climate, wave activity, tidal waves, and currents (bird, 2008; ningsih & mutaqin, 2024; widantara & mutaqin, 2024). based on the results of dsas calculations, cross-profile 2 (figure 5b) is associated with transect 23, which experienced changes in the shoreline in the form of erosion. the erosion process has a rate of -26.38 meters per year and a change distance of -527.51 meters. cross-profile 2 has topography that tends to be gentle with low elevation differences and has empty land covered with vegetation for 45 meters, which is then bordered by a 12meter asphalt road and a 7-meter river. sloping conditions with low elevation differences may cause waves to hit the land further when the extreme waves occur (mutaqin, 2017). at this location, various types of plastic waste were found along the shoreline (figure 6b). plastic in the aquatic environment will last for a very long time, and because it is light, the plastic will concentrate on the surface of the water, potentially affecting the quality of the coastal area (lebreton et al., 2017; noya & tuahatu, 2021; isnain & mutaqin, 2023; wahid & mutaqin, 2024; hibatullah & mutaqin, 2024). cross-profile 3 (figure 5c) is the closest point to the espp, and it is associated with transect 33 with an erosion rate of -18.6 meters per year and a change distance of -371.86 meters. crossprofile 3 has quite significant and varied elevational differences. agricultural land situated near the beach and beach ridge is interspersed with 50 meters of empty land. the significant elevation difference between the beach and agricultural land in the form of a beach ridge (figure 6c) may become a natural barrier for agricultural land during high and extreme waves (dias & kjerfve, 2009; isla et al., 2023; cescon et al., 2024). cross-profile 4 (figure 5d) is the closest point to the espp in the east part. this cross-profile is associated with transect 54, which experienced shoreline changes in the form of erosion. the erosion process has a rate of -11.47 meters per year and a change distance of -229.41 meters. locals use this area for tourism-related activities, i.e., to build temporary stalls to sell food and beverages (figure 6d). even though there were numerous pine plants (casuarina equisetifolia) in this area, it cannot protect this area from coastal erosion since casuarina equisetifolia is a wind barrier and not strong enough to face wave energy (mutaqin, 2017). ex-fishermen who switched careers because fishing was no longer profitable developed the agricultural land in this area. due to extremely high coastal erosion in this area, the sustainability of tourism and agricultural activities was at risk, not only due to land loss but also due to the potential for seawater intrusion and coastal flooding (ningsih & mutaqin, 2024; widantara & mutaqin, 2024; purnama et al., 2024). cross-profile 5 (figure 5e) is associated with transect 70, which experienced erosion with a rate of -13.95 meters per year and a change distance of -279.01 meters. hard rock structures that can withstand waves serve as a limit to the difference in beach elevation and empty land (figure 6e). even though the hard rock structure can withstand wave energy, coastal conditions directly facing the sea will become increasingly eroded, increasing the potential for seawater intrusion (zamroni et al., 2021; putriany & sejati, 2023; ningsih & mutaqin, 2024; widantara & mutaqin, 2024; purnama et al., 2024). cross-profile 6 (figure 5f) is associated with transect 89, which also experienced coastal erosion. this area has an erosion rate of -5.22 meters per year and a change distance of -104.34 meters. this area has almost the same conditions as cross-profile 1 (figure 5a), which is dominated by empty land, but vegetation in the form of grass and casuarina equisetifolia is found in the middle (figure 6f). apart from coastal erosion, other issues in this area include marine debris, which is also similar to environmental problems faced by yogyakarta province (isnain & mutaqin, 2023; wahid & mutaqin, 2024; hibatullah & mutaqin, 2024). shoreline dynamics in proximity to river estuaries are influenced by various factors, including the quantity of river water supply, transported sediment material, climate conditions, wave activity, tidal waves, and currents (bird, 2008; ningsih & mutaqin, 2024; widantara & mutaqin, 2024). these conditions give rise to both erosion and accretion processes, which exhibit a high degree of dynamism. https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 172 (a) (b) (c) (d) (e) (f) figure 5. a 100-meter cross-sectional profile analysis at 6 locations that experience massive erosion along bunton coastal area (not-to-scale) https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 173 (a) (b) (c) (d) (e) (f) source: ariko v. munandar, 2023 figure 6. present condition along the 100-meter cross-sectional profile at cross-profile 1 (a) to 6 (f) that experience massive erosion along bunton coastal area in the bunton coastal area, the environmental problems that occur are quite serious, especially regarding the decline in land function due to coastal erosion, which has been going on for 20 years. seawater may submerge tens of hectares of privately owned agricultural land due to extreme erosion and the effects of climate change (ningsih & mutaqin, 2024; widantara & mutaqin, 2024). this environmental damage in bunton certainly threatens the sustainability of businesses and/or people's livelihoods. moreover, the presence of marine debris in this area is quite high, and it may affect tourism activities and other coastal ecosystems (isnain & mutaqin, 2023; wahid & mutaqin, 2024; hibatullah & mutaqin, 2024). this is a very serious condition and requires exceptional action to address the problem immediately. erosion, if left untreated, could lead to further consequences that not only harm agricultural land but also jeopardize water sources (marfai et al., 2020; purnama et al., 2024), causing disruption to the residents of the bunton coastal areas. the environmental conditions and vulnerable coastal natural resources are impacting the socio-economic and socio-cultural aspects of the population living in this area (mutaqin, 2017; quesada-román et al., 2023). each coastal area has different characteristics and problems depending on location, human activity, and how it is managed through the application of laws and regulations that apply in that area (bird, 2008; kay & adler, 2005; mutaqin et al., 2020). the regional disaster management agency and the bunton communities https://doi.org/10.14710/geoplanning.11.2.165-176 mutaqin et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 165-176 doi: 10.14710/geoplanning.11.2.165-176 174 are already aware that the very high level of erosion in the bunton coastal area may exacerbate the existing problems. even though several mitigation measures have been put in place, they still have not stopped coastal erosion because the communities are using simple materials without considering any detailed hydrooceanographic conditions. moreover, the existence of espp in adipala may also accelerate erosion processes. to mitigate the impact of coastal erosion on human life, environmental damage, and its effects on social and economic problems, bunton must implement sustainable management of coastal areas and mitigation plans for potential disasters (bell et al., 2017; luijendijk et al., 2018; marfai et al., 2022; alwi et al., 2023; vasconcelos et al., 2024). 4. conclusion over a 20-year period, from 2002 to 2022, the bunton coastal area experiences dynamic changes that are primarily due to erosion, with an average distance change of -255.5 meters and an average speed of -14.6 meters per year, both of which are considered to be very high erosion. on the other hand, the accretion process, with an average distance change of +12.8 meters and an average speed of +0.6 meters/year, falls into the category of moderate accretion. the existence of the espp, which built a breakwater in 2012, had increased the erosion process. before the construction of the breakwater, the shoreline changed due to erosion, with an average distance of -106.34 meters in 10 years (2002–2012) and an average rate of -11.06 meters per year. after the adipala espp was established, the average erosion distance increased to -189.59 meters in 10 years (2012–2022), with an average rate of -20.03 meters per year. the environmental issues in the bunton coastal region are highly severe, particularly over the deterioration of land functionality caused by ongoing coastal erosion for the past two decades. due to severe erosion and the impacts of climate change, seawater has the potential to inundate tens of hectares of privately owned agricultural land in bunton. furthermore, there is a significant proliferation of marine debris in this region, which has the potential to impact tourism operations and other coastal ecosystems. in order to mitigate the adverse effects of coastal erosion in the bunton coastal area, it is imperative to implement sustainable management strategies and develop mitigation measures for prospective disasters. left untreated erosion may result in additional repercussions that not only damage agricultural land but also endanger water sources, therefore disrupting the lives of the inhabitants of the bunton coastal regions. the ecological conditions and delicate coastal natural resources are affecting the socio-economic and socio-cultural dimensions of the inhabiting population in this region. 5. acknowledgments universitas gadjah mada funded this research through the postgraduate school research grant 2023, with dr. rika harini as the principal investigator. the author thanks master students of environmental science, especially veronika permata, adjeng lestari, anisa florensia, kimintha wibowo, and ilya maulida, for their help and assistance during the data collection, as well as ntrl and tuan tigabelas for their support during the writing process. furthermore, the authors also thank anonymous reviewers for their helpful comments on this paper. 6. references alwi, m., mutaqin, b.w., marfai, m.a. 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[crossref] https://doi.org/10.14710/geoplanning.11.2.165-176 https://doi.org/10.1016/j.envdev.2023.100935 https://doi.org/10.1007/978-3-319-48657-4_409-1 http://doi.org/10.21163/gt_2021.163.04 https://doi.org/10.1016/j.jsames.2024.104832 https://doi.org/10.1007/s11852-024-01036-3 https://doi.org/10.1007/s11069-024-06506-3 https://doi.org/10.1088/1755-1315/782/2/022006 | 99 geoplanning vol 6, no 2, 2019, 99-112 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.2.99-112 the spatial patterns and local economic determinant of industrial agglomeration in semarang district, indonesia r. a. pangarsoa*, r. suharyadia, r. rijantaa a geography faculty, gadjah mada university, indonesia abstract: urbanization and industrialization are actual phenomena in metropolitan cities, including semarang district, a part of semarang metropolitan. on the other hand, this district is still facing economic problems such as unequal income and unemployment. in this context, it is interesting to identify the linkage between industrial agglomeration and the local economy. this research aims: to identify the spatial patterns of industrial agglomeration; and to identify the main factors of the local economy and how do they determine the industrial agglomeration. the research was done at the district and subdistrict levels from january to july 2017. it uses variables of geographical data and workers of large and medium industries (lmis) and the local economy. nearest neighbor analysis, ellison-glaeser index, and specialization index are used to analyze industrial agglomeration's spatial pattern. factor analysis is used to identify the local economy's main factors, and geographically weighted regression to identify how the factors determine the industrial agglomeration. the result shows lmis in semarang district geographically clustered, strongly agglomerated, and highly specialized in some subdistricts that occur in sub-sectors: food; beverages; wearing apparel; non-metal mining; and furniture industry. the main factors of the local economy that determine the industrial agglomeration are: (1) factors of livestock and horticulture region positively affect the food industry agglomeration; and (2) factors of urban and industrial region positively affect to the wearing apparel industry agglomeration. the strong agglomeration and sectoral specialization reflect the spillover in the inter-firm relationship, employment opportunity, and knowledge transfer. the linkage of the food industry with the agriculture-based local economy (horticulture and livestock) illustrates the spatial integration and linkages between rural-urban areas. the linkage of the apparel industry that dominantly footloose with urban and industrial areas shows a great dependence on international markets and suppliers. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): pangarso, r., suharyadi, r., & rijanta, r. (2020). the spatial patterns and local economic determinant of industrial agglomeration in semarang district, indonesia. geoplanning: journal of geomatics and planning, 6(2), 99-112. doi: 10.14710/geoplanning.6.2.99-112 1. introduction indonesia’s economic growth has been accompanied by rapid urbanization that has transformed indonesian cities. the urbanization has progressed rapidly since the 1990s, and in 2015 about 67.5% of indonesia's population lives in urban areas. urbanization opens up opportunities to generate regional economic growth and encourage the formation of the metropolitan regions. urbanization can drive productivity, economic opportunities, and increase income. urban areas are generally economically more productive and competitive than rural due to positive externalities in the form of agglomeration. urban areas create opportunities for the establishment of localization economies through the clustering of related activities. in contrast, urbanization economies may emerge in dense urban areas where the transaction cost of doing business is lower, and knowledge spillover opportunities are high. with the benefits of agglomeration, businesses within such economies tend to be more economically productive, as demonstrated by a faster rate of growth in grdp than rural areas. the issue of urbanization and industrialization cannot be separated in the growth of cities in indonesia (bappenas, 2012). article info: received: 2 august 2017 in revised form: 2 october 2017 accepted: 2 december 2017 available online: 30 december 2019 keywords: industrial agglomeration; spatial pattern; local economy. *corresponding author: r. agung pangarso geography faculty, gadjah mada university, indonesia email: pangarso@gmail.com open access https://orcid.org/0000-0002-5374-7840 https://doi.org/10.14710/geoplanning.6.2.99-112 mailto:pangarso@gmail.com pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 100 | industrialization is dominated by large and medium industries (lmis), which are significant in production and employment. kuncoro (2002) identified lmi's agglomeration was concentrated in the metropolitan areas. specialized lmis generally have vertical relationships with global suppliers and access to international markets, and has better access to infrastructure (ports, arterial roads). the proximity of the geographical concentration of lmis to urban areas aims to obtain urbanization economies, as demonstrated by the market size in urban areas. industrialization in indonesia has been biased in lmis and urban areas. on the other side, small and micro industries (smis) is still become a 'marginal' player in the scheme of industrial development or only gets a small profit. in some industrial regions, poverty incidence has even emerged that showed poverty emerged along with the growth of modern industry (medium-large) because its local economic base did not grow and local communities were marginalized (kuncoro, 2002; muta’ali, 2011). the industrial agglomeration (dominated by lmis) closely related to urbanization tends to grow rapidly by utilizing the benefits of agglomeration economies, but on the other hand, smis generally still faces low productivity issues (tambunan, 2011). inward investments included in lmis should impact job creation and encourage local businesses that are dominated by micro and small-scale businesses. investment in a particular industrial sector should encourage the growth of related industries (rostow & rostow, 1990; ward et al., 2002). local economic development concerns in linkages, cooperation, or synergy among local economic actors. the linkage among the industrial sector with the local economy in order to increase the competitiveness of the local economy, as porter (1990) argues that the local economy as the product of the competitiveness of the local economic actors such as local companies and local industries. the research on the linkage of industrial agglomeration and the local economy in semarang district focuses on economic actors (the business sector) related to the local economy. semarang district was chosen as a research area because the region is growing significantly become the domination of urban areas toward a metropolitan region, namely metropolitan semarang, one of the economic development regions in indonesia with a significant peri-urbanization (firman, 1998; bappenas, 2012). the manufacturing industry is the largest sector contributing to the district's grdp reaching above 40%. the region is also growing rapidly in urban areas, about 40% of its population lives in urban areas, thus showing the phenomena of industrialization and urbanization in the region. on the other side, the problem of unemployment and poverty still occurs despite the relatively large contribution, value-added, and industrial sector growth in the region. the unemployment rate in this region is still around 2.3% or 15,864 people and the poor population is still quite large, reaching 81,310 people or 8.5% (bps kabupaten semarang, 2016). these phenomena show that people's income distribution in the region has not been equally realized. previous studies show that semarang district, in general, is not supported by strong industrialization because of the domination of footloose industries in the region, which do not have strong forward and backward linkages with the region. the linkages of industrial and agricultural sectors are weak, indicating industrialization, which is expected to occur linkages, but leakages are happening in the region. the region only becomes the location of industrial activities and the weak linkages among the sectors cause significant benefits in the local economy (wilonoyudho & keban, 2011; hardati, 2014). some of the findings in this research show that industrial agglomeration in semarang district is still dominated by the wearing apparel industry sub-sector, which tends to be located around urban and industrial areas, has a strong vertical relationship with foreign suppliers and international market access. industrial agglomeration with the labor-intensive and footloose industry characteristics. but, the research also found the food industry sub-sector in the region has a strong relationship with the local economy that differs from previous studies by wilonoyudho & keban (2011) and hardati (2014). the food industry sub-sector establishes sectoral specialization at the local (sub-district) level and potentially forms industrial clusters. this sub-sector geographically tends to be located around the raw material area and has a strong linkage with the horticulture and livestock sub-sector or the agriculture sector, which is generally located in rural areas. https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 | 101 2. data and methods the spatial pattern analysis of industrial agglomeration on 307 units of large and medium industries (lmis) consists of 66 large companies and 241 medium companies. the main factors analysis of the local economy and its effects on industrial agglomeration is carried out in the sub-district unit, in 19 sub-districts. the research flow chart can be seen in figure 1. this research uses primary and secondary data for analysis. primary data is the geographical coordinate data of the lmis companies (307 units) were obtained by field mapping using gps. secondary data include: (1) lmis data according to the industrial classification or industrial sub-sectors based on the indonesian standard of industrial classification (isic), the number of workers in each lmis and data on types of investments, value of investments, and markets; (2) local economic conditions per sub-district include 102 variables; and (3) data on the characteristics of the study area. secondary data was obtained through institutional surveys at the cooperative, smes, trade and industry agency; statistic agency (bps); planning agency (bappeda); and investment and one-stop service agency (dpmptsp) of semarang district. figure 1. research flow chart https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 102 | spatial analysis of industrial agglomeration measures three variables: (1) industrial spatial distribution patterns (yunus, 2010); (2) the scale of industrial agglomeration is measured by indicators of labor (ellison & glaeser, 1997; kuncoro, 2002; ellison, glaeser, & kerr, 2007); and specialization to measure the industrial concentration in the region (porter, 1990; kuncoro, 2002). nearest neighbor analysis is used to analyze the spatial pattern of industrial agglomeration is the district level unit. the calculation results nearest neighbor index (nni) values that interpret to form a pattern: (1) clustered; (2) random; or (3) dispersed (o’sullivan & unwin, 2010; muta’ali, 2015). industrial agglomeration is shown by the nni results that illustrate the clustered pattern of lmis. the strength (size) of industrial agglomeration is measured by the ellison and glaeser index or eg index (ellison & glaeser, 1997; ellison et al., 2007). the eg index calculation follows up on the nni results, which shows certain industrial sub-sectors that are agglomerated by adding a number of workers variable. the unit of analysis of the specialization index is in the sub-district unit. high specialization in a certain industry will accelerate industrial growth (kuncoro, 2002). factor analysis is used to determine the main factors in the local economy. factor analysis in the research uses 102 local economic variables in units of sub-districts that describe local economic resources, types of business at the local level, and production of the main commodities of the local economy (rustiadi, 2018; ward et al., 2002). spatial regression using geographically weighted regression (gwr) is used to identify industrial agglomeration's determinant factors. gwr is a geographical or local regression that can explain the relationship between data variables spatially. the gwr method results in local models that vary for each location, thus differentiating it from global regression models (fotheringham, brunsdon, & charlton, 2000; scott & janikas, 2010; arsyadana, 2015). the dependent variable (y) is the specialization index per industrial sub-sector, while the independent variable (x) is the main factor of the local economy as a result of factor analysis. the unit of analysis in the spatial regression analysis is at the sub-district level. there are five calculation of variable y: y1 = food industry specialization index; y2 = beverage industry specialization index; y3 = wearing apparel industry specialization index; y4 = non-metallic mineral industry specialization index; and y5 = furniture industry specialization index. the x variable used in each calculation are: x1 = factor score of livestock-associated with horticulture region; x2 = factor score of horticultural region; x3 = factor score of horticultural associated with plantations region; x4 = factor score of plantation region; x5 = factor score of plantation associated with livestock region; x6 = factor score of urban region; and x7 = factor score of industrial region. 3. result and discussion 3.1. spatial pattern of industrial agglomeration the spatial distribution of the lmis companies locations in semarang district is illustrated in figure 2. the spatial pattern analysis results with nni show there are 10 of 21 sub-sectors of lmis geographically form clustered patterns (see table 1). the clustered patterns of lmis means industrial agglomeration happened in the region. the eg index calculation with the labor variable complements the result of nni, which only uses geographical location variables. the results of the eg index (ϒ) calculation shows there are 5 of 10 lmis subsectors in semarang district most localized or agglomerated (see table 1). https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 | 103 figure 2. spatial distribution of lmis by sub-sector (isic) in semarang district the most localized industries in semarang district are: (1) wearing the apparel industry; (2) food industry; (3) beverage industry; (4) furniture industry; and (5) non-metallic mineral product industry. the most localized agglomerated lmis sub-sectors can be identified as having advantages and spillover, and potentially provide positive agglomeration externalities. the five most localized sub-sectors generate large workers: wearing the apparel industry (79,805 workers), food industry (3,133 workers), beverage industry (2,445 workers), furniture industry (2,693 workers), and non-metallic mineral products industry (1,637 workers). the five sub-sectors' total employment generation is 89,713 workers (72% of the total lmis workers). table 1. result of nni and eg index calculation (result of analysis) sub-sector (isic two digit) nni eg index nn ratio interpretation y interpretation 1. wearing apparel industry (isic-14) 0.23 clustered 2.53 most localized industries 2. food industry (isic-10) 0.82 clustered 2.08 3. beverage industry (isic-11) 0.33 clustered 0.30 4. furniture industry (isic-31) 0.58 clustered 0.33 5. non-metallic mineral products industry (isic-23) 0.70 clustered 0.26 6. wood and products of wood and cork, except furniture, articles of straw, and plaiting materials industry (isic-16) 0.41 clustered 0.12 least localized industries 7. printing and reproduction of recorded media industry (isic-18) 0.37 clustered -0.01 8. chemicals and chemical products industry (isic-20) 0.60 clustered -0.15 9. textile industry (isic-13) 0.32 clustered -0.28 10. rubber, rubber goods, plastic industry (isic-22) 0.18 clustered 3.62 note: isic = indonesian standard of industrial classification https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 104 | industrial specialization index analysis is carried out in more detail at the sub-district level, so it shows the character of the economic locality as a special sub-sector region. the lmis specialization index calculation results show 14 of all 19 sub-districts in semarang district are specialized regions in certain sub-sectors. the specialization region shows a comparative advantage over other districts. high specialization in a certain industry will accelerate the growth of the industry in the region. the spatial description of industrial agglomeration in the specialization regions can be seen in table 2. table 2. specialization region of lmis in semarang district industrial specialization region sub-district description of spatial pattern 1. specialization region of the food industry (isic10) ambarawa, bergas, getasan, jambu, pringapus, suruh sumowono, and east ungaran ⚫ proximity some specialization regions to urban areas due to market factors. ⚫ proximity some specialization regions to agriculture and livestock areas as sources of raw materials. proximity to local commodities potentially establishes industrial clusters. 2. specialization region of the beverage industry (isic-11) bawen, banyubiru, west ungaran, and east ungaran proximity specialization regions to the location of water sources as the main raw material for industry. the beverage industry does not create significant value-added in the local economy. 3. specialization region of wearing apparel industry (isic-14) bergas, pringapus, and west ungaran proximity specialization regions to the urban areas and transportation infra-structure due to import content, export orientation and labor factors. agglomeration creates positive externalities in the interrelationship among companies that potentially establish industrial clusters. 4. specialization region of non-metallic mineral products industry (isic23) bergas, ambarawa, sumowono, tuntang and tengaran ⚫ proximity some regions to urban areas and infrastructure due to market factors (construction). ⚫ proximity some regions to mining areas as sources of raw materials but unsustainable in the long term due to limited time to extract mining. 5. specialization region of furniture industry (isic31) pringapus, tengaran, jambu, and susukan ⚫ proximity some regions to urban areas and infrastructure due to export orientation and outward materials. ⚫ proximity some regions to forestry and plantations areas as sources of raw materials (wood). 3.2. local economic factors determinant of industrial agglomeration the main factors of the local economy in semarang district are the results of factor analysis using 102 variables. factor analysis results in 7 of 15 factors or components (with cumulative eigenvalues of 77.13%). then identification of the characteristic of the factor based on commodity categories or economic activities of the initial variables of analysis. the characteristic of a factor in line with the characteristics of the local economy at the sub-district level. the main factors of the local economy in semarang district are: factor 1 livestock associated with horticulture region; factor 2 horticulture region; factor 3 horticulture associated https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 | 105 with plantations region; factor 4 plantation region; factor 5 plantations associated with livestock region; factor 6 urban region; and factor 7 industrial region. the factors in each sub-district have different strength levels indicated by the value of factor score. the spatial regression analysis using gwr identifies how the local economic factors determine industrial agglomeration. spatial regression analysis is carried out on five industrial sub-sectors that have sectoral specialization. the dependent variable (y) in the spatial regression analysis is the value of each lmis subsector's specialization index in each sub-district. the explanatory variable (x) in the analysis is the local economic factors in each sub-district. the results of the gwr analysis are described in table 3. table 3. indicators of gwr model (results of analysis using arcgis software) sub-sector (isic two digit) residual squares aicc adjusted r2 interpretation 1. food industry (isic-10) 522.65 154.92 0.54 significant (54%)*) 2. beverage industry (isic-11) 1,208.92 170.85 -0.37 not significant (37%) 3. wearing apparel industry (isic-14) 1.01 36.19 0.57 significant (57%)*) 4. non-metallic mineral products industry (isic-23 465.60 152.73 0.29 not significant (29%) 5. furniture industry (isic-31) 403.47 150.00 -0.52 significant (52%)*) *) the coefficient of determination or absolute value of adjusted r2 is significant if it is close to 1 or 100% (we consider significant if the absolute adjusted r2 is above 50%) the result of gwr shows a significant spatial regression model explains how much the independent variables together determine the sub-sectors: (1) food industry (adjusted r2 54%); (2) wearing apparel industry (adjusted r2 57%); and furniture industry (adjusted r2 52%). the spatial regression model for other sub-sectors is considered not significant because the determination coefficient value is ≤50%. the spatial regression method using gwr produces a different local model for each location that it differentiates with global regression. to find out the predictor variables (local economic factors), which significantly determine the response variable for each sub-district conducted t-test by calculating the value of t (t count), namely the comparison between estimated value with standard error in each variable for each sub-region, then the value of t count compared to t table. the t count value greater than t table shows significant variables in each subdistrict (see table 4). table 4. significant local economic factors determinant of lmis agglomeration (result of analysis) sub-sector (isic two digit) predictor variable location (sub-district) 1. food industry (isic10) (+) x1 (livestock associated with horticulture region) all sub-district (+) x2 (horticulture region) all sub-district (-) x3 (horticulture associ-ated with plantations region) all sub-district (-) x7 (industrial region). ambarawa, bancak, bandungan, ba-nyubiru, bawen, bergas, bringin, kaliwungu, pabelan, tuntang, pring-apus, sumowono, , west ungaran, east ungaran 2. wearing apparel industry (isic-14) (+) x6 (urban region) all sub-district (+) x7 (industrial region) all sub-district 3. furniture industry (isic-31) no variable significant all sub-district https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 106 | the results of the spatial regression analysis showed that local economic factors significantly determine the lmis agglomeration in two sub-sectors: (1) food industry; and (2) wearing the apparel industry, as the following explanation. 3.2.1 local economic factors determinant of industrial agglomeration the livestock associated with the horticulture region (x1) and the horticultural region (x2) has a positive effect on the agglomeration of the food industry (see figure 3 and figure 4). the increase in local economic factors in the livestock and horticulture region is influenced by the increase in these commodities. the bigger production of livestock and horticultural commodities can increase the agglomeration of the food industry. figure 3. results of gwr analysis, the livestock-associated with horticulture region determinant of food industry agglomeration the influence of local economic factors on food industry agglomeration can be seen in getasan subdistrict as a center of dairy milk production (production up to 20.7 million liters or 80% of total district production). 5 of 8 cooperatives/groups of dairy farmers/collectors in semarang district are located in getasan sub-district. companies in dairy milk processing are cv. cita nasional in getasan sub-district and pt. cimory in bergas sub-district. https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 | 107 figure 4. results of gwr analysis, the horticulture region determinant of food industry agglomeration the food industry that produces meat/eggs product is related to livestock businesses in semarang district. producer of chicken products (fresh meat, sausage, and nugget) pt. ciomas adisatwa in pabelan subdistrict and cv. indocipta mitra sejahtera in getasan sub-district) get the raw material from the surrounding area. semarang district is a potential region of the livestock sub-sector, such as chickens, with total production up to 3,888 tons (bps kabupaten semarang, 2016). pt. java egg specialties in bergas sub-district produces eggs and mayonnaise products get the supply of eggs from a group of farmers in tuntang sub-district, despite competing with egg suppliers from east java province. eggs are a potential product in semarang district, with production up to 228 million (bps kabupaten semarang, 2016). the growth of the livestock sub-sector in the region encourages the breeding industry, pt. japfa comfeed tbk. in tengaran sub-district, which produces doc to supply farmers in the region and to other regions in central java province. jambu, banyubiru, bandungan, ambarawa, and sumowono sub-districts are horticulture regions that produce commodities such as herbal plants, mushrooms, cassava, and yam to supply the raw materials for producers of herbal and food products such as ud wijaya and ud pertiwi in jambu sub-district, ud. bumi lestari in sumowono sub-district, and kub makmur sentosa in ambarawa sub-district. the supply of raw materials of horticultural products also occurs across sub-districts, even across districts, as happened at pt. sumber boga abadi and pt. mangkok mas in bergas sub-district. 3.2.2 local economic factors determinant of wearing apparel industrial agglomeration the urban region (x6) and industrial region (x7) determine the wearing apparel industry agglomeration (see figure 5 and figure 6). this influence shows that the stronger the region's character as an urban and industrial area will increase the concentration of the wearing apparel industry. the proximity of industrial agglomeration to urban and industrial regions shows this sub-sector spatially depends on infrastructure, particularly transportation accessibility in an urban and industrial region. the infrastructure supports the accessibility of labor, raw materials, and product distribution. the wearing apparel lmis is the largest industrial sub-sector in semarang district, consists of 87 companies and generates 79,805 workers (917 workers per company on average). it is categorized as a laborhttps://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 108 | intensive industry. the study results by wijaya, kurniawati, & hutama (2018) show that the labor-intensive industry in the region has attracted many migrant workers from other regions in central java province. daily workers' mobility or commuters and the growth of settlement areas around the industrial region become issues in semarang district. figure 5. results of gwr analysis, the urban region determinant of wearing apparel industry agglomeration figure 6. results of gwr analysis, the industrial region determinant of wearing apparel industry agglomeration https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 | 109 data from dpmptsp of semarang district in 2016 shows 57% of wearing apparel lmis companies are foreign direct investment (fdi) companies. 60% of wearing apparel companies are located in industrial bonded zones. the high dependence on import-content materials in the wearing apparel industry agglomeration confirms the findings by kuncoro (2002) that the lmis agglomeration in the metropolitan area is associated with import content and export orientation. the concentration of fdi companies in semarang regency also tends to proximity to the same country of origin (for example, fdi companies from south korea) that confirms the findings by rofi (2015). 4. conclusion the spatial patterns of industries in semarang district are geographically concentrated or clustered, which shows the phenomena of industrial agglomeration. the particular sub-sectors of the industry are strongly agglomerated. it reflects the interrelationship among companies (including business cooperation), the concentration of employment, and knowledge transfer that increase the positive externalities of industrial agglomeration. industrial agglomeration also shows high sectoral specialization in certain sub-sectors that potentially establish industrial clusters, as occurred in the food industry and wearing the apparel industry. high specialization in a certain industry will accelerate the growth of the industry, that means potential industrial agglomerations will lead to positive externalities in the job opportunities, capital attractiveness, increased skills or knowledge, growth of related industries and services, and other functional relationships among industries, and improve regional competitiveness (porter, 1990; kuncoro, 2002; fan & scott, 2003). certain sub-sectors of industry tend to be located around urban areas and infrastructure, which shows the proximity of industrial agglomeration to the population concentration to utilize urbanization externalities as reflected in market size in urban areas. industrial agglomeration in metropolitan areas is influenced by import content, export orientation, labor, and better access to infrastructure (kuncoro, 2002; fujita, 2002; qi, fang, & song, 2008). other sub-sectors such as the food industry that primary process products such as livestock products and plantations, the beverage industry, the wood industry, and the furniture industry geographically tend to be located around the raw material concentration. the proximity of industries to the raw material sources is in line with one of marshall's industrial agglomeration theories that economically industrial locations consider the proximity to input suppliers to save transportation costs (marshall jr, lynch, & smith, 1919). the spatial regression model shows some local economic factors that significantly determine industrial agglomeration in semarang district. it occurs in two sub-sectors: the food industry and the wearing apparel industry. the local economic factors of livestock and horticulture regions determine the industrial food agglomeration. meanwhile, the local economic factors of urban and industrial regions determine the wearing apparel industrial agglomeration. the strong relationship of food industry agglomeration and horticultural and livestock region shows the proximity of industry to the source of raw materials. it means the proximity of food industry agglomeration to the rural areas. so the development of the food industry needs to be integrated with the rural economic development based on the agricultural sector (including the livestock and horticulture sub-sectors). in this context, industrial and agricultural development can be carried out within the rural diversification concept framework. this concept emphasizes the importance of non-agricultural employment opportunities and the increased income of rural households due to the relative increase in industrial and commercial activities related to agricultural activities (rijanta, 2012). rural industrialization is in line with the concept of spatial integration in rural-urban linkages as mutually relationships in the term of economic linkages (rondinelli, 1985; douglass, 1998). the finding that the food industry sub-sector in semarang district has a strong relationship with the local economy differs from previous studies (wilonoyudho & keban, 2011; hardati, 2014) because of the different methods and variables used. the study by wilonoyudho & keban (2011) used descriptive analysis, focus group discussion, interviews, and observations, not specifically examining spatial determinants of industry. a study by hardati (2014) used the variables of agriculture and non-agriculture sectors in spatial analysis. in contrast, our study based on the geographical distribution of industries and employment is classified in subsectors and uses spatial statistical analysis techniques. https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 110 | the relationship of urban region and the wearing apparel industrial agglomeration is in line with literature on the positive correlation between industrial agglomeration and urban agglomeration (kuncoro, 2002; fujita, 2002; qi, fang, & song, 2008). our findings that show the industrial region determine the wearing apparel industrial agglomeration, particularly in the peri-urban, confirms the study by kuncoro (2002), fan & scott (2003), and rofi (2015). this phenomenon shows industrialization and urbanization issues cannot be separated in urban development, including in semarang district. the inter-sectoral linkages in the wearing apparel industry are not significant compared to the food industry because most wearing apparel companies in semarang district are categorized as the footloose industry. the footloose industry relies heavily on imported raw materials, export markets, and works within bonded zones. however, there are other business activities related to suppliers or service providers for lmis wearing apparel (embroidery, fabric printing, packaging, and other subcontracts) and utilization of wearing apparel production waste by sis to produce various goods (mattresses, doormats, and household appliances). those businesses economically are not significant compared to the value of the lmis wearing apparel. the spillover impact of the wearing apparel industry sub-sector mainly occurs in the concentration of employment and knowledge or skills transfer. the wearing apparel sub-sector in semarang district mostly labor-intensive industry which attract large migrant workers (around 50%) from other regions in central java province even outside the province, resulting in highly inter-regional labor mobility. this finding is in line with the study by bartik (1991) that high inter-regional labor mobility causes an increase in labor demand, which will be followed by an increase in labor supply from other regions, so that the industrial sub-sector is not optimal in reducing unemployment at the local level in the long run. spillover in knowledge and skills transfer of in the field of wearing apparel encourages the growth of small scale similar businesses, although not economically significant. high inter-regional labor mobility causes transportation problems and some migrant workers who settle around industrial areas triggering land use changes in the region. labor is a major issue in the wearing apparel industry sub-sector. this study concludes that industrial agglomeration in semarang district, as part of the metropolitan area, is still dominated by the wearing apparel industry sub-sector, which tends to be located around urban and industrial areas, has a strong vertical relationship with foreign suppliers and international market access. industrial agglomeration with the labor-intensive and footloose industry characteristics explains the dependency theory that metropolitan cities in developing countries have a high dependence on the economic system of developed countries. high dependence in terms of supply and international markets in the long term can lead to inequality, including inequality in urban and rural areas (myrdal, 1968; rustiadi, 2018). industrial agglomeration in certain sub-sectors in semarang district, such as the food industry sub-sector, establishes sectoral specialization at the local (sub-district) level and potentially to form industrial clusters. this sub-sector geographically tends to be located around the raw material area and has a strong linkage with the horticulture and livestock sub-sector or the agriculture sector, which is generally located in rural areas. food industry agglomeration encourages spatial integration or linkages between rural and urban areas (rural-urban linkage) can explain the interdependency theory, which bridges the modernization theory and dependency theory by reducing the gap between regions. an approach to the interdependency theory focuses on developing regional networks based on the clustering model by providing opportunities for the development of local economic factors (endowment) in a region. the development of small towns in periphery regions or rural areas can counterbalance the tendency of over-urbanization in the metropolitan area (rondinelli, 1985; douglass, 1998; rustiadi, 2018). the theoretical implications in the scientific field of geography and regional development and policy implications based on the study results can be input for regional development, particularly in semarang district as follows. 1. the linkage of industrial agglomeration and the local economy in the metropolitan region can complement the concept of spatial integration or rural-urban linkages (rondinelli, 1985; douglass, 1998). the spatial linkage that works in peri-urban and rural areas can be a counterbalance to the tendency of over-urbanization in metropolitan areas (friedmann, 1992; mcgee, 1994; firman, 1998; rustiadi, 2018; wilonoyudho & keban, 2011). https://doi.org/10.14710/geoplanning.6.2.99-112 pangarso, suharyadi, rijanta / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 99-112 doi: 10.14710/geoplanning.6.2.99-112 | 111 2. the linkage of industrial agglomeration and a local economy based on cluster model opens up opportunities for the development of local economic endowment factors in a region (porter, 1990), so that further studies on cluster dynamics are suggested, particularly in the prospective commodities of food industry sub-sector (e.g., cow milk processing, meat, and egg processing, or horticulture products processing). 3. the food industry sub-sector, which processes local agriculture products (horticulture and livestock) potentially to be developed in rural and peri-urban areas, therefore it is recommended to develop competitive and industry-oriented local agricultural commodities, such as dairy milk, beef, chicken, eggs, fruits, vegetables, and herbs. in the other side, the industrial sector development needs to be focused on the investment of industries that functionally related with the local economy. 4. spatial linkage in the form of spatial integration requires reliable regional infrastructure, particularly to improve the accessibility of rural and urban areas, so that it is necessary to improve the transportation network system among sub-districts as well as regional economic zones. 5. references arsyadana, h. h. 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(2010). metodologi penelitian wilayah kontemporer. yogyakarta: pustaka pelajar. https://doi.org/10.14710/geoplanning.6.2.99-112 10.1002/9780470549094.ch1 https://doi.org/10.1007/s11769-008-0291-2 https://doi.org/10.1007/bf00127550 https://doi.org/10.1088/1755-1315/123/1/012037 | 1 geoplanning vol 5, no. 1, 2018, 1-16 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.1-16 spatial explicit modeling to understand the dynamics of landuse switch using open source satellite data s. kalluvettya, s. bandopadhyayb a indian institute of remote sensing (indian space research organisation), india b meteorology department, faculty of environmental engineering and spatial management, poznan university of life sciences, poland abstract: restless global urbanization needs to monitor in order to design a stable and sustainable urban habitat. in this regard, remote sensing and gis are considered as an efficient monitoring and decision-support tool in sustainable urban planning and practices. in this paper we accumulate the results of a research undertaken to measure the urban sprawl and land use dynamics of the dehradun city, uttarakhand using vast sixteen years data and spatially explicit cellular automata ca-markov model. furthermore, future scenario of the city and land use was also examined. to achieve the desired goal, sixteen years large temporal images of landsat were used to analyze the spatial decoration of land use change in the study area. the outcome of this study was clearly reviled that there was a substantial change was take place in the dehradun city and its surroundings in last sixteen years. modeling proposed a clear trend of various land use classes’ transformation in the area of urban built up expansions and urban encroachment whereas agricultural lands and forest covers are reduced at an alarming rate over the time. dynamically increasing population of the city can be approximated by the predicted future scenarios. in order to promote a balance in between urban growth and environmental protection towards a sustainable urban habitat and environmental, local community involvement and capacity building program can be an efficient drive in this regard. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. kalluvetty, s., & bandopadhyay, s. (2018). spatial explicit modeling to understand the dynamics of landuse switch using open source satellite data. geoplanning: journal of geomatics and planning, 5(1), 1-16. doi: 10.14710/geoplanning.5.1.1-16 1. introduction changing aspects of landuse at the peri-urban areas is a complex and dynamic process that involves both natural and human systems (xiao et al., 2006). over recent decades the suburbs of the metropolitan area are designed by urban sprawl (glaeser & kahn, 2003). urban sprawl is known as a multifarious concept that dealing with the expansion of auto-oriented, low-density development and has a considerable impact on the surrounding ecosystem (yuan et al., 2005). it is characterized by the expansion of human population from a core urban area into previously rural and remote areas. the discussions about urban sprawl are often made partial by the view of metropolitan growth is inefficient and causes environmental degradation. but the other side of the issue says the beneficiaries of sprawl may be a silent majority who are not as politically active as center city boosters, environmentalists and the urban layman in voicing their views on the merits of the ongoing decentralization of jobs and people taking place across cities. in india, urban sprawl is in its zenith phase at the cost of farmland, forest, and other ecologically sensitive areas. rapid urbanization in india is changing the dynamics between ecology, economy and the society (bardhan et al., 2016). the exponential growth of urban population has clearly figured out by last few censuses results. the alarmingly diminishing forest coverage is upholding these findings. as like other states of india, cities of uttarakhand is also experienced with population exploration evident by the census of india 2011 report illustrated in figure 1. open access article info: received: 01 september 2017 in revised form: 10 dec 2017 accepted: 30 january 2018 available online: 30 april 2018 keywords: land-use change, urban sprawl, ca-markov, sustainable urban habitat, dehradun city corresponding author: subhajit bandopadhyay meteorology department, poznan university of life sciences poznan; poland email: subhajit.iirs@gmail.com http://orcid.org/0000-0002-8657-3488 https://doi.org/10.14710/geoplanning.5.1.1-16 https://doi.org/10.14710/geoplanning.5.1.1-16 mailto:subhajit.iirs@gmail.com kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 2 | figure 1. uttarakhand population growth from 2001 to 2011 (source: census of india, 2011) this report clearly suggests the tremendous increase in population in the districts of dehradun, udham singh nagar, and nanital, while districts which are in hilly regions like garhwal, almora showing negative growth rate. this upward nature of population growth in dehradun which is the capital city of the uttarakhand state results in unplanned urbanization and changes in the land use patterns. in order to design a sustainable city profile through appropriate urban planning system the temporal land use transformation must be evaluated (deep & saklani, 2014). so as to figure out the increasing rate of urban sprawl an attempt has to be made to comprehend sprawl dynamics and to develop appropriate management strategies that could aid in the region’s sustainable development. in early days studies, cadastral maps (scale, usually 1: 4000) were utilized in mapping for land use/land cover and to investigate their changes (jat et al., 2017). from twentieth century onwards the mapping of lulc was first replaced by aerial photographs, later on research was focused on multispectral satellite images. in recent past, implementation of mathematical techniques into satellite images have been utilized for the assessment of urban growth through preparation of lulc maps in this domain. but nearly all landscape models are spatially implicit in nature, since landscapes are fundamentally spatial entities where location is a prime factor. spatial locations matters to the process which should be modeled. spatial explicit model allows us to model the locations with answering all landscape-relevant questions and also providing precise relative location of each landscape category. several researches have already conducted to quantify the urban growth using various methods. myint and wang (2006) used post-classification change detection approach to identify the land use land cover changes in norman, oklahoma using landsat multispectral scanned and thematic mapper (tm) images. an integration of markov chain analysis and a cellular automata approach were employed to predict future land use land cover using multi-criteria decision-making and fuzzy parameter standardization approach. they compared projected results against the classified output of the same landsat tm image. the study explained the usefulness of markov and cellular modeling for urban landscape changes. they found that combination of markov and a ca filter is reasonably accurate for projecting future land use land cover. one of the important studies in this domain was done by deep and saklani (2014) showing the applicability of ca-markov model which is effectively used to study the urban dynamics in rapidly growing cities using the commercial liss-iv high-resolution data. modeling suggested a clear trend of various land use transformation in the form of built up expansions. islam & ahmed (2012) carried the similar kind of research in dhaka city, bangladesh. they executed the markov chain modeling for land use change prediction with the aid of gis. supervised classification image was given as the input for markov chain and obtained accuracy less than 70% in the absence of a sufficient number of authentic, influencing variables. they observed that accuracy can be enhanced by increasing the influencing variables. memarian et al. (2012) demonstrated the ca-markov for simulation of land use and land cover change using validation metrics, allocation disagreement, quantity doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 3 disagreement and figure of merit in a 3d space. the results were poor for land use and land cover change due to uncertainties in the source data and the model calibration. nouri et al. (2014) predict the urban land use changes using the cellular automata technique. outcome reviles that major expansions in urban areas were witnessed around western and eastern borders of the city, particularly close to the eastern border. but this study did not show the future trend of that particular city over the temporal scale. another study was done by aithal et al. (2013) in bangalore city using the same technique. the results indicate that the future expansion of greater bangalore will take place in the periurban landscapes. the current clumped urbanization at the city core will have little scope for further urban densification. this study was limited to less amount of datasets as they use only three years of datasets and predict the future of the bangalore city for year 2020. in this present research, we have used a large amount of freely available satellite datasets (table 1) and predict the future urban growth of the dehradun city with a higher level of accuracy which makes this research different from other existing studies. in this era of rapid urbanization where we need a strong quantification technique to understand the nature of the urbanization trend, remote sensing and gis can be an effective tool for policy makers to design sustainable urban habitats (deep & saklani, 2014). the wellestablished ca-markov model incorporated with rs-gis platform can act as one of the planning support tools for analysis of temporal changes and spatial distribution with a higher level of competence and accuracy. applied ca–markov technique can also use for future prediction and trend analysis for a specific region (park et al., 2010). the core idea behind this model is that model transits the one-pixel transition probability with respect to neighboring pixels (pontius & malanson, 2005). markov chain modeling proved to be an effective approach for calculating change probabilities, but the adopted procedure followed in this model that the transition probabilities might not change over time. in other words, markov chain analysis predicts the future land use pattern only on the basis of the known land use patterns of the past. this is a limitation of the method in terms of simulating urban growth since new influences on the urban structure cannot be evaluated (sun et al., 2007). the objective of this study is to prepare an urban sprawl model for the growth of dehradun city using available satellite data set and validate the model by comparing predicted result and obtained result. finally, predict the year wise growth of urban sprawl of dehradun city at near future. the remainder part of this paper as follows: the second section of this study presents related literature to urban sprawl assessment and modeling. the section 3 deals with datasets and methodology adopted followed by the description of the study area in section 4. the final section 5 discusses the findings and conclusion with recommendations followed by the future scope of this study. 2. data and methods 2.1. study area the twin cities of dehradun and mussoorie are located in dehradun district of uttarakhand state, india (figure 2). dehradun is the capital and it is situated at the north-west corner of the state. the district is bounded by uttarkashi district on the north, tehri garhwal and pauri garhwal districts on the east and saharnpur district (uttar pradesh) on the south. its western boundary adjoins sirmour district of himachal pradesh separated by rivers tons and the yamuna. the geographical coordinates of the study area are extended from 30°13’44.60” n to 30° 25’23’61” n latitudes and 77° 53’29.63”e to 78°09’06.81”e longitudes having an area of about 349.3495 sq /km with an average altitude of 640 m above msl. dehradun manifests its position as an important city in the most fertile region of doon valley between rivers ganga and yamuna. it is, in fact, the most developed city in the shivalik foothills of the great himalayas and gateway to the hilly areas of uttarakhand. the study area is cosmopolitan in nature, characterized by forest, river and agriculture along with city agglomeration. major rivers through the study area are asan river in the doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 4 | west, song river in the east, rispana river and bindal rao through the center. the forest is mainly located on the northern side of the study area. it is a part of the shivalik range of himalaya. the district has within its limits lofty peaks of the outer himalayas as well as the dun valley with climatic conditions nearly similar to those in the plains. here, temperature depends on the elevation. the climate of the district, in general, is temperate. in the hilly regions, the summer is pleasant but in the doon valley, the heat is often intense. the temperature drops below freezing point not only at high altitudes but also even at places like dehradun during the winters, when the higher peaks are under snow. the summer starts by march and lasts up to mid of june when the monsoon sets in. generally, the month of may and early part of june is hottest with mean temperatures shooting up to 36.20°c at dehradun and 24.80° c at mussoorie. the maximum temperature rises to over 42°c at dehradun while at mussoorie it doesn’t exceed 32°c. winter starts from november and continues up to february. the highest maximum temperature recorded at dehradun was 43.9°c on june 4, 1902, and that at mussoorie was 34.4°c, on may 24th, 1949. the mean daily maximum temperature during winter is 19.1°c at dehradun and 10.2°c at mussoorie. the mean daily minimum temperature in january is 6.10°c at dehradun and 2.50°c at mussoorie. in mussoorie the temperature drops to about -60°c to -70°c when snowfall occurs. the lowest minimum temperature at dehradun during winter was 1.10°c, on february 1st, 1905 and january 1945 while at mussoorie it was -6.70°c, on february 10th 1950. monsoon starts by the mid of june and lasts up to september. the district receives an average annual rainfall of 2,073.3 mm. most of the rainfall is received during the period from june to september, july and august being the wettest months. the region around raipur gets the maximum rainfall, while the southern part receives the least rainfall in the district. about 87% of the annual rainfall is received during the period june to september. 2.2. population, agriculture and other activities according to the 2011 census, dehradun district is the second most populous district in the state having a population of 1,696,694 after haridwar. the urban agglomeration has experienced a high growth rate of a population which has become almost double in one decade from 447,808 in 2001 to 714,223 in 2011 (census of india, 2011). agriculture fields are also present in outer parts of the city. major kharif crop in this region is paddy. in addition to that maize, mandus, jhngora, kulath, arhar and sugarcane are also cultivated during that period. on the other hand, wheat is the principal rabi crop. maize, mustard are the other rabi crops. it should be noted that a chunk of land in this region is owned by the government. so it highly influences the dynamics of the urban sprawl. figure 2. location map of study area: landsat 7 etm+ image of dehradun mussoorie region. mussoorie dehradun india doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 5 it is home to numerous state and central government institutions. it is the most vital service center, which meets the trade and commerce requirement of its hinterland. major activates of dehradun are education, commercial, defence, and tourism. these are also driving factors of the local economy. administrative area of mussoorie dehradun development authority (mdda) boundary was chosen as the study area for this analysis. 2.3. datasets landsat 7 with the multispectral sensor of enhanced thematic mapper plus or etm+ and landsat 8 with the multispectral sensor of operational land imager (oli) images were used for the study. landsat 7 is having 8 bands with a spatial resolution of 30m for bands 1 to 5, 7, and 15 m for the panchromatic band 1 and 60 m for the thermal band 6. it is available from 1999 and after 2003 may 31 there are noises in the acquired data due to the failure of scan line corrector (slc). landsat 7 was used for lulc classification in this study. on the other hand, landsat 8 was launched on february 11, 2013, and is having 9 bands with a 15m resolution for panchromatic band 1 and 30 m for the other bands. it was used for the lulc classification for the year 20132014 in this work. following table 1 shows the details of the datasets used. table 1. description of satellite data used sl. no. satellite and sensor path no row no date of satellite pass 1 landsat 7 etm+ 146 039 22 october 1999 2 landsat 7 etm+ 146 039 25 november 2000 3 landsat 7 etm+ 146 039 11 october 2001 4 landsat 7 etm+ 146 039 30 october 2002 5 landsat 7 etm+ 146 039 01 october 2003 6 landsat 7 etm+ 146 039 19 october 2004 7 landsat 7 etm+ 146 039 06 october 2005 8 landsat 7 etm+ 146 039 25 october 2006 9 landsat 7 etm+ 146 039 12 october 2007 10 landsat 7 etm+ 146 039 30 october 2008 11 landsat 7 etm+ 146 039 18 november 2009 12 landsat 7 etm+ 146 039 20 october 2010 13 landsat 7 etm+ 146 039 08 n0vember 2011 14 landsat 7 etm+ 146 039 25 october 2012 15 landsat 8 oli 146 039 18 september 2013 16 landsat 8 oli 146 039 16 may 2014 2.4. tools used to achieve the goal of this research three main image processing and gis software had been used. for data preparation, classification, analysis were executed using erdas imagine 2013, whereas data conversion, reclassification, and mapping were performed in arcgis 10.1. ca-markov analysis was the major phase in this project which had been done through terrset (formerly idrisi taiga) developed by clark labs at clark university, usa. the satellite data corresponding to the present study area (landsat, 1999 to 2014) in the form of multiple bands was stacked to a single image using the ‘layer stack’ feature in erdas imagine. for landsat 8 images, layer stack operation involves the participation of consecutive bands from 2 to 7 and landsat 7, consecutive bands from 1 to 7 except 6. in the case of landsat data for the years 2003 to 2012, an additional step, focal analysis has to be carried out using the same software to remove a prevalent error in the image that arises due to the absence of scan line corrector in the sensor. the stacked image and the respective pan image of the study area are subsets to highlight the area of interest. since the layer stacked image and pan image are of different resolutions, they are subject to resolution merge in order to achieve a unified resolution of 15m. the resulting image can be referred to as the target image. maximum likelihood approach based parametric supervised classification was carried out doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 6 | satellite data data pre-processing classification of respective data accuracy assessments  layer stack  focal analysis  subset  resolution merge classes  settlement  forest  agriculture  river bed/water body  open land predict map of 2013 & 2014 ca-markov model predict map of 2013 & 2014 validation of map (lulc & predicted map of 2013 & 2014) predict future trend if yes no with 70 training sets for each class on the target images, followed by an accuracy assessment. the supervised classification meant for preparation of land use/land cover based on five different classes, namely, (i) forest, (ii) settlements, (iii) agriculture, (iv) water body and riverbed, (v) open land. if the resulting accuracy was satisfactory, then classified map was prepared. making use of the ca-markov algorithm, predicted maps for the years 2013 and 2014 were prepared. prediction was done by considering different temporal data for the time period between inputs images are 1 to 7 years. the actual classified map and the predicted map of these two years are compared and checked the prediction accuracy. proving the validation successful, the future trend was predicted using the model accurately. year wise future trend is predicted till 2020 by the same method. the detailed of research framework is illustrated in figure 3. 2.5. markov chain model markov model has been extensively used in ecological modeling (brown et al., 2000; muller & middleton, 1994). markov model takes into account past states to predict how a particular variable changes was done over time. the applicability of markov model in land-use change modeling is promising because of its capability to quantify not only the states of alteration between land-use types but also the rate of conversion among the land-use types (sang et al., 2011). a homogenous markov model for predicting land-use change can be represented mathematically as equation 1: 𝐿(𝑡+1) = 𝑃𝑖𝑗 ∗ 𝐿(𝑡) ………………………………………………………………………………………… (1) and [ 𝑃11 𝑃12 … 𝑃𝑛 𝑃21 𝑃22 … 𝑃𝑛 𝑃𝑛1 𝑃𝑛2 … 𝑃𝑛𝑛 ] where, 𝐿(𝑡+1) and 𝐿(𝑡) are the land-use status at time t+1 and t respectively. { and , } is the transition probability matrix in a state. 2.6. ca-markov model markov model combines with cellular automata (ca), is used to predict land cover change over time (sang et al., 2011). it adds into markov model not only spatial contiguity but also the probable spatial transitions occurring in a particular area over time. in ca-markov module, the basic land cover image is given as reference image. in addition to that markov transition area, file which is the output from markov chain and output land cover projection are required for simulation. a number of cellular automata iterations, ca filter type is the important process parameter which has to be selected based on image and output requirement. figure 3. framework of the study doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 7 0 5 10 15 20 25 30 35 40 45 open land forest settlement agriculture river bed/water body p er ce n ta ge o f c h an ge lulc classes 1999 2014 3. results and discussion 3.1. temporal lulc change interpretation the lulc percentage and the area coverage of each land category were derived from the satellite images. classification of the image was done by supervised classification. the study area was classified into five classes as stated before. the results are illustrated in table 2. it is be observed that there is a sharp rise in the area of the settlement over time. the percentage area of the settlement in 1999 was 8.16% and it has risen to 20.27% in 15 years and remarkable changes are noted in agriculture where the percentage area dropped from 28% to 13.03%. it is also evident from the table 2 and figure 4 that the 4.41% deforestation was also taking place in the study area which is might be converted to built-up areas. so from the given table and figure it can strongly state that urban sprawl was already processed in the study area over the time. table 2. changes in lulc in between 1999-2014 lulc classes 1999 2014 area (km²) area (%) area (km²) area (%) open land 120 34.80 145 41.14 forest 100 28.36 84 23.95 settlement 29 8.16 71 20.27 agriculture 91 27.86 46 13.03 river bed/water body 5 1.53 6 1.60 total 351 100 351 100 figure 4. lulc changes in between 19992014 3.2. lulc classification of 2013 and 2014 parametric supervised classification technique was considered to understand the lulc of the study area in the year of 2013 and 2014 (see figure 5). for the change analysis lulc classification (figure 5a) for 2013 has been done with an accuracy of 80.0% and the overall kappa statistics is 0.7561. 2014 lulc classification (figure 5b) has been done with an accuracy of 90% with overall kappa statistics 0.8678. doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 8 | figure 5. (a) lulc map of 2013 (b) lulc map of 2014. 3.3. validation testing between projected and reference lulc we tested the output lulc maps of the year 2013 and 2014 and validate the classified maps with the existing lulc maps of the study area. the validation of the predicted trend is carried out by estimating the kappa statistics of the comparison between projected and reference lulc map for the years 2013 and 2014. the kappa statistic was calculated for all the prediction maps. the prediction with the input images with time gap of one year to seven years. table 3 shows the validation result using the kappa values. table 3. validation table time interval (year) 2013 kappa statistics 2014 kappa statistics 1 0.6008 0.6003 2 0.6528 0.6910 3 0.6762 0.7028 4 0.5975 0.7249 5 0.6908 0.7275 6 0.6563 0.6169 7 0.6375 0.6521 3.4. comparative analysis of actual lulc map and predicted maps in the first case of predicted lulc map of 2013 of the study area, the prediction has been done on the basis of a one-year temporal gap. lulc map of 2011 and lulc map of 2012 has been taken as input. it is clearly shown in the following (see figure 6 (ai, aii, aiii)) that open land and settlement class has been increased at a major portion. however, there was very little change in agriculture and forest class. in the second case with the temporal change at a gap of two years, there is a marked increase in settlements (see figure 6 (bi, bii, biii)). in this case, lulc map of 2009 and lulc map of 2011 has been taken as input. it was also showing considerable changes in the agricultural area that has been decreased in 2013 output. in the third case, change analysis was made on the basis three years gap. for which lulc map of 2007 and lulc map of 2010 have been taken as inputs. there was a marked reduction of agricultural land observed in the predicted lulc map of 2013 (see figure 6 (ci, cii, ciii)). the results are showing rapid changes with increased open land and settlement areas. doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 9 figure 6. (ai) lulc map of 2011 (aii) lulc map of 2012 (aiii) predicted map of 2013; (bi) lulc map of 2009 (bii) lulc map of 2011 (biii) predicted map of 2013; (ci) lulc map of 2007 (cii) lulc map of 2010 (ciii) predicted map of 2013 in the fourth case change analysis has been done on the basis of a gap of four years that was from 2005 to 2009 and from 2009 to 2013 (see figure 7 (ai, aii, aiii). in this case, lulc map of 2005 and 2009 has been taken as input for the prediction of lulc of the year 2013. it is very clear from the following figure that settlement class has been increased by a big amount of percentage. however, there is a marked reduction in agricultural fields. some of the agriculture lands have been converted into an open area. in the fifth case, change analysis has been done on the basis of a gap of five years (see figure 7 (bi, bii, biii). in this case, lulc map of the year 2003 and 2008 has been taken as input for the prediction of the year 2013. it has been observed that there was a clear change in terms of increase for class settlements and surprisingly agriculture land was also increased by area. in the sixth case, change analysis has been done on the basis of a gap of six years that is from the year 1999 to 2005 and from 2005 to 2013 (see figure 7 (ci, cii, ciii). as it is clear that lulc map of the year 1999 and 2005 has been taken as input for the prediction of lulc of the year 2013. this change was also representing a remarkable upsurge in settlement class. there was an increase of settlements by area. however, a noticeable decrease has been observed in class open lands. in the seventh case, change analysis has been done on the basis of a gap of seven years that is from the year 1999 to 2006 and from the year 2006 to 2013 (see figure 7 (di, dii, diii)). for this change analysis, lulc map of the year 1999 and 2006 has been taken as doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 10 | input. in this case, there was also an upward trend by area in settlement class. after the prediction of lulc map of the year 2013, lulc map of 2014 has been taken under consideration. figure 7. (ai) lulc map of 2005 (aii) lulc map of 2009 (aiii) predicted map of 2013; (bi) lulc map of 2003 (bii) lulc map of 2008 (biii) predicted map of 2013; (ci) lulc map of 2001 (cii) lulc map of 2007 (ciii) predicted map of 2013; (di) lulc map of 1999 (dii) lulc map of 2006 (diii) predicted map of 2013 doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 11 in the first case, a change analysis for the prediction of lulc of the year 2014 has been made on the basis of a gap of one year (see figure 8 (ai, aii, aiii)). for this analysis lulc map of 2012 and lulc map of 2013 has been taken as input for the prediction of lulc of the year 2014. a reduction has been noticed in open area class. however settlement class is showing a little increase by percentage. in the second case, a change analysis has been done on the basis of a gap of two years (see figure 8 (bi, bii, biii)). for this case, lulc map of 2010 and lulc map of 2012 has been taken as input for the prediction of lulc of the year 2014. the corresponding change for different classes has been given the following images. in the third case, a change analysis has been done on the basis of a gap of three years (see figure 8 (ci, cii, ciii)). for this change analysis, lulc map of 2008 and lulc map of 2011 has been taken as input for the prediction of lulc of the year 2014. in this case, open land has been increased by a big amount of percentage. figure 8. (ai) lulc map of 2012 (aii) lulc map of 2013 (aiii) predicted map of 2014; (bi) lulc map of 2010 (bii) lulc map of 2012 (biii) predicted map of 2014; (ci) lulc map of 2008 (cii) lulc map of 2011 (ciii) predicted map of 2014 doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 12 | in the fourth case, a change analysis has been done on the basis of a gap of four years (see figure 9 (ai, aii, aiii)). in this case for the lulc prediction of the year 2014, lulc maps of the year 2006 and 2010 has been taken as input. agriculture class was showing a remarkable change. also, there was an increase in settlement classes as usual. in the fifth case, change analysis has been done on the basis of a gap of five years (see figure 9 (bi, bii, biii)). for this analysis lulc map of 2004 and lulc map of 2009 have been taken as input for the prediction of lulc change of the year 2014. both inputs are showing a big difference in each class. in 2009, open land is larger than the lulc of 2004. in the year 2004, agriculture area was large but in the year 2009 it has been decreased much. also, there was an increment of settlement has found in 2009 than the year 2004. based on these two inputs lulc of 2014 has been predicted which is showing an abrupt increase in settlement class. in the sixth case change analysis has been done on the basis of a gap of six years (see figure 9 (ci, cii, ciii)). for this analysis, lulc of the year 2002 and lulc of the year 2008 has been taken as input. as it is clear from the figure that in the year 2002 there was a big agricultural land while it was decreased in the year 2008. also, a settlement has also been increased during these years. based on these two inputs, lulc map of 2014 has been predicted which was also showing an increment of settlement class by area. in the seventh case change analysis has been done on the basis of a gap of seven years (see figure 9 (di, dii, diii)). for this analysis, lulc of the year 2000 and lulc of the year 2007 has been taken as input and predict the lulc map of 2014. it was again observed that like the previous. agricultural land was decreased over the time whereas settlement has been increased along with the reducing forest areas. 3.5. predicted future maps by using ca-markov model the visual validation with respect to actual and predicted maps by using the kappa statistics with one year gap are showing better results. the ca-markov chain derived predicted future maps are illustrated in figure 10. in this research using the above model predicted map for future changes in the proposed area with five year gap for year 2013 showing 0.6908 kappa statistics and for year 2014 is 0.7275. for third year gap are showing 0.6762 for year 2013 and for year 2014 is 0.7028. the agricultural area is gradually decreased in between these year gaps. the urbanization and climatic variability would be the probable factor for the future changes. this research establishes the application of remote sensing and gis in mapping of urban sprawl and changes in land use system. dynamically increasing population of the city can be approximated by the predicted future scenarios. the predictions for future land use or land cover changes on the basis of a ca-markov model strongly propose a continuous rise in urban settlement built up and a subsequent decrease in agriculture, forests covers. the outcome of one of the study done by jat et al. (2008) only focuses on the quantification of the urban form (impervious area) using shannon’s entropy model and also shows the urban growth but in spatial implicit manner. whereas this study primly dedicated to represent the urban sprawl with accuracy assessment using cellular automata model which will help the decision makers to formulate the future plan. another study done by taubenböck et al. (2012) monitor the urbanization rate over 27 cities but no mathematical model was done to show how the cities are changing in its structure. our results concentrated on dehradun city and proved that the outlook of the city is changing over fifteen years (1999-2014) in its spatio-temporal manner. study done by yin et al. (2011) showing the urban expansion and land use/land cover changes using normal maximum likelihood supervised classification algorithm on four landsat images over the time of 1979–2009. no statistical modelling was implemented in this study to predict the changes over the time. this is important to do the statistical modelling like ca-markov when we are dealing with large scale temporal images to consider the accuracy of the outcomes. studies done by bagan and yamagata (2012) combined remote sensing and socio-economic data to quantitatively analyze urban growth using square grid cells method considering only three years intervals whereas we consider seven years interval and predict the future urban growth which is highly necessary for city planners from sustainable and correct planning perspective. doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 13 figure 9. (ai) lulc map of 2006 (aii) lulc map of 2010 (aiii) predicted map of 2014; (bi) lulc map of 2004 (bii) lulc map of 2009 (biii) predicted map of 2014; (ci) lulc map of 2002 (cii) lulc map of 2008 (ciii) predicted map of 2014;(di) lulc map of 2000 (dii) lulc map of 2007 (diii) predicted map of 2014 doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 14 | figure 10. (a) predicted map of 2015 (b) predicted map of 2016 (c) predicted map of 2017 (d) predicted map of 2018 (e) predicted map of 2019 (f) predicted map of 2020 4. conclusion the robustness of this study represents the reality of the dehradun city which is defined by the accelerate rate of urban expansion with decreasing forest and croplands. the increasing rate of open lands supports the massive deforestation within the study area. the significant kappa values of lulc classification for each of the 7 years interval between 2013 and 2014 make the prediction more accurate. finally this study predicts next 6 years (2015-2020) lulc as well as the urban expansion which has never done by any other long term spatio-temporal studies in this domain. this sort of prediction presented in this study using ca markov model can help to project sustainable urban systems. so, the mapping of urban sprawl using geospatial technology doi:%2010.14710/geoplanning.5.1.1-16 kalluvetty and bandopadhyay / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 1-16 doi: 10.14710/geoplanning.5.1.1-16 | 15 and mathematical model can be a device of decision support system (dss) for policy makers to design urban expansion plans with an approach of sustainable city development. in order to predict the future land use changes we need to study the important role of human activities and its impact on the environment. besides the ecosystem and its functions with related to biodiversity, land use changes are the major contribution to the rapid destruction of agriculture and forest areas. sometimes the transformation of land cover and fast urbanization leads to decrease the daily diurnal rage which ultimately punching with local level climate change (bandopadhyay, 2016). moreover, to minimize all this processes, it requires the involvement of the local community and capacity building programs, local governments (bandopadhyay, 2017) regarding how to protect the environment for the future and move towards a sustainable urban habitat in a balanced approach. 5. acknowledgments the authors would like to thank indian space research organisation (isro) and national aeronautics and space administration (nasa), for providing the freely available satellite data used in this study. we are also grateful to meteorology department, faculty of environmental engineering and spatial management, poznan university of life sciences for providing a nice work environment. interpreting and writing of the manuscript were supported by the polish national research centre (ncn) within the project no 2016/21/b/st10/02271. lastly, we extend our thanks to the anonymous reviews for their kind consideration and significant improvements through their comments. 6. references aithal, b. h., vinay, s., durgappa, s., & ramachandra, t. v. 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[crossref] doi:%2010.14710/geoplanning.5.1.1-16 https://doi.org/10.4236/jgis.2012.46059 https://doi.org/10.5589/m06-032 https://doi.org/10.1007/s13369-014-1119-2 https://doi.org/10.5389/ksae.2010.52.1.061 https://doi.org/10.1080/13658810410001713434 https://doi.org/10.1016/j.mcm.2010.11.019 https://doi.org/10.1007/s11067-007-9030-y https://doi.org/10.1016/j.rse.2011.09.015 https://doi.org/10.1016/j.landurbplan.2004.12.005 https://doi.org/10.1016/j.rse.2005.08.006 | 75 geoplanning vol 5, no. 1, 2018, 75-90 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.75-90 gis-based landslide susceptibility assessment and factor effect analysis by certainty factor in upstream of jeneberang river, indonesia p. f. nurdina, t. kubotab a graduate school of bio-resources and environmental science, kyushu university, japan b faculty of agriculture, kyushu university, 6-10-1 hakozaki higashi-ku fukuoka 812-8581, japan abstract: this study aimed to assess landslide susceptibility by employing certainty factors model (cf) to select the causative factors for landslide susceptibility mapping in upstream of jeneberang river, south sulawesi. indonesia. the landslide causative factors were: soil, slope angle, aspect, elevation, lithology, land use, distance to the river, drainage density, and precipitation. for validation purpose, landslide inventory map was randomly partition into two groups, 30% for the validation and 70% for the training. landslide susceptibility maps were produced by logistic regression using original factor (all nine factors) and selected factor (four factors with positive cf value). the result of certainty factor analysis shows cf value is positive for elevation, land use, slope and drainage density. the accuracy of two landslide susceptibility maps were evaluated by calculating the area under the curve of receiver operating characteristic (roc) curves. the result shows the the success rate curve for nine factor map (80.2%) is higher than four factor map (78%). but in case of closeness between success rate curve and predictive rate curve, certainty factors model has a closer distance. in this study, effect analysis studies show how the accuracy changes when the input factors are changed. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. nurdin, p. f., & kubota, t. (2018). gis-based landslide susceptibility assessment by certainty factor and logistic regression in upstream of jeneberang river, indonesia. geoplanning: journal of geomatics and planning, 5(1), 75-90. doi:10.14710/geoplanning.5.1.75-90 1. introduction a landslide occurs worldwide. however, the impact is greater in developing country. the spatial probability of landslide occurrence, also known as susceptibility (brabb, 1985), is the probability that any given region will be affected by landslides, given a set of environmental conditions (guzzetti et al., 2005). many landslide causative factors have been considered in the literature for landslide susceptibility mapping, but it is not certain which factors produce the optimal result for an area under analysis. with the availability of increasing number of landslide causative factors, finding the best combination of factors has become an important research issue. currently, there are no universal guidelines for the selection of landslide causative factors. determining the causative factors are a difficult task, van westen et al. (2003) stated that every study area has its particular set of factors, which triggering landslides. a factor can be a contributing one for landslide occurrence in an area but not in another one. landslide causative factors could have different degrees of effects on the accuracy of landslide susceptibility maps that has been investigated by several types of research in the literature. cuesta, sánchez, & garci’a (1999) assessed 209 landslide events from 1980 to 1994 in the cantabrian mountains in northwestern spain and found that precipitation was the most influential causative factor. moreiras (2005) considered lithology and slope as the most influential factors in landslide mapping based on the study area of the rio mendoza valley in argentina. glenn et al. (2006) stated that topographic factors are highly influential parameters in landslide studies. they assessed the efficiency of laser scanning data (lidar)-derived topographic factors in characterizing landslide morphology and activity. for instance, open access article info: received: 31 july 2017 in revised form: 10 dec 2017 accepted: 20 march 2018 available online: 30 april 2018 keywords: landslide, susceptibility map, gis, certainty factor, logistic regression corresponding author: putri fatimah nurdin kyushu university, fukuoka, japan, email: putrinurdin@kyudai.jp https://doi.org/10.14710/geoplanning.5.1.75-90 https://doi.org/10.14710/geoplanning.5.1.75-90 mailto:putrinurdin@kyudai.jp nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 76 | costanzo et al. (2012) analyzed the relationships between a priori ranking of controlling factors and predictive performances. the results showed that slope angle, roughness, land use and topographical wetness index were main causative factors. on 26th march 2004, the huge collapse occurred in northern caldera wall of bawakaraeng caldera (elev. 2,830 m). the collapsed area has caused a ridge including mt. sarongan (elev. 2,514 m), and the collapsed mass volume was estimated more than 200 million m3. the collapsed mass has been running down as debris flow, it is reached the bili-bili reservoir and treated life time of the dam. the collapse was predicted to be caused by a combination of several factors; such as weak geological structures, steep high walls of the caldera and high rainfall intensity (tsuchiya et al., 2004). the main portion of the collapse is two twin ridges of the caldera. these ridges were initially formed by the steep slope surface slipping down or creeping; the creep zone eventually expanded and settled in an unstable condition (cti, 2006). low cementation rocks, stock and second deposits of volcanic rocks are distributed at the base of mt. bawakaraeng forming a low strength base for the high walls of the caldera (cti, 2006). the scope of this study aims to investigate the geo-environmental factors that contribute to landslide and assess the most significant causative factors to generate the landslide susceptibility map with better accuracy. the outline of this study is to select the most significant causative factors by certainty factor analysis, produce landslide susceptibility map using the selected and original causative factors by logistic regression and do the comparison between two models. 2. data and methods 2.1. study area the study area (figure 1) is located in the upstream of jeneberang river (lengkese sub-watershed). the study area is bounded by the latitude of 05°18 ′10″ and longitude 119°53′20″ with an area about 128.40 km2 and most of the terrain is mountainous with highest peaks exceeding 2,795 m. the study area located around 70 km east of capital city of south sulawesi province. jeneberang river rises in mt. bawakaraeng, which has an elevation of 2,833 m above msl and flows from the east to the west. sulawesi island has a tropical climate with characteristics of two seasons within a year, rainy seasons from november to may and a dry season from june to october. the precipitation is more than 700 mm in the month of february and rises to 900 mm in january (tsuchiya et al., 2009). this area is a productive land but regularly experience a small to a big landslide, especially during the rainy season. due to increasing of rainfall intensity, the probability of landslide occurrence, particularly shallow landslides increases and it is very sensitive to short lasting, high intensive rainfall (hasnawir and kubota, 2012). figure 1. hillshaded map of the study area and the landslide inventory https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 | 77 figure 2. flow chart of the study. the lithological layer of the study area was created from geology map of indonesia with scale 1:250,000. the land use layer was based on indonesia ministry of forestry 2014. river and soil layers were based on big (badan informasi geospasial / geospatial information agency). another factor such as mean annual rainfall data, collected from rainfall gauge stations was available around the study area. landslide inventory of the area was detected by satellite image interpretation and verified by field investigation. 2.1.1. landslide inventory map according to guzzetti et al. (1999), landslides which occurred in the past and present are keys to predicting landslides happening in future. however, due to the absence of historical records of landslides and their triggering factors, insufficient information and heterogeneity of subsurface conditions and lack of knowledge about their behavior make it very difficult to predict spatial and temporal probabilities of landslides. indirect mapping methods use either statistical models or deterministic models to predict landslide prone areas, based on information obtained from the interrelation between landslide causative factors and the landslide distribution. therefore landslide inventory is an essential component of landslide hazard zonation techniques. a total of 380 known landslides with a total of 9,901 pixels were prepared from field observations and remote sensing of the study area (figure 1). some of the archived landslide inventory databases were also used in the previous research to produce landslide hazard map. landslides in the area include rotational slides and translational slides. for building the susceptibility map models, the landslide inventory was randomly partitioned into two groups: a training data set (70%, 266 landslides) and a validation data set (30%, 144 landslides). https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 78 | figure 3. landslide site in study area 2.2. geospatial database of geo-environmental factors influencing landslides data preparation is the first fundamental and important step for landslide susceptibility analysis. to mapping the potential landslide in sub-watershed jeneberang, it first conducted studies on the factors that cause landslides. factors that caused landslides are well known by experts or landslide researchers, but the main factor of avalanche between one watershed and others will vary due to the different of biophysical conditions. in this study, nine landslide causative factors were used, namely: elevation, slope aspect, slope angle, lithology, drainage density, distance to river, soil texture, mean annual rainfall and land cover or land use (figure 4). each category was divided into different classes by its value or feature. digital elevation model (dem), remotely sensed imagery and geological maps of the study area were used to create maps of the factors that were employed in subsequent stages. digital elevation model (dem) was the key to generate various topographic parameters related to landslide activity of the study area. with cell size 10 x 10 meter, elevation (<820 – 2,795 m), slope angle (0 >45 degrees) and slope aspect layers have been extracted. the resolution and accuracy have a direct influence on the quality of these factors (lee, 2005). 2.3. probabilistic analysis the probabilistic analysis is performed using a methodology integrating the results into a spatial database using gis. 2.3.1. certainty factor analysis the certainty factor (cf) model is a method for managing uncertainty in rule-based systems. shortliffe and buchanan (1975) developed the cf model in the mid-1970s for mycin, an expert system for the diagnosis and medical treatment. in this study, cf is applied to selecting the optimal causative factor related to landslide occurrence. certainty factor can be calculated using the following functions: (1) https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 | 79 where ppa is the conditional probability of having a number of landslides event in a class of parameter and pps is the prior probability of a total number of landslides in the study area. for each of the causative factors, the weights and contrast were calculated using the certainty factor method. the cf approach transforms each class into interval between -1 and 1, and it indicates a measure of belief and disbelief. a cf value of -1 indicates that an increasing uncertainty of landslide occurrence or the certainty of the hypothesis being true is very small, as compared with a high cf near to 1 means that decreasing uncertainty or the indication strongly supports the hypothesis as true. a value close to 0 means that there is no information of the landslide occurrence. the ppa and pps values were determined by overlaying each parameter layer with the landslide inventory layer in arcgis and landslides falling in each parameter class were determined. these values were used to determine the cf value of each classes. 2.3.2 the multivariate approach: logistic regression (lr) it is admitted that among the wide range of statistical methods proposed in landslide susceptibility mapping, logistic regression analysis has proven to be one of the most reliable approaches (ayalew et al., 2005). logistic regression analysis relates the probability of landslide occurrence (having values from 0 to 1) to the “logic” z (where −1<z <0 for higher odds of non-occurrence and 0<z <1 for higher odds of occurrence). in the lr formula, the probability of landslide occurrence is expressed by: where p is the estimated probability of landslide occurrence and ranges from 0 to 1; y is an indicator variable, x is the independent variables (landslide causative factors), x = (x0, x1, x2,. . . xn), x0 = 1; b is regression coefficient. to linearize the mentioned method as well as remove the 0/1 boundaries for the original dependent variable, the estimated p probability is transformed by the following formula: the alteration is referred to as the logit transformation. theoretically, the logit transformation of binary data can ensure that the dependent variable is continuous and the logit transformation is boundless. moreover, it can ensure that the probability surface will be continuous within the range [0, 1]. using the logit transformations, the standard linear regression models can be obtained as follows: here, b0 is the constant or intercept of the formula, b1, b2, . . . bn represents the slope coefficients of the independent parameters, x1, x2, . . . xn in the logistic regression and ε is the standard error. multivariate regression analysis plays a central role in statistics that cause one of the most powerful and commonly used techniques (mccullagh and nelder, 1989). (2) (3) (4) (5) https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 80 | figure 4. thematic maps used in this study, (a) elevation; (b) slope; (c) aspect; (d) lithology; (e) landuse; (f) soil; (g) distance to river; (h) drainage density; (i) precipitation. https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 | 81 3. results and discussion 3.1. physical factor of lanslide 3.1.1. elevation elevation is commonly used to assess landslide susceptibility. the variation in elevation may be related to several environmental settings such as rainfall and vegetation variety. a digital elevation model (dem) can categorize the local relief and locate points of maximum and minimum heights within the terrain. the elevation of the study area is 820 to 2,795 meters. figure 3 shows that landslides mostly occurred at 8201,137 m (26%). one of the reason is the land use at those elevation is dominated by agriculture which mostly is paddy field. 3.1.2. slope angle slope is often used to study landslide probability, and all studies into the probability of landslides consider slope (dai et al., 2001; s. lee and talib, 2005). highly sloped areas and cleared areas receive exposure to direct sunlight, which dries the soil and increases the chances of landslides. the slope angle is frequently considered to be one of the most influential factors for landslide modeling because it influences the shear forces acting on hill slopes (dai et al., 2001). in the study area, landslides increased with increasing slope steepness. most landslides occurred on slopes of 30-45 degrees (20%). 3.1.3. slope aspect the slope aspect describes the slope direction, and it identifies the downslope direction of the maximum rate of elevation change. although the relationship between the aspect and the mass movement has been investigated for a long time, there is no general decision regarding the aspect/landslide relationship (ercanoglu & gokceoglu, 2004). however, the aspect is a significant factor in producing landslide susceptibility maps (saro lee et al., 2004). the slope aspect also plays an important role in exposing the topography to sunlight and drying winds, which control the soil moisture. this is an important factor in landslide studies (magliulo et al., 2008). according to the number of pixels affected by slope failure, a southwest-facing slope (21,34%) ranks first followed by the north(19,87%), northwest(15,91%), and west-facing slopes (14,92%). moreover, slopes facing south are more prone to landslides because they receive heavy rainfall during the monsoon season. 3.1.4. lithology lithology is the most important parameter in this study of landslides because different lithology units have varying degrees of landslide vulnerability (cuesta et al., 1999; dai et al., 2001). the lithological units shown in the surface geologic maps were reclassified according to geology and development center. the result was a generalized geologic map. finally, the map describes the distribution of six types of lithology:  tmc (tertiary miocene camba): marine sediment rocks vary with volcanic rocks, tuff sand vary with sandy tuff and clay stone; and have insertion marl, limestone, conglomerate, volcanic breccias, and coal.  qlvp, qlv, and qlvb (quarter lompobatang volcanic): agglomerates, lava, breccias, lahar deposition and tuff.  tpbv (tertiary pliocene baturape cindako volcanic): lava and breccias, with insertion tuff and conglomerate.  qac (quarte aluvium): gravel, sand, clay, mud and coral limestone. the relationship between landslide occurrence and lithological condition showed that qlv has the highest percentage of landslides (63,88%) of the six other lithology classes. qlv is a common volcanic sediment formation in south sulawesi. https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 82 | figure 5. the spatial relationship between landslide occurrence and causative factors. landslide occurrence correlates strongly with environmental factors that might trigger its mechanism. https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 | 83 3.1.5. soil the physical properties of soil are often used for parameter analysis of landslides via a probabilistic approach to soil texture. soil texture can affect the other physical soil properties such as water infiltration, porosity, and permeability of water and power to pass groundwater. in indonesia, soil is classified via the united states department of agriculture system, the food and agriculture organization of the united nations (fao), and the centre of soil and agroclimatic research. soil in the study area is from the andosol family and is divided into three types: dystrandepth, dystropepts, and tropaquepts. landslides are mostly accumulated on dystrandepts soil type (77,25%). this might be related to the location of dystrandepts deposition, which are mostly found at higher altitudes. this class accounts for the largest proportion of the areas. 3.1.6. drainage density drainage density is the total stream length per unit area of a river basin. hasegawa et al. (2009) noticed that if precipitation increased, then an area with a higher drainage density is more often prone to a shallow landslide. a large-scale landslide is frequent in areas with less drainage density. in this study area, the 0.004-0.008 km subclass has the highest landslide percentage (26%). 3.1.7. distance to river rivers play a major role in modifying the terrain by incising different rocks (meten et al., 2015). runoff plays an important role and is a triggering factor in landslides. according to meten et al. (2015), rivers have a significant role in facilitating landslides. the analysis assessed the influence of distance to river and drainage density on landslide. in the case of the relationship between landslide occurrence and distance to river, subclasses of 0 – 60 have the highest landslide percentage (70%). gully erosion along the river may initiate landslides. areas closer to the river network have more erosive forces that erode the base of the slope to a greater degree. 3.1.8. precipitation rainfall is the principal climatic variable that influences landslide distribution. it is affected by topography, elevation, and vegetation—factors that are all interrelated. mountainous areas cause the air currents to rise and cool resulting in increased precipitation with elevation (walker and shiels, 2013). the climate of the study area is tropical humid, and precipitation varies with elevation. the average annual precipitation is between 3,100 – 3,800 mm. the rainfall data area was obtained from two weather stations: lengkese station and malino station. malino has the highest landslide percentage (79%) followed by lengkese area. 3.1.9. land use land use also plays an important role in the stability of the slope. the land covered by forest regulates continuous water flow. water regularly infiltrates this area whereas the cultivated land affects the slope stability due to saturation of the covered soil. land use in the study area is mainly occupied by dry land agriculture, mixed garden, forested area, paddy field, and savannah. paddy field covered mostly located in the lowland and river floodplain. the landslide area was mostly in agricultural (27,57%) and secondary forest (27,18%). 3.2. factors selection using certainty factor (cf) the landslide distribution for each class is expressed by the number of occurring pixels and was used to calculate cf values. table 1 shows the result of z value of each causative factor. based on the certainty factor method, four causative factors were detected with high influence to slope instability in the study area: land use (z value: 0.82), elevation (z value: 0.56), drainage density (z value: 0.25), and slope angle (z value: 0.30). these four factors have positive relationships with landslide occurrence. therefore, these four factors were selected for further process to create an optimized landslide susceptibility map. the highest z value is land use. landslides especially correspond to the primary forest subclass (cf value: 0.80). https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 84 | table 1. the relationship between landslide occurrence and causative factors theme class pixels in class landslides cf z soil dytstrandepts 508,608.00 7,649.00 0.49 (0.72) dystropepts 732,874.00 2,128.00 -0.63 tropaquepts 42,449.00 124.00 -0.62 lithology camba formation (tmc) 65,868.00 195.00 -0.62 (1.00) parasitic eruption (qlvp) 199,491.00 565.00 -0.63 lompobattang volcanic rock (qlv) 572,811.00 6,325.00 0.30 baturape volcanic rock (tpbv) 31,343.00 129.00 -0.47 aluvium deposition (qac) 27,945.00 0.00 -1.00 breksi (qlvb) 386,473.00 2,687.00 -0.10 landuse primary forest 15,192.00 582.00 0.80 0.82 secondary forest, forest plantation 279,523.00 2,691.00 0.20 brush & savana 165,513.00 2,458.00 0.48 open land 50,810.00 499.00 0.22 agriculture, plantation & settlement 539,506.00 2,730.00 -0.35 paddy field 201,138.00 343.00 -0.78 water & sand 32,249.00 598.00 0.59 aspect north 233,598.00 1,967.00 0.08 (0.21) northeast 118,658.00 998.00 0.08 east 50,658.00 349.00 -0.11 southeast east 56,486.00 370.00 -0.15 southeast 146,194.00 1,052.00 -0.07 south west 198,444.00 2,113.00 0.28 west 224,796.00 1,477.00 -0.15 northwest 255,097.00 1,575.00 -0.20 elevation <820 281,570.00 1,005.00 -0.54 0.56 820 1,137 323,730.00 2,594.00 0.04 1,137 1,451 235,103.00 1,615.00 -0.11 1,451 1,845 278,558.00 2,223.00 0.03 1,845 2,299 129,473.00 1,592.00 0.38 2,299 2,795 35,497.00 872.00 0.69 distance to river 0 – 60 818,631.00 6,944.00 0.09 (1.00) 60 – 120 384,836.00 2,254.00 -0.24 120 – 200 78,739.00 697.00 0.13 200 – 250 1,579.00 6.00 -0.51 250 -350 146.00 0.00 -1.00 drainage density 0 — 0.004 172,338.00 905.00 -0.32 0.25 0.004 – 0.008 750,490.00 4,384.00 -0.24 0.008 – 0.012 319,030.00 4,110.00 0.40 0.012 – 0.019 42,073.00 502.00 0.36 slope angle 0 – 5 115,433.00 157.00 -0.82 1.00 5 – 10 235,161.00 424.00 -0.77 10 – 15 248,266.00 811.00 -0.58 15 – 20 209,183.00 1,104.00 -0.32 20 – 25 158,647.00 1,293.00 0.05 25 – 30 111,267.00 1,578.00 0.46 30 – 35 74,494.00 1,373.00 0.59 35 – 45 82,741.00 1,972.00 0.68 >45 48,739.00 1,189.00 0.69 precipitation malino 947,840.00 7,807.00 0.06 (0.14) lengkese 336,091.00 2,094.00 -0.19 https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 | 85 most landslide cases seen here occurred on natural slopes where vegetation grows perennially. elevation plays a dominant role in landslide occurrence in the study area. the cf value is positive for intermediate elevation (820-1,137 m) and increased further above 1,451 m. from 2,299 m – 2,795 m, dystrandepts dominate the soil, and landslides are common (cf value: 0,691). it is widely accepted that the slope angle directly influences slope instability. landslide probability increases from 20 -25 degrees, and it increases further and corresponds to slope angle. the increase in slope angle is affected by the shear stress in the soil or unconsolidated material increases. gentle slopes generally have a lower frequency of landslides than steep slopes because of the lower shear stresses seen in low gradients (lee & talib, 2005). in the study area, most landslides are associated with slope angles greater than 45 degrees (cf value: 0,69). the drainage density negatively impacts landslide susceptibility due to their abrasive forces along the base of the slope. in the study area, the subclass of 0.08-0.012 km/km2 has the highest cf value of 0.40 where most shallow landslides are observed. costanzo et al. (2012) identified the factors based on the ranks associated with the factor’s expected contribution to the predictive skill of a multivariable model. approaches adopting discriminant analysis and logistic regression on the forward selection of variables, however fail when most of the variables are statistically significant. although this method includes less computation, it requires to categorize the data into landslide and non-landslide groups which is rather exhausted. the proposed model using cf eliminated these limitations because it used only landslide pixels in the computation, and hence is very fast. prior definition of hazard classes is not required in cf approach and it also supplies advantage of rendering the definition of susceptible classes transparent. moreover, the proposed model is a relatively straightforward method that allows the causative factors to be ranked according to their certainty values in the range between -1 to 1. it is assumed that positive cf values have a high correlation with the landslide occurrence, and vice versa. 3.3 landslide susceptibility mapping using logistic regression in this study, a logistic regression model was developed using an equal proportion of landslide and nonlandslide pixels over ten iterations and all non-landslide data as a comparison. the constant and coefficient of independent variables were provided by logistic regression analysis using spss. landslide data were randomly selected by spss based on the number of balanced proportion of non-landslide pixels,. hence, this study proposes to examine ten iterations to get optimal results and a sense of fairness as shown in tables 2 and 3. by applying a logistic regression model, the landslide occurrence probability was measured. if the values are closer to unity, then landslides are more likely to occur. table 2. iteration for all causative factor models iteration data equal drainage density distance to river soil landuse lithology elevation aspect precipitation slope constant roc iteration 01 b 0.69 1.34 0.87 0.28 -0.42 0.10 0.55 1.44 0.59 -6.06 0.80 iteration 02 b 0.67 1.03 0.80 0.31 -0.36 0.12 0.74 1.40 0.62 -5.94 0.80 iteration 03 b 0.60 1.22 0.87 0.31 -0.39 0.08 0.64 1.04 0.64 -6.00 0.80 iteration 04 b 0.66 1.18 0.89 0.32 -0.37 0.05 0.63 1.47 0.62 -6.10 0.80 iteration 05 b 0.62 1.09 0.84 0.30 -0.45 0.13 0.71 1.65 0.60 -6.10 0.79 iteration 06 b 0.67 1.18 0.83 0.30 -0.39 0.12 0.59 1.47 0.62 -6.04 0.80 iteration 07 b 0.67 1.08 0.86 0.28 -0.41 0.12 0.74 1.55 0.60 -6.11 0.80 iteration 08 b 0.68 1.06 0.89 0.29 -0.46 0.15 0.72 1.49 0.60 -6.06 0.80 iteration 09 b 0.69 1.18 0.83 0.31 -0.37 0.12 0.69 1.53 0.60 -6.21 0.80 iteration 010 b 0.66 1.12 0.84 0.26 -0.46 0.18 0.56 1.38 0.60 -5.77 0.79 table 3. iteration for selected causative factor models iteration data equal drainage density landuse elevation slope constant roc iteration 01 b 0.74 0.43 0.33 0.67 -2.62 0.77 iteration 02 b 0.73 0.45 0.34 0.70 -2.65 0.78 iteration 03 b 0.67 0.46 0.33 0.72 -2.63 0.78 iteration 04 b 0.72 0.48 0.31 0.70 -2.66 0.78 iteration 05 b 0.66 0.44 0.33 0.69 -2.55 0.77 iteration 06 b 0.85 0.62 0.39 0.71 -3.00 0.77 iteration 07 b 0.71 0.43 0.35 0.68 -2.61 0.77 iteration 08 b 0.72 0.44 0.41 0.70 -2.69 0.78 iteration 09 b 0.74 0.46 0.34 0.68 -2.67 0.77 iteration 010 b 0.72 0.40 0.41 0.68 -2.65 0.77 https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 86 | (6) based on logistic regression analysis for all causative factors (table 2), precipitation has the highest coefficient of 1.47 which means that in the study area precipitation plays the important rules in triggering the landslide. meanwhile, table 3 shows that slope plays the most important rules than three others causative factors. in this case, precipitation was excluded because it was not selected by certainty factors analysis (z= -0.14). the natural break method or jenks optimization is also known as the goodness of variance fit (gvf) method. it has been used widely especially by planners. it determines the best arrangement of values for the different classes. this method maximizes values between classes and reduces the variance within classes. the five classes include very low, low, moderate, high, and very high values describing the level of landslide susceptibility in the study area. figure 4 (a) shows the landslide susceptibility map using nine causative factors. the lsm model for nine causative factors was obtained using the coefficient values as the equation 6 : z = 0.05 (elevation) + 0.62 (slope) + 0.63 (aspect) + (-0.37) (lithology) + 0.32 (landuse) + 0.89 (soil) + 1.18 (distance to river) + 0.66 (drainage density) + 1.47 (precipitation) – 6.10 ……………………………………. figure 6. (a) landslide susceptibility map using nine causative factors generate by logistic regression. (b) landslide susceptibility map using four causative factors generate by logistic regression. (c) enlarged image of lsm using nine causative factors. (d) enlarge image of lsm using four causative factors. based on the certainty factor, further lr analysis was conducted using the four highest impact causative factors of landslide occurrences. optimization was conducted to gain insight into whether the accuracy of the landslide susceptibility map can be increased. the lsm model using four causative factors was obtained using the coefficient values as the equation 7. z = 0.05 (elevation) + 0.62 (slope) + 0.32 (land use) + 0.66 (drainage density)-2.63 …………………………… (7) (a) (b) (c) (d) https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 | 87 finally, the regression coefficients of predictors were imported to generate the landslide susceptibility map in gis as shown in figure 6 (a) and (b). the enlarged images in figure 6 (c) and (d) facilitate comparison between these two maps. the distribution of medium to high susceptibility areas are much more wide spread in the map of nine causative factors than the four-factor map which is more specific to some location. 3.4. accuracy assessment of susceptibility maps landslide susceptibility maps without validation are less meaningful (chung and fabbri, 2012). to validate the landslide susceptibility maps, landslides in the study area were divided into two parts based on random partitions. these partitions divided the area into two groups: prediction (training) and validation (testing). the roc curve is a graphical representation of the trade-off between the false negative and false positive rates for every possible cut-off value. the area under curve (auc) is a useful indicator to validate the prediction performance of the model. the success rate curve describes how well the model and controlling factor predict landslides (chung and fabbri, 2003). accuracy is evaluated by the area under the roc curve. the auc value lies between 0.5 to 1. an area of 1 represents an excellent classifier and an area of 0.5 represents a worthless classifier. in this study, both the training data (70% of 380 landslide polygons) and validation (the remaining 30% of 380 landslide polygons) datasets were selected to assess the models. the training data was used for the lsm success rate, and the validation data was used for prediction. the success rate and prediction rate can be obtained by comparing the landslide susceptibility results at known landslide locations. in spss software, the auc of the success rate was derived by linking the landslide index in logistic regression model and the cf model after using landslide data for training. subsequently, the auc of predictive rate was obtained using landslide data for validation. figure 7. the area under the curve (auc) represents a) success rate and b) prediction rate curve using four causative factors. c) success rate and d) prediction rate curve using nine causative factors. b) prediction rate curve = 70,6% success rate curve = 78% a) predictive rate curve = 66,9% d) success rate curve = 80% c) https://doi.org/10.14710/geoplanning.5.1.75-90 nurdin and kubota / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 75-90 doi: 10.14710/geoplanning.5.1.75-90 88 | the roc curves for all nine causative factors or four causative factors are shown in the figure 6. the auc of the success rate curve and the predictive rate curve for nine causative factors are 0.80 and 0.67, respectively. it means that the accuracy of the nine causative factors is 80% for training and 66.9% for the validation. in the case of the four causative factors model, the success rate and predictive rate curve return accuracies were 78% and 70%, respectively, with an auc of 0.78 and 0.70. this proves that the nine causative factor models are better at explaining the cause of landslide occurrences than the four causative factors model. the auc curve determined with the validation data set should be approximately equal to the auc curve determined with the training dataset, but it is generally lower than the success curve because the landslide data about validation areas are not used for modeling (ngadisih et al., 2013). it is interesting that the similarity of the success rate and the predictive rate values of the four factors models is closer than the nine-factor model. meten et al. (2015) stated that the proximity of success rate and predictive rate values are also important because it shows how the logistic regression helps predict landslides. 4 conclusion this study shows the selection of optimum causative factors to build an effective landslide susceptibility map. four out of nine causative factor was selected by using a certainty factor analysis (elevation, slope, landuse and drainage density). higher prediction accuracy was obtained from the landslide susceptibility map based on a combination of nine causative factors. the result shows that decreasing the number of causative factors may not always result in higher prediction accuracy. for instance, the combination of nine causative factors showed a higher success rate (80%) than the combination of four landslide factors (success rate 78%). this proved that the landslide susceptibility map from nine causative factors is quite acceptable and should have a greater degree of influence in causing landslides. there are some limitations and assumptions in this method. the limitations are related to the landslide inventory data. these data did not include the total number of the landslide events within the study area. furthermore, the output ls map presents only the predicted spatial distribution of landslides and not their temporal probability. despite these limitations, the produced landslide susceptibility map could be useful to the community and local officials. it could help design future land-use plans and implementation of developments. 5 acknowledgments the first author would like to thank indonesia endowment fund for education (lpdp) for the scholarship grant to pursue the ph.d. study. 6 references ayalew, l., yamagishi, h., marui, h., & kanno, t. 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[crossref] https://doi.org/10.14710/geoplanning.5.1.75-90 https://doi.org/10.1017/cbo9780511978685.008 | 187 geoplanning vol 4, no. 2, 2017, 187-200 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.187-200 land-soil characteristics for mapping paddy cropping intensity using decision tree analysis from single date ali imagery in magelang, central java, indonesia s. arjasakusumaa,b , p. danoedoroa,b , s. herumurtia,b, y.a. nugrohoa, p.a. aryagunaa,b a remote sensing dept., geographic information science major, faculty of geography, gadjah mada university, indonesia b graduate school of remote sensing, faculty of geography, gadjah mada university, indonesia abstract: paddy field area and its cropping intensity are main information used to measure the crop production and the response of crop to changing climate conditions. remote sensing technology has been widely used to map cropping pattern of paddy mostly using spectral analysis of multi-date multispectral data of remote sensing. however, the cropping intensity of paddy was also influenced by the characteristics of planted land to paddy field which defines the level of land suitability for planting paddy. this research aimed to map paddy rotation using single date ali imagery by assessing the land and soil characteristics based on the land suitability parameters for planting paddy. soil characteristics such as texture, acidity level, p205 (phosphor) and c-organic level collected from field work and terrain characteristics such as landform, surface water, and drainage density from visual delineation of srtm 90 m were collected as inputs for the decision tree analysis to map the repetition of paddy planting throughout the year. the results showed the overall accuracy of 85% ± 8% (95 % level of confidence) for the final paddy rotation map where 2-times paddy per year was mostly found in the study area. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. arjasakusuma, s., danoedoro, p., herumurti, s., nugroho, y.a., aryaguna, p.a., (2017). land-soil characteristics for mapping paddy cropping intensity using decision tree analysis from single date ali imagery in magelang, central java, indonesia. geoplanning: journal of geomatics and planning, 4(2), 187-200. doi: 10.14710/geoplanning.4.2.187-200 1. introduction rice is the product of the paddy (oryza sativa) which is the world dominant staple food for human. around 480 million tons of rice were annually produced to supply the demand of global citizen especially the poor fulfilling up to 50 % of their calories (muthayya, sugimoto, montgomery, & maberly, 2014). same situation occurred in indonesia, rice is one of the main foods for most of the poor citizen of the country where 20-25 % of total expenditure was used for rice consumption (timmer, 2004). considering the large rice consumption, population increase and the declining of paddy field area due to land use conversion in indonesia, accurate and annual information about the area of paddy field and the intensity of paddy planting is important to measure the potential of annual rice production. these two main parameters can be used to measure the actual and potential deficit of rice production so that further policy can be carried to prevent further problem such as inflation. in addition, information regarding crop intensity can be used as baseline information for measuring the impact of climate variability to the agricultural area as the dependence of agricultural area to climate conditions (xiao et al., 2006) as well as the effect of intensified agriculture pressure to soil quality and biogeochemical cycles (yan et al., 2014). remote sensing has been considered as valuable technology to provide important information regarding precise crop management with the ability to monitor the seasonality of crop and soil conditions and time dependence crop management (moran, inoue, & barnes, 1997). seasonal and time dependence cropping pattern in paddy field area is one of the main information that can be monitored using remote sensing. various remote sensing methods have been employed to map the cropping pattern information of open access article info: received: 15 dec 2016 in revised form: 1 may 2017 accepted: 7 july 2017 available online: 30 oct 2017 keywords: decision tree, cropping intensity, paddy field, land characteristics corresponding author: sanjiwana arjasakusuma gadjah mada university email: sanjiwana.arjasakusuma@uqconn ect.edu.au https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 mailto:sanjiwana.arjasakusuma@uqconnect.edu.au mailto:sanjiwana.arjasakusuma@uqconnect.edu.au arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 188 | paddy field using single date or multi temporal remote sensing data (foerster, et al., 2012; li, et al., 2012; pan et al., 2015; panigrahy & sharma, 1997; wang et al., 2015; xiao et al., 2006). analysis of paddy field distribution and crop rotation was mainly carried out using multi temporal data due to differences in spectral value in the paddy field during the phase of flooding, paddy growth and harvesting (panigrahy & sharma, 1997; wang et al., 2015). current analysis of remote sensing data using multi temporal analysis relies on the high temporal remote sensing data in order to be able to catch the seasonality pattern in spectral values at paddy field area. high temporal resolution remote sensing usually have coarse spatial resolution which consequently mixed pixel. this becomes problematic when it is implemented on monitoring the smallholder agricultural area generally having smaller area compared to the remote sensing pixel size (jain, et al., 2013). meanwhile, medium spatial resolution remote sensing data tends to have less intensive revisit time to capture the seasonality in the paddy field (wang et al., 2015). cropping intensity at detailed scale is influenced by the socio-economic factor such as farmer’s decision and crop price, and climate such as precipitation (biradar & xiao, 2011). in addition to climate and socio-economic factor, land physical constraints such as soil and water availability to supplement rainfall plays role in the farmer’s decision for cropping intensity (osman, et al., 2015; panigrahy & sharma, 1997). effort to map cropping pattern information using landscape-ecological approach from remote sensing data has been demonstrated to be able to map cropping intensity with decent accuracy (danoedoro, n.d.) in this study, we aimed to predict the potential cropping intensity information (1) by combining ancillary data such as soil characteristics and land morphology;(2) by employing data mining algorithm using decision tree analysis; and (3) by further measuring how accurate the potential cropping intensity map matches with the reality in the world. the decision tree algorithm was used because it gives comparable accuracy with the other remote sensing classification algorithm (friedl & brodley, 1997; xu, et al., 2005). furthermore, it is also able to handle data with different scales, no statistical assumption and fast, with the output of tree model that is easy to be interpreted (tso & mather, 2009). 2. data and methods 2.1. study area this study took place in magelang districts, central java, indonesia located between 110° 01’ 51” 110° 26’ 58” e and 7° 19' 13” 7° 42’ 16” s with total area of 1112 km2 (figure 1). furthermore, this area has various land morphologies which can be seen from the hill shade background in the figure 1. therefore, considering the variety of land morphology, this area is suitable for assessing the land physical characteristics relationships to paddy cropping intensity. 2.2. data 2.2.1. earth observation ali imagery earth observation – advanced land imagery (eo ali) is an experimental satellite that was launched by nasa in november 2000. the main objective of this satellite was to test the new system of hyperspectral and multispectral sensor for the next landsat mission. the data has similar orbit with previous landsat system with 30 m spatial resolution. however ali has more bands with extra near infrared and middle infrared bands sensors with the total of 9 bands of multispectral (bicknell, et al., 1999). details of eo ali sensors and technical configuration are shown in the following table 1. 2.2.2. field data field data was conducted to collect the input data for decision tree analysis and validation data for accuracy assessment. input data that was collected from the field data were the soil characteristics such acidity level (ph), phosphor level (p2o5), c-organic level, soil permeability and soil texture while the collection of validation data included the land use data and cropping pattern mapping data to assess the accuracy of crop intensity map. https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 | 189 figure 1. map of study area, magelang district, central java, indonesia with srtm 90 m hill shade as the background in the inzet map (analysis, 2015) table 1. technical configuration of eo-ali (lencioni, et al., 2005) characteristics specification details numbers of bands (pixel size) panchromatic (0.48 – 0.69 µm) 1 band (10 m) visible near infrared (0.433 – 0.89 µm) 6 bands (30 m) shortwave infrared (1.2 – 2.35 µm) 3 bands (30 m) orbit sun-synchronous with 705 km altitude aligned with landsat 7 satellite scanning system push-broom scanning field of view (fov) 15o cross-track by 1.26o in-track swath width 37 km temporal resolution 16 days radiometric resolution 12 bits 2.3. methods there are three main analyses in methods that cover 1.) identification of mapping unit boundary using visual inspection of land morphology from srtm data, 2.) implementation of two-stage decision tree analysis on remotely sensed data where the first decision tree was used to extract paddy field area using based on at surface radiance-spectral values and the second decision tree was used to add cropping intensity information to the extracted paddy field area, and 3.) accuracy assessment of the paddy field area as well as the cropping intensity. systematic general workflow of the aforementioned analyses is shown in the figure 2. 2.3.1. identification of mapping unit boundary in this study, land terrain unit acted as the mapping unit which is the smallest unit used in the analysis. the consideration of using terrain unit as the mapping unit was based on the assumption of homogenous terrain morphology having homogeneous lithological unit. therefore, when the area was undertaken by the geomorphological processes such as erosion and sedimentation, it will create similar https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 190 | output of land morphology depending on the resistance of rock material to the erosion process and the direction of the sedimentation. terrain attributes were identified as important variables for soil properties mapping as used by (elnaggar & noller, 2009) which shows the importance of using of terrain unit as mapping unit. the terrain was modeled by srtm hill shade in which delineation was visually made by looking at the similar pattern of morphology such as slope, stream flow, hills and canyon. srtm used in this research to generate hill shade has the spatial resolution of 90 m so that the mapping unit was identified at medium scale. figure 2. workflow to extract cropping intensity from ali – imagery using decision tree analysis (analysis, 2015) 2.3.2. two stages decision tree analysis decision tree is a method to categorize data using hierarchical splitting procedures. this method offers the ability to comprehensively understand the relationship between the data attributes for data classification (tso & mather, 2009). the final classification is represented in a form of a model tree composed of root, interior, and terminal nodes depicting the decision stages that were taken to categorize the data. the creation of the tree model can be manually done based on the user’s knowledge or automatically using a set of training area or known as supervised decision tree or tree induction. many algorithms have been developed for creating automatic tree model such as id3, c.45, cart and see5.0. see5.0/c5.0 algorithm applied in the software of see5® were used in this study in the formation of tree model. this algorithm was a development of the previous c4.5 algorithm which uses information or normalized gain ratios to perform the splitting process. the information gain was calculated based on the shannon’s entropy measures. see 5.0 offers boosting techniques which are able to increase the model accuracy by repeatedly performing a tree-generating process. this process assigned weight to the training area with the ratio value of misclassified training area to the correctly labeled area. in this study, two stages of decision tree induction were performed to produce land cover data and paddy rotation map. the first tree induction to produce land cover associated with paddy field and nonsrtm 90 m hillshade eo1 – ali imagery visual delineation spectral based decision tree field sampling reclassification reclassification decision tree analysis land cover map terrain unit map tentative paddy field map soil properties cropping intensity terrain characteristics paddy field map cropping intensity map https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 | 191 paddy field employed the input value taken from each bands spectral values of the training area. the tentative map of the paddy rice distribution was further reclassified using the terrain unit derived from visual delineation of srtm 90m using boolean logic from field observation. the second stage of tree induction was carried out to map the frequency of paddy rotation by employing the set of training area from (1) land characteristics such as drainage density which qualitatively estimate stream flow density, relief, surface water and morphology and (2) soil characteristics such as texture, acidity level, p2o5 and corganic level taken from the field work. the land characteristics that described the terrain configuration were considered useful to identify the distribution of soil properties (sumfleth & duttmann, 2008) and water availability (samson, ali, rashid, mazid, & wade, 2004). in the other hand, soil characteristics resembles the crop management such as cropping intensity and tillage which affect the dynamic of soil chemical, nutrient and quality (liebig, tanaka, & wienhold, 2004; mikha et al., 2006). therefore, mapping cropping intensity using the combination of land-soil characteristics and remote sensing observation is possible to be conducted. 2.3.3. mapping paddy field and reclassification process to extract paddy field category from the single date ali imagery, cautious selection of training area were taken to map the land cover information from the imagery. sixty training areas were taken to resemble various land cover visible in the 30 m of spatial resolution ali imagery. average spectral value from each training area was used as the input for decision tree analysis with the standard deviation less than three to ensure the training area representing homogeneous land cover class. land cover class information that was derived from the imagery was used as the base information for next classification of paddy and non-paddy field classes. to successfully deriving this information using single date imagery data, the possibility of visual appearance of paddy field at different states of condition should be taken into account. paddy field which is recently planted will have water body that dominates spectral pattern meanwhile paddy field which is dominated with paddy cover had vegetation spectral pattern. lastly paddy field that is recently harvested had bare soil typical spectral pattern (figure 3). to accommodate those three characteristics of paddy field in single date imagery, different training classes were assigned to specifically record and training the algorithm to recognize these spectral pattern. according to the crop calendar from the indonesian ministry of agriculture, in the time when ali imagery that was recorded in june 2004 during this study, the paddy fields in the area were mostly at the growth stage. in line with that, the paddy fields with the vegetation spectral pattern were mostly found in the imagery even though several unplanted and wet paddy fields were correspondingly found at this time. to capture the variability of paddy field in the study area, from 60 training areas that has been randomly collected, 23 of the training area were affiliated to paddy field in which 12 of the training areas are affiliated to the planted paddy fields dominated with vegetation cover, 4 training areas represent wet or moist soil covered paddy fields and 7 training areas affiliated to bare soil covered paddy fields. those classes related paddy field were easily distinguished from other existing land cover classes in the 30 m spatial resolution of the imagery due to the homogenous shape and association with the pathway and the linearity of possible irrigation network. although other non-paddy vegetation cover such as woody vegetation and smallholder plantation equally exists in the imagery, this vegetation can also be easily distinguished by looking at the tone and density of vegetation for woody vegetation and location for the smallholder non-paddy plantation. however, misclassification especially for smallholder plantation and vegetation covered paddy field was still possible due to the use of spectral information solely as an input during the classification process. thus, additional post-processing was performed to rectify this misclassification using matching method based on the identified terrain unit. tree model was produced using see5.0(c) software which average values from each training area were employed as the base information to do the splitting process in decision tree in this study. there are 75 times of trial with 25 % pruning to avoid excessive leaf nodes in the output tree model so that over-fitting in the output model can be avoided. from 75 trials, there are 75 output tree models with different number of leaf node, splitting decision and error. error of each model was counted by assessing the number of misclassified input training areas that did not match with the output tree model. the land cover classification can be linked into 2 main land use categories which are paddy and nonpaddy fields. by performing this simple reclassification, tentative spatial distribution of paddy field was able https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 192 | to be extracted. visual assessment matched with the ground checking revealed misclassification in the tentative map especially between covered paddy field and dry smallholder plantation. both have similar spectral signature due to vegetation coverage although both were associated with different landform unit. based on ground checking, smallholder plantation is generally located at higher elevation with steeper slope and thin soil solum. with minimum need of irrigation for the majority of the crops in the smallholder plantation, planting these crops in this landform is feasible. on the other hand, developing paddy field in this area would be problematic as water availability is a limiting factor. with distinct differences in terms of landform for smallholder plantation and paddy field, binary matching using land terrain unit from the previous analysis was used to remove the falsely identified paddy field. figure 3. different visual appearance of paddy field at different states of condition as appeared in ali multispectral imagery false color composite such as (a). paddy field at flooding stage, (b). paddy field with bare soil reflectance, and (c). paddy field with vegetation (analysis, 2015) 2.3.4. accuracy assessment accuracy assessment was carried using error matrix based calculation developed by (olofsson, foody, stehman, & woodcock, 2013) in order to derive the producer’s, user’s and overall accuracy in percentage. the confidence interval for the producer’s, user’s and overall accuracy were calculated using this method which gives deviation of error range for the accuracy. as accuracy assessment of the map was calculated from statistical analysis using sample data representing the entire population in the map, the confidence interval calculation was necessary. therefore, the accuracy value should not be merely represented by a single number but the margin of error should be included in the accuracy assessment. this was to give the value of uncertainty of the final accuracy number (olofsson et al., 2013). the research implemented conventional error matrix as the baseline information of the accuracy calculation. however instead of using normal pixel or point count calculation in the conventional error matrix (table 2), another error matrix was developed using the estimated area proportion for class i,j (pij) in the cell entries (table 3). table 2. conventional error matrix using sample counts (n) with additional information of area and area proportion (wi) to transform sample counts into estimated area proportion class 1 2 .. q total area wi 1 n11 n21 .. nq1 n1. a1 a1/a.tot 2 n12 n22 .. nq2 n2. a2 a2/a.tot .. .. .. .. .. .. .. .. q n1q n2q .. nqq nq. aq aq/a.tot total n.1 n.2 .. n.q n a.tot 1 a b c https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 | 193 table 3. modified error matrix using estimated area proportion (p) in the cell entries (olofsson et al. 2013) class 1 2 .. q total 1 p11 p21 .. pq1 p1. 2 p12 p22 .. pq2 p2. .. .. .. .. .. .. q p1q n2q .. pqq pq. total p.1 p.2 .. p.q p here, rice field data was collected using ground checking combined with google earth© observation while cropping intensity validation was collected by performing interview to the local farmers. there are 6171 pixels for validation area from the ground checking and extrapolation process for validating rice field area and 42 points of validation data for estimating the accuracy of cropping intensity map. 3. results and discussion 3.1. identification of mapping unit boundary there are approximately 26 main land units that were identifiable by visual interpretation from srtm 90 m hillshade data (figure 4). these land units represented various morphology, surface roughness and slope steepness indicating different process and intensity of geomorphological processes occurring in the background. the study area was dominated by main geomorphological processes such as volcanic (v), fluvial (f) and denudation (d). paddy field seems to be centered on fluvial landform with least steep slopes and good water availability in the surface. while in the upper volcanic neck and denudation hill with rough surface roughness and intensive stream flow indicating the undergoing process of gully erosion and less capability of soil to hold water, dry smallholder plantation are often discovered. the association of land physical unit characteristics with paddy field and dry plantation is useful for performing reclassification process to further eliminate possible misclassification in the land use mapping. 3.2. mapping and reclassification of paddy field first stage of decision tree analysis resulted in 75 tree models with different nodes and error. from these trees the best model having least nodes and lower error was chosen (figure 5). this particular model had 12 nodes and 8.25 % of error indicating that from the total of 60 training areas, 5 of them were misclassified or did not match with the output tree model. the output of tree model depends highly on the input training area so that changes in training area will correspondingly change the output of the tree model. therefore, the binary splitting decision in the tree model is area-specific and cannot be applied to other imagery and other areas. figure 4. map of terrain unit identified from srtm 90 m hillshade data and their corresponding land morphology and topography attributes (analysis, 2015) https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 194 | figure 5. output tree model produced by using average spectral value of different land cover in the training area (analysis, 2015) the association of paddy field to certain land terrain characteristics (figure 3) was developed based on the ground checking in order to map the paddy field. in the study area, we observed that paddy fields were mostly distributed in flat to least steep with less surface stream flow. this indicated low gully erosion in this area indicating thick soil solum and the ability to sustain water. these characteristics are mostly associated with fluvial process even though some paddy fields were also found in volcanic and denudation landforms such as low to middle volcanic plain, and denudation landform. using these observations, the association was transformed into binary rules (table 4) to rule out the false identified paddy field from the tentative map. table 4. binary matching to rule out false identified paddy field from the tentative map (analysis, 2015) class mapping unit d11 d12 f11 f12 s1 v1 v2 v31 v32 non-paddy field + + + + + + + + + paddy field + + + + class mapping unit v33 v41 v42 v51 v52 v53 v61 v62 v63 non paddy field + + + + + + + + + paddy field + + + + + legends : + = exist, = not exist the resulted map showed the removal of pre-identified paddy field especially in the area with higher latitude and coarse surface roughness which agricultural related land cover were associated to smallholder non-paddy plantation (figure 6). this process reduced the detected paddy field although it still has to be justified by conducting the accuracy assessment. it is worth noted that the use of the binary matching resulted in a discrete distribution of where paddy fields are located. while in the real world, the boundary might be fuzzy even though it is still controlled by the landform characteristics. https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 | 195 (a) (b) figure 6. paddy field distribution derived by using (a) decision tree model employing the spectral values and further corrected by using (b) binary matching by considering the land morphology – land use relationships 3.3. mapping paddy cropping intensity the remaining paddy fields after reclassification process were labeled with the paddy cropping intensity. this analysis was performed using similar decision tree analysis with land and soil characteristics as the training data to select the significant variables influencing the paddy cropping intensity. eighteen soils samples were taken from different landform where paddy field was found. these samples carrying the information of land physical and soil characteristics were used as an input for the decision tree analysis. seventy five trials of decision tree analysis were executed to produce 75 decision tree models. to assess one best output model, error and the number of leaf nodes were used as the criteria. here, tree model with 5.6 % error and 6 leaf nodes were selected where the phosphate level (p2o5), soil texture and ph were significant variables that influence paddy field cropping intensity (figure 7). the selected soil parameters of phosphate level, acidity level and soil textures in the tree model indicated the significance of those parameters for supporting cropping intensity although the relationships between cropping intensity and the parameters are most likely indirect. the parameters detected in the tree model are often used for indicating the chemical and physical properties needed to study the soil nutrients in the paddy fields among other parameters (cho & han, 2002). in particular, p2o5 is often used in the chemical fertilizer in which its amount of substance in the soil is needed to sustain and/or to improve paddy productivity (inthavong, fukai, & tsubo, 2011; mishima, taniguchi, & komada, 2006). in addition, the productivity of paddy fields is also influenced by the water holding capacity factor. one of the parameters that control the water availability is soil texture besides the topographic configuration and rainfall intensity (inthavong et al., 2011). furthermore, soil texture with less sand will have higher productivity due to its capability to preserve waters, nutrients and high electric conductivity (mzuku et al., 2005). in the above tree model, sandy loam was the threshold category of soil texture which is still able to be planted maximum 2 times per year. the other condition of soil texture with p2o5 below 97 could only yield rice for 1 time per year according to the model in the left side branch. in the right side of the branch, in order to obtain maximum 3 times per year planting intensity, ph level close to neutral (equal or above 5) and high soil nutrient of phosphate (equal or above 97) is needed. otherwise, land is only able to be planted once per year due to the acidic condition of the soil. output tree model showed that soil characteristics have more influence compare to land physical characteristics that were also included as the training area. the conclusion of whether soil characteristics has more influence to paddy cropping intensity compared to land physical characteristics cannot be uniquely justified based on this analysis. this is due to the nature of https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 196 | decision tree analysis that is sensitive to the input training area. thus, the need for more samples to become more stable is indispensable. however, these selected variables in the tree results seems to align with the statement of soil characteristics that influenced more on the paddy field productivity compared to the location or the spatial distribution of paddy field (keersebilck & soeprapto, 1985). however, similar to the previous decision tree model, this output model is highly sensitive to the input of training area. it cannot be implemented for another study using different input even though the same parameters were used. figure 7. output tree model to map the cropping intensity using land physical and soil characteristics as the input training data (analysis, 2015) cropping intensity using these rule sets from decision tree analysis showed that 2 times a year paddy cropping is common in this area which occupies 47 % (13,520 ha) of the total paddy field (figure 8). the boundary of different crop intensity category in the map is distinct due to the use of land terrain unit as the smallest mapping unit for mapping cropping intensity. two to three times a year, paddy seems to be clustered around the alluvial plain and at the lower level of merapi volcanic slopes. figure 8. final cropping intensity map where dominant of 2 times a year paddy field was found in the area, the distribution of different types of cropping intensity appear distinct due to the use of terrain unit as the mapping unit (analysis, 2015) p205 <= 97 texture = sandy loam ph => 5 1 times/year 2 times/year 1 times/year 3 times/year : yes : no https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 | 197 3.4. accuracy assessment accuracy assessment was performed to both tentative and reclassified maps of paddy field distribution using 6171 pixel count based on the field observation and extrapolated using google earth imagery. based on the calculation performed, the tentative map produced the overall accuracy of 80 % ± 1% with the 79 % ± 1.5 % and 76 % ± 1.4 % for user accuracy and producer accuracy, respectively (table 5). after using landform association with the paddy field existence to remove the possible false identified paddy field from the tentative map, 9 % increase in the overall accuracy has been achieved (table 6). the overall accuracy for the reclassified map is now 89 % ± 1 % with 86 % ± 1.2 % and 82 % ± 1.6 % for the user accuracy and producer accuracy. this accuracy is at par with the accuracy of rice field mapping of different studies that were able to map rice field at the accuracy around 78 90 % (gumma, thenkabail, maunahan, islam, & nelson, 2014; wang et al., 2015). the increasing of accuracy after the post processing is the proof that using the relation between landform and land use was able to remove possible paddy field mistake in the map. table 5. accuracy before post processing using landform approach (analysis, 2015) reference area (ha) wi class accuracy ci paddy field non paddy field total sample ui pi ui pi map paddy field 2353 620 2972 43287.97 0.431 0.79 0.76 0.015 0.014 non rice field 599 2600 3199 57033.25 0.569 0.81 0.84 0.014 0.010 total sample 2951 3219 6171 100321.21 overall accuracy : 0.80 ± 0.01 table 6. accuracy after reclassification using landform approach (analysis, 2015) reference area (ha) wi class accuracy ci paddy field non paddy field total sample ui pi ui pi map paddy field 2646 425 3071 33790.78 0.337 0.86 0.82 0.012 0.016 non rice field 306 2794 3100 66530.76 0.663 0.90 0.93 0.010 0.006 total sample 2951 3219 6171 100321.55 overall accuracy : 0.89 ± 0.01 table 7. accuracy of cropping intensity of paddy field (analysis, 2015) reference area (ha) wi class accuracy ci 1 times/year 2 times/year 3 times/year total sample ui pi ui pi map 1 times/year 4 0 0 4 3699.05 0.129 1.00 0.66 0.00 0.21 2 times/year 4 20 5 29 13520.70 0.470 0.69 1.00 0.17 0.00 3 times/year 0 0 9 9 11555.41 0.402 1.00 0.83 0.00 0.11 total sample 8 20 14 42 28775.16 overall accuracy : 0.85 ± 0.08 second accuracy assessment was performed to estimate the accuracy of cropping intensity map using decision tree analysis from 42 samples collected by interviewing local farmers on the area. here, overall accuracy of the final cropping map showed 85 % ± 8 % which is good for the classification using medium resolution imagery (table 7). however, in the individual class accuracies, producer accuracy for cropping intensity of 1 times of paddy per year and user accuracy for class 2 times a year is rather moderate to low with big deviation for the confidence interval. producer accuracy of 1 times paddy per year was 66 % ± 21% and user accuracy for class 2 times a year was 69 % ± 17 %. this moderate to low accuracy was compared to related research using multidate imagery that able to map 90-97 % of producer accuracy of crop rotation (nguyen, 2013; panigrahy & sharma, 1997). the low accuracy for individual class might be caused by the coarse unit of mapping that was used for generalizing the cropping intensity for paddy field within the specific landscape. https://doi.org/10.14710/geoplanning.4.2.187-200 arjasakusuma et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 187-200 doi: 10.14710/geoplanning.4.2.187-200 198 | the drawback of using 90 m srtm as the base data for identifying the boundary of land terrain is that only major terrain can be identified resulting in general landform mapping unit. cropping intensity in paddy field was mostly influenced by the availability of water so that if the paddy field has a good irrigation system, then 2 or 3 times paddy per year can be achieved. however, variation of cropping intensity within clustered paddy field in the same area also possible. topography controls the distribution of the surface water flows so that the distribution of water in the same paddy field may be different. in slope or terraced paddy fields will consistently receive water compared to paddy fields in the plain and higher elevation which will sustain less water especially in the dry season. in term of irrigation management, terraced paddy field will arranged to be keep inundated especially in the dry season to prevent the landslide which gives the farmer the chance to plant another crop (adachi, 2007). in addition, further assessment to include to climate data such as rainfall and temperature in the analysis to the dynamic of cropping intensity can be performed with the dependence of paddy field to water availability. 4. conclusion the accuracy of predicted cropping intensity map revealed the possibility of using the landform approach and characteristics. it was used to improve the identification of paddy field and its cropping intensity attributes with respectable accuracy. decision tree was used as the algorithm to develop the rule set from the collected training areas. binary matching using landform approach was able to increase the overall accuracy of 9 % indicating the benefit of using logical relationships between landform and land use/land cover to omit the misclassification from the result. rule set developed from decision tree analysis using soil parameters was also able to map cropping intensity with respectable level. however, future assessment using additional parameters such as monthly rainfall and temperature data is required to improve the result as well as socio-economic parameters such as the availability of labor to work on the land. in addition, the infrastructures of the paddy field such as irrigation systems require more detailed data in the future to show the irrigation systems. detailed land morphology delineation could also be made by using higher spatial resolution of terrain data such as srtm 30 m or aster gdem to enhance the detail of land unit map and identify the micro-topography that affect the water distribution especially in rain-fed paddy field. the main drawback of this study lies particularly in the algorithm of decision tree, although decision tree can give respectable accuracy, the algorithm was strongly sensitive to the input of training area where small changes in the input can entirely change the output model. this also suggests that collection of training area should be taken carefully and the more number of samples should be taken to give more valid output of the tree model. further assessment using ensemble method, random forest or neural network analysis also could be explored in this study application. 5. acknowledgments the authors would like to acknowledge graduate school of remote sensing, faculty of geography, gadjah mada university, yogyakarta, indonesia for the support in this study. the authors also would like to extend the gratitude to statistical agency (bps) of magelang districts, central java, indonesia and bappeda magelang districts for the permission to conduct field research and the support of the statistical data. the author is grateful for being granted with the beasiswa unggulan dikti for providing support during the study. we also thank anonymous reviewers for the comments and suggestions which significantly improve the quality of the manuscript. 6. references adachi, s. 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(2014). multiple cropping intensity in china derived from agro-meteorological observations and modis data. chinese geographical science, 24(2), 205– 219. https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.14710/geoplanning.4.2.187-200 https://doi.org/10.1016/j.jag.2014.08.011 https://doi.org/10.1016/s0924-2716(97)83003-1 https://doi.org/10.1626/pps.7.101 https://doi.org/10.1016/j.ecolind.2007.05.005 https://doi.org/10.2139/ssrn.997415 https://doi.org/10.1038/srep10088 https://doi.org/10.1016/j.rse.2005.10.004 https://doi.org/10.1016/j.rse.2005.05.008 | 157 geoplanning vol 4, no. 2, 2017, 157-170 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.157-170 the performance of land use change causative factor on landslide susceptibility map in upper ujung-loe watersheds south sulawesi, indonesia a. s. soma a,b, t. kubota c a graduate school of bio-resources and environmental science, kyushu university, japan b faculty of forestry, hasanuddin university, indonesia c faculty of agriculture, kyushu university, japan abstract: the study aims to develop and apply land use change (luc) performance on landslide susceptibility map using frequency ratio (fr), and logistic regression (lr) method in a geographic information system. in the study area, upper ujung-loe watersheds area of indonesia, landslides were detected using field survey and air photography from time series data image of google earth pro from 2012 to 2016 and luc from 2004 to 2011. landslide susceptibility map (lsm) was constructed using fr and lr with nine causative factors. the result indicated that luc affect the production of lsm. validation of landslide susceptibility was carried out in this study at both with and without luc causative factors. first, performances of each landslide model were tested using auc curve for success and predictive rate. the highest value of predictive rate at with luc in both fr and lr method were 83.4 % and 85.2 %, respectively. in the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.lr method with luc had the highest accuracy of 80.24 %. taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): soma, a. a., & kubota, t. (2017). the performance of land use change causative factor on landslide susceptibility map in upper ujung-loe watersheds south sulawesi, indonesia. geoplanning: journal of geomatics and planning, 4(2), 157-170. doi:10.14710/geoplanning.4.2.157-170 1. introduction land use changes (luc) has increased the level of vulnerability to landslides, especially in mountainous regions. it is recognized throughout the world as one of the most important factors influencing the occurrence of rainfall-triggered landslides (glade, 2003). it implies to landslide occurrence on a steep slope (mugagga, kakembo, & buyinza, 2012). in south sulawesi indonesia, especially in ujung-loe upper watershed, luc has been translated into numerous landslide incidents triggered by the intensity of rainfall compared to other factors such as earthquakes. the topography is extremely steep and naturally mountainous (38.8 % class slope >20 degrees). it has a very high level of instability, especially during the rainy season. the rainfall can reach 2,976 to 7,114 mm/year with average annual rainfall of 4,524 mm/year. farming which is located in the mountainous area is the primary occupation of social community in that area is. it is hard to avoid this agricultural practice. this has become people's culture for agriculture in mountainous regions and they have made it hereditary (soma & kubota, 2017). landslide susceptibility defined as quantitative or qualitative assessment classification, volume (or area), and the spatial distribution of landslides or potentially may occur in the zone. susceptibility can also include a description of the speed and intensity of existing or potential landslides (fell et al., 2008). using scientific analysis of landslides, this study can assess and predict landslide susceptibility and decrease landslide damage through proper preparation (lee et al., 2002). article info: received: 31 july 2017 in revised form: 10 august 2017 accepted: 10 october 2017 available online: 30 october 2017 keywords: land use change, landslide susceptibility, frequency ratio, logistic regression corresponding author: andang suryana soma graduate school of bio-resources and environmental science, kyushu university, japan email: suryaandang@kyudai.jp open access https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 mailto:suryaandang@kyudai.jp soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 158 | a few studies have evaluated land use change (luc) that contributed to landslide occurrence (garcíaruiz et al., 2010; glade, 2003; mugagga, kakembo, & buyinza, 2012; soma & kubota, 2017). however, the use of luc as a causative factor to see the performance of luc and to build landslide susceptibility has not yet implemented. therefore, the luc will be as a new causative factor to change a land use as a human factor. land use is simply implemented to look at the current time and different with the luc. luc can prepare what land has used before. example, early land use change was primary forests to farmland, then compared with the open land change to agricultural, of these two conditions have different slope stability (soma & kubota, 2017). based on these factors, previous researchers have not drawn the performance of land use changes as a causative factor. the objective of the study was to examine the performance of land use change as a causative factor to produce landslide susceptibility map using frequency ratio, logistic regression, and comparison. 2. data and methods 2.1. study area upper of ujung-loe watersheds was located in bulukumba and sinjai regency, south sulawesi province, indonesia. it provides a fertile land but frequently suffers from landslide disasters. landslide disasters occur almost every year, especially during the rainy season, which induces flash floods and debris flows in the upstream (soma & kubota, 2017). the upper of ujung-loe watersheds is located at 119° 55' 42.34"e to 120° 8' 43.12"e and 5° 18' 19.07" s to 5° 24' 43.33" s with the altitude of 255 – 2,860 meters above sea level with areas of 79.79 km2 (figure 1). it provided forests covering an area of cultivation and farming. some areas are particularly in the upstream part. according to geological maps of sulawesi, it is dominated by volcanic rock of lompobatang (qlv and qlvc) and members of volcanic breccia rocks of lompobatang (qlvb). the volcanic rocks of lompobattang mountain consist of extrusive, mafic, and polymict, which form a broad stratovolcano and quarter lompobattang volcanic estimated start from the last of pliocene to early pleistocene of volcanic rock. members of volcanic breccia rocks of lompobatang (qlvb) consist of extrusive, mafic, and polymict, and are estimated to start from first pleistocene to early holocene of volcanic rock. the slope is around 38.8% with slope class >20 degrees including a particular area at the upstream is very steep (>40 degrees). figure 1. study area the tropical climate of south sulawesi has individual characteristics of the two seasons of the year: the rainy season and dry season. the northeast monsoon giving raises rainy season between november and july (march to july has the maximum precipitation), and the southwest monsoon causes the dry season, from august to october. the annual rainfall data were recorded at three stations, i.e., pasir putih station, https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 | 159 malino station and tanete/bulo-bulo station from year 2010 to 2015. the annual rainfall data recorded at pasir putih station was 2,976 to 7,114 mm/year with average annual rainfall 4,524 mm/year. rainfall recorded at malino station was 3,271 to 5,346 mm/year with average annual rainfall 3,933 mm/year; and recorded at tanete/bulo-bulo station was 2,237 to 5,711 mm/year with average annual rainfall 3,538 mm/year. the monthly rainfall is more than 400 mm in the month of december and rises to 627 mm in july (agency of climatology and geophysical, makassar, indonesia, 2016). due to the increasing intensity of rainfall leading to the possibility of landslides and especially in shallow landslides correspondingly increased with the high-intensity rainfall in a short time (hasnawir et al., 2017). 2.2. preparation of data data selection is the important thing in the preparation of the landslide susceptibility map. the good data selection for analysis helps to find satisfactory results. management and collection or selection using arc gis 10.3 must be accurate in establishing a spatial data landslide inventory and also a causative factor. for the analysis of the frequency ratio (fr) calculation was carried out using microsoft excel, while for the logistic regression (lr) used the program statistical package for social sciences (spss). more detail of the research, it can be consulted in figure 3. 2.2.1. landslide inventory landslides inventory can involve field surveys, expression of morphological, and interpretation of remote sensing images based on spectral characteristics, shape, and contrast (kanungo et al., 2006). this study used data landslide from 2012 to 2016 using air photography of google earth pro and ground survey (figure 1 and figure 2). the purpose was to find a correlation between the occurrence of landslides and land use change from 2004 to 2011. the study area was limited to the upper of ujung-loe watersheds. a total of 188 landslides were identified covering an area of 43.65 hectares (0.44 km2). most of the landslides are of the shallow type with minimum and maximum landslide area of 137 m2 and 15,600 m2, respectively. using the landslide data from the survey and digitizing high-resolution from google earth pro to arc gis 10.3, it digitized the time series imaging data by image interpretation landslide, and these files were saved as gis compatible format as .kml (extension). then, the data was again subsequently changed into shape file and into raster format 10 x 10 meter. figure 2 shows the location of all landslide data divided into two group i.e. landslide for training at 2,873 pixels (70 %) and a landslide for validation at 1,230 pixels (30 %). figure 2. landslide inventory a) old landslide, b) new landslide 2.2.2. landslide causative factors in susceptibility map, the most important assumption that the incidence of landslides will occur in the same condition is affected by the cause of the landslides that have been occurred. there are no strict guidelines for the selection of causative factors to be used in logistic regression analysis and certainty factor, and as such, the selected covariates vary widely between studies (ayalew & yamagishi, 2005; dou et al., 2015). correspondingly, the determination of landslide causative factors was associated with the availability of data. therefore, we selected causative factors based on the general knowledge found in a) b) house farming area house farming area https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 160 | previous studies and its availability in the target location. the entire landslide causative factors that have been used for the independent variable in the landslide susceptibility mapping (figure 4). the independent variable was nine (9) causative factors including elevation, slope, aspect, curvature, lithology, and distance from fault, distance to river, drainage density, and land use change (luc). elevation, slope, aspect, and curvature were extracted from contour data digital interval 12.5 meters. contour data of rupa bumi indonesia (rbi) on map scale 1: 25,000 from geospatial information agency of indonesia (big) was obtained using arc toolbox raster surface in arcgis 10.3, elevation, slope, aspect, and curvature were extracted. using the uniform isotropic material, increased slope correlates with increased likelihood of failure. in this study, we have used six (6) slope categories (0–10°, 10–20°, 20–30°, 30–40°, 40– 50°, and above 50°) which were considered and represented in the form of slope thematic data layer. likewise, the aspect map plays a significant role in slope stability assessment (chauhan et al., 2010). in this study, aspect was divided into nine classes namely, flat, north, northeast, east, southeast, south, southwest, west, and northwest. curvature was classified using the curvature of the profiles into three categories: concave, flat and convex. the values represented the morphology topography curvature. in the case of profile curvature, it was related to the puddle condition after heavy rain. moreover, the reason is that, following heavy rainfall, a more upwardly concave or convex slope has more water and retains it longer (lee & lee, 2006). figure 3. research framework the geology of the area was using digital data produced by indonesia government, namely geology map by geological research institute, at a scale of 1:250,000. this map includes the current study area. the geology data consist of lithology, structure (fault or lineament), and rock type. lithology is the primary data or parameters for analysis of the landslide map. lithology is a standard variable that controls the landslide danger. it related to the strength of the material, because lithological composition and structure vary for different types of rocks (kanungo et al., 2006). in addition, resistance to the driving force depends on the strength of rocks and stones that will be more resistant. faults are structural features, which describes the zones/areas of weakness, fractures, and among lineament going higher susceptibility to landslides. it has been observed that the increased probability of landslide occurrence in a location close to faults not only affect the surface structure of the material but equally contributes to the permeability and cause slope instability. for this purpose, the distance from faults was used to analyze the incidence of landslides at a distance of faults. the proximity of the fault was obtained by buffering the map of faults (rasyid, bhandary, & yatabe, 2016). both drainage lines and landslide occurrence in the hilly area had a strong association due to erosional activity in this location. the distance from the river has been calculated by buffering analysis of stream https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 | 161 lines. this information was derived from a topographic map of scale 1:25,000 called peta rupa bumi indonesia (rbi) prepared by geospatial information agency of indonesia (big) at 2012. the class starts from 0 to 100 m to > 500 m. similarly, drainage density was calculated using arc toolbox kernel density in km/km2. the class of drainage density was grouped in five class starting from 0 to 1 km/km2 to >4 km/km2. besides topographic factors and geology, land use (cover) is an essential element/factor responsible for landslide occurrences. the incidence of the landslide is inversely related to the vegetation density. this research used land use change (luc) as vegetation density. luc was as a new causative factor to change land use pattern to build a landslide susceptibility mapping. to the critical slope, luc triggered a series of shallow and profound landslides (mugagga et al., 2012). the luc map was derived from overlay land use 2004 and land use 2011. land use was derived from interpretation landsat 5 tm (date recorded september, 21th 2004) and landsat 7 etm+ (time filed october, 11th 2011) images, each with a 30 m resolution, collected from united states geological survey (usgs). figure 4. eleven causative factor of landslide the unsupervised classification method was applied to classify land use. unsupervised classification consists of three steps: (1) the map creation of n spectral class using iterative self-organizing data analysis technique algorithm, (2) the development of land use (lu) map with the help of reference data, and (3) the accuracy measurement of the ratings of all lu reference map using independent data and selection of a map lu with the highest accuracy (lang et al., 2008). this method was applied to classify land use into seven (7) such as type: open area, paddy field, farming area, scrub, savanna, secondary forest and primary forest (soma & kubota, 2017). overall accuracy values of lu 2004 and lu 2011 were 86 % and 90 %, respectively. kappa values of 0.83 and 0.88 were achieved for the unsupervised classified maps of lu 2004 and lu 2011, respectively. moreover, luc was built by classifying once more lu 2004 and 2011 in four classes: (1) open area, paddy field, (2) farming area and shrub, savanna, (3) secondary forest and (4) primary forest. in the next step, each other was overlaid using arc gis 10.3 and founded 13 classes as a class of luc. they were 1 – 1 (change from open area and paddy field to open area and paddy field) , 1 – 2 (change from open area and paddy field to farming area and scrub, savanna), 2 – 1 (change from farming area and scrub, savanna to https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 162 | open area and paddy field), 2 – 2 (change from farming area and scrub, savanna to farming area and scrub, savanna), 2 – 3 (change from farming area and scrub, savanna to secondary forest), 3 – 1 (change from secondary forest to open area and paddy field), 3 – 2 (change from secondary forest to farming area and scrub, savanna), 3 – 3(change from secondary forest to secondary forest), 3 – 4(change from secondary forest to primary forest), 4 – 1 (change from primary forest to open area and paddy field), 4 – 2, 4 – 3 (change from primary forest to secondary forest), and 4 4 (change from primary forest to secondary forest), and 4 4 (change from primary forest to primary forest). luc was downgraded from pixel size of 30 x 30 meter to pixel size of 10 x 10 meter. landslide was described as the dependent variable and causative factor i.e. elevation, slope, curvature, distance to river, drainage density, lithology, and distance to faults. luc was described as the independent variable. independent and dependent variables were used as a map input and then processed to turn it into a raster map with a pixel size of 10 m × 10 m. we can observe the causative factor map in figure 4. the study area included 795,227 pixels. the landslide data used in the model included 2,873 pixels (70% of landslide) and 1,230 pixels (30%) for validation. 2.3. data analysis there are two analyses methods to understand the performance of each causative factor (frequency ratio (fr), and logistic regression) to produce landslide susceptibility map. frequency ratio analysis was implemented to define the performance of which class each causative factor and logistic regression methods could describe the performance of the susceptibility of landslide occurrence. 2.3.1. frequency ratio the landslide and the causes were related and it can be concluded between areas where the landslide occurs with the causative factors of landslides. simple statistical techniques to determine the closeness of the relationship has been applied to the frequency ratio (fr) approach. frequency ratio for each causative factor was calculated by dividing the landslide occurrence rate by the area ratio. if the ratio is bigger than 1.0, the relationship between the landslide and the causative factor is higher, and, if the relationship is less than 1, the connection is low (lee & lee, 2006). a ratio value in each class shows the level of relationship the frequency ratio value calculated by the equation (1). ………………………………………………………………… (1) where, pixel (nm) number of pixel with landslide within class n of j parameter, pixel (nm) number of pixel in class n of m parameter, σpnxl total pixel of m parameter, and σpnx whole pixel of the area. to create an index susceptibility to landslides, all causative factors were charted in the form of raster maps of the value fr then summed by using equation (2). lsi = fr1 + fr2 + … + frn ………………………………………………………. (2) where fr1, fr2, fr3… frn is the frequency ratio raster maps of landslide causative factors. 2.3.2. logistic regression logistic regression resulted in landslide susceptibility index. a simple introduction to logistic regression available in (chau & chan, 2005) which defines the probability occurrence of landslides divided by the probability of no occurrence of landslides. it is useful to predict the presence or absence of a characteristic or outcome based on values of a set of variable predictors. generally, in the logistic regression, spatial prediction can be modeled using the independent and the dependent variables (shirzadi et al., 2012). it is useful when the variable is a binary or dichotomous. variables can be continuous, or discrete, or a combination of the two types and they do not always have a normal distribution. the probability of regression can be understood as the possibility of state dependent variables. data analysis created iteration in ten tests using equal data of landslide and no landslide. using an equal data of occurrence of landslide and no landslide will result better and fair for logistic regression analysis (rasyid et https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 | 163 al., 2016). they were restricted to fall within a range of values from 0 to 1 (xu et al., 2013). the value of zero shows probability of 0 % landslide occurrences, and one shows a 100% probability (dai et al., 2004). the logistic regression followed on logistic function –z expressed by the equation (3). ……………………………………………………………. (3) z = c0 + c1cf1 + c2cf2 + …+ cncfn ………………………………………………………. (4) where p is the probability of landslide occurrence that estimated values varying from 0 to 1. variable z is landslide causative factor and is assumed as a linear combination of the causative factors xi (i = 1,2,…n). moreover, z calculates using equation (4). c0 is the intercept, and c1, c2,..., cn are coefficient, which measures the contribution of independent factors (cf1, cf2, . . ., cfn) to the variations in z. 2.4. validation and verification in addition to a decrease in prediction of accuracy and probability, validation can improve the reliability. during the modeling predictions, the most important and critical component is to carry out the validation of the results of prediction (chung & fabbri, 2003). in this study, the landslide inventories were divided into two parts; one for training and the other for validation. this study used a 2,873 pixel (70%) inventories landslides to produce models and 1,230 (30%) of pixels for validation. the fundamental assumptions election landslide of data for training and validation of the model was randomly taken on each part of landslide occurrence in the area of research and also based on the representation of the landslide area. to illustrate the procedure, a small portion of the landslide-prone areas was selected as the data for validation. size, area, depth of landslides and distribution significantly varies from place to place. also, we used the roc curve to plot predicted probabilities in order to understand the problem of accuracy, selection criteria, and interpretation. for validating the landslide susceptibility map, auc curve was used as a measure of overall fit and comparison of modeled prediction. the area determines the success rate under the curve (auc) of the training data set, and predictable level calculated from the auc of the validation dataset. roc curves were used to evaluate the predictive accuracy of the model selected in the statistical approach, such as logistic regression (gorsevski et al., 2006). the auc obtained from the roc plot statistics is the most preferred type that can influence rating (akgun et al., 2012). predicted probabilities generated by logistic regression can be seen as an indicator to be continuously compared with a binary response variable observed. in this study, the validation process further demonstrates the level of accuracy of landslide susceptibility map to calculate the ratio of the data for validation of landslides that fall into each class of vulnerability. it was generally assumed that most of the landslides for validation must occur on a high-class to a higher susceptibility (h + vh). 3. results and discussion 3.1. frequency ratio table 1 indicates a correlation between landslide occurrence and each class of landslide causative factors. in the case of the relationship between landslide occurrence and luc, class of primary forest to open area and paddy field (4-1) had the highest probability of landslide occurrence with frequency ratio 8.70. moreover, class of secondary forest to the farming area, savanna, scrub (4-2) had frequency ratio 2.20. the vegetation which cause this frequency ratio affects the stability of the slope. land with forest having the root system would reinforce the soil strength and stabilizes the slope (kubota, sanchez-castillo, & soma, 2015). forest clearance seems to have manifested primarily through increased rates of landslide activity (glade, 2003). https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 164 | table 1. the value of frequency ratio and certainty factor for each landslide causative factors factor class pixel class* % class landslide pixel** % landslide frequency ratio elevation (meter) <500 126010 15.85 0 0.00 0.00 500 – 750 113821 14.31 0 0.00 0.00 750 – 1000 117886 14.82 382 13.30 0.90 1000 – 1250 99735 12.54 544 18.93 1.51 1250 – 1500 80401 10.11 446 15.52 1.54 1500 – 1750 73551 9.25 665 23.15 2.50 1750 – 2000 62583 7.87 452 15.73 2.00 2000 2250 63418 7.97 202 7.03 0.88 2250 – 2500 38830 4.88 180 6.27 1.28 >2500 18992 2.39 2 0.07 0.03 slope (degree) 0 -10 277391 34.88 233 8.11 0.23 10 – 20 193490 24.33 504 17.54 0.72 20 – 30 142736 17.95 519 18.06 1.01 30 – 40 114954 14.46 613 21.34 1.48 40 – 50 56795 7.14 819 28.51 3.99 >50 9861 1.24 185 6.44 5.19 curvature concave 335269 42.16 1,614 56.18 1.33 flat 100826 12.68 158 5.50 0.43 convex 359132 45.16 1,101 38.32 0.85 aspect flat 48980 6.16 43 1.50 0.24 north 105139 13.22 963 33.52 2.54 northeast 140313 17.64 599 20.85 1.18 east 128555 16.17 311 10.82 0.67 southeast 155292 19.53 191 6.65 0.34 south 127354 16.01 515 17.93 1.12 southwest 48881 6.15 43 1.50 0.24 west 11324 1.42 23 0.80 0.56 northwest 29389 3.70 185 6.44 1.74 lithology qlvb 195818 24.62 0 0.00 0.00 qlv 562441 70.73 2,826 98.36 1.39 qvlc 36968 4.65 47 1.64 0.35 distance to faults (meter) 0 – 2500 228372 28.72 913 31.78 1.11 2500 -5000 123498 15.53 1,333 46.40 2.99 5000 – 7500 106243 13.36 472 16.43 1.23 7500 – 10000 92127 11.58 155 5.40 0.47 >10000 244987 30.81 0 0.00 0.00 distance to river (meter) 0 100 325991 40.99 1,489 51.83 1.26 100 – 200 240871 30.29 726 25.27 0.83 200 – 300 139539 17.55 397 13.82 0.79 300 – 400 59549 7.49 189 6.58 0.88 400 – 500 19942 2.51 42 1.46 0.58 >500 9335 1.17 30 1.04 0.89 drainage density (km/km2) 0 – 1 147677 18.57 698 24.30 1.31 1 2 228100 28.68 635 22.10 0.77 2 3 252005 31.69 829 28.85 0.91 3 4 121676 15.30 512 17.82 1.16 >4 45769 5.76 199 6.93 1.20 luc (1=open area, paddy area; 2=farming area, savanna, scrub; 3=secondary forest; 4=primary forest) 1 1 167966 21.12 608 21.16 1.00 1 – 2 44883 5.64 276 9.61 1.70 2 – 1 127015 15.97 134 4.66 0.29 2 2 140425 17.66 215 7.48 0.42 2 3 3971 0.50 2 0.07 0.14 3 – 1 24542 3.09 157 5.46 1.77 3 2 88061 11.07 513 17.86 1.61 3 3 30715 3.86 158 5.50 1.42 3 – 4 4602 0.58 26 0.90 1.56 4 – 1 954 0.12 30 1.04 8.70 4 – 2 19912 2.50 177 6.16 2.46 4 – 3 55800 7.02 180 6.27 0.89 4 – 4 86381 10.86 397 13.82 1.27 *total pixel area 795,227 **landslide training 2,873 https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 | 165 in slope class, slope above 20° has a ratio of >1 which indicates a high probability of landslide occurrence. moreover, slope below 20° has a ratio of <1, which shows a very low probability of landslide occurrence. in class of elevation, the values between 1000 to 2000 meters (m) have indicated a high degree of likelihood of the landslides’ occurrence. in the class of curvature, the concave class has a higher probability of landslide occurrence with ratio value >1. in the case of the class aspect, north, northwest, south and northeast-facing slopes have frequency ratio > 1, which shows a high rate of probability of the landslides’ occurrence. in the case of lithology, quarter lompobattang volcanic (qlv) has a ratio of >1, which indicates a high probability of landslides’ occurrence. qlv is one of the volcanic and sediment formations in south sulawesi. causative factor i.e. distance to fault and rivers, the ratio of the distance/proximity is used to understand the degree of influence on the landslide. distance to faults below 7500 m has a ratio > 1. it shows that more narrow distance to the fault, the probability of landslide occurrence will increase. similarly, the distance to river below 100 m has frequency ratio > 1. it indicates the probability of landslide will increase if the distance to the river is nearer. to create an index of susceptibility to landslides, all causative factors were mapped in the form of raster maps of the value fr then summed using equation (2). the index value of frequency ratio of with luc was in the range of 2.70 to 25.41 and without luc was in the range of 2.46 to 17.97 (table 4). the higher lsi value showed greater susceptibility to landslides. the results showed that luc change creates a higher value than without luc meaning that luc is better to predict of landslide occurrence. 3.2. logistic regression hence, this study proposes to investigate ten tests to acquire best result and sense of fairness as shown in table 2 and table 3. luc as a new causative factor for landslide had value of 0.589 (number test seventh) that affects landslide occurrence. forest land with root system would reinforce the soil strength and stabilizes the slope to reduce surface erosion or shallow landslides (kubota, sanchez-castillo, & soma, 2015). the highest value of 3.081 shows the distance to the river having the greatest effect on landslide occurrence. moreover, the lowest value of elevation (0.353) indicated a small effect on landslide occurrence in this research. table 2. logistic regression coefficient of landslide causative factors using equal proportion of landslide and non-landslide pixel with luc causative factor number test variable in the equation elevation slope aspect curvature lithology distance to faults distance to river drainage density luc constant 1 0.344 0.553 0.576 0.659 1.681 0.400 3.044 1.214 0.512 -10.496 2 0.353 0.562 0.548 0.534 1.696 0.476 3.081 0.995 0.551 -10.355 3 0.274 0.475 0.624 0.605 1.631 0.473 2.630 1.184 0.518 -9.882 4 0.302 0.533 0.561 0.452 1.612 0.439 2.817 0.703 0.425 -9.304 5 0.335 0.532 0.590 0.391 1.684 0.459 2.934 1.175 0.521 -10.136 6 0.307 0.535 0.627 0.376 1.722 0.371 2.914 0.907 0.497 -9.741 7 0.245 0.571 0.551 0.524 1.682 0.448 2.818 0.897 0.481 -9.693 8 0.317 0.511 0.525 0.388 1.572 0.506 2.986 0.814 0.539 -9.638 9 0.400 0.552 0.498 0.430 1.541 0.445 3.119 0.597 0.378 -9.421 10 0.348 0.563 0.498 0.329 1.723 0.446 2.985 0.803 0.355 -9.530 table 3. logistic regression coefficient of landslide causative factors using equal proportion of landslide and non-landslide pixel without luc causative factor number test variable in the equation elevation slope aspect curvature lithology distance to faults distance to river drainage density constant 1 0.486 0.587 0.554 0.671 1.618 0.376 2.983 1.317 -10.047 2 0.509 0.600 0.533 0.537 1.643 0.442 3.026 1.130 -9.927 3 0.409 0.509 0.603 0.635 1.566 0.448 2.573 1.325 -9.479 4 0.417 0.562 0.544 0.472 1.578 0.419 2.791 0.814 -9.014 5 0.471 0.568 0.576 0.406 1.617 0.428 2.902 1.317 -9.740 6 0.448 0.571 0.611 0.393 1.669 0.342 2.892 1.031 -9.397 7 0.372 0.605 0.533 0.538 1.647 0.422 2.793 1.032 -9.368 8 0.464 0.546 0.504 0.411 1.503 0.481 2.916 0.956 -9.200 9 0.504 0.579 0.485 0.446 1.517 0.422 3.090 0.698 -9.160 10 0.453 0.591 0.491 0.341 1.680 0.424 2.985 0.931 -9.339 https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 166 | a) b) 3.3. validation table 4 shows results of auc curve for both success rate and predictive rate for each test. some landslide and non-landslide pixels were used to obtain auc success and predictive rate. in general, the auc of roc curves representing excellent, good, and valueless tests were plotted on the graph. it classifies the accuracy of a diagnostic test i.e. the value ranges from 0.50 to 0.60 (fail), 0.60–0.70 (poor), 0.70–0.80 (fair), 0.80–0.90 (good), and 0.90–1.00 (excellent) (rasyid et al., 2016). the results showed that the entire test of fr and lr methods both of with and without luc are included in the good category. the value ranged from 0.833 to 0.854 in success rate and 0.833 to 0.852 in predictive rate, respectively (table 4 and fig. 5). moreover, success rate and predictive rate value for all methods were near to the interval of 0.02 indicating that all the methods were more reliable to a predictive landslide in the future. the proximity of success rate and predictive rate values show how the method helps in landslide prediction in the future (meten, prakashbhandary, & yatabe, 2015). tabel 4. auc of roc curve of success and predictive rate and ratio of landslide validation on landslide susceptibility map using fr, and lr method method fr number test of lr 1 2 3 4 5 6 7 8 9 10 with luc: auc success rate 0.835 0.854 0.854 0.853 0.854 0.854 0.854 0.854 0.854 0.854 0.854 auc predictive rate 0.834 0.852 0.852 0.851 0.852 0.852 0.852 0.852 0.852 0.851 0.852 h+vh (%) 79.35 80.08 80.24 78.70 79.43 79.92 80.00 80.00 79.19 79.67 79.76 without luc: auc success rate 0.833 0.850 0.851 0.850 0.,851 0.850 0.850 0.851 0.850 0.850 0.851 auc predictive rate 0.833 0.848 0.849 0.848 0.848 0.848 0.848 0.849 0.848 0.848 0.849 h+vh (%) 78.46 77.97 77.56 77.24 78.38 77.40 77.97 79.19 77.07 79.19 78.94 figure 5. auc of roc of landslide susceptibility of with and without luc causative factor using fr, and lr method; a) success rate and b) predictive rate in this study, lr method conducted one more validation to choose the best statistical model for creating landslide susceptibility map and the best equation in logistic regression approach from the ten tests. the sum of fr value and equation of the lr models were used to create landslide susceptibility map (lsm). all lsm classes were created by reclassifying lsi of the models using natural breaks method. overlaid landslide data validation on lsm described another level of accuracy beside auc curve. the natural breaks method or jenks optimization method has been widely used especially by planners. it is designed to determine the best arrangement of values into different classes. this approach maximizes the variance between classes and reduces the variation within classes. the description of landslide susceptibility level on location was grouped into five categories, namely very low, low, medium, high and very high. the accuracy of landslide susceptibility map wasverified by a landslide susceptibility map by overlaying it with 30% of landslide data https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 | 167 validation. validation on lsm for the lr model was better than fr model, and causative factor with luc was better than without luc (figure 5). validation of fr method with luc (0.835) in success rate value had slightly higher accuracy than without luc (0.833). similarly, thelr method luc (0.854) had slightly greater accuracy than without luc (0.851). these show that the fr and lr model with luc are good model for identifying landslide as opposed to the model without luc. in the case of auc curve for predictive rate, fr method with luc (0.834) value had slightly higher accuracy than without luc (0.833), and lr method luc (0.852) had slightly greater than without luc (0.849). fr and lr model with luc are better prediction tool for landslide occurrence as opposed to the model without luc. the curve of the model and validation proves that the susceptibility model is acceptable and the model could be applied to predict the potential landslides in the future. as an interesting point to be noticed in table 4, the seventh tests for lr had a good result in auc curve, which is 0.857 in success rate and 0.856 in predictive rate, respectively. figure 6 shows the landslide susceptibility map with and without luc causative factor using fr, and the second test equation of lr model with luc and seventh test equation of lr without luc. the lsm by lr model with luc was obtained using the coefficient values of landslide causative factors as in the equation (5); z = -10.355 + 0.353 elevation + 0.562 slope + 0.548 aspect + 0.534 curvature + 1.696 lithology+ 0.476 faults + 3.081 distance to river + 0.995 drainage density + 0.551 luc ……………………………………… (5) the lsm by lr model without luc was obtained using the coefficient values of landslide causative factors as in the equation (6); z = -9.368 + 0.372 elevation + 0.605 slope + 0.533 aspect + 0.538 curvature + 1.647 lithology+ 0.422 distance to faults + 2.793 distance to river + 1.032 drainage density ………………………………………. (6) the ranges of the index value of each model in five categories were established using natural breaks method. can et al. (2005) and bai et al. (2010) stated two important guidance for validating landslide susceptibility map i.e. (1) the high to very high classes should cover only small areas and (2) landslide data validation should lie in high or very high classes. figure 6. landslide susceptibility map of with and without luc causative factors using fr, and lr method https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 168 | table 5 shows the characteristics of susceptibility class for fr and lr models with and without luc causative factors. it indicates that the ratio of high to very high vulnerability classes cover a small area or less than 32 % for fr and less than 26 % of the total area for lr. the data validation landslide included in the class shows the ratio below 10 %. the accuracy of the predicted future landslide from the lsm should have lower ratio in low to very low classes and higher in the high to very high classes (rasyid et al., 2016). figure 7 shows high to very high vulnerability classes for lr with luc (80.24 %) having a higher value for validation than lr without luc (79.19 %). moreover, fr with luc (79.35 %) had a higher value than fr without luc (78.46 %). it indicates that the performance of luc as a causative factor both using fr and lr model gives a good result. taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence. table 5. the characteristic of susceptibility classes on landslide susceptibility map using fr, and lr method with and without luc causative factor class number reclassified index value vulnerability class number of pixels % area covered number of landslide validation pixel % area of landslide validation covered frequency ratio with luc 1 2.700 5.906 very low 187187 23.54 0 0.00 2 5.906 8.578 low 153294 19.28 19 1.54 3 8.578 10.893 moderate 208585 26.23 235 19.11 4 10.893 13.476 high 181945 22.88 452 36.75 5 13.476 25.410 very high 64216 8.08 524 42.60 logistic regression with luc 1 0.0013 0.1187 very low 292856 36.83 3 0.24 2 0.1187 0.3067 low 162173 20.39 62 5.04 3 0.3067 0.5063 moderate 133513 16.79 178 14.47 4 0.5063 0.7216 high 113755 14.30 331 26.91 5 0.7216 0.9996 very high 92930 11.69 656 53.33 frequency ratio without luc 1 2.460 5.1971 very low 188730 23.73 18.90 0 0.00 2 5.1971 7.630 low 150267 18.90 24 1.95 3 7.630 9.820 moderate 220097 27.68 241 19.59 4 9.820 12.253 high 178338 22.43 466 37.89 5 12.253 17.970 very high 57795 7.27 499 40.57 logistic regression without luc 1 0.0013 0.1187 very low 272313 34.24 4 0.33 2 0.1187 0.3067 low 171069 21.51 60 4.88 3 0.3067 0.5063 moderate 148090 18.62 192 15.61 4 0.5063 0.7216 high 112403 14.13 322 26.18 5 0.7216 0.9996 very high 91352 11.49 652 53.01 figure 7. percentage of landslide susceptibility classes and rate of landslide susceptibility validation on landslide susceptibility of fr and lr method https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 soma and kubota / geoplanning: journal of geomatics and planning, vol 4, no. 2, 2017, 157-170 doi: 10.14710/geoplanning.4.2.157-170 | 169 4. conclusion in conclusion, land use change (luc) showed a good demonstration as a new causative factor to build landslide susceptibility map. the result indicated that luc have the effect to produce lsm. validation of landslide susceptibility was carried out in this study at both with and without luc causative factors. firstly, performances of each landslide model were tested using auc curve for success and predictive rate. the highest value of predictive rate was at with luc in both fr and lr methods (83.4 % and 85.2 %, respectively). secondly, the ratio of landslides on high to very high classes of susceptibility was obtained, which indicates the accuracy level of the method. lr method with luc had the highest accuracy of 80.24 %. these results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence. . 5. acknowledgments the authors thank esri indonesia for supporting the arcgis 10.3 in collaboration with hasanuddin university, indonesia. this doctoral program was supported by dikti scholarship batch 2, 2015. 6. references akgun, a., sezer, e. a., nefeslioglu, h. a., gokceoglu, c., & pradhan, b. 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[crossref] https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.14710/geoplanning.4.2.157-170 https://doi.org/10.3390/rs4010271 https://doi.org/10.1016/j.enggeo.2006.03.004 https://doi.org/10.1016/j.cageo.2007.10.011 https://doi.org/10.1016/j.asr.2006.03.036 https://doi.org/10.1186/s40677-015-0016-7 https://doi.org/10.1016/j.catena.2011.11.004 https://doi.org/10.1186/s40677-016-0053-x https://doi.org/10.1007/s11069-012-0321-3 https://doi.org/10.1007/s11069-013-0661-7 doi: 10.14710/geoplanning.4.2.157-170 copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): soma, a. a., & kubota, t. (2017). the performance of land use change causative factor on landslide susceptibility map in upper ujung-loe watersheds south sulawesi, indonesia. geoplanning: journal of geomatics and planning, 4(2), 157-170. doi:10.14710/... 1. introduction keywords: land use change, landslide susceptibility, frequency ratio, logistic regression corresponding author: andang suryana soma graduate school of bio-resources and environmental science, kyushu university, japan email: suryaandang@kyudai.jp 2. data and methods 2.1. study area 2.2. preparation of data 2.3. data analysis 2.4. validation and verification 3. results and discussion 4. conclusion 5. acknowledgments 6. references volume 1, no 2, 2014, 85-92 http://ejournal.undip.ac.id/index.php/geoplanning | 85 open access geoplanning e-issn: 2355-6544 lokasi optimal pengembangan tpi untuk mendukung perkembangan kawasan pesisir kecamatan kragan kabupaten rembang k.d. astutia, r.viyetrib a universitas diponegoro, indonesia, email: khristiana.dwiastuti@undip.ac.id buniversitas diponegoro, indonesia, email: ricyeviyetri@ymail.com abstract: geographically, district kragan is located in northern coast of java in rembang regency. based on that, one of the potential sector in the district is marine fisheries. that sector is an alternative livelihood and increase the economy of rembang regency. to support the marine fisheries sector, it needed adequate infrastructure, such as fish comercial trader (a.k.a.tpi). there are 3 tpi in district kragan: tpi pandangan, tpi karanglincak, and tpi karanganyar, but not operating optimally. so, there’s less result of productivity, processing and marketing of fish in district kragan. that problem effect on people's income and revenue of district kragan.the method that used is quantitative methods, and approach to determine the minimum time and cost, marketing area, and maximum profit. in addition to analyzing the availability of infrastructure and facilities that support the development of coastal areas kragan is using scoring analysis. base on that analysis, the optimal location of the development of tpi in the district kragan is on tpi padangan because it has a large area, and thus allowing to support the development of tpi and accommodate the availability of infrastructure and facilities. abstrak: secara geografis kecamatan kragan terletak di pesisir pantai utara jawa di kabupaten rembang. berdasarkan hal tersebut salah satu potensi di kecamatan kragan adalah dibidang perikanan laut yang menjadi alternatif mata pencaharian dan salah satu faktor sektor unggulan yang meningkatkan perekonomian. untuk menunjang sektor tersebut diperlukan sarana dan prasarana yang memadai. salah satu sarana penunjang pengembangan perikanan laut di kecamatan kragan adalah tersedianya tempat pelelangan ikan (tpi). adanya tpi di kecamatan kragan yaitu desa pandangan, desa karanglincak, dan desa karanganyar belum beroperasi secara maksimal. hal ini yang mendasari pokok permasalahan sehingga dampak yang ditimbulkan adalah kurang maksimalnya hasil produktivitas, pengolahan dan pemasaran ikan di kecamatan kragan serta berakibat juga terhadap pendapatan masyarakat dan pendapatan asli daerah (pad) kecamatan kragan. metode yang digunakan adalah metode kuantitatif, dan alat analisis yang digunakan adalah analisis teori lokasi dengan pendekatan-pendekatan untuk menentukan waktu dan biaya minimum, pendekatan daerah pemasaran dan pendekatan keuntungan maksimum. selain itu untuk menganalisis ketersediaan prasarana dan sarana yang mendukung perkembangan wilayah pesisir kragan menggunakan analisis skoring. melalui analisis tersebut, lokasi optimal pengembangan tpi di kecamatan kragan adalah di tpi padangan karena mempunyai ketersediaan fisik lahan yang luas, sehingga memungkinkan untuk pengembangan kedepannya, dalam mewadahi aktivitas perikanan yang semakin berkembang didukung ketersediaan prasarana dan sarana yang lebih memadai. 1. pendahuluan wilayah indonesia yang sebagian besar merupakan wilayah lautan memberikan karakteristik tersendiri bagi potensi sumber daya alam yang dimilikinya. sumber daya alam yang berupa sumberdaya perikanan merupakan salah satu sektor potensial pada kawasan pesisir yang terdapat di seluruh wilayah indonesia. info artikel; diterima: 20 september 2014 hasil revisi : 23 september 2014 disetujui: 25 september 2014 publikasi on-line: 1 oktober 2014 kata kunci: tpi, kawasan pesisir, lokasi optimal article info; received: 20 september 2014 in revised form: 23 september 2014 accepted: 25 september 2014 available online: 1 october 2014 keywords: tpi, coastal area, optimum location geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 86 berkaitan dengan perkembangan wilayah pesisir, salah satu wilayah yang mempunyai potensi dibidang sumberdaya perikanan adalah kabupaten rembang yang terletak dipesisir pantai laut utara jawa. kabupaten rembang memiliki potensi yang besar dibidang perikanan baik perikanan tangkap maupun perikanan budidaya. kabupaten rembang memiliki panjang pantai ± 63 km yang meliputi enam kecamatan termasuk kecamatan kragan. kecamatan kragan memiliki luas yaitu 6.166 ha atau sekitar 6,08% dari total wilayah kabupaten rembang. berdasarkan jumlah penduduk 10 tahun keatas menurut pekerjaan utama kecamatan kragan tahun 2013, penduduk yang bekerja dibidang perikanan sebesar 7.219 jiwa atau sekitar 15,07% dari jumlah penduduk, merupakan mata pencaharian terbesar kedua setelah pertanian. lokasi yang strategis menghubungkan kota semarang menuju surabaya juga memberikan pengaruh berkembangnya sektor perikanan dan berimplikasi terhadap perkembangan ekonomi wilayahnya. berdasarkan data dari dinas perikanan dan kelautan kabupaten rembang tahun 2013 jumlah produksi dan nilai produksi perikanan laut kabupaten rembang selalu meningkat setiap tahunnya. total produksi pada tahun 2012 8.666,733 ton dengan nilai produksi rp 83.825.356.000,00 menjadi 37.583,359 ton dan nilai produksi rp 194.644.331.680,00 pada tahun 2013. untuk menunjang sektor perikanan diperlukan sarana dan prasarana yang memadai. salah satu sarana penunjang pengembangan perikanan laut di kecamatan kragan adalah tersedianya tempat pelelangan ikan (tpi) sebagai tempat pemasaran hasil laut. adanya 3 tpi di kecamatan kragan yaitu tpi pandangan, tpi karanglincak dan tpi karanganyar belum beroperasi secara maksimal. kondisi fasilitas tempat pelelangan ikan (tpi) yang ada di kecamatan kragan saat ini kurang baik, kumuh dan tidak layak seperti yang dikeluhkan oleh para nelayan dan pedagang dilokasi tersebut oleh karena itu memerlukan perhatian dan perbaikan oleh semua pihak yang terlibat. hal ini yang mendasari pokok permasalahan sehingga dampak yang ditimbulkan adalah kurang maksimalnya hasil produktivitas, pengolahan dan pemasaran ikan di kecamatan kragan. dengan demikian apabila tpi tidak berfungsi dengan semestinya maka transaksi dan penawaran dilakukan diluar lokasi tpi. selain itu hasil perikanan kecamatan kragan juga dipengaruhi oleh keberadaan ppi tasik agung di kecamatan rembang yang menjadi pusat ppi di kabupaten rembang secara keseluruhan. berdasarkan pada kondisi dan kenyataan tersebut untuk menunjang bagi perkembangan kawasan pesisir di kabupaten rembang, khususnya di kecamatan kragan, perlu ditentukan lokasi optimal tpi di kecamatan kragan, tidak hanya dari sisi lokasi namun juga ketersediaan prasarana dan sarana yang menunjang aktivtas tpi. 2. data dan metode wilayah pesisir adalah daerah pertemuan antara darat dan laut; kearah darat wilayah pesisir meliputi bagaian daratan, baik kering maupun terendam air, yang masih dipengaruhi sifat-sifat laut seperti pasang surut, angin laut dan perembesan air asin; sedangkan kearah laut wilayah pesisir mencakup bagian laut yang masih dipengaruhi oleh proses-proses alami yang terjadi di darat seperti sedimentasi dan aliran air tawar, maupun yang disebabkan oleh kegiatan manusia di darat seperti pengundulan hutan dan pencemaran (dahuri, 1996). wilayah pesisir dibagi menjadi 4 (empat) kriteria (budiharsono, 2001), yaitu: 1. wilayah homogen, sebagai wilayah homogen yang dipandang dari satu aspek/kriteria mempunyai sifat-sifat atau ciri-ciri yang relatif sama. dengan demikian wilayah pesisir termasuk wilayah homogen dimana pada wilayah pesisir adanya produksi ikan, dan dilihat dari tingkat pendapatan penduduknya tergolong di bawah garis kemiskinan. 2. wilayah nodal adalah wilayah yang secara fungsional mempunyai ketergantungan antara pusat (inti) dan daerah belakangnya (hinterland). wilayah pesisir seringkali sebagai wilayah belakang, sedangkan daerah perkotaan sebagai intinya. sebagai wilayah belakang, wilayah pesisir merupakan penyedia (input) bagi inti, dan merupakan pasar bagi barang-barang jadi (output) dari inti. 3. wilayah administratif, sebagai wilayah adminstratif wilayah pesisir dapat berupa wilayah administrasi yang relatif kecil yaitu kecamatan atau desa, namun juga dapat berupa kabupaten/kota yang berupa pulau kecil. 4. wilayah perencanaan, batas wilayah pesisir sebagai wilayah perencanaan lebih ditentukan dengan kriteria ekologis, batas tersebut sering melewati batas-batas satuan wilayah adminstratif, karena pada dasarnya termasuk dalam satu kesatuan wilayah perencanaan yang saling memberikan dampak. geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 87 sesuai dengan karakteristik yang dimiliki oleh wilayah pesisir tersebut, maka tentunya banyak potensi yang bisa dikembangkan, terutama dari aktivitas masyarakat yang mengandalkan dari hasil perikanan. menurut direktorat jenderal perikanan departemen pertanian ri (1981) dalam sulistyani (2005), pelabuhan perikanan adalah pelabuhan yang secara khusus menampung kegiatan masyarakat perikanan baik dilihat dari aspek produksi, pengolahan maupun aspek pemasarannya. pelabuhan perikanan berfungsi sebagai tempat pelayanan umum bagi masyarakat nelayan dan usaha perikanan, sebagai pusat pembinaan dan peningkatan kegiatan ekonomi perikanan yang dilengkapi dengan fasilitas di darat dan di perairan sekitarnya untuk digunakan sebagai pangkalan operasional tempat berlabuh, mendaratkan hasil, penanganan, pengolahan, distribusi dan pemasaran hasil perikanan. selain pelabuhan perikanan, fasilitas lain yang secara langsung berpengaruh terhadap perkembangan fungsi perikanan dikawasan pesisir adalah adanya tpi yang digunakan sebagai tempat untuk transaksi hasil produksi perikanan. agar fungsi tpi tersebut bisa memberikan dampak yang signifikan bagi perkembangan wilayah pesisir secara keseluruhan, perlu adanya lokasi yang optimal yang didasarkan pada beberapa kriteria. kriteria yang digunakan untuk pemilihan lokasi pengembangan tpi menggunakan pendekatan yang digunakan oleh pemerintah kota semarang dalam mengoptimalkan peran tpi yang ada. pendekatan ini dilakukan dengan pertimbangan bahwa karakteristik wilayah pesisir di kota semarang dan kabupaten rembang hampir sama. kriteria pertimbangan pemilihan lokasi tpi didasarkan pada aspek fleksibilitas lahan, aksesibilitas, utilitas, operasi dan proses kegiatan, biaya pembangunan, citra arsitektur, kondisi perairan untuk operasi kapal perikanan, nilai ekonomi, dan dampak lingkungan. tabel 1. pertimbangan pemilihan lokasi tpi (bappeda, 2011) no. pertimbangan keterangan 1. fleksibilitas lahan berupa ketersediaan lahan bagi pengalokasian tpi dan kemungkinan perluasannya 2. aksesibilitas berupa kemudahan pencapaian baik kelokasi tpi maupun kelokasi pusat-pusat pemasaran tangkapan ikan. 3. utilitas merupakan ketersediaan jaringan prasarana penunjang aktivitas tpi 4. operasi dan proses kegiatan berupa kemudahan operasi dan proses kegiatan bongkar muat hasil tangkapan. 5. biaya pembangunan yakni anggaran biaya yang dibutuhkan dalam pembangunan tpi 6. citra arsitektur berupa bangunan dan lingungan sekitar yang terlihat pada lokasi 7. kondisi perairan untuk operasi kapal perikanan perairan yang tenang dengan memungkinkan kapal bersandar lebih dekat dengan lokasi bongkar muat. 8. nilai ekonomi berkaitan dengan pengembalian modal dan keuntungan dari investasi saran tpi serta harga lahan. untuk kedepannya adalah pemasukan dari pembangunan tpi. 9. dampak lingkungan akibat yang ditimbulkan terhadap lingkungan sekitar dengan adanya pembangunan tpi seperti pencemaran, pendangkalan pantai, dll. keberadaan prasarana dan sarana pada lokasi tpi sangat diperlukan untuk menunjang aktivitas yang dilakukan pada tpi tersebut. berikut ini profil tpi pandangan, tpi karanglincak, dan tpi karanganyar. tabel 2. profil tpi pandangan, tpi karanglincak, dan tpi karanganyar ( dinas kelautan dan perikanan rembang, 2013) keterangan tpi pandangan tpi karanglincak tpi karanganyar luas lahan 0,880 ha 192 m2 940 m2 luas bangunan 452 m2 96 m2 650 m2 fasiitas  1 ruang kantor pengelola  1 los tempat pelelangan ikan  tidak memiliki sentra pengolahan ikan  1 ruang kantor pengelola  1 los tempat pelelangan ikan  tidak memiliki sentra pengolahan ikan  1 ruang kantor pengelola  31 los tempat pelelangan ikan  memiliki sentra pengolahan ikan, stasiun pengisian bahan bakar nelayan, tempat labuh kapal geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 88 kapal motor > 30 gt = 130 unit 5-10 gt = 250 unit 10-30 gt = 350 unit alat tangkap purseseine = 103 unit gillnet = 200 unit trammelnet = 70 unit cantrang = 70 unit dogol = 200 unit purseseine = 350 unit sarana pemasaran bergerak (spg) kendaraan roda 2 = 1 unit kendaraan roda 4 = 1 unit kendaraan roda 3 = 10 unit kendaraan roda 2 = 10 unit keranjang = 250 unit akses jalan jalan desa jalan desa jalan desa jaringan listrik 450 watt 450 watt 450 watt sumber air sumur sumur sumur sarana kebersihan 1 pompa air pompa air, dan mempunyai saluran pembuangan air selain prasarana dan sarana, masing-masing tpi mempunyai sumber daya manusia yang berbeda. berikut ini ketersediaan sumber daya manusia yang dimiliki oleh tpi yang terdapat di kecamatan kragan. tabel 3. sumber daya manusia di tpi pandangan, tpi karanglincak, dan tpi karanganyar (dinas kelautan dan perikanan rembang, 2013) no jenis tpi pandangan tpi karanglincak tpi karanganyar 1. jumlah pengolah 30 6 2. jumlah pemasar ikan segar 16 orang 15 20 3. jumlah pemasar ikan olahan 15 15 4. jumlah pemasar ikan segar maupun olahan 30 15 5. jumlah karyawan tpi 11 7 22 6. jumlah nelayan 2510 1000 5500 metode yang digunakan dalam studi ini adalah metode kuantitatif dengan menggunakan teknik skoring. selain itu analisis juga dilakukan secara kualitatif, berdasarkan kriteria yang sudah ditentukan sebelumnya. adapun data yang digunakan berdasarkan data yang diperoleh dari instansi, yaitu dinas perikanan dan kelautan kabupaten rembang, serta data yang diperoleh dari hasil wawancara dan observasi lapangan pada ketiga tpi yang menjadi wilayah studi. 3. hasil dan pembahasan 3.1 analisis lokasi analisis lokasi dari setiap tpi didasarkan pada beberapa pendekatan, yaitu: 1. pendekatan biaya terkecil  jarak tpi pandangan ke sumber bahan mentah ± 20 m karena berada dipinggir pantai utara jawa, dan dekat dengan daerah pemasaran yaitu pasar sumbergayam dengan jarak ±1 km.  jarak tpi karanglincak ke sumber bahan mentah ± 20 m, dan jarak ke daerah pemasar yaitu pasar karangharjo ± 0,5 km.  jarak tpi karanganyar ke sumber bahan mentah ± 35 m, dekat dengan daerah pemasaran yaitu pasar kebloran dengan jarak ± 1 km. geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 89 2. pendekatan daerah pemasaran distribusi dari hasil perikanan dan kelautan dapat dilakukan di dalam dan keluar daerah, jika pemasaran keluar daerah dapat dilihat seberapa jauh pemasaran tersebut dan membentuk pasarpasar baru untuk memudahkan pendistribusian hasil tangkapan. secara keseluruhan pendistribusian hasil perikanan sekitar 30% didistribusikan di kecamatan kragan sendiri, selain pemasaran di tpi juga dilakukan pemasaran di pasar-pasar lokal baik berupa ikan segar maupun ikan hasil olahan. 70% didistribusikan keluar daerah seperti semarang, demak, blora dan surabaya. tpi karanganyar dan karanglincak lebih banyak didistribusikan ke arah blora dan jawa timur, sedangkan dari tpi pandangan wetan banyak didistribusikan ke demak. 3. pendekatan keuntungan maksimum lokasi optimal merupakan keuntungan terbesar yang dapat diperoleh, baik dari biaya yang dikeluarkan maupun penerimaan berdasarkan lokasinya. dalam hal ini lokasi optimal dilihat berdasarkan jumlah produksi dan distribusi ikan paling tinggi di tpi tersebut untuk mendapatkan keuntungan yang maksimum.  tpi pandangan memiliki jumlah produksi menengah, tetapi didukung dengan berbagai faktor seperti kelengkapan fasilitas dan daya dukung kawasan. tpi ini dapat beroperasi lebih optimal untuk meningkatkan hasil produksi menjadi tinggi.  tpi karanglincak memiliki jumlah produksi paling rendah, hal ini bisa disebabkan karena fasilitas yang kurang. tetapi jika dikembangkan akan meningkatkan hasil produksi karena di tpi ini memiliki sumber daya manusia yang memadai. untuk alat tangkap tpi karanglincak memiliki alat lebih lengkap.  dilihat dari jenis ikan, jumlah produksi dan nilai produksi perikanan laut di tpi karanganyar paling tinggi. banyak faktor yang mendukung hal tersebut seperti ketersediaan sarana dan prasarana yang lengkap, jumlah sumber daya manusia yang memadai, bentang lahan yang mendukung. dari teori lokasi dengan ketiga pendekatan-pendekatannya tersebut, untuk meningkatkan nilai ekonomi kecamatan kragan maka dapat diambil kesimpulan bahwa : 1. tpi pandangan melalui pendekatan biaya terkecil yaitu dekat dengan sumber bahan mentah dan daerah pemasaran. untuk distribusi pemasaran yaitu dekat dengan semarang dan demak. untuk pendekatan keuntungan maksimum, faktor yang menguatkan untuk dijadikan lokasi optimal adalah ketersediaan fisik yaitu lahan yang luas, sehingga untuk pengembangan kedepannya, jika semua sarana (penambahan jumlah alat tangkap dan kapal motor), prasarana (seperti perbaikan jalan menuju lokasi tpi, menyediakan sarana persampahan, memperbaiki saluran drainase, menyediakan tempat pengolahan limbah) dan mendirikan sentra pengolahan ikan atau industri pengolahan ikan. dari hal tersebut juga dapat membuka lapangan pekerjaan baru bagi masyarakat maka tpi ini dapat beroperasi lebih optimal untuk meningkatkan hasil produksi lebih tinggi. 2. tpi karanglincak melalui pendekatan biaya terkecil yaitu dekat dengan sumber bahan mentah dan daerah pemasaran. untuk distribusi pemasaran yaitu dekat dengan blora dan jawa timur. untuk pendekatan keuntungan maksimum, tpi karanglincak memiliki jumlah produksi paling rendah, tetapi bisa dikembangkan karena di tpi ini memiliki sumber daya manusia yang memadai dan alat tangkap ikan yang lebih lengkap. 3. tpi karanganyar melalui pendekatan biaya terkecil yaitu dekat dengan sumber bahan mentah dan daerah pemasaran. untuk distribusi pemasaran yaitu ke arah blora. geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 90 3.2 analisis kelengkapan prasarana dan sarana analisis ini didasarkan pada ketersediaan prasarana dan sarana penunjang aktivitas di setiaptpi, yang kemudian digunakan untuk menentukan ranking prioritas sebagai lokasi optimal tpi. a. kriteria kelangkapan fasilitas nilai ketersediaan kantor pengelola los pelelangan yang memadai tempat parkir sentra pengolahan ikan stasiun pengisian bahan bakar nelayan tempat labuh kapal nilai 3 v v v v v v nilai 2 v v v v x x nilai 1 v v x x x x b. kriteria fleksibilitas lahan nilai ketersediaan lahan kemungkinan perluasan nilai 3 luas v nilai 2 cukup v nilai 1 terbatas x c. kriteria sarana bergerak dan pemasaran nilai kapal motor alat tangkap spg nilai 3 v v v nilai 2 v v x nilai 1 v x x d. kriteria prasarana nilai akses jalan jaringan listrik sumber air sarana kebersihan nilai 3 v v v v nilai 2 v v v x nilai 1 v v x x e. kriteria sdm nilai karyawan tpi pemasar ikan pengolah ikan nilai 3 v v v nilai 2 v v x nilai 1 v x x f. hasil penilaian kriteria tpi pandangan tpi karanglincak tpi karanganyar kelengkapan fasilitas 2 1 3 fleksibilitas lahan 3 1 2 sarana bergerak dan pemasaran 3 3 1 prasarana 3 2 3 sdm 2 3 3 ranking ii iii i geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 91 gambar 1. peta analisis ketersediaan prasarana dan sarana tpi (analisis, 2014) sesuai dengan analisis penilaian terhadap prasarana dan sarana yang terdapat di masing-masing tpi, diperoleh hasil bahwa lokasi optimal pengembangan tpi yang paling mendukung bagi perkembangan sektor perikanan di kawasan pesisir kragan adalah di tpi pandangan. 4. kesimpulan berdasarkan hasil analisis yang dilakukan dengan menggunakan pendekatan lokasi serta ketersediaan prasarana dan sarana yang ada, tpi pandangan merupakan lokasi optimal bagi pengembangan tpi yang mendukung perkembangan sektor perikanan di kecamatan kragan. tpi ini memiliki kemungkinan yang tinggi untuk dikembangkan, karena faktor yang menguatkan untuk dijadikan lokasi optimal adalah ketersediaan fisik yaitu lahan yang luas, sehingga untuk pengembangan kedepannya, jika semua sarana (penambahan jumlah alat tangkap dan kapal motor), prasarana (seperti perbaikan jalan menuju lokasi tpi, menyediakan sarana persampahan, memperbaiki saluran drainase, menyediakan tempat pengolahan limbah) dan mendirikan sentra pengolahan ikan atau industri pengolahan ikan. selain itu pada berdasarkan rdtr kecamatan kragan, tpi pandangan termasuk diprioritaskan penanganannya untuk dijadikan kawasan terpadu bahari sebagai sentra perikanan laut dan industri perikanan (agromina). berdasarkan upaya pengembangan tersebut, diharapkan tujuan dari adanya tpi sesuai dengan yang tercantum dalam peraturan daerah kabupaten rembang nomor 4 tahun 2009 tentang pengelolaan tempat pelelangan ikan (tpi), yang meliputi memperlancar pelaksanaan penyelenggaraan pelelangan, menjaga dan mengusahakan stabilitas harga ikan, meningkatkan taraf hidup dan kesejahteraan nelayan, melaksanakan pendataan pengelolaan sumber daya ikan, dan meningkatkan pendapatan daerah dapat tercapai a b c keterangan a. tpi pandangan b. tpi kranglincak c. tpi karanganyar geoplanning 2014, vol: 1, no: 2, 85-92 astuti dan viyetri | 92 5. daftar pustaka aktualisasi peta dasar kabupaten rembang tahun 2011. bappeda kabupaten rembang. budiharsono, sugeng. 2001. teknik analisis pembangunan wilayah pesisir dan lautan. jakarta : pt pradnya paramita. dahuri, r, dkk. 1996. pengelolaan sumber daya wilayah pesisir dan lautan secara terpadu. jakarta : pt pradnya paramita. dinas kelautan dan perikanan kabupaten rembang. laporan tahunan 2013. dyah, sulistyani. 2005. “analisis efisiensi tpi (tempat pelelangan ikan) kelas 1, 2 dan 3 di jawa tengah dan pengembangannya untuk peningkatan kesejahteraan nelayan”. tesis tidak diterbitkan, program studi magister manajemen sumberdaya pantai, universitas diponegoro, semarang. djojodipuro,marsudi.1992. teori lokasi. jakarta: lembaga penerbit fakultas ekonomi universitas indonesia. kragan dalam angka 2013. bps jawa tengah. laporan tahunan 2013. dinas kelautan dan perikanan kabupaten rembang. peraturan daerah kabupaten rembang nomor 4 tahun 2009 tentang pengelolaan tempat pelelangan ikan undang-undang republik indonesia nomor 26 tahun 2007 tentang penataan ruang. undang-undang republik indonesia nomor 27 tahun 2007 tentang pengelolaan wilayah pesisir dan pulaupulau kecil. rencana tata ruang wilayah (rtrw) kabupaten rembang tahun 2011-2031. lokasi optimal pengembangan tpi untuk mendukung perkembangan kawasan pesisir kecamatan kragan kabupaten rembang 1. pendahuluan keywords: tpi, coastal area, optimum location 2. data dan metode 3. hasil dan pembahasan analisis lokasi dari setiap tpi didasarkan pada beberapa pendekatan, yaitu: 1. pendekatan biaya terkecil  jarak tpi pandangan ke sumber bahan mentah ± 20 m karena berada dipinggir pantai utara jawa, dan dekat dengan daerah pemasaran yaitu pasar sumbergayam dengan jarak ±1 km.  jarak tpi karanglincak ke sumber bahan mentah ± 20 m, dan jarak ke daerah pemasar yaitu pasar karangharjo ± 0,5 km.  jarak tpi karanganyar ke sumber bahan mentah ± 35 m, dekat dengan daerah pemasaran yaitu pasar kebloran dengan jarak ± 1 km. 2. pendekatan daerah pemasaran distribusi dari hasil perikanan dan kelautan dapat dilakukan di dalam dan keluar daerah, jika pemasaran keluar daerah dapat dilihat seberapa jauh pemasaran tersebut dan membentuk pasar-pasar baru untuk memudahkan pendistribusian hasil tangkapan. secar... 3. pendekatan keuntungan maksimum lokasi optimal merupakan keuntungan terbesar yang dapat diperoleh, baik dari biaya yang dikeluarkan maupun penerimaan berdasarkan lokasinya. dalam hal ini lokasi optimal dilihat berdasarkan jumlah produksi dan distribusi ikan paling tinggi di tpi ters...  tpi pandangan memiliki jumlah produksi menengah, tetapi didukung dengan berbagai faktor seperti kelengkapan fasilitas dan daya dukung kawasan. tpi ini dapat beroperasi lebih optimal untuk meningkatkan hasil produksi menjadi tinggi.  tpi karanglincak memiliki jumlah produksi paling rendah, hal ini bisa disebabkan karena fasilitas yang kurang. tetapi jika dikembangkan akan meningkatkan hasil produksi karena di tpi ini memiliki sumber daya manusia yang memadai. untuk alat tangkap tpi k�  dilihat dari jenis ikan, jumlah produksi dan nilai produksi perikanan laut di tpi karanganyar paling tinggi. banyak faktor yang mendukung hal tersebut seperti ketersediaan sarana dan prasarana yang lengkap, jumlah sumber daya manusia yang memadai, bentan� dari teori lokasi dengan ketiga pendekatan-pendekatannya tersebut, untuk meningkatkan nilai ekonomi kecamatan kragan maka dapat diambil kesimpulan bahwa : 1. tpi pandangan melalui pendekatan biaya terkecil yaitu dekat dengan sumber bahan mentah dan daerah pemasaran. untuk distribusi pemasaran yaitu dekat dengan semarang dan demak. untuk pendekatan keuntungan maksimum, faktor yang menguatkan untuk dijadikan � 2. tpi karanglincak melalui pendekatan biaya terkecil yaitu dekat dengan sumber bahan mentah dan daerah pemasaran. untuk distribusi pemasaran yaitu dekat dengan blora dan jawa timur. untuk pendekatan keuntungan maksimum, tpi karanglincak memiliki jumlah pr� 3. tpi karanganyar melalui pendekatan biaya terkecil yaitu dekat dengan sumber bahan mentah dan daerah pemasaran. untuk distribusi pemasaran yaitu ke arah blora. analisis ini didasarkan pada ketersediaan prasarana dan sarana penunjang aktivitas di setiaptpi, yang kemudian digunakan untuk menentukan ranking prioritas sebagai lokasi optimal tpi. a. kriteria kelangkapan fasilitas b. kriteria fleksibilitas lahan c. kriteria sarana bergerak dan pemasaran d. kriteria prasarana e. kriteria sdm f. hasil penilaian sesuai dengan analisis penilaian terhadap prasarana dan sarana yang terdapat di masing-masing tpi, diperoleh hasil bahwa lokasi optimal pengembangan tpi yang paling mendukung bagi perkembangan sektor perikanan di kawasan pesisir kragan adalah di tpi ... 4. kesimpulan 5. daftar pustaka 173 geoplanning journal of geomatics and planning geoplanning: journal of geomatics and planning, vol. 12, no. 2, 2025, 173 – 186 original research urban flood susceptibility analysis using multi criteria decision analytical hierarchy process (ahp) method: case study of bandung city rena denya agustina1*, riki purnama putra2,1*, seni susanti1, agustinus bambang setyadji2, riantini virtriana2 1. department of physics education, faculty of tarbiyah and teaching uin sunan gunung djati bandung, indonesia 2. master’s program in geodesy and geomatics engineering, faculty of earth sciences and technology, institut teknologi bandung, indonesia doi: 10.14710/geoplanning.12.2.173-186 abstract flood is one of the natural disasters and is supported by bad human habits, of course, this disaster can cause enormous losses, which can take lives. flood handling certainly requires proper analysis before handling is carried out. various methods for mapping flood susceptibility can be done, one of which is using the ahp multi-criteria decision method which is considered the most up-to-date and very accurate method in terms of accuracy. this study aims to map the susceptibility of flood hazard in urban areas, especially in the city of bandung with the help of satellite imagery. the method in this study uses the ahp multi-criteria decision method, where five experts are needed to carry out an assessment in determining the variable weight value, with the variable in question namely; (1) twi; (2) elevations; (3) slopes; (4) precipitation; (5) land cover; (6) ndvi; (7) distance from rivers; and (8) distance from roads. in addition, this study validates the results of the mapping by comparing the real events of flooding in the city of bandung in 2002-2022 with the map of the susceptibility of flood hazard in the city of bandung. the results obtained in this study are flood hazard susceptibility maps created well with validation of 80.20%. in addition, areas that are very at hazard of being affected by flooding are the east bandung area (mandalajati, ujungberung, cibiru, gedebage, and panyileukan) with a high hazard of over 75%, and an extreme hazard of above 0.1%. copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction floods are one of the natural disasters caused by nature and supported by bad human behavior, such as the neglect of rivers, sewers, and gutters (liu et al., 2020; sejati et al., 2024). bad human behavior is stated to be the biggest factor besides heavy and frequent rains (berndtsson et al., 2019). especially in dense urban areas where some drains are sometimes clogged or provided but cannot hold much water, or in other words, the ditches are made too small. as a result, urban areas often experience severe flooding even though the rainfall that occurs is quite light (singh et al., 2018). the consequences of flooding in urban areas can be detrimental to many parties, such as many social or educational activities that are disrupted. in addition, floods in urban areas sometimes always release their floodwater towards residential areas which submerges the residential areas so that many material losses occur, not even a few urban area floods claim lives (bertilsson et al., 2019). as the preliminary study conducted in this research shows that during the 2018-2020 period, the bandung city area experienced frequent flooding in various regions. even though the bandung area has high slopes in the e-issn: 2355-6544 received: 01 march 2023; revised: 11 may 2025; accepted: 19 may 2025; available online: 31 october 2025; published: 31 october 2025. keywords: ahp, flood, gis, multi-criteria decision, probability *corresponding author(s) email: purnamariki20@gmail.com https://doi.org/10.14710/geoplanning.12.2.173-186 mailto:purnamariki20@gmail.com agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 186 doi: 10.14710/geoplanning.12.2.173-186 174 northern area, this does not prevent the central to southern areas from being flooded. according to hosseiny et al. (2020), floods can be classified into three main classifications, but there can also be four to five classifications depending on the strongest variable used, the three main classifications are; (1) light flooding (0.1 – 0.8 meters); (2) moderate flood (0.9 – 1.5 meters); and (3) high flood (>1.3 meters). the city of bandung according to data from the "central bureau of statistics for the city of bandung" (bps kota bandung) (bps, 2021) noted that there were around 54 flood events in 2018, including 35 mild floods, 14 moderate floods, and 7 high floods, in 2019 there were around 50 flood events including 32 mild floods, 12 moderate floods, and 6 high floods, in 2020 around 38 flood events occurred including 21 light floods, 10 moderate floods, and 7 high floods. based on preliminary studies conducted by researchers, it appears that the city of bandung has many problems regarding flood disasters, where floods often occur even though rainfall is small. floods in urban areas often occur due to the small number of waterways and small ditches, so water spills more easily onto the streets and is difficult for the soil to absorb (o’donnell & thorne, 2020). in addition, the dense factor of adjacent buildings makes it very easy to trap rainwater that overflows from the ditches, so due to the lack of soil that can absorb rainwater it can cause the overflow to be absorbed for a long time (nkwunonwo et al., 2020). other researchers also stated that urban areas often easier to trap water overflow due to a less vegetation index, even in the worst conditions when there is no bio pores between buildings (mignot et al., 2019). in terms of handling, flooding in urban areas is a little difficult to do, because it requires very complex regional planning, plus many clashes of political needs in urban areas, so the processing time needed to deal with flooding in urban areas is relatively long (zhou et al., 2019). therefore, planning to handle flooding in urban areas must be based on complex analysis, so that accuracy can be trusted. with an analysis of the probability of flooding in urban areas, researchers, communities, and the government will know the main overview of regional priorities that must be addressed, in order to reduce the probability of flooding in the area (abebe et al., 2019). one possible effort to obtain the probability of flooding in urban areas is by using satellite imagery based on the geographic information system (gis). gis is able to present data widely and fairly accurately if the treatment of the data is used correctly (quattrochi et al., 2023). it is felt that the use of gis in disaster hazard analysis will shorten time because the entire process is carried out by computing and assisted by satellite imagery which can record very large areas (dikau, 2020). in addition, in terms of accuracy, gis is fairly accurate because according to supriadi & oswari (2020) in his research stated that gis accuracy is 98% accurate, and it all depends on the dataset used, if all datasets used have the same timeframe, and there are no clouds blocking, will be very accurate results. including flood hazard probability analysis, which can be carried out using satellite imagery and assisted by gis. flood hazard probability analysis using satellite imagery and the assistance of gis can be done by looking at the various variables used. according to motta et al. (2021), states that an analysis of the probability of flooding in urban areas can pay attention to the main variables, namely; (1) topographic wetness index (twi); (2) land slope (ls); and (3) distance from road (dro). however, feng et al. (2020) revealed that other variables in the probability analysis of flood hazard in urban areas can pay attention to variables such as; (1) elevation (el); (2) precipitation (pc); (3) land use/land cover (lulc); and (4) distance from the river (drv). in addition, ndvi is felt to be very necessary for the analysis of flood hazard probabilities, because ndvi will play an important role in urban water absorption (putra et al., 2022). of course, all the variables used by various researchers need to be re-analyzed according to the characteristics of the urban area to be studied, as was done by previous researchers, using the seven variables mentioned by previous researchers, but not using ndvi, because the area is in the barren dominated by dry land (nsangou et al., 2022). based on the variables, of course, the data needs to be analyzed using a method in disaster hazard probability analysis, for example by using the lstm neural network model, where the lstm neural network prioritizes connections between variable weight reliability, without regard to data validation, so that the accuracy for flood disaster hazard probabilities uses lstm neural network needs to be questioned again. what is possible to do for flood hazard probability analysis is to use the multi-criteria decision analysis (mcda) model because the mcda model is one of the most recent disaster hazard models (boulomytis et al., 2022). in addition, mcda https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 – 186 doi: 10.14710/geoplanning.12.2.173-186 175 is in the process, it requires joint analysis with several experts, where the expert must come from the area to be analyzed and the number of experts must be at least five experts so that accuracy is maintained (morales-ruano et al., 2022). however, the mcda model will not be completely perfect if there is no weighting to be done, mcda must therefore be integrated with the analytical hierarchy process (ahp), which utilizes pairwise comparisons to determine the relative importance of each variable by evaluating them against predefined criteria. (msabi & makonyo, 2021). the ahp technique offers a key advantage over other methods by allowing researchers the flexibility to select variables, provided they carefully consider essential aspects when assigning weights in flood susceptibility analysis (abdullah et al., 2021). in contrast, other approaches are often viewed as less effective and adaptable. for instance, methods based on the geomorphological characteristics of basins cannot fully substitute traditional hydraulic modeling and usually require detailed field studies, making them more time-consuming and laborintensive. moreover, such methods are only applicable in areas with multiple basins, and their accuracy becomes questionable in regions with generally flat terrain (adnan et al., 2019). statistical methods like frequency ratio and logistic regression also present limitations, as their effectiveness heavily relies on the relevance of input variables and the size of the dataset used (rahmati et al., 2016; tehrany et al., 2015). each method and model used to analyze flood probability in urban areas comes with its own strengths and weaknesses. therefore, selecting an appropriate method for flood susceptibility mapping must consider the clarity of cumulative effects and the spatial continuity influenced by flood-triggering parameters. moreover, a critical factor in mapping flood susceptibility is the spatial scale, whether it is conducted at a local or national level. this study focuses on identifying and analyzing the probability of urban flooding in bandung city at a regional scale using the ahp-mcda model, supported by satellite imagery and gis tools. for validation purposes, a gis-based point database will be included, documenting flood events that occurred between 2002 and 2022, with at least one recorded event in each of those years. 2. data and methods 2.1. study area the study area used in this study uses the city of bandung, where the city of bandung has an area of 167.64 km2, and consists of thirty districts and one hundred and fifty-one sub-districts. based on the population as of 2023, there were 2,469,589 people consisting of 1,242,674 male residents and 1,226,915 female residents (bps, 2023). the city of bandung is an area surrounded by quite high mountains, and the city of bandung is often referred to as a sunken area like a bowl. figure 1. study area of bandung city in terms of climate, it was recorded from the bandung city geophysical station that in 2021 the average temperature for the city of bandung has an average temperature of 23.5°c, with the lowest temperature reaching https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 186 doi: 10.14710/geoplanning.12.2.173-186 176 15.6°c, and the highest reaching 32.2°c. in addition, it was also recorded from the bandung city geophysical station that in 2021 the rainfall for bandung city was 180.89 mm/year, with the lowest rainfall occurring in july which reached 33.2 mm/month, and the highest rainfall occurring in november with rainfall reaches 454.3 mm/month. in full, geographic information about the city of bandung as a study area can be seen in figure 1. 2.2. data and process this study utilizes a diverse range of data obtained from creditable and reliable sources, which are subsequently processed using gis. as outlined in the literature review presented in the introduction, the data used for analyzing flood conditioning factors is summarized in table 1. table 1. types of data used and their sources to process flood conditioning criteria no data type gis data type scale or resolution source of data spatial database derived map spatial database 1 twi grid slope gradient (°) 30 m aster gdem version 3 2 elevation elevation (m) 3 slope topographic wetness index 4 rainfall grid precipitation map (mm/yr) 1:50.000 (harris et al., 2020) 5 lulc arc/info grid land use 10 m esri land cover (2020) 6 ndvi arc/info grid ndvi 30 m landsat 8 oli/tirs + images 7 distance from river arc/info grid line coverage distance from river 30 m indonesian geospatial information agency (big) 8 distance from road arc/info grid line coverage distance from road 30 m indonesian geospatial information agency (big) 9 flood inventory point and polygon indonesian national disaster management agency (bnpb) eight datasets are used as flood conditioning factors in this study, chosen specifically to minimize the complexity of data processing at a regional scale encompassing city of bandung. these factors include: (1) twi, (2) elevation, (3) slope, (4) rainfall (annual precipitation), (5) land use, (6) ndvi, (7) distance from river, and (8) distance from road. a ninth dataset is used for validating the resulting flood susceptibility map of the city of bandung, consisting of recorded flood events from 2002 to 2022, with at least one event documented each year. the overall research flow is illustrated in figure 2. figure 2. research flowchart this study employed arcgis 10.8 software to compile and analyze all flood conditioning factors within a local gis database. the processing steps, including the reclassification of conditioning factor maps, application of the weighted linear combination (wlc) method, and the validation of the flood susceptibility map for the city of bandung area were entirely conducted using arcgis 10.8. all gis-based analyses were performed in a twodimensional spatial format, without incorporating 3d analysis. meanwhile, the ahp was executed using microsoft excel, applying a quantitative approach to determine preferences among various decision alternatives https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 – 186 doi: 10.14710/geoplanning.12.2.173-186 177 (chourabi et al., 2019). a pair-wise comparison matrix (pcm) was utilized in the ahp method to rank the parameters, enabling the construction of weighting factors for each criterion based on individual judgments using a standardized ranking scale (lin & kou, 2015). the weighting scale ranges from 1 to 9, where a score of 1 indicates equal importance between two factors, and a score of 9 signifies an extreme preference for one factor over another. the resulting consistency index (ci) values derived from pcm calculations vary depending on the number of conditioning factors, with corresponding matrix sizes. the consistency ratio (cr) is then used to quantitatively validate the input data through mathematical equations, as outlined in equations 1 and 2. 𝐶𝑅 = 𝐶𝐼 𝑅𝐼 …………... (equation 1) 𝐶𝐼 = (𝜆𝑚𝑎𝑥−𝑛) 𝑛−1 …………. (equation 2) where cr is the consistency ratio, ci is consistency index, λ is average value of consistency vector, n is the number of criteria, and ri is random ci randomly generated by pcm. the random index (ri) values were derived from pairwise comparison matrices (pcms) generated through random, inconsistent pairwise selections (althuwaynee et al., 2016). to validate the assigned weights of the conditioning factors, the consistency ratio (cr) must be less than 0.1. if the cr exceeds 0.1, the weighting matrix developed by experts must be revised. weight normalization within the pcm is conducted through various techniques, based on expert judgment and methodological preferences (bozorgi-amiri & asvadi, 2015). once the final weights are determined from expert evaluations, an aggregation method is applied by multiplying each conditioning factor map in arcgis 10.8 with the corresponding weight, following the procedure outlined in equation 3. 𝐹𝑆 = ∑𝑤𝑖𝑥𝑖…………... (equation 3) where, fs is flood susceptibility, wi is weight of factor i, and xi is classes of flood susceptibility for each factor i. 2.3. flood validation by flood inventory database for accuracy validation of the flood susceptibility map generated through various data processing and analytical stages, a gis-based flood event database was utilized, encompassing flood occurrences from 2002 to 2022. during this period, data were collected for one or more flood events per year. the information on these events was sourced from the disaster information data provided by the indonesian national disaster management agency (bnpb). however, the dataset for city of bandung does not cover all flood incidents, only those classified within levels one to three. according to bnpb classifications, level one refers to a critical condition where flooding persists for more than six hours without receding; level two indicates a situation where floodwaters have begun to spread; and level three refers to non-critical inundation events, often identified as flash floods. 3. result and discussion this study focuses on the probability analysis of flood hazard in urban areas, especially the area of bandung city, west java, indonesia. before displaying the flood hazard probability map for the city of bandung, this study will display eight data that are used as conditioning factors that will affect the probability of flooding hazard for the city of bandung, with a list of data and their sources which can be seen in table 1. the conditioning factor data that has been obtained is then checked. reconsidering the values in the raster data, and readjusting the units used, so that when weighting is carried out it is relevant to the conditions that occur in the study area. sometimes the data obtained has units that are different from what the researcher believes and what has been determined according to the law in force in the research area, for example, most areas use the temperature unit as celsius, but some data will provide temperature data in fahrenheit units. because the data manager is in an area that applies temperature in fahrenheit units, conversions must be carried out so that they are aligned and there is no confusion in units later when a disaster hazard probability map has been made (cabrera & lee, 2019). the results of the data obtained in this study can be seen in figure 3. https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 186 doi: 10.14710/geoplanning.12.2.173-186 178 after all the data is obtained properly, the units are in accordance with the applicable provisions, and the resolution used is quite good or in accordance with what is desired, even if all the resolutions used are more or less similar to other data, then the data classification is carried out according to the values that have been determined. previously defined. classifying raster data aims to produce equivalent values that are used when doing weighting so that no data weight has more or less value between one data and another (shahiri tabarestani & afzalimehr, 2021). in addition, the classification aims to provide specific values which later these values will appear as a new classification, but sometimes the classification that is set does not match the final results obtained so the classification must be considered properly according to the data presented in the raster (wijaya & buchori, 2022). the following is the result of reclassification based on the class or value of each map data which can be seen in table 2. table 2. classes of conditioning factors, and estimated ratings for reclassify no. factor abbreviation class rating 1. twi (level) tw <5 1 6 – 10 2 11 – 15 3 16 – 20 4 20> 5 2. elevation (m) el <650 5 651 – 700 4 701 – 750 3 751 – 800 2 800> 1 3. slope (°) sl 0 – 10 5 11 – 30 4 31 – 50 3 51 – 70 2 70> 1 4. precipitation (mm/yr) pc <195 1 196 – 197 2 198 – 199 3 200 – 201 4 201> 5 5. land use lu water 1 agriculture land 2 building 3 bare land 4 vegetation 5 6. ndvi nd -0.00649 – -0.012 1 -0.012 – 0.125 2 0.126 – 0.200 3 0.201 – 0.300 4 0.301 – 0.476 5 7. distance from rivers (m) drv <85 5 85 – 184 4 185 – 296 3 297 – 431 2 431> 1 8. distance from roads (m) dro <160 5 161 – 316 4 317 – 474 3 475 – 632 2 633> 1 source: analysis, 2023 the classification presented in table 2 was conducted utilizing the jenks natural breaks optimization (nbo) method, a technique that groups data based on inherent distribution patterns to minimize intra-class variance and maximize inter-class differences. this approach ensures alignment with the dataset’s empirical distribution curve, thereby mitigating risks of data fragmentation or voids during classification (hadipour et al., https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 – 186 doi: 10.14710/geoplanning.12.2.173-186 179 2020). following this, a weighted assessment was performed by a panel of five regional experts from bandung city to contextualize the classification outcomes according to local geographical and socio-environmental conditions. each expert’s input was subjected to a consistency validation process, with a predetermined threshold for the consistency ratio (cr) set at ≤0.1 to ensure analytical reliability. the individual expert weightings were subsequently aggregated using a composite scoring framework, and the synthesized results are comprehensively detailed in table 3. figure 3. the map of (a) twi; (b) elevation; (c) slope; (d) precipitation; (e) land cover; (f) ndvi; (g) distance from rivers; and (h) distance from roads in city of bandung table 3. the results of the weighting matrix along with the normalized principal eigenvector values tw el sl pc lu nd drv dro normalized principal eigenvector tw 1 1.51 1 1.27 1.32 1.55 2.11 1.24 16.29% el 0.66 1 0.51 1.12 0.85 1 1.24 1.24 11.22% sl 1 1.93 1 1.38 1.14 1.64 1.71 1.14 16.31% pc 0.78 0.89 0.72 1 1.11 0.92 1.14 1.24 11.82% lu 0.75 1.17 0.87 0.89 1 1.32 2.22 1.55 14.18% nd 0.64 1 0.60 1.08 0.75 1 1.38 0.87 10.79% drv 0.47 0.80 0.58 0.87 0.45 0.72 1 0.92 8.57% dro 0.80 0.80 0.87 0.80 0.64 1.15 1.08 1.00 10.82% source: analysis, 2023 https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 186 doi: 10.14710/geoplanning.12.2.173-186 180 by applying ahp, the weighting criteria were determined through the normalized principal eigenvector, which was derived by comparing the values in each row to calculate the total weight for all flood conditioning factors in the city of bandung. the analysis produced a maximum eigenvalue (λmax) of 8.08 and a consistency ratio (cr) of 0.08, indicating that the weighting results are valid, as the cr value (0.08) is less than the acceptable threshold of 0.1. the resulting weights for each conditioning factor are as follows: twi in 16.29%, elevation in 11.22%, slope in 16.31%, precipitation in 11.32%, land use in 14.18%, ndvi in 10.79%, distance from rivers in 8.57%, and distance from roads in 10.82%. these weights were then applied in arcgis 10.8 using the weighted overlay method to produce the flood susceptibility map (see figure 4) for the city of bandung. in order to analyze more deeply regarding the percentage and area of the area affected, an analysis per sub-district was carried out (see table 4). this per-sub-district analysis was carried out by pasting the subdistrict boundary shapefiles, which were then cut using the overlay method, so the researchers would find out how many areas were affected along with their flood hazard classification. table 4. area coverage of flood susceptibility by its classes subdistrict low hazard (sq km) % quite hazard (sq km) % high hazard (sq km) % extreme hazard (sq km) % total area (sq km) total % andir 0 0 4.09 97.10 0.12 2.89 0 0 4.21 100 astana anyar 0 0 2.44 99.31 0.01 0.69 0 0 2.46 100 antapani 0 0 3.47 71.82 1.36 28.17 0 0 4.83 100 arcamanik 0 0 3.31 48.38 3.51 51.26 0.02 0.35 6.85 100 babakan ciparay 0.08 1.18 6.55 96.49 0.15 2.32 0 0 6.79 100 bandung kidul 0 0 3.76 71.92 1.46 28.07 0 0 5.21 100 bandung kulon 0.11 2.04 5.33 96.26 0.09 1.69 0 0 5.53 100 bandung wetan 0 0 2.49 60.48 1.62 39.51 0 0 4.11 100 batununggal 0 0 4.18 86.61 0.64 13.38 0 0 4.82 100 bojongloa kaler 0.00 0.23 3.01 98.40 0.04 1.37 0 0 3.06 100 bojongloa kidul 0 0 4.57 93.74 0.30 6.25 0 0 4.87 100 buahbatu 0 0 5.20 75.25 1.71 24.75 0 0 6.90 100 cibeunying kaler 0 0 2.90 63.70 1.64 35.92 0.01 0.37 4.56 100 cibeunying kidul 0 0 3.46 85.52 0.58 14.47 0 0 4.04 100 cibiru 0 0 0.78 10.01 6.43 82.30 0.60 7.67 7.81 100 cicendo 0 0 6.77 89.55 0.79 10.44 0 0 7.56 100 cidadap 0 0 5.45 73.49 1.96 26.50 0 0 7.42 100 cinambo 0 0 1.61 41.29 2.29 58.62 0 0.07 3.91 100 coblong 0 0 5.39 74.34 1.86 25.65 0 0 7.25 100 gedebage 0 0 2.51 26.22 7.05 73.67 0.01 0.10 9.57 100 kiaracondong 0 0 4.58 80.56 1.10 19.43 0 0 5.69 100 lengkong 0 0 4.41 84.54 0.80 15.45 0 0 5.22 100 mandalajati 0 0 0.95 20.27 3.70 78.91 0.03 0.80 4.69 100 panyileukan 0 0 2.53 43.17 3.34 56.82 0 0 5.88 100 rancasari 0 0 3.85 45.74 4.57 54.25 0 0 8.42 100 regol 0 0 4.54 95.04 0.23 4.95 0 0 4.78 100 sukajadi 0 0 4.64 90.32 0.49 9.67 0 0 5.14 100 sukasari 0 0 3.60 61.18 2.29 38.81 0 0 5.89 100 sumur bandung 0 0 2.32 73.31 0.84 26.68 0 0 3.17 100 ujungberung 0 0 1.41 20.61 5.16 75.05 0.29 4.33 6.87 100 total 0.19 0.11 110.20 69.15 56.25 30.27 0.99 0.45 167.64 100 source: analysis, 2023 according to the percentages presented in table 4, bandung kulon has the highest proportion of areas classified as low flood hazard, accounting for 2.04% of its total area. the highest percentage of areas under moderate (quite) flood hazard is found in astana anyar, with 99.31%. for high flood hazard zones, cibiru exhibits the greatest extent, comprising 82.30% of its area. additionally, cibiru also records the largest proportion of areas under extreme flood hazard, at 7.67%. generally, administrative regions with larger land areas tend to exhibit higher absolute values of flood hazard zones compared to smaller regions (rincón et al., 2018). https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 – 186 doi: 10.14710/geoplanning.12.2.173-186 181 figure 4. susceptibility flood hazard map in city of bandung then, the results of the flood hazard susceptibility mapping in bandung city were validated to determine the validity percentage of the flood hazard susceptibility that was carried out. validation was carried out by comparing the real events of flooding in the city of bandung in the 2002-2022 timeframe with the results of the flood hazard susceptibility map for the city of bandung. previously, the criteria for real flood event data in the city of bandung were classified according to the laws in force in indonesia, such as “siaga 1” will be classified as low flood hazard, up to “siaga 4” will be classified as extreme flood hazard. validation criteria that data can be said to be valid if the average validation of all flood classification levels reaches > 50% (de moel et al., 2015). the validation results can be seen in table 5. table 5. flood validation in city of bandung based on bnpb data and flood susceptibility map flood susceptibility level number of flood validation points % of total number of flood validation points % average and it’s validation low hazard 12 76.60 80.20% (valid) quite hazard 117 84.20 high hazard 304 82.70 extreme hazard 54 77.30 source: analysis, 2023 careful selection of flood conditioning factors and appropriate spatial scale is essential for valid flood susceptibility analysis (al-juaidi et al., 2018; wing et al., 2017). in particular, finer spatial resolution typically yields more accurate and valid data, whereas coarse or mismatched data scales can compromise model validity. in this study, a primary issue is that the input data are at a relatively coarse scale compared to the broad regional extent of the analysis, which likely undermines validation accuracy. hasanloo et al. (2019) similarly observed that excessively large study areas relative to the data resolution result in low validation and coarse-detail outputs. furthermore, employing mcda with the ahp introduces significant subjectivity, since expert judgments are required to weight the conditioning factors. in large, multicity analyses, differing expert opinions can lead to one or two factors receiving disproportionately large weights. consistent with this, doorga et al. (2022), ha-mim et al. (2022), and vignesh et al. (2021) reported that researchers often rebalance or adjust their data to equalize the influence of all conditioning factors, thereby avoiding one-sided bias. the analysis performed in table 4 shows information that the high to extreme flood probability is centered on east bandung which includes the mandalajati, ujung berung, cibiru, gedebage, and panyileukan areas. globally, similar patterns emerge in diverse contexts. for instance, anelli et al. (2022) in rome, italy, found that https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 186 doi: 10.14710/geoplanning.12.2.173-186 182 proximity to rivers and impervious surfaces drove flood susceptibility, aligning with bandung’s high-risk zones near rivers and roads. however, rome’s historical drainage infrastructure mitigated central-area risks, unlike bandung’s limited drainage capacity. in lagos, nigeria, nkwunonwo et al. (2016) highlighted elevation and vegetation loss as key factors, mirroring bandung’s challenges, though coastal tides added complexity. contrastingly, new orleans’ reliance on levees (abbott, 2024) and são paulo’s impervious surfaces (young & papini, 2020) underscore the variability in mitigation strategies and risk drivers across continents. these comparisons emphasize that while bandung’s flood susceptibility aligns with global urban patterns, localized infrastructure and planning critically shape outcomes. flood hazard with a high and extreme level of probability in east bandung has a high percentage of coverage, which is above 75% for high-hazard probability, and above 0.1% for extreme-hazard probability. of course, this is because, in the east bandung area, the roads are close together, with the rivers also close together. the proximity of roads and rivers can increase the probability of flood hazard, because the distance between the roads that are close together will make it difficult for water to be absorbed by the ground, besides that the distance between rivers that are close to urban areas will make the accumulated water easily overflow onto the roads so that the absorption capacity of the soil will increase. fills up quickly when roads and rivers are close together, let alone coincide (khosravi et al., 2020; rafiei-sardooi et al., 2021). from a review of land cover, which is shown in figure 3e, it can be seen that the east bandung area is filled with buildings, and little vegetation, and also that the vegetation is relatively far from settlements, so water absorption when it rains only applies to areas that have vegetation. less or far distances between vegetation and settlements will certainly make flood handling inappropriate, so it is essential to notice the distance between vegetation and settlements (ferrini et al., 2020). machado et al. (2019) in his research revealed that the role of vegetation is of course very large in absorbing water when it rains so as to avoid flooding in urban areas, however, the layout of the vegetation must be considered again. to be more effective. o’donnell & thorne (2020) suggests building a vegetation area at the headwaters of a river, or the corner of a road to make it easier for water to infiltrate when rain occurs in urban areas, besides that, maragno et al. (2018) provides the best design for developing urban vegetation, namely by building the vegetation area encircles several parts of the residential area, the circular shape is expected to be able to hold water coming from other directions towards the residential area with the aim that the water can be absorbed first. in addition, the construction of floating buildings is considered very possible to deal with flooding, because floating buildings will create new paths for river flow so that water will move quickly and reduce overflows in other parts of the river (piątek & wojnowska-heciak, 2020). in terms of environmental review and regional planning for the east bandung area, it is indeed deemed inadequate to withstand flooding. this can be seen by the very small width of the ditch in residential areas, and the absence of qualified bio pores. schools and bio pores in urban areas are indeed the most important thing in overcoming flooding because the provision of sufficient wide ditches and an adequate number of bio pores and strategic locations will make water infiltration and flow faster so that water that falls when it rains will not quickly accumulate and causing flash floods to occur (hamel & tan, 2022; karunia et al., 2021). in addition, the gedebage area has a market on the border between gedebage and panyileukan, where the market produces and accumulates quite a lot of waste, around ten tons a year (idris, 2022), where this waste is one of the supporting factors. in the occurrence of floods in the city of bandung, especially the east bandung area which is vulnerable to being affected. the accumulation of garbage in urban areas will certainly result in clogged river flows or drainage so that the water will quickly overflow due to obstacles from the garbage (mensah & ahadzie, 2020). moreover, solid waste will seriously interfere with river flow and drainage, because its accumulation will block the flow of water in rivers and drainage and become a dead end for water flow (zambrano et al., 2018). this study provides critical insights into flood susceptibility in bandung city, indonesia, through a multicriteria decision framework. the most significant finding is the identification of east bandung, which encompasses mandalajati, ujungberung, cibiru, gedebage, and panyileukan, as the region with the highest flood https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 – 186 doi: 10.14710/geoplanning.12.2.173-186 183 risk. over 75% of this area falls under "high" to "extreme" susceptibility categories, driven by its dense road networks, proximity to rivers, and limited vegetation cover (ndvi < 0.3). these factors collectively hinder natural water infiltration, exacerbating surface runoff during rainfall events. such spatial clustering of risk aligns with patterns observed in other flood-prone urban regions globally, such as são paulo, brazil, where impervious surfaces and informal settlements amplify flood hazards (young & papini, 2020). the ahp weighting revealed that topographic and hydrological factors, specifically the topographic wetness index (twi, 16.29%) and slope (16.31%), were the most influential criteria. this underscores the role of bandung’s "bowl-like" topography, where lower elevations and gentle slopes in central-southern zones trap floodwaters. conversely, anthropogenic factors like distance from roads (10.82%) and land cover (14.18%) highlighted human contributions to vulnerability, such as inadequate drainage systems and urban sprawl. this dual emphasis on natural and human-driven factors mirrors methodologies applied in rome, italy, where ahpgis frameworks similarly prioritized river proximity and impervious surfaces (anelli et al., 2022). the validation accuracy of 80.20%, achieved by cross-referencing predicted susceptibility zones with 20 years of historical flood data (2002–2022), underscores the model’s reliability. this high accuracy stems from two pillars of robustness; (1) methodological rigor; and (2) data diversity and resolution. the integration of five expert assessments ensured localized relevance while minimizing subjectivity. the consistency ratio (cr = 0.08) confirmed logical coherence in pairwise comparisons, adhering to saaty’s threshold (cr < 0.1). this approach contrasts with single-expert studies, such as maskrey et al. (2022), stated that limited expert input reduced decision transparency. combining high-resolution satellite imagery (30 m) with multi-source datasets (e.g., aster dem, landsat ndvi, and bnpb flood inventories) enabled granular analysis. for instance, the 30 m resolution of distance from rivers and roads allowed precise identification of at-risk settlements, a refinement absent in broader-scale studies like lagos, nigeria (nkwunonwo et al., 2016), which relied on coarser administrative boundaries. the ahp-mcda framework’s adaptability further demonstrates robustness. unlike machine learning models (e.g., lstm) that require vast datasets, this method delivered accuracy despite bandung’s moderate data availability. this flexibility is critical for cities in developing regions, where data scarcity often impedes flood modeling. for example, in new orleans, usa, advanced hydraulic models depend on extensive levee and rainfall data (abbott, 2024). globally, these findings resonate with flood susceptibility drivers identified across continents. in rome, historical drainage infrastructure reduced central-zone risks despite river proximity (anelli et al., 2022), whereas bandung’s infrastructural gaps amplified vulnerabilities. similarly, lagos shares bandung’s challenges with elevation and vegetation loss but faces compounded risks from coastal tides (nkwunonwo et al., 2016). these parallels highlight the universality of topographic and anthropogenic factors in flood risk, while contextual differences emphasize the need for tailored mitigation strategies. 4. conclusion the study, which aimed to map flood susceptibility in the city of bandung using the ahp-mcda approach integrated with arcgis 10.8, revealed that, following the reclassification of each conditioning factor and the weighting assessment provided by five expert evaluators, four distinct flood susceptibility classes were identified within the city of bandung; (1) low hazard; (2) quite hazard; (3) high hazard; and (4) extreme hazard. the east bandung area which includes mandalajati, ujungberung, cibiru, gedebage, and panyileukan is an area that is very at hazardof flooding with a high-hazard percentage of above 75% and extreme hazard above 0.1%, where this needs to be considered again for bandung city policymakers and the community must take part in dealing with floods in the city of bandung, especially east bandung. based on the results of the validation carried out by comparing the real events of flooding in the city of bandung in 2002-2022, a validation result of 80.20% showed that the map of susceptibility to flood hazard in the city of bandung is valid. this research is expected to make the bandung city flood susceptibility map the main basis for local government to deal with flooding in the bandung city area so that the determination of development plans for urban areas is more organized and can cope with bandung city flooding more efficiently. https://doi.org/10.14710/geoplanning.12.2.173-186 agustina et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 173 186 doi: 10.14710/geoplanning.12.2.173-186 184 this study successfully mapped flood susceptibility in bandung city using the ahp-mcda-gis framework, identifying east bandung as the highest-risk zone due to its dense infrastructure, proximity to rivers, and limited green spaces. the model’s 80.20% validation accuracy against historical flood events underscores its reliability for urban planning and disaster mitigation. however, future research should address several avenues to enhance practical applicability like expanding criteria to include socio-economic factors e.g., population density, poverty levels, infrastructure resilience to assess human vulnerability and prioritize equitable mitigation strategies. also, pilot and quantify the effectiveness of proposed solutions (e.g., bio-pores, decentralized drainage systems, or urban greening) in high-risk zones like gedebage and cibiru, using preand post-intervention flood data. 5. acknowledgments researchers are grateful to litapdimas kemenag, and lp2m uin sunan gunung djati bandung for financial assistance provided to researchers so that this research can run well and smoothly from the initial stages until this research is published. 6. references abbott, m. j. o. 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untuk identifikasi permukiman kumuh daerah penyangga perkotaan (studi kasus : kecamatan mranggen kabupaten demak) r.a.ramadhana, b.pigawatib a universitas diponegoro, indonesia, email: rezkyarieframadhan@gmail.com b universitas diponegoro, indonesia, email: bitta.pigawati@gmail.com abstract: mranggen subdistrict is one of the buffer zone area for semarang city that slums area can be found. emergence of slums area in mranggen subdistrictis because the impact of increased population in semarang capital. increasing of the settlements area and housing become a logical consequence that comes from the main case. this condition is a major trigger from the formation of slums area. the purpose of this research is to identify characteristics of slums area in mranggen subdistrict, as a buffer area for semarang capital, with a quantitative approaching method. instruments used in this research were observation, interview, and kuesioner with descriptive and spatial analytical techniques. interpretation of alos satellite’s citra is about to know the distribution of slums area. the results showed that some of the slums area in mranggen subdistrict has a bad quality of buildings, semi-permanent and nonpermanent buildings still stands, varying distance between the buildings was like (< 1,5 meters, 1,5 – 3,0 meters, and > 3,0 meters). location of the slums area is very strategic that so many people could make an easy access towards the working place. condition of the road was good, but condition of the drainage system, clean water, and sanitation were bad. many house that have not been certified yet, low level of population growth and density, low rate income, also migrants people that prefers to live in this location rather than the other places, because near from their working place. so, strategic ways to handle slums area in mranggen subdistrict can be done using poverty development, community based development, and guided land development approach. abstrak: kecamatan mranggen merupakan salah satu daerah penyangga kota semarang yang teridentifikasi memiliki kawasan permukiman kumuh. munculnya kawasan permukiman kumuh di kecamatan mranggen merupakan dampak dari meningkatnya jumlah penduduk kota semarang. pertambahan luas permukiman dan pemadatan rumah mukim menjadi konsekuensi logis yang selalu menyertai gejala ini. hal ini menjadi pemicu terbentuknya permukiman kumuh. penelitian ini bertujuan untuk mengkaji karakteristik kawasan permukiman kumuh di kecamatan mranggen sebagai daerah penyangga kota semarang dengan menggunakan pendekatan kuantitatif. instrumen penelitian yang digunakan adalah observasi, wawancara, dan kuesioner dengan teknik analisis deskriptif dan spasial. teknik analisis spasial digunakan untuk mengetahui persebaran kawasan permukiman kumuh di kecamatan mranggen melalui interpretasi citra satelit alos. hasil penelitian menunjukkan bahwa karakteristik permukiman kumuh di kecamatan mranggen kualitas bangunannya rendah, banyak terdapat bangunan semi permanen dan non permanen, jarak antar bangunan bervariasi (< 1,5 meter, 1,5 – 3,0 meter, dan > 3,0 meter). lokasi kawasan permukiman kumuh sangat strategis sehingga memungkinkan penduduk dapat mengakses tempat kerja dengan mudah. kondisi prasarana jalan baik namun kondisi drainase, air bersih, dan sanitasi buruk. masih banyak rumah yang belum bersertifikat, tingkat pertumbuhan dan kepadatan penduduk rendah, tingkat pendapatan rendah, dan alasan penduduk pendatang yang memilih tinggal di kawasan permukiman kumuh karena dekat dengan tempat kerja. untuk penangan kawasan permukiman kumuh di kecamatan maranggen dapat dilakukan melalui pendekatan poverty development, community based development, dan guided land development. info artikel; diterima: 23 september 2014 hasil revisi : 25 september 2014 disetujui: 24 september 2014 publikasi on-line: 1 oktober 2014 kata kunci: karakteristik, permukiman kumuh, daerah penyangga, kecamatan mranggen article info; received: 23 september 2014 in revised form: 25 september 2014 accepted: 27 september 2014 available online: 1 october 2014 keywords: characteristics, slums, buffer area, mranggen subdistrict mailto:rezkyarieframadhan@gmail.com mailto:bitta.pigawati@gmail.com geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 103 1. pendahuluan identifikasi karakteristik kawasan permukiman kumuh di daerah penyangga perkotaan menjadi cukup strategis manakala kawasan tersebut memiliki kaitan atau berbatasan langsung dengan pusat kota. penelitian ini bermaksud untuk melengkapi kajian tentang permukiman kumuh di kawasan pesisir kota semarang yang telah di lakukan sebelumnya oleh bitta pigawati (2014). kecamatan mranggen merupakan salah satu daerah penyangga kota semarang yang teridentifikasi memiliki kawasan permukiman kumuh. munculnya kawasan permukiman kumuh di kecamatan mranggen merupakan dampak dari meningkatnya jumlah penduduk kota semarang. wilayah kecamatan mranggen, khususnya desa kangkung, desa batursari, dan desa mranggen yang letaknya dekat dengan kota semarang merupakan sasaran bagi pendatang-pendatang baru untuk bertempat tinggal, baik pendatang dari bagian dalam kota maupun pendatang dari bagian yang lebih jauh dari itu. bagi pendatang yang berasal dari bagian dalam kota, kecamatan mranggen merupakan daerah yang sangat menarik untuk bertempat tinggal, karena menawarkan tingkat kenyamanan yang jauh lebih tinggi ketimbang suasana yang ada di bagian dalam kota. sementara bagi pendatang yang berasal dari luar kota, kecamatan mranggen merupakan daerah yang tepat untuk memperoleh peluang kerja yang lebih besar. pertambahan luas lahan permukiman dan pemadatan rumah mukim menjadi konsekuensi logis yang selalu menyertai gejala ini. hal ini merupakan pemicu utama terjadinya taudifikasi atau proses terbentuknya permukiman kumuh. peta citra kecamatan mranggen dapat dilihat pada gambar 1. gambar 1.peta citra kecamatan mranggen (citra alos, 2011) berdasarkan kondisi tersebut, penelitian ini bertujuan untuk mengidentifikasi karakteristik kawasan permukiman kumuh di kecamatan mranggen sebagai daerah penyangga kota semarang. untuk penanganan kawasan permukiman kumuh dapat dilakukan diantaranya melalui pendekatan poverty development, community based development (cbd), dan guided land development (gld). poverty development adalah pendekatan yang berangkat dari pemahaman bahwa kawasan permukiman kumuh akan dikelola secara komersial agar ekonomi lokasi yang tinggi dimanfaatkan semaksimal mungkin bagi kepentingan kawasan. community based development (cbd) dan guide land development (gld) adalah pendekatan berdasarkan pemahaman bahwa kawasan kurang bahkan hampir tidak mempunyai nilai ekonomis komersial. perbedaannya, cbd menempatkan masyarakat sebagai pemeran utama. sementara, gld lebih mengarah dan melindungi hak penduduk asal untuk tetap tinggal (dirjen cipta karya, 2006). perkembangan kondisi fisik dan penduduk memunculkan masalah permukiman yang terkait dengan kualitas lingkungan. kondisi ini terus berkembang sehingga di dalam penyelenggaraan pembangunan permukiman perlu pendekatan yang terpadu dan dukungan kebijakan yang meliputi berbagai aspek (pigawati. b, 2014). geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 104 2. data dan metode 2.1 penginderaan jauh penginderaan jauh berasal dari dua kata dasar yaitu indera berarti melihat, dan jauh berarti jarak jauh. jadi berdasarkan asal katanya (epistimologi), penginderaan jauh berarti melihat objek dari jarak jauh. lillesand dan kiefer (1999) dalam mulyadi (2007) mendefinisikan penginderaan jauh sebagai ilmu dan seni untuk memperoleh informasi tentang objek, daerah atau gejala dengan jalan menganalisis menggunakan kaidah ilmiah, dan data yang diperoleh dengan menggunakan alat tanpa kontak langsung terhadap objek, daerah, atau gelaja yang dikaji. 2.2 permukiman kumuh permukiman kumuh adalah suatu kawasan dengan bentuk hunian yang tidak berstruktur, tidak berpola (misalnya letak rumah dan jalannnya tidak beraturan, tidak tersedianya fasilitas umum, prasarana dan sarana air bersih, mck), dan bentuk fisiknya tidak layak misalnya secara regular tiap tahun kebanjiran (yudohusodo, 1991). sementara menurut khomarudin (1997), kawasan permukiman kumuh dapat didefinisikan sebagai : a. lingkungan yg berpenghuni padat (melebihi 500 org per ha); b. kondisi sosial ekonomi masyarakat yang rendah; c. jumlah rumah yang sangat padat dan ukurannya dibawah standar; d. sarana prasarana tidak ada atau tidak memenuhi syarat teknis dan kesehatan; e. hunian yang dibangun diatas tanah milik negara atau orang lain dan diluar perundang-undangan yang berlaku. menurut un-habitat (2003) faktor-faktor yang menyebabkan kawasan menjadi kumuh diantaranya adanya migrasi penduduk dari desa ke kota, urbanisasi, dan kombinasi urbanisasi dan migrasi sebagai akibat dari perpindahan konflik antar penduduk. giok ling (2007) memiliki pendapat yang berbeda, menurutnya permukiman kumuh dapat pula terbentuk tanpa adanya tingkat urbanisasi yang sangat cepat. tetapi lebih disebabkan karena pemerintah tidak memiliki kapasita. dalam mengatasi urbanisasi yang cepat. hasil penelitian bitta pigawati (2014), menunjukkan ada keterkaitan antara jumlah penduduk miskin dengan kondisi kumuhnya suatu kawasan permukiman, sehingga salah satu pendekatan penanganan permukiman kumuh dapat ditempuh melalui srtategi penanggulangan kemiskinan. ciri-ciri kawasan permukiman kumuh menurut hari srinivas (2003) dapat tercermin dari : 1. penampilan fisik bangunannya yang miskin konstruksi, yaitu banyaknya bangunan-bangunan temporer yang berdiri serta nampak tak terurus maupun tanpa perawatan; 2. pendapatan yang rendah mencerminkan status ekonomi mereka, biasanya masyarakat kawasan kumuh berpenghasilan rendah; 3. kepadatan bangunan yang tinggi, dapat terlihat dari tidak adanya jarak antar bangunan maupun siteplan yang tidak terencana; 4. kepadatan penduduk yang tinggi dan masyarakatnya yang heterogen; 5. sistem sanitasi yang miskin atau tidak dalam kondisi yang baik; 6. kondisi sosial yang tidak baik dapat dilihat dengan banyaknya tindakan kejahatan maupun kriminal; 7. banyaknya jumlah masyarakat pendatang yang bertempat tinggal dengan menyewa rumah. 2.3 daerah penyangga daerah penyangga merupakan daerah yang tumbuh akibat proses pertumbuhan wilayah-wilayah tertentu dengan aktivitasnya yang bersifat agraris dan non agraris yang ditandai dengan interaksi antara manusia dan komoditasnya (bintarto, 1983). menurut tarigan (2005), daerah penyangga diartikan sebagai daerah yang langsung berbatasan dengan wilayah kota / areal terbangun (built up area). daerah penyangga tersebut saat ini tidak termasuk dalam wilayah terbangun secara penuh (full developed), namun dalam waktu mendatang akan mengalami perubahan karena perkembangan kota, atau dengan kata lain batasan tersebut dapat mencakup suatu pengertian bahwa daerah penyangga adalah wilayah dalam lingkungan administratif yang bersentuhan dengan wilayah administratif lain. yunus (2008) mengemukakan definisi yang berbeda yakni daerah penyangga merupakan wilayah yang geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 105 berada di antara wilayah kekotaan dan wilayah kedesaan. ciri khas wilayah ini sangat istimewa yang tidak dimiliki oleh wilayah lain yaitu dalam hal keterkaitan yang begitu besar dengan aspek kehidupan kota maupun desa yang tercipta secara simultan. menurut bintarto (1983), beberapa alasan tumbuhnya daerah penyangga antara lain; 1. peningkatan pelayanan transportasi kota; 2. pertumbuhan penduduk yang tinggi; 3. meningkatnya taraf hidup masyarakat; 4. gerakan pemilikan rumah oleh masyarakat; menurut malaque (2007) tumbuhnya daerah penyangga juga dapat dilihat dari adanya perubahan penggunaan lahan. malaque (2007) menambahkan bahwa perubahan penggunaan lahan di daerah penyangga atau kawasan pinggiran dapat ditandai dengan berubahnya status kepemilikan lahan dan munculnya permintaan akan tempat tinggal. yunus (2008) sependapat dan menambahkan bahwa daerah penyangga perkotaan merupakan sasaran perkembangan fisikal baru dari suatu kota. transformasi spasial yang terjadi di daerah penyangga merupakan proses berubahnya penggunaan lahan yang berorientasi pada kepentingan kedesaan menjadi penggunaan lahan yang berorientasi pada kepentingan kekotaan. penelitian yang berjudul karakteristik kawasan permukiman kumuh daerah penyangga perkotaan semarang (studi kasus : kecamatan mranggen kabupaten demak) menggunakan pendekatan kuantitatif. instrumen penelitian yang digunakan adalah observasi, wawancara, dan kuesioner. untuk mengetahui jumlah sampel, teknik sampling yang digunakan adalah simple random sampling dan stratified sampling dengan jumlah sampel 269 (tingkat kesalahan 10%). teknik analisis yang digunakan adalah teknik analisis deskriptif dan spasial. analisis dalam penelitian ini diantaranya adalah analisis penggunaan lahan di kecamatan mranggen yang dilakukan untuk mengetahui persebaran kawasan permukiman kumuh melalui interpretasi citra satelit alos tahun 2011. analisis permukiman kumuh dilakukan untuk mengetahui karakteristik kawasan permukiman kumuh berdasarkan kondisi bangunan, aksesbilitas, prasarana, status tanah, kondisi kependudukan, dan kondisi sosial ekonomi serta tingkat kekumuhannya secara deskriptif dan spasial. analisis karakteristik permukiman kumuh dilakukan untuk mengetahui karakteristik permukiman kumuh kecamatan mranggen sebagai daerah penyangga kota semarang secara deskriptif dan spasial. kerangka analisis dapat dilihat pada gambar 2. 3. hasil dan pembahasan 3.1 analisis penggunaan lahan kecamatan mranggen kawasan permukiman kumuh di kecamatan mranggen memiliki luas 1652,94 ha atau 54% dari luas total permukiman. persebaran kawasan permukiman kumuh di kecamatan mranggen dapat dilihat pada tabel 1. tabel 1. persebaran kawasan permukiman kumuh kecamatan mranggen (hasil analisis, 2014) desa kawasan permukiman kumuh luas (ha) prosentase (%) banyumeneng 116,93 7,07 sumberejo 49,97 3,02 kebonbantur 166.15 10,05 batursari 182,56 11,04 kangkung 109,15 6,60 kalitengah 148,13 8,96 kembangarum 94,15 5,70 mranggen 149,88 9,07 bandungrejo 36,42 2,20 brumbung 45,90 2,78 geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 106 desa kawasan permukiman kumuh luas (ha) prosentase (%) ngemplak 89,07 5,39 karangsono 67,31 4,07 tamansari 87,09 5,27 menur 61,82 3,74 jamus 28,01 1,69 wringinjajar 69,81 4,22 waru 32,28 1,95 tegalarum 75,33 4,56 candisari 42,99 2,60 1652 100 berdasarkan tabel 1 dapat diketahui, desa batursari merupakan desa dengan kawasan permukiman kumuh terluas yakni 182,56 ha (11,04%) dari luas total permukiman karena lokasinya yang strategis yakni berada di pusat kota kecamatan dan dilalui jalan semarang-purwodadi sehingga penduduk lebih memilih tinggal di wilayah ini. sementara, desa jamus merupakan desa dengan kawasan permukiman kumuh terkecil yakni 28,01 ha (1,69%) dari luas total permukiman karena lokasinya yang kurang strategis. untuk lebih jelasnya, peta citra dan persebaran kawasan permukiman kumuh di kecamatan mranggen secara spasial dapat dilihat pada gambar 3. gambar 2. kerangka analisis (hasil analisis, 2014) penggunaan lahan kecamatan mranggen jenis penggunaan lahan luas penggunaan lahan kondisi bangunan kualitas bangunan jarak antar bangunan aksesbilitas jarak ke tempat kerja letak strategis kawasan prasarana jalan drainase air bersih sanitasi status tanah kondisi kependudukan tingkat pertumbuhan penduduk kepadatan penduduk kondisi sosial ekonomi tingkat pendapatan lama tinggal analisis penggunaan lahan kecamatan mranggen analisis permukiman kumuh berdasarkan kondisi bangunan analisis permukiman kumuh berdasarkan aksesbilitas analisis permukiman kumuh berdasarkan prasarana analisis permukiman kumuh berdasarkan status tanah analisis permukiman kumuh berdasarkan kondisi kependudukan analisis permukiman kumuh berdasarkan kondisi sosial ekonomi persebaran permukiman kumuh di kecamatan mranggen permukiman kumuh berdasarkan kondisi bangunan permukiman kumuh berdasarkan aksesbilitas permukiman kumuh berdasarkan prasarana permukiman kumuh berdasarkan status tanah permukiman kumuh berdasarkan kondisi kependudukan permukiman kumuh berdasarkan kondisi sosial ekonomi analisis karakteristik kawasan permukiman kumuh di kecamatan mranggen sebagai daerah penyangga kota semarang karakteristik kawasan permukiman kumuh di kecamatan mranggen sebagai daerah penyangga kota semarang kesimpulan dan rekomendasi interpretasi citra deskriptif kuantitatif deskriptif kuantitatif deskriptif kuantitatif deskriptif kuantitatif deskriptif kuantitatif deskriptif kuantitatif deskriptif kuantitatif geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 107 gambar 3. peta citra kecamatan mranggen (kiri), dan peta persebaran kawasan permukiman kumuh kecamatan mranggen (kanan)(citra alos dan interpretasi alos, 2011) 3.2 analisis permukiman kumuh berdasarkan kondisi bangunan jenis bangunan kawasan permukiman kumuh di kecamatan mranggen merupakan bangunan semi permanen dan non permanen. sebanyak 43% jenis bangunan rumah penduduk merupakan bangunan semi permanen, dan sebanyak 57% jenis bangunan rumah penduduk merupakan bangunan non permanen. jarak antar bangunan rumah bervariasi yakni < 1,5 meter, 1,5 – 3,0 meter, dan > 3,0 meter. untuk jarak antar bangunan < 1,5 meter, antara bangunan satu dengan yang lain hanya dipisahkan oleh tanah, sementara untuk jarak antar bangunan 1,5 – 3,0 meter dan > 3,0 meter, antara bangunan satu dengan yang lain dipisahkan oleh tanah atau pekarangan. kondisi ini dipengaruhi oleh kemampuan finansial penduduk dalam membangun tempat tinggal. penduduk yang tinggal di rumah dengan bangunan semi permanen dapat dikatakan memiliki kemampuan finansial lebih baik daripada tinggal di rumah dengan bangunan non permanen. jenis bangunan dan jarak antara bangunan permukiman di kecamatan mranggen dapat dilihat pada gambar 4; gambar 4. jenis bangunan non-permanen (kiri), dan jarak antar bangunan < 1,5 meter (kanan), (hasil survei primer, 2014) 3.3 analisis permukiman kumuh berdasarkan aksesbilitas tingkat aksesbilitas kawasan permukiman kumuh di kecamatan mranggen tinggi. sebanyak 42% penduduk menyatakan jarak dari rumahnya menuju tempat kerja < 1 km, sebanyak 26 % penduduk menyatakan jarak dari rumahnya menuju tempat kerja antara 1-5 km, sebanyak 19% penduduk menyatakan jarak dari rumahnya menuju tempat kerja antara 6-10 km, dan sisanya sebanyak 13% penduduk menyatakan jarak dari rumahnya menuju tempat kerja > 10 km. desa bandungrejo, desa batursari, dan desa mranggen memiliki lokasi yang strategis karena dilalui jalan semarang-purwodadi, dekat dengan pusat perdagangan (pasar dan pertokoan) dan pusat pemerintahan (kantor kecamatan). desa bandungrejo, desa kangkung, desa jamus, desa wringinjajar, dan desa kebonbatur juga memiliki lokasi yang strategis karena dekat dengan kota semarang. desa tegalarum, desa waru, desa geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 108 brumbung, desa menur, desa ngemplak, desa kalitengah, desa tamansari, desa kembangarum, dan desa karangsono memiliki lokasi yang cukup strategis karena berada pada “lapis kedua” dari wilayah desa yang dekat dengan kota semarang dan jalur yang menghubungkan kota semarang dengan kabupaten purwodadi. fungsi lain yang berada di kawasan ini berupa fungsi campuran yakni permukiman dan perdagangan dan jasa. desa candisari, desa banyumeneng, dan desa sumberejo memiliki lokasi yang kurang strategis karena berada pada “lapis ketiga” dari wilayah desa yang dekat dengan kota semarang dan jalur yang menghubungkan kota semarang dengan kabupaten purwodadi. wilayah ini berada di ujung utara dan selatan kecamatan mranggen. fungsi lain yang berada di kawasan ini berupa fungsi permukiman dan persawahan. 3.4 analisis permukiman kumuh berdasarkan prasarana kondisi jalan kawasan permukiman kumuh di kecamatan mranggen baik sehingga yang dapat mendukung tingkat mobilitas penduduk tinggi. namun kondisi drainase, air bersih, dan sanitasi buruk. kondisi drainase terkesan kurang terpelihara karena sebagian besar saluran drainase kering dan dipenuhi dengan sampah. kondisi air bersih keruh karena kurangnya kontrol pemerintah terhadap kualitas air bersih. sebagian besar kawasan permukiman kumuh tidak memiliki jamban atau memiliki tapi dalam kondisi buruk. penduduk yang tidak memiliki jamban biasanya memanfaatkan sungai atau saluran untuk buang air besar. foto kondisi jalan, drainase, air bersih, dan sanitasi dapat dilihat pada gambar 5. gambar 5. kodisi prasarana jalan, drainase, air bersih, dan sanitasi, (hasil survei primer, 2014) 3.5 analisis permukiman kumuh berdasarkan status tanah status tanah atau hak atas tanah adalah hak penguasaan atas tanah untuk dapat memberikan kewenangan kepada pemegang haknya, agar dapat memakai suatu bidang tanah tertentu yang dihaki dalam memenuhi kebutuhan pribadi atau usahanya (undang-undang nomor 5 tahun 1960). sebanyak 67% rumah kawasan permukiman kumuh di kecamatan mranggen belum bersertifikat, sebanyak 33 % rumah bersertifikat hak milik dan tidak ada rumah yang bersertifikat hak guna bangunan. rumah yang belum bersertifikat merupakan rumah yang memang tidak memiliki sertifikat dan rumah yang memiliki sertifikat lama bekas rumah orangtua/ orang lain namun sudah tidak berlaku. alasan penduduk yang rumahnya belum bersertifikat adalah besarnya biaya untuk mengurus sertifikat rumah. kawasan permukiman kumuh yang identik dengan pendapatan penduduknya yang rendah, tidak mampu membayar pengurusan sertifikat rumah. disisi lain, peran pemerintah daerah setempat dalam menyediakan pelayanan publik untuk pengurusan sertifikat rumah kurang optimal. 3.6 analisis permukiman kumuh berdasarkan kondisi kependudukan jumlah penduduk kecamatan mranggen tahun 2011 adalah sebanyak 161.680 jiwa yang terdiri atas 80.238 laki-laki dan 81.442 perempuan. sementara, jumlah penduduk kecamatan mranggen tahun 2012 adalah sebanyak 163.773 jiwa terdiri atas 81.156 laki-laki dan 82.617 perempuan. tingkat pertumbuhan penduduk kecamatan mranggen sebesar 1,3%. tingkat pertumbuhan penduduk ini cukup rendah karena angka kelahiran dan migrasi masuk besar tetapi angka migrasi keluar juga besar. kondisi ini terjadi secara alamiah karena pada dasarnya manusia menginginkan kehidupan yang lebih baik. faktor keahlian juga dapat mempengaruhi kondisi tersebut dimana penduduk yang memiliki keahlian lebih bisa bertahan karena memperoleh kesempatan kerja yang lebih luas. kepadatan penduduk di kecamatan mranggen sebesar 22,38 jiwa/ha. kepadatan penduduk ini cukup rendah karena jumlah penduduk yang ada tidak sebanding dengan luas wilayah. jalan drainase air bersih sanitasi geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 109 3.7 analisis permukiman kumuh berdasarkan kondisi sosial ekonomi penduduk kawasan permukiman kumuh di kecamatan mranggen ada yang bekerja di sektor kekotaan seperti buruh industri, ada pula yang bekerja di sektor kedesaan seperti petani. sebanyak 99% penduduk memiliki pendapatan rp 1.000.000,00 – rp 1.500.000,00 per bulan dengan jumlah pengeluarannya rp 1.500.000,00 – rp 1.200.000,00 per bulan dan sisanya sebanyak 1% penduduk memiliki pendapatan rp 1.500.000,00 – rp 2.000.000,00 per bulan dengan jumlah pengeluaran > rp 2.000.000,00 per bulan. kondisi ini menunjukkan bahwa tingkat pendapatan penduduk kawasan permukiman kumuh rendah. dengan kata lain, pendapatan mereka habis digunakan untuk membiaya kebutuhan hidup saja. sebagian besar penduduk kawasan permukiman kumuh di kecamatan mranggen merupakan penduduk pendatang. penduduk yang tinggal < 5 tahun merupakan penduduk pendatang yang memilih kecamatan mranggen sebagai tempat tinggal karena dekat dengan tempat kerja. sementara penduduk yang tinggal > 5 tahun merupakan penduduk asli yang sudah tinggal sejak lahir yang memiliki ciri kedesaan. untuk lebih jelasnya, tingkat kekumuhan kawasan permukiman kumuh berdasarkan kondisi bangunan, aksesbilitas, prasarana, status tanah, kondisi kependudukan, kondisi sosial ekonomi dapat dilihat pada gambar 6, 7, dan 8. gambar 6. peta tingkat kekumuhan kawasan permukiman kumuh berdasarkan kondisi bangunan (kiri), dan aksesbilitas (kanan) (hasil survei primer, 2014) gambar 7. peta tingkat kekumuhan kawasan permukiman kumuh berdasarkan prasarana (kiri), dan status tanah (kanan), (hasil survei primer, 2014) geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 110 gambar 8. peta tingkat kekumuhan kawasan permukiman kumuh berdasarkan kondisi kependudukan (kiri), dan kondisi sosial ekonomi (kanan), (hasil survei primer, 2014) 3.8 analisis karakteristik kawasan permukiman kumuh di kecamatan mranggen sebagai daerah penyangga kota semarang berdasarkan hasil analisis, karakteristik kawasan permukiman kumuh di kecamatan mranggen sebagai daerah penyangga kota semarang adalah sebagai berikut; a. kawasan permukiman kumuh di kecamatan mranggen memiliki luas 1652,94 ha atau 54% dari luas total permukiman. b. kondisi bangunan kawasan permukiman kumuh di kecamatan mranggen buruk. 43% jenis bangunan rumah merupakan bangunan semi permanen, dan 57% jenis bangunan rumah merupakan bangunan non permanen. jarak antar bangunan rumah bervariasi yakni < 1,5 meter, 1,5 – 3,0 meter, dan > 3,0 meter. c. tingkat aksesbilitas kawasan permukiman kumuh di kecamatan mranggen tinggi. penduduk dapat mengakses tempat kerja dengan mudah, karena sebagian besar penduduk bekerja di kota semarang. sehingga kawasan permukiman kumuh memiliki lokasi strategis karena dekat dengan kota semarang. d. kondisi jalan baik karena tingkat mobilitas penduduk tinggi. namun kondisi drainase, air bersih, dan sanitasi buruk. kondisi drainase terkesan kurang terpelihara karena sebagian besar saluran drainase kering dan dipenuhi dengan sampah. kondisi air bersih keruh karena kurangnya kontrol pemerintah terhadap kualitas air bersih. sebagian besar kawasan permukiman kumuh tidak memiliki jamban atau memiliki tapi dalam kondisi buruk. penduduk yang tidak memiliki jamban biasanya memanfaatkan sungai atau saluran untuk buang air besar. e. status tanah kawasan permukiman kumuh tidak jelas. sebanyak 67% rumah belum bersertifikat, sebanyak 33 % rumah bersertifikat hak milik dan tidak ada rumah yang bersertifikat hak guna bangunan. f. tingkat pertumbuhan penduduk rendah yakni < 1,7% karena angka kelahiran dan migrasi masuk besar tetapi angka migrasi keluar juga besar. kepadatan penduduk juga rendah yakni < 400 jiwa/ha karena jumlah penduduk tidak sebanding dengan luas wilayah yang ada. g. kondisi sosial ekonomi penduduk kawasan permukiman kumuh rendah. tingkat pendapatan rendah yakni sebanyak 99% penduduk kawasan permukiman kumuh memiliki pendapatan rp 1.000.000,00 – rp 1.500.000,00 per bulan dengan jumlah pengeluarannya rp 1.500.000,00 – rp 1.200.000,00 per bulan sehingga pendapatan mereka habis digunakan untuk membiaya kebutuhan hidup saja. banyak penduduk pendatang yang memilih kecamatan mranggen sebagai tempat tinggal karena dekat dengan tempat kerja. geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 111 h. tingkat kekumuhan kawasan permukiman kumuh di kecamatan mranggen terdiri dari tingkat kekumuhan rendah, sedang, dan tinggi. tingkat kekumuhan rendah berada di desa ngemplak. tingkat kekumuhan sedang berada di desa banyumeneng, desa sumberejo, desa kembangarum, desa brumbung, desa ngemplak, desa karangsono, desa tamansari, desa menur, desa jamus, desa wringinjajar, desa waru, desa tegalarum, dan desa candisari. sementara tingkat kekumuhan tinggi berada di desa kebonbatur, desa batursari, desa kangkung, desa mranggen, dan desa bandungrejo. untuk lebih jelasnya, karakteristik tingkat kekumuhan kawasan permukiman kumuh dapat dilihat pada tabel 2 berikut. tabel 2. karakteristik tingkat kekumuhan kawasan permukiman kumuh kec. mranggen (hasil analisis, 2014) untuk lebih jelasnya, tingkat kekumuhan kawasan permukiman kumuh di kecamatan mrangen sebagai daerah penyangga kota semarang dapat secara spasial dapat dilihat pada gambar 8 berikut. geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 112 gambar 8. peta tingkat kekumuhan kawasan permukiman kumuh kecamatan mranggen (hasil survei primer, 2014) 4. kesimpulan berdasarkan hasil penelitian, maka dapat disimpulkan bahwa tingkat kekumuhan kawasan permukiman kumuh di kecamatan mranggen terdiri dari tingkat kekumuhan rendah, sedang, dan tinggi. tingkat kekumuhan rendah berada di desa ngemplak. tingkat kekumuhan sedang berada di desa banyumeneng, desa sumberejo, desa kembangarum, desa brumbung, desa ngemplak, desa karangsono, desa tamansari, desa menur, desa jamus, desa wringinjajar, desa waru, desa tegalarum, dan desa candisari. sementara tingkat kekumuhan tinggi berada di desa kebonbatur, desa batursari, desa kangkung, desa mranggen, dan desa bandungrejo. tingkat kekumuhan kawasan permukiman tersebut dilihat dari kondisi bangunan, aksesbilitas, prasarana, status tanah, kondisi kependudukan, dan kondisi sosial ekonomi. berdasarkan hasil penelitian, maka dapat direkomendasikan upaya penanganan kawasan permukiman kumuh di kecamatan mranggen sebagai berikut: a. untuk desa kalitengah, dapat ditangani dengan menggunakan pendekatan community based development atau guided land development dengan prioritas perbaikan pada: 1. memperbaiki kualitas bangunan dari semi permanen menjadi permanen dengan memberikan insentif dana untuk perbaikan rumah. 2. menyediakan sarana transportasi yang memadai untuk penduduk kawasan kumuh. 3. memperbaiki kondisi jalan lokal sekunder dan jalan lingkungan. 4. memperbaiki saluran perpipaan air bersih dan peningkatan kualitas air bersih melalui pengecekan berkala. 5. memperbaiki saluran drainase dari sedimentasi tanah dan tumpukan sampah. 6. membangun mck komunal bagi penduduk kawasan permukiman kumuh yang tidak memiliki mck. 7. memberikan peringatan dan punishment bagi rumah penduduk yang belum bersertifikat. untuk rumah yang bersertifikat namun sudah tidak berlaku, pemerintah sebaiknya memfasilitasi pengurusan sertifikat rumah. 8. memberikan modal pinjaman usaha melalui koperasi agar penduduk kawasan kumuh dapat memiliki pendapatan di sektor lain untuk meningkatkan taraf hidup . b. untuk desa banyumeneng, desa sumberejo, desa kembangarum, desa brumbung, desa ngemplak, desa karangsono, desa tamansari, desa menur, desa jamus, desa wringinjajar, desa waru, desa tegalarum, dan desa candisari, dapat ditangani dengan menggunakan pendekatan community based development atau guided land development dengan prioritas perbaikan pada: 1. memperbaiki kualitas bangunan dari semi permanen menjadi permanen dengan memberikan insentif dana untuk perbaikan rumah. geoplanning 2014,vol: 1, no: 2, 102-113 ramadhan dan pigawati | 113 2. melakukan perbaikan jalan, drainase, air bersih, dan sanitasi. 3. menyediakan sarana transportasi yang memadai untuk penduduk kawasan kumuh. 4. memperbaiki dan membangun jalan lokal sekunder dan jalan lingkungan. 5. memperbaiki dan membangun saluran perpipaan air bersih dan meningkatkan kualitas air bersih melalui pengecekan berkala. 6. memperbaiki dan membangun saluran drainase. 7. membangun mck komunal bagi penduduk kawasan permukiman kumuh yang tidak memiliki mck. 8. memberikan peringatan dan punishment bagi rumah penduduk yang belum bersertifikat. 9. memberikan modal pinjaman usaha melalui koperasi agar penduduk kawasan kumuh dapat memiliki pendapatan di sektor lain untuk meningkatkan taraf hidup. c. untuk desa kebonbatur, desa batursari, desa kangkung, desa mranggen, dan desa bandungrejo, dapat ditangani dengan menggunakan pendekatan poverty development dengan prioritas perbaikan pada: 1. membuat program rumah murah dengan kualitas bangunan non permanen. 2. menyediakan sarana transportasi yang memadai untuk penduduk kawasan kumuh. 3. memperbaiki dan membangun jalan kolektor sekunder, jalan lokal sekunder dan jalan lingkungan. 4. memperbaiki dan membangun saluran perpipaan air bersih dan meningkatkan kualitas air bersih melalui pengecekan berkala. 5. memperbaiki dan membangun saluran drainase. 6. membangun mck komunal bagi penduduk kawasan permukiman kumuh yang tidak memiliki mck. 7. memberikan peringatan dan punishment bagi rumah penduduk yang belum bersertifikat. 8. memberikan modal pinjaman usaha melalui koperasi agar penduduk kawasan kumuh dapat memiliki pendapatan di sektor lain untuk meningkatkan taraf hidup. 5. daftar pustaka bintarto. 1983. interaksi desa kota dan permasalahannya. jakarta : ghalia indonesia. direktorat pengembangan permukiman direktorat jenderal cipta karya departemen pekerjaan umum.2006. konsep pedoman identifikasi kawasan permukiman kumuh penyangga kota metropolitan. giok ling ooi and kai hong phua. 2007. urbanization and slum formation. journal of urban health: bulletin of the new york academy of medicine, vol. 84, no. 1. hari srinivas, 2003, slum, squatter areas and informal settlement, 9th international conference on sri lanka studies, matara, sri lanka, arawinda nawagamuwa and nils viking. khomarudin. 1997. menelusuri pembangunan perumahan dan permukiman. jakarta : yayasan real estate indonesia, pt. rakasindo. malaque, isidoro and yokohari makoto. 2007. urbanization process and the changing agricultural landscape pattern in the urban fringe of metro manila, philiphines. http://www.sagepublications.com. mulyadi. 2007. penginderaan jauh dan interpretasi citra. jakarta : lapan. pigawati, bitta. 2014. seminar nasional kota hijau pesisir tropis: kajian permukiman pesisir kota semarang berkelanjutan; karakteristik, tata ruang dan kebijakan. tidak diterbitkan. tarigan, robinson. 2005. perencanaan pembangunan wilayah. jakarta : pt. bumi aksara. un-habitat. 2003. the challenge of slum : global report on human settlements. united nations huma settlemnets programme. undang-undang nomor 5 tahun 1960 tentang undang-undang pokok agraria. yunus, hadi sabari. 2008. dinamika wilayah peri urban determinan masa depan kota. yogyakarta : pustaka pelajar. yudohusodo, s. 1991. tumbuhnya pemukim-pemukim liar di kawasan perkotaan. jakarta : yayasan padamu negeri. http://www.sagepublications.com/ pemanfaatan penginderaan jauh untuk identifikasi permukiman kumuh daerah penyangga perkotaan (studi kasus : kecamatan mranggen kabupaten demak) abstract: mranggen subdistrict is one of the buffer zone area for semarang city that slums area can be found. emergence of slums area in mranggen subdistrictis because the impact of increased population in semarang capital. increasing of the settlemen... abstrak: kecamatan mranggen merupakan salah satu daerah penyangga kota semarang yang teridentifikasi memiliki kawasan permukiman kumuh. munculnya kawasan permukiman kumuh di kecamatan mranggen merupakan dampak dari meningkatnya jumlah penduduk kota se... keywords: characteristics, slums, buffer area, mranggen subdistrict 1. pendahuluan gambar 1.peta citra kecamatan mranggen (citra alos, 2011) 2. data dan metode teknik analisis yang digunakan adalah teknik analisis deskriptif dan spasial. analisis dalam penelitian ini diantaranya adalah analisis penggunaan lahan di kecamatan mranggen yang dilakukan untuk mengetahui persebaran kawasan permukiman kumuh melalui ... 3. hasil dan pembahasan gambar 2. kerangka analisis (hasil analisis, 2014) 4. kesimpulan 5. daftar pustaka 103 geoplanning journal of geomatics and planning vol. 9, no. 2, 2022 original research digital earth surface model for the estimation of solar panel electric power towards renewable energy baskara suprojo1*, westi utami1, luthfi a. saraswati1, diffa a. nabila1, m. nazir salim1 1. sekolah tinggi pertanahan nasional, indonesia (national land academy, indonesia) doi: 10.14710/geoplanning.9.2.103-120 abstract the development of geographic information systems (gis) is able to create future value in various sectors and become a solution to the problem of limitations and disparity of electricity resources in indonesia. this condition encourages gis to be an analytical solution to the problem of electricity resources, which is by utilizing solar radiation as a source of renewable energy. this study aimed to optimize gis in the use of solar radiation on the slope of building roofs which affects the estimated number and average electric power. this study used the mixed method. research data includes aerial photos, which were analyzed digitally using the area of solar radiation and the slope angle of building roofs so as to produce a spatial analysis of the utilization of solar panels on derawan island. the data analysis showed that buildings in derawan island can produce 17,355.254 mwh per year with each building producing an average of 28,686 kwh annually. the result of the study is expected to encourage the realization of the use of renewable energy as part of the sdgs by utilizing solar panels as a source of electricity, replacing fossil-derived energy. this study is also expected to be applied in other small inhabited islands to support the sustainability of electricity use and increase the use of renewable energy. copyright © 2022 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction utilization of sustainable energy is an important issue in the 21st century, especially in the face of climate change that has a massive impact on people's lives (missoum & loukarfi, 2021; zawadzki et al., 2022). on the other hand, development that is constantly encouraged in various aspects certainly requires energy as the main raw material supply (guenther, 2018). one of the fundamental energy to support development and encourage economic growth is the fulfillment of electricity needs (ahmad & byrd, 2013). in reality, around 25 million indonesian people live without electricity. at the same time, electricity is limited in several inland areas, border areas, and small islands (adam, 2016; setyowati, 2021). several studies and data showed that up to now, the fulfillment of electricity in indonesia is largely still sourced from fossil energy (erdiwansyah et al., 2021; guenther, 2018). the limitation of fossil raw materials that is non-renewable is certainly a problem. besides, the residue of the burning of fossil fuels has adverse implications, leading to the damage to the atmosphere that triggers global climate change (hasan et al., 2012). the use of solar energy is hoped to help reduce co2 and so2 emissions, an important part to suppress the rate of climate change (wattana & aungyut, 2022). the challenges and targets for the use of renewable or environmentally friendly energy have been agreed by various countries and are outlined in the paris agreement and sdgs (elavarasan et al., 2022; grubler et al., 2018; setyowati, 2021). in response to the agreement, indonesia aims to change the utilization of renewable e-issn: 2355-6544 received: 16 september 2022; accepted: 29 november 2022; published: 08 december 2022. keywords: renewable energy, digital surface model, gis, roof slope, solar radiation *corresponding author(s) email: baskarasuprojoo@gmail.com https://doi.org/10.14710/geoplanning.9.2.103-120 mailto:baskarasuprojoo@gmail.com suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 104 energy by 25% in 2025 (guenther, 2018). for this reason, innovations in the use of environmentally friendly energy or sustainable energy need to be developed, one of which is through the use of solar panel energy to maintain electricity supply in various regions, both for remote settlements and even urban areas (ahmad & byrd, 2013; guno et al., 2021). indonesia's location on the equator is an advantage in optimizing solar panels. several studies have shown that the average solar radiation potential is around 4.8 kwh/m2 per day (khotama et al., 2020; kurniawan & shintaku, 2021; nasruddin et al., 2018). the technology development of geographic information systems (gis), remote sensing, and photogrammetry through unmanned aerial vehicles (uavs) can be used to analyze the potential use of solar power (mallon, 2019). this system has the advantage to be operated directly at the same time; the distance and the flying height can be adjusted by the controller, resulting in higher spatial resolution images following the area sought; the time needed is faster; and it can be processed directly (stewart & martin, 2020). uavs are indispensable for identifying buildings in detail that will be analyzed using the solar radiation. a digital surface model (dsm) as a model of the earth's surface shows all the details on the face of the earth in the form of land, buildings, and vegetation in real appearance as seen in satellite imagery, including details of buildings and land cover as well as their height. dsm images has a difference with digital elevation model (dem) and digital terrain model (dtm) images. dem and dtm images usually only display the height of the ground level without the buildings or vegetation on it. to analyze the effect of the slope of building roofs on solar radiation, a dsm is needed. analysis of the use of dem data that provides information on the height of the natural landscape of the earth's surface has been carried out by nusantara & dewanto (2020). the analysis is combined with a map source on open street map (osm) which visually displays buildings in 3d. meanwhile, desthieux used a vertical sky view factor (svf) model to calculate the radiation of a single point located on the roof, ground, and facade of buildings (desthieux et al., 2018). boz et al. (2015) in their study used light detection and ranging (lidar) to determine suitable buildings to install solar panels. the planning automation model conducted by (boz et al., 2015) is only limited to determining suitable roofs using gis with slope analysis without calculating the amount of electricity that can be generated through the photovoltaic system. in another study, (song et al., 2018) conducted a similar analysis of the potential of solar energy on flat roofs using dsm data and google maps imagery in the chinese plain through gis by paying attention to several factors such as wind direction on the roofs, shadows generated due to obstruction with buildings or vegetation, slope, and the type of the roofs. based on previous studies above, it can be seen that dsm and dem data sources provide different information details. in this study, the researchers used dsm data, which does not only display information about the elevation of the earth's surface, but also on the slope of building roofs. in this study, the researchers also paid attention to the analysis of the use of solar panels to be installed or placed on the roof of buildings, so that the data on the roof slope in the form of the degrees produced were able to answer whether the roof of the building is feasible to install solar panels. thus, the researchers initiated a new idea, namely to use roof slope data from aerial photos that were processed into 2d and dsm images. in addition, based on several previous studies as mentioned above, there was an opportunity for the researchers to calculate the amount of electric power produced on each roof of the building so that an assumption could be made whether a building is feasible and eligible to be installed with solar panels and whether electricity demand of the building can be met. the purpose of this study was to estimate the electrical power produced by solar panels on the building roofs on derawan island using the results of aerial photographs which are analyzed at an advanced level into dsm as the basis for determining the slope of building roofs and solar radiation as the basis for determining the absorption of sunlight on building roofs. https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 105 2. data and methods 2.1. study area derawan island has an area of 44.6 ha and is part of the derawan archipelago, located in berau regency, east kalimantan province. it is located between 2° 17' 3.00" north latitude and 118° 14' 39.00" east longitude. the abundant natural potential and natural beauty under the sea have made derawan island a tourist attraction that interests domestic and foreign tourists, so half people who live are fishermen and divers (febriyanto et al., 2021) as well as others in the tourism sector such as homestays, restaurants, and boat rental. in 2018, there was an increase in the amount of tourists who visited berau regency with an achievement target of 253% higher than the previous year (dinas pariwisata berau., 2020).this indicates that derawan island needs more renewable energy supplies considering the amount of tourists, small areas, marine tourism, and maintaining marine conservation. figure 1 is location of study area, source: data analysis results, 2021 figure 1. location of study area 2.2. method the method was to use the mixed method in which the quantitative method to calculate and estimate total electric energy for each building roofs from spatial analysis by using aerial photos with a spatial approach (muryono & utami, 2020), whereas the qualitative method was used when data collection and analysis were done (tarfi & amri, 2021) then reviewing the function of installing solar panels become the renewable energy as a solution of electricity problem by using literature review. there are two types of data collection, namely primary and secondary data collection. primary data collection was obtained by field observation by taking various data in order to make spatial-based decisions in determining electric power estimation. secondary data was obtained from books, websites, and journals that were relevant to the research writing as a support for strengthening spatial analysis and its effect on stakeholders from various aspects. this activity began with (i) the installation and processing of ground control points (gcps) as an air quality control of actual coordinates on the earth's https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 106 surface, (ii) the photographing and processing of aerial photos using uavs/drones to produce 2d and dem images in accordance with the current land cover appearance, and (iii) performing advanced gis analysis by rapid mapping by performing a combination of processing in the form of building digitation (to get the right value), solar radiation area (the amount of solar radiation emitted for a full year), and slope (the degree of the slope angle of the building roofs). in addition, the description process needed to be carried out to further elaborate on the impact of the use of solar energy as renewable energy in reducing carbon emissions and supporting the implementation of sustainable development goals (sdgs) in the form of the transition of non-renewable energy consumption to environmentally friendly energy and reducing the effects of climate change. figure 2 is a flowchart of research that illustrates the process of determining electric power estimation carried out on derawan island. source: data analysis results, 2021 figure 2. workflow diagram 2.3. gcp installation and processing preparation of aerial photos began with the installation of ground control points or gcps at the location of the shoot. a gcp generates planimetric coordinates (x, y) and elevation (z) by trilateration, triangulation, polygon, and gps methods (subakti, 2017). a gcp is a coordinating point found at the aerial photo shoot location in the form of a cross-shaped pre-mark with tarpaulin or wood material. a pre-mark must have a color that contrasts with its surroundings to be visible during aerial photo processing (prayogo, et al., 2020). the next https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 107 stage was the implementation of gcp data collection by installing a gnss receiver on top of the pre-mark that had been installed. data collection was carried out by recording data statically by making several parameter settings in the form of a coordinate system, recording time, and the type of rinex data used. 2.4. aerial photoshoot the aerial photo shooting using uav was done both manually and automatically using a control radio that had been programmed through a flight path designed using ardupilot software. the flight process requires good flight path planning with regard to the mathematical modelling formulation consisting of network, uav technical, environmental, decision variables, sets, and constraints on aoi (thibbotuwawa et al., 2020). the results of the aerial photo shooting must be processed to become ready-to-use raster images. 2.5. aerial photo processing the aerial photo processing needed in this study were 2d and dsm photos using agisoft metashape professional software. the photo processing steps consisted of nine steps: (1) adding photos, (2) aligning photos, (3) inputting gcps, (4) building a dense cloud, (5) building a mesh, (6) building the texture, (7) building a dem, (8) building orthomosaic images, and (9) exporting. 2.6. aerial photo accuracy test based on the regulation of the geospatial information agency no. 6 of 2018 concerning the basic map precision technical guidelines, to realize the standard of map precision, technical guidelines are needed to produce accurate, reliable, trustworthy, and accountable calculations. the value of root mean square error (rmse) of the processed aerial photos is required, which can be known by comparing the gcp coordinate value as the processing result with the gcp coordinate value as seen in the aerial photos. the purpose of searching for rmse values was to obtain ce90 values that served to determine the quality of aerial photos as a base map. table 1 shows the classification of the scale and the ce90 value, where the smaller the ce90 value, the better the quality of the aerial photos that are aligned with the scale value. table 1. classification of base map precision no scale contour (m) map precision class 1 class 2 class 3 horizontal (ce90 in m) vertical (le90 in m) horizontal (ce90 in m) vertical (le90 in m) horizontal (ce90 in m) vertical (le90 in m) 1 1:1,000,000 400 300 200 600 300 900.0 400 2 1:500,000 200 150 100 300 150 450.0 200 3 1:250,000 100 75 50 150 75 225.0 100 4 1:100,000 40 30 20 60 30 90.0 40 5 1:50,000 20 15 10 30 15 45.0 20 6 1:25,000 10 7.5 5 15 7.5 22.5 10 7 1:10,000 4 3 2 6 3 9.0 4 8 1:5,000 2 1.5 1 3 1.5 4.5 2 9 1:2,500 1 0.75 0.5 1.5 0.75 2.3 1 10 1:1,000 0.4 0.3 0.2 0.6 0.3 0.9 0.4 source: geospatial information agency regulation number 6 of 2018 2.7. processing of determination of electric power estimation this is in line with the use of dsms that can be applied to the identification of buildings on derawan island in determining the installation of solar panels on each roof. the calculation of solar radiation entering the earth's surface used the area solar radiation tool in arcgis pro by entering dsm raster. the determination of the sun position was carried out for a full year with time intervals throughout the day. to provide profits, solar https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 108 panels were set to produce at least 850 kwh of power (aybar et al., 2018). to identify building roofs that comply with the above criteria, the remap tool was performed on arcgis pro by eliminating (no data) each cell that has a value below 850 kwh. the slope calculation in the dsm was done to determine building roofs suitable for receiving solar radiation using the slope tool in arcgis pro. the slope of the solar panels installed on the building roofs should not be more than 45° with respect to the wind which puts pressure on the solar panels and tends to receive less sunlight (stathopoulos, xypnitou, & zisis, 2012). to identify this, the remap tool was used on simplified raster with a value of 1 for each cell with an appropriate slope (below 45°) and no data for each cell with an inappropriate slope (above 45°). the next stage combined solar radiation raster with slope raster using the times tool in arcgis pro to identify the results that matched the criteria described above. the next process was to find the vector value of the amount of solar radiation per building using the zonal statistics as table tool by combining combined raster and digitized shapefiles. the type of value used was the mean, which was used to get the average value of all cells in the same zone. for the calculation of electric power estimation, area (m²) and mean (kwh) values must be combined with vector data using the join fields tool. each generator has a capacity of 1 kwp for 10 m²(boz et al., 2015). the suitability of the building roof area above 10 m² was identified using select layer by attribute and export features tools. furthermore, the creation of a new field to determine the total amount of solar radiation received per year by each building was done by multiplying the "area" field by the "mean" field. changes in the value of solar radiation was made to obtain total electric energy for each building roof by creating a new field and (kohak et al., 2019)entering the formula below: e = a * r * h * pr [1] where: e = energy in kwh unit a = roof area of each building using the "area" field. r = efficiency of solar panels around 15.2%, which is considered as the best percentage based on the national renewable energy laboratory (nrel) in 2019 h = average value of solar radiation on the roof of each building using the "mean" field pr = performance ratio and loss coefficient of 86%; this value was calculated using pvwatts calculator with the location of derawan island. 3. result and discussion 3.1. results and analysis of gcp observation on derawan island, there was no basic point of technique or benchmark (bm) found to be made as base stations, so the binding of gcp data recording was carried out on the inacors big berau station with coordinates of 2° 08' 58' 'n, 117° 29' 49'' e, 64.2 m based on the srgi 2013 reference system. the gnss receiver used in the gcp was a chcnav with a long recording time of about 90-120 minutes because the location of the base station was very far from the gcp. gcp data processing used the post processing method with trimble business center software. the post processing calculation used the trilateration mesh method, so that gcp points mutually made data quality corrections with fellow gcp points and base stations to obtain precise and accurate coordinate values. the coordinate system used was transverse mercator 3° zone 50.2 which can be seen https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 109 in table 2. gcps with five pre-marks were spread evenly across derawan island. gcp 1 was located in the east in the open area and away from buildings. gcp 2 was in the south on the side of the road, close to the mosque, and not obstructed by buildings and vegetation. gcp 3 was located in the southwest on the side of the road and not obstructed by buildings and vegetation. gcp 4 was located in the north on an empty land. gcp 5 was located at the center of derawan island on a football field. table 2. results of gcp processing coordinates code coordinate x (m) y (m) h (m) gcp 1 171,878.829 1,752,600.182 67.888 gcp 2 171,326.557 1,752,440.109 61.932 gcp 3 171,156.305 1,752,512.844 61.935 gcp 4 171,577.123 1,752,785.756 62.895 gcp 5 171,416.955 1,752,567.219 64.630 source: data analysis results, 2021 the determination of ce90 was based on united states national map accuracy standards (us nmas) with ce90 =1.5175 x rmser. the rmser value was obtained by summing the rmsex and rmsey values divided into two. rmsex and rmsey values were obtained by the square root of the difference in aerial photo coordinates and the actual coordinates divided by the number of gcp points. table 3 shows the calculation results of planimetric coordinates for aerial photos, obtaining a ce90 value of 0.294 m, which means that the horizontal precision accuracy test falls into the 1:1,000 scale map category, namely the class 1 (good), with a maximum precision of 0.3 meters. a scale of 1:1,000 means 1 cm in aerial photos, which equals to 10 m in reality, so that these aerial photos can be used for large-scale mapping, specifically building modeling. table 3. horizontal precision test code x gcp (m) y gcp (m) x photo (m) y photo (m) difference of x (m) difference of y (m) rmse of x (m) rmse of y (m) ce90 (m) gcp 1 171,878. 829 1,752,600 .182 171,878. 588 1,752,599 .980 0.241 0.202 0.111 0.277 0.294 gcp 2 171,326. 557 1,752,440 .109 171,326. 596 1,752,440 .019 -0.039 0.090 gcp 3 171,156. 305 1,752,512 .844 171,156. 348 1,752,512 .949 -0.043 -0.104 gcp 4 171,577. 123 1,752,785 .756 171,577. 113 1,752,785 .683 0.010 0.073 gcp 5 171,416. 955 1,752,567 .219 171,416. 945 1,752,567 .782 0.010 -0.563 source: data analysis results, 2021 3.2. results and analysis of dsm processing dsm processing based on the aerial photo dense cloud is able to provide the most accurate results when compared to other data sources. dsm processing was done using agisoft metashape professional on dem build tool, but it could also be generated by setting up the point classes. the processing results show that the dsm resolution obtained is 29.4 cm/pixel with a resolution below the orthophoto because the dsm is the result of the dense cloud interpolation process of aerial photo extraction. based on the dsm, it can be seen that the height of the area of interest is (-43.96) m-115.523 m. the description of dsm results is shown in figure 3, where the lowest location is marked in grey with existing conditions in the form of coral reefs to the deep ocean. the green area in the form of beaches, reefs, roads, and vacant land is very dominating. the blue area appears to be buildings and shrubs. the red and purple area have a higher value with the highest of 115.523 m and the area is a coconut grove. https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 110 source: data analysis results, 2021 figure 3. digital surface model aerial photo visualization 3.3. results and analysis of orthophoto and building digitation aerial photo processing used dsm data as photo reconstruction to produce the most optimal orthophoto compared to other data. the aerial photo resolution is 7.35 cm/pixel. the resolution value of aerial photos is much higher than that of dsm images due to the incorporation of aerial photos from the acquisition results. the digitation of the building was done manually using arcgis pro software to provide the right shape and area in determining building roofs in the process of electric power estimation. as a result, 625 buildings on land and in water with an area of 30 m²-861 m² were obtained as shown in figure 4. buildings visible on land consist of homestays, community houses, places of worship, educational facilities, government buildings, and other supporting facilities, while buildings on the water are hotels. source: data analysis results, 2021 figure 4. visualization of building orthophoto and digitation https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 111 3.4. results and analysis of solar radiation areas the processing of solar radiation on derawan island produces a radiation value of 15.4799 kwh-1,794.43 kwh. figure 5 shows that the dominating colors on the building roofs are red and orange, which indicate that the amount of solar radiation received is quite high. yellow and blue indicate a lower amount of radiation. this is because buildings facing north tend to receive less solar energy than those facing south, west, and east. in addition, the roofs of buildings that detected covered by vegetation, others buildings, and other factors receive little solar radiation. source: data analysis results, 2021 figure 5. results of solar radiation processing using dsm rasters figure 6 shows the results of solar radiation that was adjusted to the criteria for solar panels with a minimum value of 850 kwh. the distribution of radiation values on the building roofs ranges from 850 kwh to 1,794.43 kwh with an average of 1,609.97 kwh. the results of this analysis show that the radiation produced on derawan island as a whole is quite large as presented in figure 6. this analysis shows that the higher the diagram, the greater the kwh value, with the highest value at 1,714 kwh. source: data analysis results, 2021 figure 6. histogram of solar radiation rasters https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 112 3.5. results and analysis of the slope the slope processing on derawan island resulted in a value of 0°-90°. the angle value of 90° has a steep or vertical slope: the lower the degree, the flatter the slope, and at 0° it has no slope at all (flat). figure 7 shows the black area dominating the building roofs which shows a low slope. roofs with a maximum slope angle of 45° is very good for the installation of solar panels. source: data analysis results, 2021 figure 7. results of slope processing using dsm rasters figure 8 shows the slope results that were adjusted to fulfil the requirement for the installation of solar panels, which is with a slope angle of 0°-45°. the slope angle starting at 0° increases steadily and reaches the highest in quantity at an angle of 21.1°, then there is a slow decrease up to an angle of 45°. as seen in figure 8, the highest number of cell quality is at an angle of 16°-24°, which shows that buildings on derawan island have relatively flat roofs. source: data analysis results, 2021 figure 8. histogram of slope rasters https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 113 3.6. results and analysis of electric power estimation estimation of electric power was carried out through the analysis of the combination of solar radiation and the slope, which was by multiplying the two rasters as seen in table 4. based on the tested analysis, only the first row produces a matched value, while the other one does not. based on the table below, it can be explained that solar radiation that has a value above 850 kwh with a slope below 45° is included in the most appropriate category in the installation of solar panels on the building roofs. solar radiation that has a value below 850 kwh does not meet the criteria even though it has a slope below 45°. solar radiation that has a value above 850 kwh is within the criteria because it has a slope angle above 45°. solar radiation that has a value below 850 kwh and has a slope angle above 45° does not meet the criteria for solar panel installation. the implementation of dsm and solar radiation data in derawan island has been in line with the study that has been developed by (song et al., 2018) on the use dsm data in 3d building and solar radiation reconstruction processing in satellite images in the chao yang district of beijing, china. table 4. results of solar radiation and slope classification appropriate solar radiation x appropriate slope = corresponding result ≥ 850 kwh ≤ 45 ° ✓ ≤ 850 kwh ≤ 45 °  ≥ 850 kwh ≥ 45 °  ≤ 850 kwh ≥ 45 °  source: data analysis results, 2021 based on the classification in table 4, it can be found that from a total of 625 buildings, 605 buildings meet the criteria for solar panel installation, so only 4% of buildings on derawan island do not meet the criteria. the data analysis shows that the amount of electric energy produced by 605 buildings on derawan island for a full year is up to 17,355.254 mwh or 17,355,254 kwh. the energy produced annually has an average of 28.686 mwh or 28,686 kwh per building. in this study, to determine the distribution of electric energy in a year, the following classification was carried out: 1) power of 0-10,649 kwh of 39 buildings; 2) 10,650 kwh-20,000 kwh of 176 buildings; 3) 20,001 kwh-30,000 kwh of 173 buildings; 4) 30,001 kwh-40,000 kwh of 102 buildings; 5) 40,001 kwh-50,000 kwh of 64 buildings; 6) 50,001 kwh-70,000 kwh of 31 buildings; 7) 70,001 kwh-100,000 kwh of 12 buildings; and 8) 100,001 kwh-154,000 kwh of 8 buildings. an overview related to the classification of electric energy is presented in figure 9 below. source: data analysis results, 2021 figure 9. diagram of number of buildings and electric energy classification https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 114 based on a study from the energy information administration (eia), the average annual electricity consumption for households in the united states in 2020 was 10,649 kwh (hayibo et al., 2020). electricity demand in the united states can be a comparative data to the electricity produced on derawan island. table 5 shows that 39 buildings generate less than 10,649 kwh of electricity, which means that there is insufficient household electricity. the analysis shows that there are more buildings that produce electricity above 10,649 kwh, namely 566 buildings, so that the need for electricity is not a big problem in the implementation of solar panel installation in derawan island. in this study, a mapping of electric power estimates as presented in figure 10 was also carried out to determine the distribution of electric power generated by each building. the coloring of the electric energy classification in figure 9 is equated with the electric power estimation map presented in figure 10 due to its classification range using the same data. the shapefile data which used the "area" field and the interpretation of aerial photos as displayed on the electric power estimation map shows a red area in the form of community houses covering an area of 30 m²-56 m². a quite dominating purple area consists of houses and resorts with an area of 57 m²-10 m². the orange area that is also dominating the map has an area of 101 m²-150 m² is in the form of homestays, resorts, and community houses. the yellow one has an area of 150 m²-190 m² in the form of homestays, resorts, and community houses. the light green area consists of homestays, community houses, and dining houses with an area of 191 m²-280 m². the dark green area consists of homestays, resorts, community houses, dining houses, and educational facilities with an area of 281 m²-350 m². the light blue one has an area of 351 m²-485 m² consisting of resorts, village offices, and other supporting facilities. the dark blue area has an area of 486 m²-861 m² in the form of resorts, educational facilities, and mosques. source: data analysis results, 2021 figure 10. map of derawan island electric power estimation the map of derawan island electric power estimation as shown in figure 10 shows that the number of buildings and the amount of power estimation analyzed are very diverse due to different types of buildings. the classification results as displayed show the following: 1) power energy of 0-10,649 kwh (6.4%) consisting of houses; 2) power energy of 10,650 kwh-20,000 kwh (29.1%) consisting of houses and resorts; 3) power energy of 20,001 kwh-30,000 kwh (28.6%) consisting of homestays, resorts, and houses; 4) power energy of 30,001 kwh-40,000 kwh (16.85%) consisting of homestays, resorts, and houses; 5) power energy of 40,001 kwh-50,000 kwh (10.57%) consisting of homestays, houses, and dining houses; 6) power energy of 50,001 kwh-70,000 kwh (5.12%) consisting of homestays, resorts, houses, restaurants, and educational facilities; 7) power energy of https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 115 70,001 kwh-100,000 kwh (1.98%) consisting of resorts, village offices, and other supporting facilities; and 8) power energy of 100,001 kwh-15,000 kwh (1.32%) consisting of educational facilities, resorts, and mosques. 3.7. renewable energy as a solution to electricity problems climate change triggered by greenhouse gas (ghg) emissions causes hot temperatures to be trapped in the earth's atmosphere (fawzy et al., 2020) greenhouse gas emissions are influenced by various aspects where co2 emissions sourced from fossil sources contribute a fairly high number, in addition to some human activities such as land transfer, increasing urbanization, industrial activities (eickemeier et al., 2014; fawzy et al., 2020; lin & zhu, 2019), and natural conditions, namely volcanic activity, which also contributes to (fawzy et al., 2020; xi-liu & qing-xian, 2019). based on the report of the intergovernmental panel on climate change (ipcc) on 2018, he increase in earth's temperature since 2018 to date has reached 0.8oc to 1.2oc and is expected to reach 1.5oc by 2030 to 2052 (fawzy et al., 2020; ipcc, 2018). efforts to suppress global warming will not be successful if industrial activities along with economic and business development are still carried out as usual without land, energy, and environmental sustainability management plans (hellin & fisher, 2019). as an effort to address climate change, various studies, action plans, conferences, monitoring of extreme weather changes, sustainable development goal agendas, and the paris climate change agreement have been conducted. as part of mitigation efforts in addressing the impact of climate change, 161 out of 188 member countries of paris agreement agree on sectoral policies to curb the rise in greenhouse gas emissions (fawzy et al., 2020; nieto, carpintero, & miguel, 2018) table 5. types of power generation energy and co2 emissions from power utilities power generation carbon emissions million kwh million metric tons million short tons pounds per kwh coal 757,763 767 845 2.23 natural gases 1,402,438 576 635 0.91 petroleum 13,665 13 15 2.13 source: u.s. energy information administration, 2021 data from the u.s. energy information administration shows that the use of electric energy sourced from the combustion of coal, natural gases, and petroleum fuels accounts for very large co2 gas emissions (lai et al., 2017). table 5 is data related to carbon emissions caused by electricity generation with non-renewable energy. the fuel supplies 62% of fuel use for electric energy in the united states, pollutes the atmosphere, and accounts for 99% of carbon emissions generated (administration, 2021). the power of fossil energy produces the amount of emission gases such as carbon dioxide (co2), nitrogen oxide (nox), and sulfur dioxide (so2) on a large scale (shahsavari & akbari, 2018). source: u.s. energy information administration, 2021 figure 11. percentage of total carbon emissions from various energy sources for power generation https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 116 based on the data display in figure 11, efforts to replace fossil-fueled electricity are important to be pursued. as the study of rodriguez & barau (2018) revealed, people must change old patterns and get used to utilizing non-renewable energy as wisely as possible. this idea of renewable energy is believed to reduce greenhouse gas emissions, where fawzy et al. (2020) propose several climate change mitigation measures, one of which is conventional mitigation that can be done by diverting fuel and encouraging the realization of new and renewable energy. in this study, it is stated that the effort to manifest renewable energy is carried out through the transition of the utilization of fossil energy as a source of electric energy to the utilization of sunlight through the use of solar panels as a source of electricity and storage of backup electric energy. the use of solar panels that is proven to be clean energy, do not cause pollution, and can suppress the use of fossil energy will prevent environmental damage as well as produce low carbon emissions and less pollution (hellin & fisher, 2019; mitra, 2021). an illustration of the utilization of solar energy is presented in figure 12. source: data analysis, 2022 figure 12. benefits of using solar home system to slow climate change utilization of solar energy through solar panel technology is the implementation of the 7th sdgs, namely clean energy (ce) that is affordable, efficient, and safe (santika et al., 2020). in accordance with the concept of renewable energy technology innovation (reti) initiated by (lin & zhu, 2019) , it was revealed that ideas and innovations regarding technology development are needed to encourage the transition to renewable energy sources. this energy should encourage the concept of carbon neutral or low carbon footprint production. by the transition of renewable energy, using solar panels has improved decrease from carbon emission which this emission could happen when gas released from the combustion of carbon containing compounds, such as co2, coal burning, diesel, and so on. table 6 are the carbon footprint levels produced by solar panels in various empirical literature in various countries. this technological change allows the construction of solar panels to be carried out on each house (sharma & goyal, 2020) with cheaper materials (al-shahri et al., 2021). various efforts have been made to support the energy transition (lucas, carbajo, machiba, zhukov, & cabeza, 2021) and suppress the adverse effects of the use of fossil energy whose availability is increasingly limited (al-shahri et al., 2021)) the geographical location of indonesia as a tropical country crossed by the equator with a daily intensity level of solar radiation of 4.8 kwh/m2 should be optimally made use of so that energy sustainability can be realized. (wullandari & hakim, 2021) stated that the intensity of solar radiation is a factor determining the success of the work of solar cells contained in solar panels. with this condition, indonesia has a high chance of success in optimizing solar panels as an environmentally friendly source of electricity and renewable energy (wullandari & hakim, 2021). https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 117 the development of a clean energy concept that produces zero carbon emission (aste et al., 2020) is a challenge for many countries. measuring the success of the utilization of geographic information systems through dem analysis becomes one of the alternative solutions. the accuracy of building roof selection by considering the level of slope and the consideration of the intensity of sunlight is one approach to determine how much energy is generated and how far the level of adequacy is. shs can be a solution to be implemented to address the complex problems of fossil fuel scarcity and environmental pollution (rigo et al., 2020). in addition, due to its easy-to-install shape, this technology can be installed on various types of building roofs so that it can reach even the most remote houses and can become a technology to fulfill the 100% electrification goal in various parts of the world (rabuya et al., 2021). this effort that requires multi-stakeholder cooperation is expected to be successful and able to be used by all groups so that the transition from fossil energy to clean energy can be achieved following the sdgs' 7.1 target (santika et al., 2020). this study and several other empirical studies prove that the use of solar panels as a technology based on solar energy is part of the efforts to reduce carbon emissions and earth's temperature to prevent climate change (al, 2022). table 6. empirical studies on large carbon emissions generated by solar panels method empirical study result life-cycle analysis (lca), energy payback time (epbt), greenhouse gas emission rate (ghge-rate) constantino, freitas, fidelis, & pereira, 2018 a study in brazil the emission rate generated by solar panels for a 25year validity period is only about 911co2-eq/kwh for each m2 data envelopment analysis (dea) ren et al., 2020 a study in china the use of solar panels reduces co2 emission level by 0.5468 life-cycle analysis (lca) olek, 2021 a study in poland for a validity period of 25 years, solar panels produce 55 gco2eq/kwh for every 1222kwh/m2/year life-cycle analysis (lca) shahsavari & akbari, 2018 a study in several countries. the operation of solar panels results in zero gas emission, but the installation process will produce approximately 4 gigatonnes of carbon emissions from a total of 4600 gw for a period of up to 2050. source: literature review results, 2022 4. conclusion the goal of this study was observed an estimated total electric power using gis utilizing slope of the buildings, aerial photography data quality for dsm model, and criteria of solar radiation received by the roof. the main result found that the energy produced per year is 17,355.254 mwh or 17,355,254 kwh and an average power per building of 28.686 mwh or 28,686 kwh. as a comparison, the average annual electricity consumption for households in the united states was 10,649 kwh in 2020. according to the eia, only 39 out of a total of 566 buildings on derawan island produce below average electric energy. based on the number of buildings and electric power, there are eight classifications as described in the results. in this classification, it was found that buildings in the form of community houses, resorts, and homestays have an estimated electric power of 0-40,000 kwh. the total number of identified buildings is about 490 buildings with a dominant percentage of 80.95%: 64 buildings (10.57%) consisting of homestays, community houses, and dining houses of 40,001 kwh-50,000 kwh; 31 buildings (5.12%) consisting of homestays, resorts, community houses, dining houses, and educational facilities of 50,001 kwh-70,000 kwh; 12 buildings (1.98%) consisting of resorts, village offices, and other supporting facilities of 70,001 kwh-100,000 kwh; and the largest total energy power in the range of 100,001 kwh-154,000 kwh is estimated in 8 buildings (1.32%) consisting of resorts, educational facilities, and places of worship (mosques). the successful implementation of the solar home system can have positive impacts, marking the transition to clean energy as well as becoming a real implementation to reduce the use of fossil fuels that has adverse effects on the environment and to slow the increase in earth's temperature. this big effort that requires https://doi.org/10.14710/geoplanning.9.2.103-120 suprojo et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 103-120 doi: 10.14710/geoplanning.9.2.103-120 118 the cooperation of the society on all levels is expected to go along with by the assertiveness of the government in carrying out the regulation of fossil user industries to participate in the transition to clean energy. as precise calculating continues to grow, as long as technologies, we expect more tools for the future could measure the solar radiation accepted on roof to estimate electric power on top of the building. fulfilled the demand from our emergencies in sustainable energies and help island people communities such as derawans to supply electrify needs in easy and cheap. research continues could using high-tech equipment and tools to obtained precise, accurate, and great quality data of the building roofs, such as lidar. also, to get and maintain actual data, the research could involve the surrounding communities as participants. 5. acknowledgments the author would like to say our thankful to the regional land office of east kalimantan province and especially to mohamad gugus perdana, noor santy haqim, hadi prayitno, nurjamiin ansori, and rizka dita samsudin al chodiq (the measurement infrastructure section) which has allowed engaging in aerial photoshoot on derawan island, so author could develop the data provided into something advanced. 6. references adam, l. 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morton, 2003; nichols et al., 2018). on the other hand, coastal erosion is exacerbated inflow activities like construction of recreational facilities, flowage barriers, restructuring and flood control channel dredging, tree harvesting in particular softwood stands, and coastal green space decline (owens, 2020; sahavacharin et al., 2022; van tho, 2019). additionally, these effects are aggravated by global warming. infrastructure along coastlines paired with depleting sediment supplies and increasing sea levels can deteriorate coastal erosion (fitzgerald et al., 2008; gracia et al., 2018). small pacific islands, like vietnam is some of the numerous places around the world that have been impacted by the above phenomena. the effects of coastal erosion can cause many issues including e-issn: 2355-6544 received: 05 april 2025; revised: 12 may 2025; accepted: 18 may 2025; available online: 31 october 2025; published: 31 october 2025. keywords: coastal erosion, remote sensing, deep learning, influence factors *corresponding author(s) email: giangnh@hueuni.edu.vn https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 140 loss of land from agriculture and aquaculture putting livelihood of coastal inhabitants at risk. these challenges impede sustainable development, highlighting the need of an active coastal management policy. coastal erosion is a big problem in vietnam and 17 percent of the population is affected by this problem (nga et al., 2025; nguyen et al., 2018). some major regions, such as the mekong delta, the central coastal region, and the red river delta, are more at risk because of rising sea levels, storms, decrease in river sediment, tidal waves, and monsoon winds. different studies have been conducted on the causes of coastal erosion and its impact on certain regions such as vietnam and southeast asia. dong et al. (2024), studied the impact of climate change on coastline erosion in southeast asia, pointing out several causative agents such as historical sea level changes, geological structures, sediment supply, wave movement, tides, storm surges, longshore transport of sediments, and anthropogenic activities. the research also outlined possible actions that can be taken to deal with erosion problems. thepsiriamnuay & pumijumnong (2019) displayed the simulator of climate change risks and adaptation initiatives (simclim) and its impact model, "coastclim” were utilized to project changes in relative sea level across 18 provinces of thailand from 1995 to 2100. the result of their findings forecasted sea levels rise with either solution 17.50 cm to 147.90 cm resulting to coastal retreat from 41.64m to 517.09m. karlsrud et al. (2017) used mike21 sw and swan models, together with the genesis model for longshore sediment transport to forecast sea-level rise along vietnam`s east coast. the result found that sea-level rose faster than 3 mm/year between 1993 and 2008 and that an all-time high was reached with about 20 cm overall during the last decades. in addition, ca mau's coast was identified as particularly vulnerable to subsidence/land loss. hens et al. (2018) presented an overview of threats, i.e., flooding, salinization, shoreline changes and decline in mangroves and wetlands in the asia-pacific region due to coastal erosion impacts in vietnam. the study proposed adaptation strategies to increase coastal resilience. yasuhara et al. (2016) displayed riverside and coastal erosion in mekong delta, vietnam by breakpoint statistics and explained reduced sediment supply from upstream, dykes collapse, sea-level rise, intensifying typhoons as the main reasons of undercutting. yaacob et al. (2018) deployed to evaluate the sedimentological and morphological terengganu’s coastal stretch between dungun and kemaman. the study pointed out that coarser sand deposits and steeper coastal slopes were characteristic of the region’s eastern beaches, influencing coastal stability. duc & hieu (2017) assessed the impact of sea-level rise on sea-dike stability in coastal infrastructure of hai hau vietnam due to sea-level rise. it was concluded that accelerated erosion, scouring and wave-induced soil loss on dike slopes may lead serious threat to a coastal infrastructure. veettil et al. (2021) examined the use of bio shields in conserving coastal mangroves ecosystems of vietnam. the results highlighted that bio shields not only preserve shoreline long-term ecology but also yield additional ecosystem services for environment sustainability. lin et al. (2021) identified natural and anthropogenic coastal erosion factors were delineated by case studies along the south-central coast line of vietnam. the study particularly stressed the dangers that natural risks and socio-economic activities constitute for the stability of the coast in the process. based on previous studies, the nature of coastal erosion and its vulnerability are the result of interactions between complex environmental and anthropogenic factors. the need for integrated management becomes clear before the associated long-term impacts can be observed. although many studies have been conducted on coastal erosion, a systematic review of how climate change and natural hazards interact with each other in relation to their combined impact on vietnam's coastline is still lacking. the relationship between climate change-related events and their impacts, their influence on coastal erosion, implications for livelihoods, and how adaptation measures work, requires stronger evidence. this study next gathers and analyzes existing data, classifies the existing coastal erosion in vietnam, identifies main factors determining the extent of erosion, and applies deep learning models to predict erosion trends. therefore, this study aims to analyze coastal erosion trends, identify the factors that drive them, and use https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 141 deep learning algorithms to estimate future erosion risks. in addition, this study also provides context-specific interventions to reduce risk. the results are very important for policymakers, local governments, developers and environmental researchers so they can implement proven data-driven strategies addressing the needs, capacities and resilience of coastal communities under acute or extreme environmental and social-ecological pressures. 2. data and methods the study employs a range of deep learning models, including lstm, bilstm, rnn, birnn, and hybrid rnn-lstm, to analyze and forecast coastal erosion patterns along vietnam's shoreline. the principles and functionalities of these models are described in detail below, highlighting their suitability for coastal erosion prediction and comparative performance in forecasting accuracy. 2.1. lstm it is designed to manage sequential data by effectively capturing long-term dependencies (lindemann et al., 2021; xiang et al., 2020). an overview of its architecture is presented in figure 1, and its detail is as follow (i) input layer: the model receives a sequence of input data as a sequence of data points 𝑋 = ⟦𝑥1, 𝑥2, … 𝑥𝑡⟧, where t is the sequence length. (ii) forget gate (𝑓𝑡): decides what information from the previous time step should be discarded from the cell state 𝐶𝑡−1 with formulation (see equation 1): 𝑓𝑡 = 𝜎(𝑊𝑓 ∗ 𝑥𝑡 + 𝑈𝑓 ∗ ℎ𝑡−1 𝐿 + 𝑏𝑓)……... (eq.1) this is done by applying a sigmoid activation function (σ) to produce values between 0 and 1 (0 = forget, 1 = retain). (iii) input gate (𝑖𝑡) and candidate state (�̃�𝑡): determines which values from the current input and the previous output should be stored in the cell state. the new information should be added to the cell state as the equation (2) and equation (3) 𝑖𝑡 = 𝜎(𝑊𝑖 ∗ 𝑥𝑡 + 𝑈𝑖 ∗ ℎ𝑡−1 𝐿 + 𝑏𝑖)…………... (eq.2) �̃�𝑡 = 𝑡𝑎𝑛ℎ(𝑊𝑢 ∗ 𝑥𝑡 + 𝑈𝑢 ∗ ℎ𝑡−1 𝐿 + 𝑏𝑢)……... (eq.3) (iv) cell state (𝐶𝑡): this is the memory of the lstm that can carry information across time steps. it gets updated by the input gate and forget gate. the equation (4) indicates to combines past knowledge 𝑓𝑡 ⊙ 𝐶𝑡−1and new information 𝑖𝑡 ⊙ �̃�𝑡. 𝐶𝑡 = 𝑖𝑡 ⊙ �̃�𝑡 + 𝑓𝑡 ⊙ 𝐶𝑡−1) …………………. (eq.4) (v) output gate (𝑜𝑡 𝐿) and hidden state (ℎ𝑡 𝐿): determines what the output of the current lstm cell should be. equation (5,6) are used the cell state and a sigmoid activation to compute the output, which is passed to the next lstm cell or used as the model's prediction (ℎ𝑡 𝐿). 𝑜𝑡 = 𝜎(𝑊𝑜 ∗ 𝑥𝑡 + 𝑈𝑜 ∗ ℎ𝑡−1 𝐿 + 𝑏𝑜)…………... (eq.5) ℎ𝑡 𝐿 = 𝑜𝑡 ⊙ tanh (𝐶𝑡) )…………... ……………..(eq.6) (vi)fully connected (dense) layer: converts lstm output into a final prediction in equation (7) 𝑜𝑡 𝐿 = 𝑡𝑎𝑛ℎ(𝑊𝑜 ∗ ℎ𝑡 𝐿 + 𝑏𝑜)……………………(eq.7) the optimal component model features an input layer with 52 nodes, a hidden layer comprising 84 nodes, and a single output node. the adam optimizer is employed for stochastic gradient descent, while mean squared error (mse) serves as the performance evaluation metric. the model is trained 150 epochs with a batch size of 12, and tanh (activation function) for tested in the hidden layer. https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 142 figure 1. the lstm model’s working structure 2.2. bilstm bilstm model is an extension of the lstm network, and figure 2 shows a process of input sequences in both forward and backward directions (hameed & garcia-zapirain, 2020; siami-namini et al., 2019). at each time, step t, a working method of two hidden states are indicated as below: (i) forward hidden state ℎ𝑡 𝐵𝐿⃗⃗ ⃗⃗ ⃗⃗ calculated using equation (8): ℎ𝑡 𝐵𝐿⃗⃗ ⃗⃗ ⃗⃗ = 𝑜𝑡 𝐵𝐿⃗⃗ ⃗⃗ ⃗⃗ ⊙ tanh (𝐶𝑡)⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗……………………(eq.8) (ii) backward hidden state ℎ𝑡 𝐵𝐿⃖⃗ ⃗⃗ ⃗⃗ ⃗ calculated in reverse order in accordance with equation (9). ℎ𝑡 𝐵𝐿⃐⃗ ⃗⃗ ⃗⃗ ⃗ = 𝑜𝑡 𝐵𝐿⃐⃗ ⃗⃗ ⃗⃗ ⃗ ⊙ tanh (𝐶𝑡)⃐⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ …………............... (eq.9) (iii) the final hidden state is occupied by concatenating both forward and backward states in equation (10): ℎ𝑡 𝐵𝐿 = [ℎ𝑡 𝐵𝐿⃗⃗ ⃗⃗ ⃗⃗ , ℎ𝑡 𝐵𝐿⃖⃗ ⃗⃗ ⃗⃗⃗⃗⃗] ……………………(eq.10) (iv) fully connected (dense) layer; the concatenated hidden state ℎ𝑡 is passed through a dense layer for transformation. output equation (11) presents: 𝑜𝑡 𝐵𝐿 = 𝑡𝑎𝑛ℎ(𝑊𝑜 ∗ ℎ𝑡 𝐵𝐿 + 𝑏𝑜) …………............... (eq.11) the optimal component model features are same lstm model. figure 2. the bilstm model’s working structure 2.3. rnn rnn model is architected for sequential data. it maintains a hidden state that captures information from previous time steps (ming et al., 2017; weerakody et al., 2021) (detail see figure 3). following is a detailed analysis of its structure. (i) input layer: the model receives a sequence of the input consists of sequential data, defined as 𝑋 = ⟦𝑥1, 𝑥2, … 𝑥𝑡⟧, where t is the sequence length; (ii) hidden state update (ℎ𝑡 𝑅). the equation (12) describes hidden state update: https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 143 ℎ𝑡 𝑅 = 𝑡𝑎𝑛ℎ(𝑊 ∗ 𝑥𝑡 + 𝑈 ∗ ℎ𝑡−1 𝑅 + 𝑏) …………............... (eq.12) where, ℎ𝑡 𝑅, ℎ𝑡−1 𝑅 , 𝑥𝑡 , 𝑈, 𝑊, 𝑏 are hidden state at time t, hidden state from the previous time step, input at time t, weight matrices, and bias term, respectively. the hidden state ℎ𝑡 𝑅 acts as the network memory, and allowing information to be conducted across multiple time steps. (iii) output layer (𝑜𝑡 𝑅), the final hidden state is passed through a fully connected (dense) layer to produce the desired output. equation (13) presents the output at each time step. 𝑜𝑡 𝑅 = 𝑡𝑎𝑛ℎ(𝑊𝑜 ∗ ℎ𝑡 𝑅 + 𝑏) …………............... (eq.13) the optimal component model features are same lstm model. figure 3. the rnn model’s working structure 2.4. birnn birnn is an rnn expansion, figure 4 describes a processes input sequences in both forward and backward directions (li et al., 2019; liu & singh, 2016). at each time step t, an operating breakdown of two hidden states of birnn layer are described as below; (i) forward hidden state ℎ𝑡 𝑅⃗⃗ ⃗⃗ : calculated using equation (14): ℎ𝑡 𝑅⃗⃗ ⃗⃗ = 𝑡𝑎𝑛ℎ(�⃗⃗⃗� ∗ 𝑥𝑡 + �⃗⃗� ∗ ℎ𝑡−1 𝑅⃗⃗ ⃗⃗ ⃗⃗ ⃗⃗ + �⃗� ) ……………………(eq.14) (ii) backward hidden state ℎ𝑡 𝑅⃖⃗ ⃗⃗ ⃗ calculated in reverse order in equation (15): ℎ𝑡 𝑅⃖⃗ ⃗⃗ ⃗ = 𝑡𝑎𝑛ℎ(�⃗⃗⃗⃖� ∗ 𝑥𝑡 + �⃗⃗⃖� ∗ ℎ𝑡−1 𝑅⃖⃗ ⃗⃗ ⃗⃗ ⃗⃗ ⃗ + �⃗⃗⃖�) ……………………(eq.15) (iii) the final hidden state is occupied by concatenating both forward and backward states in equation (16): ℎ𝑡 𝑅 = [ℎ𝑡 𝑅⃗⃗ ⃗⃗ , ℎ𝑡 𝑅⃖⃗ ⃗⃗ ⃗⃗ ] ………………………………………(eq.16) (iv) fully connected (dense) layer: the concatenated hidden state ℎ𝑡 is passed through a dense layer for transformation. output equation (17) presents: 𝑜𝑡 𝑅 = 𝑡𝑎𝑛ℎ(𝑊𝑜 ∗ ℎ𝑡 𝑅 + 𝑏) …………………….…(eq.17) the optimal component model features are same lstm model. figure 4. the birnn model’s working structure https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 144 2.5. hybrid rnn-lstm the hybrid rnn-lstm model (figure 5) combines two types of rnn and lstm (al-selwi et al., 2024; donkol et al., 2023). this network is designed to process sequential data by learning dependencies and patterns across time. at the same time figure 5 indicates the structural diagram for the model as (i) input layer: processes and forwards input data to the model (see equation 18); (ii) rnn layer: handles sequence data and learns temporal patterns (see equation 19); (iii) lstm layer: captures long-term dependencies in the data (see equation 20); (iv) dropout layers: prevents overfitting by randomly ignoring certain neurons during training (see equation 21); (v) dense (output) layer: generates the final predictions based on learned features (see equation 22); (vi) connections: data flows sequentially through rnn, lstm, and dense layers, with dropout layers enhancing generalization (see equation 23). in addition, the equations (18, 24) describe the working breakdown for the model. 𝑖𝑡 𝐻 = 𝜎(𝑊𝑖 ∗ 𝑂𝑡 𝑅 + 𝑈𝑖 ∗ ℎ𝑡−1 𝑙 + 𝑏𝑖) …………………….…(eq.18) 𝑓𝑡 𝐻 = 𝜎(𝑊𝑓 ∗ 𝑂𝑡 𝑅 + 𝑈𝑓 ∗ ℎ𝑡−1 𝑙 + 𝑏𝑓) …………………….…(eq.19) 𝑜𝑡 𝐻 = 𝜎(𝑊𝑜 ∗ 𝑂𝑡 𝑅 + 𝑈𝑜 ∗ ℎ𝑡−1 𝑙 + 𝑏𝑜) …………………….…(eq.20) 𝑢𝑡 𝐻 = 𝑡𝑎𝑛ℎ(𝑊𝑢 ∗ 𝑂𝑡 𝑅 + 𝑈𝑢 ∗ ℎ𝑡−1 𝑙 + 𝑏𝑢) ……………………(eq.21) 𝑐𝑡 𝐻 = 𝑖𝑡 𝐻 ⊙ 𝑢𝑡 𝐻 + 𝑓𝑡 𝐻 ⊙ 𝑖𝑡−1 𝐻 ……………………………….…(eq.22) ℎ𝑡 𝐻 = 𝑜𝑡 𝐻 ⊙ tanh (𝑐𝑡 𝐻) ……………………………………...…(eq.23) the estimating sea erosion is as (see equation 24): 𝑒𝑠𝑡 ̂ = 𝜎(𝑊𝑜 ∗ ℎ𝑡 𝐻) ………………………………………….…(eq.24) the model work such as input data is fed into the rnn layer, which captures temporal relationships in a lightweight manner. the processed information is passed to the lstm layer, which learns both short-term and long-term dependencies in the sequence. the lstm's output is regularized using the dropout layer. finally, the dense layer generates predictions based on the processed sequence. the optimal component model features are same lstm model; however, hidden layers for model include rnn hidden layer and lstm hidden layer. figure 5. hybrid rnn-lstm model’s structure 2.6. metric of accurate parameters predicting accuracies depend on computing and comparing between the actual and predicted data. these metrics of the accreting measured parameters for the study such as the mape (equation 25), rmse (equation 26), mae (equation 27), squared (r2) (equation 28), and cc (equation 29). the indicators are shown as below (kardani et al., 2020; touzani et al., 2018): https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 145 𝑀𝐴𝑃𝐸(%) = 1 𝑛 ∑ |𝑥𝑡 ′−𝑥𝑡| 𝑥𝑡 %………………………………(eq.25) 𝑅𝑀𝑆𝐸 = √∑ (𝑥𝑡− 𝑥𝑡 ′) 2𝑛 𝑡=1 𝑛 …………………………………(eq.26) 𝑀𝐴𝐸 = 1 𝑛 ∑ |𝑥𝑡 ′ − 𝑥𝑡| 𝑛 𝑡=1 …………………………………. (eq.27) 𝑅_𝑠𝑞𝑢𝑎𝑟𝑒𝑑 = 1 − ∑ (𝑥𝑡− 𝑥𝑡 ′) 2𝑛 𝑡=1 ∑ (𝑥𝑡− 1 𝑛 ∑ 𝑥𝑡 𝑛 𝑡=1 ) 2 𝑛 𝑡=1 ……………………(eq.28) 𝐶𝐶 = ∑ (𝑥𝑡− 1 𝑛 ∑ 𝑥𝑡 𝑛 𝑡=1 )×(𝑥𝑡 ′− 1 𝑛 ∑ 𝑥𝑡 ′𝑛 𝑡=1 ) 𝑛 𝑡=1 √∑ (𝑥𝑡− 1 𝑛 ∑ 𝑥𝑡 𝑛 𝑡=1 )2𝑛 𝑡=1 ×∑ (𝑥𝑡 ′− 1 𝑛 ∑ 𝑥𝑡 ′𝑛 𝑡=1 )2𝑛 𝑡=1 ……………………(eq.29) where 𝑥𝑡 , 𝑥𝑡 ′ represent the estimated and actual values at time t, and n denotes the number of observed data points in the testing phase. the mean values of the predicted and actual data are represented by �̅�: 𝑥′̅ , respectively. the mape, rmse, and mae metrics approach zero for an ideal model (kim & kim, 2016; niedbała, 2019), while r-squared (r²) can reach a maximum value of 1, indicating a highly accurate prediction (brown, 2018; gao, 2024; karch, 2020; salih & abdulazeez, 2021). figure 6 demonstrates the workflow of the modeling experiment such as data preprocessing: the collected dataset undergoes statistical processing and is divided into training and testing sets. model deployment: five models are trained and evaluated to determine the most optimal indicators. performance evaluation: key metrics such as rmse, mae, and r² are analyzed to identify the most effective forecasting model. figure 6. workflow of the study 3. result and discussion vietnam’s coastline, located along the eastern edge of the indochina peninsula, plays a vital role in the country’s history, culture, economy, and environment. stretching approximately 3,260 km from mong cai in the north to ha tien in the south, it is associated with three major water bodies: the gulf of tonkin to the north, the south–central section of the east sea, and the gulf of thailand to the southwest. this extensive coastline supports a wide range of activities, including aquaculture, fisheries, seaport operations, maritime trade, and renewable energy development. yes no end start rnn lstm eda analysis for input data the data acquisition and preprocess concepting incremental training method for models setting indicators for models follow literature and error simulating compressive strength prediction for concrete describing statistical analysis of the generated estimations appropriate results? calibration of indicators/ architecture/ approach and/or training data size hybrid rnn_lstm bilstm bỉrnn figure 6. steps for study https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 146 however, coastal erosion has emerged as a severe challenge because of overexploited, the action of natural disasters and climate refresh. most are in the southwest and central coastal provinces where river mouths as well as lowly forest cover protected areas. minimal sediment supply leads to straight beaches or the nearly angulated beaches found along the interior coast and island coasts. they are typically also haphazardly populated which packages economic erosion effect. figure 7-16 represents the spatial-temporal variability in shoreline erosion in significant coastal eroding areas of central coastal range and southwest coastal region during a fiveyear study period based on remote sensing data of each other. in which, panel (a): satellite imaged shows the specific places onshore that were target for coastal erosion. panel (b): a digital elevation model (dem) that shows topographical spatial changes occurring along the shorelines panel (c)–(f) are one bands false-color composite satellite images in different years, where green stands for vegetation, yellow shows the land of transition land, and red reveals eroded or water places. figure 7. lienhuong's shoreline erosion during 2016 – 2021 figure 8. ro hamlet’s shoreline erosion during 2016 2021 binh thuan province: in lien huong town, approximately 1,200 meters of coastline have been eroded, with land loss extending 50–100 meters inland. in addition, figure 7 illustrates the progressive shoreline erosion in the town, over a five-year period using remote sensing and gis techniques, and it reveals a gradual land retreat, with significant erosion observed along the shoreline and river mouth. coastal vegetation degradation could be observed indicating enhanced susceptibility of wave action and sediment loss. this erosion is most likely due to natural coastal processes, upwelling of sea level rise because of climate change and human induced activities (aquaculture, sand mining). phu yen province: erosion rate is 10 to below 20 meters/year in ro village, tuy hoa city has more erosion. with time this led to the loss of several to hundreds of hectares of land along rivers and coastal zones. some of these, are aerial particularly hazardous containing ~248 km14 classified as very high risk. as more erratic weather patterns we see, coastal erosion visitation becomes more erratic in (local communes' anxiety counterbalancing) the authorities. figure 8 also indicates a trend of shoreline retreat on much of the beach, (a) (b) (c) (d) (e) (f) (a) (b) (c) (d) (e) (f) https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 147 particularly in some large wave approach and tidal exposure areas. decreasing coastal vegetation signifies erosion and sediment movement reach new dimensions. figure 9. cualo beach erosion during 2016 2021 figure 10. giang hai's shoreline erosion during 2016 2021 quang nam province: the cualo beach in tam hai commune has seen substantial wave-induced erosion. affected by waves, which have eroded seaward 3-5 meters and washed hundreds of casuarina trees in a 2kilometre stretch of coast in binh trung hamlet along with the temporary houses constructed for shrimp farming many old ponds are gone, and their graves. also, shown in figure 9 is an advanced land-loss along shallow nearshore areas, especially regions of high wave energy. the loss of vegetated zones implies a significant exposure to coastal erosion and sediment transport processes. this shoreline instability may be also exacerbated by other human activities in the form of large tourism development or infrastructure expansion. hue city: sea erosion has caused loss of hundreds of hectares of trees, forests and farmlands along three seaside communes. the most extensive area had a loss of more than 150 hectares including 90 hectares of rice, crops and 60 hectares ponds and freshwater reservoirs for shrimp, crab, as well as fisheries in giang hai commune. in conjunction with figure 10 which demonstrates the tidal influences, wave agitation and anthropological factors; a substantial decrease in vegetated area may have helped increase the erosion sensitivity. quang tri province: sea dike has been entirely torn for more than 150 meters; waves breaching into the land and topple down local shop that threaten row of coastal infrastructure, in vinh thai commune. be along with, figure 11 displays the erosion is particularly prominent along the eastern shoreline, where land degradation is noticeable. factors contributing to this process include wave action, sea-level rise, extreme weather events, and anthropogenic activities. a reduction in coastal vegetation, particularly mangroves, may have further exposed the shoreline to erosion. (e) (f) (c) (d) (a) (b) (d) (c) (b) (a) (f) (e) https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 148 kien giang province: in tay yen commune, a bien district, since last decade (2011–2023) the coastal erosion has intensified so about 500 hectares of mudflats lose each year. the coastal forest belt has retreated 60 to 300 meters, or approximately 20 m/year 3 m/year degradation of coastal forest plantation. currently erosion has affected approximately 188 kilometers, out of the province's 254 kilometers of coastline. figure 12c and 12f detail data indicate a large extent of land loss in the coastal zone (red areas show up more prominent in the north and central parts). the erosion-dotted line of deposition at least hints at some environmental stresses which may increase over time through wave action, tidal and anthropogenic influence as well. figure 11. vinh thai's shoreline erosion during 2016-2021 figure 12. tay yen conmunm's coastal erosion during 20162021 tra vinh province: hiep thanh commune of the coastline received a lot of erosion from the blow of monsoon waves and high tides in tra vinh province. erosion has been highly intense (> 900 meters shoreline and 50 meters inland penetration) at some locations endangering 199 households. figure 13c and figure 13f outline the tremendous loss of land along the southern shoreline with the red areas encroaching into the land. the trend is manifested as ongoing coastal erosion possibly driven by wave action, sediment motion and human disturbances. ca mau province: between 2011 and 2022, around 5,250 hectares of mangrove forests were lost due to coastal erosion. the worst-affected area, tam giang tay, have suffered severe damage, directly impacting local livelihoods. figure 14 describes a noticeable increase in red areas over time, and the northern section of the coastline appears to have undergone significant land loss. possible causes include wave action, tidal currents, sediment transport, and human-induced activities such as aquaculture expansion. there is coastal vegetation, a lot mangroves especially reduced and be erosion speeded up. (a) (c) (d) (e) (f) (b) (f) (e) (c) (d) (b) (a) https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 149 ben tre province: approximately 4.7 km of bao thuan commune, ba tri district have been eroded which impact directly to 115 households. coastal erosion has eaten up 100 meters of concrete roads, some 650 meters embankments and more than 535 hectares land. 15 houses were damaged with 4 completely collapse and 11 families were relocated. land loss along the southern shoreline is evident in figure 15 and represents a very high rate of erosion from the coastline. possible explanations are the wave action, tidal currents and sea-level rise and anthropogenic activities such as deforestation. coastal vegetation loss coupled accelerates erosion and sediment displacement. bac lieu: erosion has taken protective forests away so the waves directly striking the dikes, in some parts. one of the most affected areas is the ganh hao. moreover, figure 16 depicts the shoreline changes to appear along the southern coastline, where land loss is more pronounced. contributing factors include wave action, sediment transport, tidal forces, and possible anthropogenic impacts. coastal vegetation cover reduces, which could in turn promote erosion by sediments destabilizing. vietnam faces an urgent environmental and socio-economic threat due to rapid coastal erosion. the elimination of protection forests, farmland and all-terrain infrastructure has grave implications for local communities and the economy. much-needed quick and effective mitigation options are required to protect and preserve vietnam coastline from development. from the above analysis, the main factors underlying coastal erosion in vietnam are classified into natural factors and human-induced factors. figure 13. hiep thanh's shoreline erosion during 2016 – 2021 figure 14. tam giang tay's shoreline erosion during 2016 – 2021 (a) (b) (c) (d) (e) (f) (a) (b) (c) (d) (e) (f) https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 150 the impacts of climate change on coastal erosion include; (1) porosity of soil is essential for its wateruptake and -drainage soil porosity, such as in poorly drained soil or compacted clay soils, restricts the infiltration of water (nearing et al., 1989; toy et al., 2002). high porosity soils like sand drain more freely. sandy soils have high porosity, which encourage quick water entry low porosity, such as compacted clay low soil waterinfiltration, can cause surface runoff and further erosion high-porosity soils, like sandy ones, increase water infiltration for example, this can help reduce surface erosion (lal & stewart, 2018; shukla, 2003); (2) the high tides, mainly occurring during storm surges increase wave energy over coastal erosion including elevated tides exacerbated by storm surges (komar, 1977; masselink et al., 2014; pugh, 2004). variations in tidal level will also change the amount of shoreward march waves may attain onto the shore (nicholls & cazenave, 2010). (3) monsoon winds stir up and move more sediment giving greater displacement in conjunction with faster shoreline retreat (anthony et al., 2021; masselink et al., 2014); (4) persistent or heavy rainfall increase runoff, exacerbating the erosion of beaches and coastal cliffs. turbulent runoff loosens soil structure, lower rain intensities (~100%) and thus make the coastal slopes prone to severe slope failures and associated sediment transport downstream to the ocean (collins & sitar, 2008; crozier, 2010; sidle & bogaard, 2016); (5) storm events significantly impact on coastal erosion. at the same time, the event surges elevate sea levels, facilitating the rapid movement of sediments and the degradation of coastal formations (sallenger jr, 2000; zhang et al., 2004). (6) coastal geomorphology (shore face) gradient drives erosion rates; steeper slopes induce greater longshore sediment transport due to the force of gravity (bird, 2008; cowell & thom, 2006). in contrast, more slowly eroding coastlines may deposit and accumulate sediments (masselink et al., 2014); (7) increased wave heights drive the most enhanced erosion due to wave forces on coastal landscapes (komar, 1977; masselink et al., 2014). continuous wave action has an important influence in the horizontal re-suspension of sediments, figure 15. bao thuan's shoreline erosion during 2016 – 2021 figure 16. ganhhao shoreline erosion during 2016-2021 (a) (b) (c) (d) (e) (f) (a) (b) (c) (d) (e) (f) https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 151 therefore moving materials up and over shore (cowell & thom, 2006); (8) sea level rise slowly submerges the coastal areas and amplify chronic erosion. higher sea levels will reach more inlandtheir waves can travel further (church & white, 2011; nicholls & cazenave, 2010), and (9) rising global temperatures expansion of ocean water is another thermal load that drives sea level rise. even more frequent and severe storms and cyclones will further exacerbate coastal erosion (hansen et al., 2013; rahmstorf, 2007). anthropogenic influences on shoreline retreat consisting of deforestation of protective mangrove forests: mangrove removal reduces natural defenses against erosion (duke et al., 2007; giri et al., 2011); coastal engineering and development: poorly planned construction of walls and dikes change the sediment flow, thereby exacerbating erosion (anthony, 2016; hapke et al., 2009); aquaculture/land use changes (excessive shrimp farming and conversion of land forces it to destabilize; amount of fishermen reported washed away landbased extraction (barbier et al., 2013; hamilton, 2013; primavera, 2006). vietnam continental type coastline degradation is brought about by a complex mixture of climatic, oceanic, geological and also anthropogenic activities. to cope with this, integrated coastal zone management (book cover report perhaps) is vital which includes restoration of mangrove systems, livelihood regulation and also construction coastal security to reduce damages further. figure 17. coastal erosion points the data for this study was sourced from the open development mekong website, the national centre for hydro-meteorological forecasting of vietnam, and landsat 8 oli/tirs satellite imagery for six years (from 2016 to 2022). using python, an analysis was conducted on 52 coastal erosion sites along the vietnamese shoreline; at the same time, 52 gathered samplers are divided into 80%, and 20% for training and testing phases. figure 17 demonstrates the locations of beach erosion, categorized by severity levels: level 5 ("hazardous points without planned repair funding") – red, level 4 ("hazardous points with planned repair funding") – orange, level 3 ("extremely hazardous points") – green, level 2 ("normal erosion points") – blue, level 1 ("points with extremely critical erosion velocity") – purple. natural negative factors affecting coastal erosion: tide height (m), wave height (m), storm intensity (km/h), storm geomorphology slope(degree), monsoon winds (km/h), rainfall (l), sea level rise (m), temperature (°c). paracel islands spratly islands https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 152 figure 18. pearson correlation matrix for coastal erosion factors for all things except coastal geomorphology the scale of these forces was mapped by their equal annual, average measured. figure 18 explains the distribution of variables based on pearson correlation coefficients, which quantify the strength and direction of their relationships. these coefficients range from -1 to 1 (asuero et al., 2006; šverko et al., 2022), where higher absolute values indicate stronger associations, while values closer to 0 suggest weaker correlations. the pearson correlation analysis revealed varying degrees of association among key environmental factors, with certain variables exhibiting strong positive correlations. contrarily another could have weak or even negative associations. specifically, the analysis also identified positive relationships including coastal geomorphology and storm intensity (cc = 0.50) with temperature (cc = 0.53), wave height. in contrast, tidal height showed negative correlations with rainfall (-0.52), storm intensity (-0.55), coastal slope (-0.39), and wave height (-0.42). sea level erosion also upon closer observation showed minor positive correlation with temperature (cc = 0.42). figure 19. statistic for level of coastal erosion table 1 illustrates some descriptive statistics of mean, standard deviation (std), minimum (min), maximum(max) parameters of data quality across 42 stations, moreover mean and std of input evaluator attributes are soil porosity(38.27,6.33), tide height (2.48,0.64), monsoon winds (43.37,8.67), rainfall (2.33,0.44), storm intensity (151.35,34.47), coastal slope (4.40,4.12), wave height(1.92,0.88), sea level rise (4.81,0.53), temperature (30.21,1.09), the level of erosion (2.77,1.29). the std range is concentrated near the mean with minimal variation, indicating acceptable reliability for forecasting coastal erosion changes. in addition, figure 19 displays the levels of coastal erosion, indicating that along the vietnamese shoreline, level 1, level 2, level 3, level 4, and level 5 correspond to 10, 15, 9, 13, and 5 points, respectively. https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 153 table 1. descriptive data analysis cate gory soil porosity (%) tidal height (m) monsoon winds (km/h) rainf all (l) storm intensity (km/h) coastal slope (degree) wave height (m) sea level rise (mm/year) tempera ture (𝒐𝒄) level of erosion mean 38.27 2.48 43.37 2.33 151.35 4.40 1.92 4.81 30.21 2.77 std 6.33 0.64 8.67 0.44 34.47 4.12 0.88 0.53 1.09 1.29 min 25 2 25 1.2 80 1 1 3 27 1 max 60 4 70 3.5 200 20 4 5 32 5 figure 20. (a)training loss, (b)validation loss for activation functions using 5 models regarding to deploy deep learning for coastal erosion analysis, figure 20 presents the combined loss curves (loss and validation loss) and accuracy curves (accuracy and validation accuracy) for the five models at the 150th epoch. the diagrams highlight the influence of convergence and model performance, revealing that the validation loss and accuracy of the rnn and its variant models failed to converge. meanwhile, other models exhibited significant fluctuations under specific training conditions, indicating instability in their learning process. additionally, the study conducted experiments on all models, and the results presented in table 2, and figure 21 present the simulation outcomes of the five models. results suggest the best forecasting model is hybrid rnn_lstm (5.85) followed by simple rnn. the other models (lstm, birnn, bilstm) also provide reasonable performance but somewhat lower in accuracy than the top two models. table 2. standard metrics for accuracy model rmse r_squared mae cc std rnn 0.77 0.74 0.43 0.90 1.62 birnn 0.93 0.53 0.57 0.86 1.80 lstm 0.83 0.66 0.77 0.85 0.61 bilstm 0.88 0.51 0.74 0.80 0.84 hybrid rnn_lstm 0.71 0.77 0.35 0.91 1.54 a taylor diagram illustrates in a graphical way how std and cc are related for varying models (rnns, birnns, lstms and bi-lstms and hybrid_rnn_lstm) against an ideal reference. in particular, figure 22 shows a visual std and cc comparisons for std from different models. the hybrid rnn‐lstm model has strongest correlation (0.91) with smallest standard deviation; hence, this is considered as a perfect forecasting model. the rnn model showed good performance with cc of ~0.90. other models (birnn, lstm, bilstm) have relatively lower cc values. https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 154 figure 21. the regression for forecasting of five models based on testing phase figure 22. taylor diagram for models’ performance figure 23 summaries shap plot to provide insights into the impact of different features on the model's predictions for coastal erosion levels. it highlights that monsoon winds, storm intensity, and tidal height have the most significant influence, as indicated by the wider spread of shap values along the x-axis. these combination of features have a positive contribution as well as a negative one, meaning that they play an important role on differences in coastal erosion. they influence the model, too: coastal slope, wave height and soil porosity have a lower shap value where, again the range is smaller than monsoon winds or tidal height but higher than more moderate impacts in contrast sea level rise and rainfall display less shap values spread suggesting a lesser overall effect. on the other hand, sea level rise and rainfall have not such rounded shap values span (smaller effect), as an example. rainfall appears to have almost zero effect, with shap values distributed around zero which would mean minor influence on the model. image the northern coast to southern regions of vietnam: one of the countries which gets maximum tempo changed weather, coastal erosion has been the most threatening issue on land from northwards. studying and predicting coastal erosion is therefore central to developing effective mitigation strategies for local communities to help reduce its impact. we used the forecasting models to predict coastal erosion at 52 sites along vietnam coastline, this study findings showed that the number of erosion points is highest in mekong delta, followed by central coast and red river delta. recent (2010 and later) landsat 8 oli/tirs satellite imagery analysis revealed that these are treeless regions and predominantly located at the https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 tran and nguyen / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 139-158 doi: 10.14710/geoplanning.12.2.139-158 155 mouths, where high sediment transport potentially balances coastal resilience across this extent. this study was based on nine main natural factors for coastal erosion as major drivers. while the model can be more precise if additional parameters such as human activities were considered, the deep learning-based approach offers accurate forecasting features. the conclusion of this research indicated that the five predictive models are good predicting models and effective for forecasting study canyon landslides in regions with specific environmental condition. figure 23. the impact of different features on the model's predictions for coastal erosion levels 4. conclusion vietnamese coastline is a pretty valuable natural capital with its mesmerizing views, marvelous biodiversity and huge economic value. nevertheless, it is currently more and more being injured naturally but also by human activities. people need to ensure sustainable development and conservation programs in order to preserve this key in next generations' use. developed deep learning models to evaluate and predict coastal erosion along vietnam's coastline by combining nine mandatory registers of the surrounding environmental conditions of this study. the results showed that the hybrid rnn_lstm model outperformed other models in terms of accuracy and reliability. some of the most important were monsoon winds, storm power, tidal height, whereas, rainfall and sea level rise only had a small effect on erosion prediction. the main consideration of this study was focused on nine input factors but with consideration of other impacting elements like impact of human intervention, subsurface sedimentogenics and ocean currents. these findings recommend a robust foundation for integrating deep learning into coastal management frameworks, offering actionable insights for climate adaptation strategies. furthermore, the analysis is based on only five predictive models which arguably might open an opportunity for improved performance without exploring the effect of other models such as hybrid birnn_lstm and birnn_bilstm. 5. acknowledgement thanks for group research number of nctb.dhh.2024.05 of hue university. 6. references al-selwi, s. m., hassan, m. f., abdulkadir, s. j., muneer, a., sumiea, e. h., alqushaibi, a., & ragab, m. g. 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[crossref] https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.14710/geoplanning.12.2.139-158 https://doi.org/10.1016/j.neucom.2021.02.046 https://doi.org/10.1029/2019wr025326 https://doi.org/10.11113/jt.v80.11196 https://doi.org/10.14456/seagj.2016.53 https://doi.org/10.1023/b:clim.0000024690.32682.48 | 35 geoplanning vol 5, no. 1, 2018, 35-42 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.35-42 estimating mangrove forest density using gap fraction method and vegetation transformation indices approach n. khakhima, a. c. p. putrab, t. u. widhaningtyasb a faculty of geography, universitas gadjah mada, yogyakarta, indonesia b remote sensing department, earthline, jakarta, indonesia abstract: mangrove forest represented a coastal ecosystem in indonesia. theoretical validation and in-field measurement by calculating the number of trees and the density data that was validated through remote sensing would not be appropriate because the remote sensing recorded canopy density and not tree stands. new method canopy photography or gap fraction method was the technique to predict sun radiation using the photograph taken upward through extremely wide lens and classification object image. the objectives of the study were (1) to examine the acuracy of the estimation of the mangrove forest density using vegetation index transformation, and (2) to map the mangrove forest condition. the location of the study was alas purwo resort grajagan national park area. the material of the study was landsat-8 oli image recorded on january 19th, 2016 using savi vegetation index transformation method. gap fraction filed measurement method was a new method in indonesia. the results of the study showed that the regression of the savi index between index transformation value and in-field condition (r2) was 0.566, the forest density estimation resulting from the savi index transformation had the rmse of 2.334178 and the density of the mangrove forest in grajagan bay of the alas purwo national park included low density of 0-12.5% (30.42 ha), medium density of 12.6-25% (116.55 ha), and high density of 25.1-37.6% (463.68 ha). . copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. khakhim, n., putra, a, c, p., & widhaningtyas, t, u. (2018). estimating mangrove forest density using gap fraction method and vegetation transformation indices approach. geoplanning: journal of geomatics and planning, 5(1), 35-42. doi: 10.14710/geoplanning.5.1.35-42 1. introduction indonesia had a very wide coastal area with 95.181 km coastal line. mangrove forest represented costal ecosystem and formed because of the presence of wave protection, fresh water input, sedimentation, tidal water flow, and warm temperature (walsh, 1974). the mangrove forest in grajagan bay represented the most natural mangrove forest in java island (sudarmadji, 2011). a part of the mangrove forest belonged to alas purwo national park or to grajagan resort. according to the decree of the minister of forestry no. 283 kpts-ii/1992 alas purwo national park was a conservation area. remote sensing was considered to be more effective in mapping mangrove condition than a conventional or terrestrial method. according to (fawzi, 2015) the remote sensing of the mangrove forest was able to provide detailed physical aspects of the forest such as species, zoning, change in land use arrangement, and mangrove physical mapping. according to (lee & yeh, 2009) mangrove had unique spectral reflection representing the combination of ground, water and vegetation because the mangrove grew in coastal area. additionally, the basic reflection of mangrove canopy also influenced the spectral reflection of the mangrove (kuenzer, bluemel, gebhardt, quoc, & dech, 2011). forest density could be estimated in the remote sensing using vegetation index transformation approach. the vegetation index transformation was a mathematical model developed to estimate forest density (danoedoro, 2012). the objectives of the study were to examine the accuracy of the estimation of mangrove forest density using remote sensing with open access article info: received: 14 dec 2016 in revised form: 31 may 2017 accepted: 3 november 2017 available online: 30 april 2018 keywords: mapping, estimate, remote sensing, mangrove forest, gap fraction, landsat-8 oli corresponding author: nurul khakhim faculty of geography, universitas gadjah mada, yogyakarta email: nurulk@ugm.ac.id https://doi.org/10.14710/geoplanning.5.1.35-42 https://doi.org/10.14710/geoplanning.5.1.35-42 mailto:nurulk@ugm.ac.id khakhim et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 35-42 doi: 10.14710/geoplanning.5.1.35-42 36 | vegetation index transformation approach and to map the mangrove forest density in grajagan bay of alas purwo national park. theoretical validation and in-field measurement by calculating the number of trees and the density data that was validated through remote sensing would not be appropriate because the remote sensing recorded canopy density and not tree stands. therefore, a new method of gap fraction or hemispherical photography that was well-known as fisheye photography or canopy photography was the technique to predict sun radiation using the photograph taken upward through extremely wide lens (rich, 1990). the resulting perspective was usually close to 180 degree that covered all sky directions. the resulting photographs recorded sky obstruction geometry by vegetation canopy or other close features. the geometry could measure objects exactly and used to calculate the sun radiation transmitted through the vegetation canopy and also to predict the canopy structure aspects such as lai (leaf area index). the measurement of the canopy width using the gap fraction was based on the calculation of observable sky proportion as the function of sky direction with the canopy fraction recorded by the photographs. scientists began to develop threshold method to separate canopy types through hemispherical photographs. the factor that influenced the classification of objects was the exposure of the taken photographs (jonckheere, nackaerts, muys, & coppin, 2005). the classification of the photographs involved picture pixels representing both observable and non-observable sky directions with the intensity value of the pixels above the classified threshold as the intensity value of the pixels and observed below the threshold classified as non-observable. recently, advancement has been made in developing automatic threshold algorithm. however, there were still many things to do till the technique was fully applicable (inoue, yamamoto, mizoue, & kawahara, 2002). the gap fraction of the hemispherical photography was useful in making the measurement of the canopy parameter easier through remote sensing, such as lai (leaf area index) to find out the productivity of a plant calculated on the basis of the width of its leaves for photosynthesis per land surface unit (watson, 1947) (jonckheere et al., 2004). the canopy width played an important role in identifying the information through the remote sensing. in the gap fraction method some assumed that leaf element was uneven so that there was significant difference between width leaf and needle-shape leaf (smith, 1991) (bolstad & gower, 1990). 2. data and methods 2.1. study area the location of the study was grajagan bay of alas purwo national park. it was geographically situated at 114o13’20,203” east longitude, 114o20’45,979” east longitude and 8o35’52,79” south latitude and 8o37’28,697 south latitude as indicated in the map (figure 1). dominant water movement taking place in estuary was sea water that salinity would influence the zoning of the location. therefore, the results of infield observation of the zoning of the mangrove forest in grajagan bay could be classified into 2, south zone and north zone on the basis of the boundary of the sea water of the estuary. based on the in-field observation there were 15 species of the mangrove of the grajagan bay of alas purwo national park, but according to wetland international there were 27 species spreading in the national park and perum perhutani. 2.2. image processing the study used the data of landsat-8 oli with 2 sensors carried by landsat of 8th generation, which were oli and thermal. however, it used the oli sensor. the landsat-9 oli consisted of 9 bands and according to (irons, 2015) with the following details: coastal band (0.43-0.45 m), blue band (0.450-0.51 m), green band (0.53-0.59 m), red band (0.64-0.67 m), near infrared band (0.85-0.88 m), swir1 band (1.57-1.65 m), swir2 band (2.11-2.29 m)), panchromatic band (0.50-0.68 m)), and cirrus band (1.36-1.38 m) with the resolution of 30 meters for multispectral imaging and 15 meters for panchromatic imaging. the reason for the use of the landsat-8 oli image was that it had complete band with minimum cloud coverage of 11.6% on ground and when the cloud coverage on the ground was >30%, the cloud would cover targeted object and the imaging result was useless. and then, radiometric and atmospheric corrections were made https://doi.org/10.14710/geoplanning.5.1.35-42 khakhim et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 35-42 doi: 10.14710/geoplanning.5.1.35-42 | 37 to obtain biophysical parameters (jensen & lulla, 1987). the atmospheric correction was made using histogram adjustment method. the histogram adjustment method was easy and simple to use by discarding the offset value of the image (danoedoro, 2012). the image was processed using software envi 4.8. figure 1. the location of the study in grajagan bay of alas purwo national park. 2.3. field measurement the activity of the field measurement was conducted in april 2016 in alas purwo national park area. the measurement was made using dslr camera nikon d5100 with 18-55 mm lens and additional fisheye converter lens to change the resulting image into fisheye image (figure 2a). the portraying was conducted using the fisheye lens because it was the easiest and more appropriate lens for processing the resulting canopy density image with the software can eye. the data of the canopy density was processed using the software can eye because it was a new software and gave more appropriate results with the in-field condition. it was necessary in the in-field portraying to consider the mean height that was breast height on squat position (figure 2b). (a) (b) figure 2. (a) field measurement. (b) the results of vertical fisheye portraying and figure (b). the in-field portraying process. 2.4. field measurement vegetation transformation index used in the study was index savi (soil adjusted vegetation index). the savi vegetation index was the best one considering the factor of land influence (l). according to (jensen & lulla, 1987), (danoedoro, 2012) the savi vegetation index was able to reduce land background because of the presence of the factor of land spectral noise during atmosphere correction. the formula of the savi (eq.1) vegetation index is: https://doi.org/10.14710/geoplanning.5.1.35-42 khakhim et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 35-42 doi: 10.14710/geoplanning.5.1.35-42 38 | the factor of land influence (l) used in the study was 0.5 because it was the result of the field identification of the mangrove forest, which was moderately dense. additionally, it was the most common practice to use the value because it could accommodate both low and high vegetation areas (champagne in ilham, 2012). the process was carried out using the savi vegetation index in which nir was near infrared band and red was red band in the landsat-8 image resulting from the atmosphere correction processing. the results of the processing of the savi vegetation index transformation were statistically analyzed using regression and correlation methods to find out the correlation between the results of the estimation of the image and the in-field condition. the regression analysis resulted in the equation y = ax+b to develop forest density estimation model. 3. results and discussion in remote sensing the recorded forest density was the canopy density so that it was more appropriate and the results were more accurate. if the measurement was conducted using canopy approach, the remote sensing recorded the canopy density and not the density of tree stands so that a new method of gap fraction or hemispherical photography was developed. the gap fraction method was new method and has not been widely used in indonesia. the measurement using this method was very simple because it used a camera and fisheye lens and the photographs were taken upward or downward depending on the objective of the photography. the results of the upward photographs might be seen in (figure 2a). the study used the upward photography to avoid noises of other objects in the resulting photographs. the average in-field canopy density was 21.6% or 0.216 of 46 distributed sample points. the resulting canopy density was processed using can eye software accessible in http://www6.paca.inra.fr/can-eye (weiss, 2002). the process was carried out by tracing the resulting photographs taken using fisheye lens that tended to be convex into low watermark canopy density as reference so that the results of the classification of the canopy and non-canopy density would be very different as illustrated in (figures 3a and 3b). (a) (b) figure 3. (a) the scheme of the processing of the forest density. (b) the results of the classification of the canopy and non-canopy density and figure (b). the results of the processing of forest density. according to (jonckheere et al., 2005), the classifying process was based on the threshold given by the processing software. the classification would give canopy density through the gap between sky and canopy object. the classification would influence the width of the coverage area of the lens, which was increasingly wider and close to 180o and the results would be better because the influencing factors of the density were the angle of sun and the ability of the camera in catching objects such as over exposure or under exposure. the photographs were usually made using auto method that the classification of the objects was based on the incoming light into the camera considered to be the same and there was not any under exposure or overexposure. the more the under exposure influenced the density, the higher the density would be, while the more the over exposure influenced the density, the smaller the classified objects would be. https://doi.org/10.14710/geoplanning.5.1.35-42 khakhim et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 35-42 doi: 10.14710/geoplanning.5.1.35-42 | 39 the resulting density of the in-field measurement would be used as observation data that would subsequently be used in statistical test using regression to find out the correlation between the in-field density and the estimated data resulting from the image processing using vegetation index transformation, which was savi because the savi was according to (putra & khakhim, 2016) was appropriate method in mangrove forest density mapping with good accuracy and appropriate with the in-field condition. the accuracy of the savi reached 80%. it was consistent with other study by (murti & others, 2013) in which a representation was made among rvi, ndvi and savi and the results were indicative of the canopy density with the accuracy above 80%. the results of the regression (r2) of the observation results of the image processing showed that the regression (r2) was 0.566 and the equation y= 107.96x + 5.2859 as illustrated in (figure 4). consequently, the equation y=107.96x + 5.2859 was applicable in spatially mapping the canopy density. the regression value above would result in canopy density estimation model in mangrove forest that was based on the estimation results of the regression equation between the in-field measurement sample and the condition of the remote sensing. actually, a linear regression might be carried out only by observing how appropriate the estimation results of the long distance image and the in-field actual condition. therefore, the regression y= ax+b would give the regression value (r2) that enables us to find out the correlation. the closer was the correlation (r), the more appropriate it would be with the in-field condition. thus, the resulting correlation of the regression model was close and able to estimate the mangrove forest density using the vegetation index transformation savi. there were 30 samples used in the regression to develop the forest density estimation model. figure 4. the regression of the results of the in-field density measurement and the results of the vegetation index transformation processing. the results of the regression equation would be returned to the long distance image in which the x value would be substituted by the result of the vegetation index transformation processing savi and it would give the estimated forest density. the estimated forest density might be observed in (figure 5). the resulting mangrove forest density was classified in 3: low, medium and high. based on the density there were low density forest (0-12.5%), medium density forest (12.6-25%), and high density forest (25.1-37.6%) in (table 1). table 1. the result table of density mangrove density class range class (%) extensive (ha) pixels low 0-12,5 30,42 338 medium 12,6-25 116,55 1295 high 25,1-37,6 463,68 5152 total 610,65 6785 https://doi.org/10.14710/geoplanning.5.1.35-42 khakhim et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 35-42 doi: 10.14710/geoplanning.5.1.35-42 40 | it was necessary to find out the accuracy of the resulting forest density in order to find the error of the forest density estimation. the accuracy was measured using rmse (roots mean square error) method commonly used in the remote sensing. according to (kamal, hartono, wicaksono, adi, & arjasakusuma, 2016), the method was used to predict the visual error between the data resulting from the observation and the estimation data. the bigger was the rmse, the lower the resulting accuracy would be. the number of the samples used in the accuracy test was 14 samples. there supposed to be 15 samples ideally to find out the accuracy of the samples. however, 14 samples were used because there in-field sampling failed to comlete the number. however, 14 samples were still considered to give accuracy in the measurement. the location of the sample drawing for the accuracy test must be widely separated to the location of the samples for the estimation model for more ideal accuracy and did not have any significant impact on the model, if the estimated model sample and the accuracy were close, the resulting accuracy would be better and hence it was less representative. the results of the calculation of the accuracy showed that the rmse was 2.334178. according to (makridakis et al., 1982) the low rmse value indicated that the resulting value variation of the estimation model was close to its observation variation. the higher was the regression value of the in-field observation results and the estimated results were high, the better the resulting rmsi would be. good rmse was the one with low value. the lower was the rmsi value, the better it was. based on (putra & khakhim, 2016), among the rmse savi value and other vegetation index transformation methods the best was the savi with the lowest rmse value. therefore, the rmse value of 2.334178 was low enough for a model to be accurate. the lower was the rmse value, the better it would be. in other words, the closer was it to 0.00, the better it would be (kamal et al., 2016). the 1:1 line in (figure 5) was helpful in understanding the estimation results of the forest density whether it was underestimate or overestimate. it was a red and diagonally dividing line indicative of overestimation or underestimation. if it was underestimated, the sample distribution would be concentrated in the in-field observation and vice versa. the results of the study showed that the forest density was underestimated and the in-field density was >25% in which there were some samples concentrated in and heading to the underestimated density. it was influenced by the tidal condition of the forest. figure 5. the results of the calculation of the rmse (root means square error) of the vegetation index transformation savi of mangrove forest. https://doi.org/10.14710/geoplanning.5.1.35-42 khakhim et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 35-42 doi: 10.14710/geoplanning.5.1.35-42 | 41 4. conclusion if the forest density was measured using remote sensing, the object for the measurement was the canopy that was measured through spectral reflection so that it was the forest canopy density that was measured in the measurement. the forest canopy density was measured using gap fraction method or hemispherical photography method. the method was more appropriate in identifying the forest density, especially the canopy density because it used the covering width of hemispherical lens. the samples of the study were measured using the vegetation index transformation savi that could give in-field mean canopy density of 21.6%, while the estimated density was 25% and the resulting rmse was 2.334178 and categorized into good enough. 5. acknowledgments we would like to 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(2002). eye-can user guide (techreport) https://doi.org/10.14710/geoplanning.5.1.35-42 https://doi.org/10.1080/02757259009532119 https://doi.org/10.1093/oxfordjournals.aob.a083148 | 107 geoplanning vol 3, no. 2, 2016, 107-116 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.2.107-116 assessment of mangrove forest degradation through canopy fractional cover in karimunjawa island, central java, indonesia m. kamal a, hartono a, p. wicaksono a, n. s. adi b, s. arjasakusuma a a faculty of geography, universitas gadjah mada, yogyakarta, indonesia b research centre for coastal and maritime resources, the ministry of maritime affairs and fisheries, indonesia abstract: the karimunjawa islands mangrove forest has been subjected to various direct and indirect human disturbances in the recent years. if not properly managed, this disturbance will lead to the degradation of mangrove habitat health. assessing forest canopy fractional cover (fc) using remote sensing data is one way of measuring mangrove forest degradation. this study aims to (1) estimate the forest canopy fc using a semi-empirical method, (2) assess the accuracy of the fc estimation and (3) create mangrove forest degradation from the canopy fc results. a sample set of in-situ fc was collected using the hemispherical camera for model development and accuracy assessment purposes. we developed semi-empirical relationship models between pixel values of alos avnir-2 image (10 m pixel size) and field fc, using enhanced vegetation index (evi) as a proxy of the image spectral response. the results show that the evi provides reasonable estimation accuracy of mangrove canopy fc in karimunjawa island with the values ranged from 0.17 to 0.96 (n = 69). the low fc values correspond to vegetation opening and gaps caused by human activities or mangrove dieback. the high fc values correspond to the healthy and dense mangrove stands, especially the rhizophora sp formation at the seafront. the results of this research justify the use of simple canopy fractional cover model for assessing the mangrove forest degradation status in the study area. further research is needed to test the applicability of this approach at different sites. copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): kamal, m., et al. (2016). assessment of mangrove forest degradation through canopy fractional cover in karimunjawa island, central java, indonesia. geoplanning: journal of geomatics and planning, 3(2), 107-116. doi:10.14710/geoplanning.3.2.107-116 1. introduction canopy fractional cover (fc) refers to the estimate of the proportion of an area that is covered by set vegetation cover per unit area. the value of fc is, therefore, unitless, with the value ranges from 0 to 1. canopy fractional cover can serve as an indicator of forest degradation (wang et al., 2005). the amount of foliage in a plant canopy represented by the fractional cover is one of the basic ecological characteristics reflecting many plant functions and attributes (hashim et al., 2014; wang et al., 2005). in mangrove and other vegetation ecosystems, the fractional cover also serves as an indicator of ecological processes which characterizes land-atmosphere energy and water exchange needed for climate modelling (jiménez-muñoz et al., 2009; zeng et al., 2000). because of its significance role in describing the fundamental property of a plant canopy, the ability to measure and estimate canopy of the fractional cover provides the key to understanding and assessing the physical condition of mangroves. remotely sensed data are ideal for assessing fc because of their capability to cover large areas, provides access to mangrove forest that is often inaccessible, and it its temporal property makes it ideal for monitoring from time to time. defries et al. (2000) developed a prototype for the global map of proportional vegetation cover that separates one pixel in coarse-resolution imagery (>1 km) into percentage cover of leaf form, leaf type and leaf longevity. this concept can also be applied in degraded forests in which each pixel is composed of tree canopies and open area as firstly applied by souza et al. article info: received: 10 august 2016 in revised form: 6 september 2016 accepted: 18 september 2016 available online: 31 october 2016 keywords: mangroves, degradation, canopy fractional cover, alos avnir-2, evi corresponding author: muhammad kamal faculty of geography, universitas gadjah mada, yogyakarta, indonesia email: m.kamal@ugm.ac.id open access http://dx.doi.org/10.14710/geoplanning.3.2.107-116 http://dx.doi.org/10.14710/geoplanning.3.2.107-116 mailto:m.kamal@ugm.ac.id kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 108 | (2005) and further developed by asner (2009) in their carnegie landsat analysis system. direct measurements of fc in mangroves from the field would yield very accurate results; however, the mangrove environment is difficult to access due to the mangrove root systems, tidal fluctuation and unconsolidated sediment. the work is labor intensive, costly in terms of time and money and some of the methods are destructive (kamal et al., 2016). therefore, it is needed to use a method that is efficient, robust and most importantly not destructive to conserve mangrove forest. fortunately, previous studies indicated that remote sensing data provided a practical indirect method to repeatedly map fc from local to global scales to assess the forest degradation status (asner, 2009; defries et al., 2000; hashim et al., 2014; souza et al., 2005; wang et al., 2005). the most operational method to estimate forest biophysical parameters (such as lai, biomass and fc) from remote sensing data is based on empirical or semi-empirical relationships formulated between in-situ parameter measurements and image pixel values from at-surface spectral reflectance or through spectral vegetation indices (svis). these svis are developed to enhance the sensitivity of vegetation spectral reflectance while minimizing the soil background influence. a study by matricardi et al. (2010) showed that the use of svis improved the accuracy of fc estimation. the svis are useful for accurately measuring cover change and forest degradation in evergreen forest, although the soil background decreases the relationship of the variables, especially in sparse vegetation system (xiao & moody, 2005). with the challenging environment of mangrove forests, the assessment of semi-empirical model between svi and fc remains open for exploration. mangrove forests are located between 30°n and 30°s and have different vegetation compositions in terms of species and canopy stand structures across the world (duke et al., 1998). these species and structural variation result in different ecological functions and processing rates between places, which is also possibly represented by the different pattern of fractional cover values. mapping mangrove conditions (such as extent and degradation level) is important to provide a current and spatially-explicit information source for coastal assessment, monitoring and planning. in particular, the map of the level of mangrove degradation is essential to show where the disturbance happens, what type of disturbance occurred and how severe is the damage. the information is required to support the development of plans and scenarios for sustainable coastal development, especially in mangrove areas. therefore, a systematic study needs to be undertaken to understand the relationship between field fractional cover value distribution and the variation of mangrove environmental setting and vegetation structures. this study aims to (1) estimate the forest canopy fc using the semi-empirical method from alos avnir-2 data, (2) assess the accuracy of the fc estimation and (3) create mangrove forest degradation map from the canopy fc results. 2. data and methods 2.1. study site the study site is mangroves areas at karimunjawa national park, central java, indonesia. it is located between 110°24’10” 110°30’10” e and 4°47’48” 5°50’12” s in the java sea between java and kalimantan islands, approximately 125 km north of semarang city (figure 1). it is a tropical archipelago of 22 islands (five of which are inhabited) with a total area of 111,625 ha (1285.50 ha of karimunjawa island, 222.20 ha of kemujan island and 110,117.30 ha of other small islands) (btnk, 2008). it has a humid tropical maritime climate with an average daily temperatures range from 26–30°c and average humidity of 70–85%. the average annual rainfall is 2632 mm; the average monthly rainfall during the dry season (april to september) is 60 mm and during wet season (october to march) is 400 mm. for this study, we focused on mangroves at karimun and kemujan islands only. in this site, the mangrove forest has variation (i.e. zonation) in vegetation structure from the landward to the edge of the sea water. there are three noticeable mangrove zones in karimunjawa island from landward to the sea water edge; low multi-stem stands, single and multi-stem low-closed forest and multi-stem closed forest. according to karimunjawa national park office (btnk, 2012), there are 45 mangrove species in this area (27 true mangroves and 18 mangrove associates), with rhizophora stylosa as the dominant mangrove species. http://dx.doi.org/10.14710/geoplanning.3.2.107-116 kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 | 109 figure 1. study area and field sample distribution in karimunjawa island, indonesia (authors, 2016) 2.2. image datasets this study used alos avnir-2 multispectral image data to estimate mangrove canopy fractional cover. this image was captured on 19 february 2009, has 10 m pixel size, and four spectral bands (blue (420-500 nm), green (520-600 nm), red (610-690 nm), nir (760-890 nm)). the reason of using this image dataset is because it has pixel resolution that is optimum for vegetation biophysical parameters estimation in this environment as indicated by laongmanee et al. (2013) and kamal et al. (2016). the avnir-2 image was geo-referenced using high resolution worldview-2 image (level 3x) to ensure high geometric accuracy and was assigned to a utm zone 49m map projection. the pixel digital numbers were converted to top of atmosphere (toa) spectral radiance (w/cm2sr.nm) and toa reflectance using the envi 4.8 software (itt systems, itt exelis, herndon, va, usa), following the procedures and correction coefficients described in bouvet et al. (2007). due to the limitation of image acquisition information, further dark object subtraction (dos) process was conducted to minimize the atmospheric effects on the image. the atmospheric correction was conducted to obtain pixel surface reflectance values as close as possible to the ground measurement condition. 2.3. fieldwork and fractional cover measurement fieldwork on karimunjawa island was conducted on july-august 2012. the time different between the the image acquisition and fieldwork was 4 years (from 2009 to 2012). this time difference, however, did not affect too much to the mangrove condition because the lifetime span of mangroves were reported for approximately 90 – 100 years or more and its growth rate is slow (verheyden et al., 2004). moreover, we have not found any major disturbance from human activities or natural disasters reported during this time period. therefore we assume that the mangrove forest condition and extent remains stable. the data used in this study extracted from fieldwork data collection in kamal et al. (2016). we laid out 7 field transects as much as possible to be perpendicular to the shoreline on the field site (figure 1) to collect hemispherical photos of the mangroves along the different zonation patterns. along these transects, we collected several 10 m by 10 m sampling plots at approximately 10 to 30 m intervals, depending on the stability of the forest floor substrate and variation of the tree stands density. ten additional individual sample sets were also collected to cover some variation outside the transects. during the fieldwork, we http://dx.doi.org/10.14710/geoplanning.3.2.107-116 kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 110 | collected 69 sampling plots. within each sample plot, the position of the plot centre was measured using gps instrument and nine random hemispherical photos were taken straight up to record its in-situ canopy cover (figure 2a, b), following weiss & baret (2014) guideline. all of the hemispherical photos were collected at a height of approximately one meter above the forest floor (figure 2c). the fc values were then calculated by the can-eye software (http://www6.paca.inra.fr/can-eye) based on the samples collected in the field. figure 2. fieldwork scheme: (a) 10 m by 10 m sampling plot, (b) example of a hemispherical photo of canopy, and (c) hemispherical photo field collection. (authors, 2016) 2.4. image-based fractional cover estimation the atmospherically corrected of avnir-2 images were used to compute the evi as a proxy of canopy fc. in this case, semi-empirical relationships were developed between the evi pixel values and the in-situ fc measurements. the evi was selected as representative of a background and atmospherically corrected vegetation index (huete et al., 2002) and the evi algorithm is expressed as: [1] where ρnir, ρred, and ρblue are near-infrared, red, and blue spectral reflectance of the image, respectively. while g, c1, c2, and l are coefficients to correct for aerosol scattering, absorption, and background brightness (set at 2.5, 6, 7.5, and 1, respectively). linear regression analyses were used to investigate the relationships between evi pixel values and in-situ fc measurements in the corresponding sample locations based on the gps measurements. the in-situ fc locations were used to extract the evi pixel values from the image for developing the regression analysis. for developing the model, we selected field samples that are at least 40 m apart to avoid the sample clustering. this process ends up with 39 points for fc model development and 30 points for model validation. following the experimental remote sensing guideline from franklin (2001), we use the in-situ fc measurement as independent variable and the evi pixel values as dependent variable. the regression algorithms were then applied inversely to the image to map the distribution of mangrove fc in the study site. 2.5. accuracy assessment of fractional cover estimation after the fc model was created, the estimated fc values produced from the evi model was compared with the in-situ fc validation samples by means of the root means squared error (rmse). to visually assess the prediction error, linear models and scatter plots between modelled and in-situ observed fc were produced and plotted in a 1:1 graph, as were done by laongmanee et al. (2013), kamal et al. (2016) and kovacs et al. (2009) . with the 1:1 graph, in an ideal situation, estimation of fc values from image (y-axis) will (c) (a) (b) http://dx.doi.org/10.14710/geoplanning.3.2.107-116 http://www6.paca.inra.fr/can-eye kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 | 111 be exactly correspond to the fc values measured from the field (x-axis), following the 1:1 diagonal line. however, this relationship is rarely happen in this type of study. any points higher than the 1:1 line indicate an over-estimation, and vice-versa. in this process, we used 30 independent validation samples. 2.6. mangrove forest degradation mapping to convert the mangrove degradation status from the fc image, we compared the estimated fc values with the qualitative observation of the mangrove appearance in the field and on the image. in this study, we consider any deterioration of canopy cover or changes from mangroves to non-mangroves cover which affect the ecological function of mangroves as forest degradation. we identified some degraded mangroves samples in the field and then pinpoint the corresponding locations on top of fc image to find out the value threshold of the degraded mangroves based on the fc map. we also extrapolate the degradation identification to other areas using pan-sharpened high spatial resolution worldview-2 image (0.5 m pixel size) and then bring the identification results back to the fc map. according to the field observation, the degraded mangroves were found mostly in water-logged areas, areas surrounding the fish ponds or agricultural fields, and at some of the sea-fringe areas. 3. results and discussion 3.1. mangrove fractional cover estimation the hemispherical photo processing resulted in the field fc values ranged from 0.17 to 0.96 (n = 69, mean = 0.72, sd = 0.19). the wide spread of fc values in this site can be explained by two factors; first, mangrove in karimunjawa island has a very high species diversity. second, in terms of vegetation structure, mangroves at karimunjawa island have high variation in canopy density, from low density in shrub formation to the dense one in the tall and mature tree formation. the combination of these two variations results in high variation of canopy fc. this result is supported by the finding by duke et al. (1998); which indicated the difference in environmental, physical and biotic setting where mangroves live was the main driving factors for mangrove biodiversity. therefore, the fc variation as a representation of mangrove forest structure is site dependent. figure 3a shows the regression function and relationships between in-situ fc measurement and evi. the kolmogorov-smirnov data normality test shows the data follow the normal distribution, thus appropriate for further processing using inferential statistics. the f-statistic and t-statistic for the models suggested that the relationship was statistically significant at p < 0.05 (with n = 39). the regression model has a positive relationship between the two variables with the coefficient of determination value (r2) of 0.6289. it means that about 63% of the field fc values can be explained by image evi values; an increase in the field fc values followed by an increase in image evi values. the low-moderate of r2 indicates that the use of hemispherical photos for fc estimation introduces some possible errors. these errors could be from the field sampling scheme design, hemispherical photos calculation, and positional error between field sample and image pixels. to apply the model to the image, we inverted the regression function to find the field fc (x-variable) using evi (y-variable). figure 3b shows the distribution of mangrove fc presented in an arbitrarily selected value ranges to indicate the variation of fc values. from figure 3b, it is obvious that most of the high density fc (>0.8) located at the mangrove-sea water fringe. there areas are dominated by mature, tall, and dense rhizophora stylosa species. the low fc values are located in the middle and landward area or the mangrove boundary. these areas are dominated by other mangrove species such as ceriops tagal, lumnitsera racemosa, bruguiera gymnorrhiza and xylocarpus granatum. http://dx.doi.org/10.14710/geoplanning.3.2.107-116 kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 112 | figure 3. (a) regression model between evi and in-situ fc, and (b) map of fc distribution (analysis, 2016) 3.2. accuracy assessment of the fractional cover estimation the 1:1 graph in figure 4 has a low coefficient of correlation (r2 = 0.55) with an rmse value of 0.14, as can also be seen from the scattered distribution pattern of the validation points. the spread of validation point’s pattern indicates that the model poses some degrees of error in estimating the fc value from the image evi. the low estimation accuracy could be caused by some factors, including absolute positional displacement of the field samples from the image pixels, inaccuracy in calculating the fc from hemispherical photos, and the sample position that were not well-distributed across the study site. the nature of mangrove zonation in the study area could also affect the result. the mangrove zonation in karimunjawa is composed from mixed-species with high variation in vegetation structure (tree height, canopy density, and (a) (b) http://dx.doi.org/10.14710/geoplanning.3.2.107-116 kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 | 113 stem density), that make the mangrove zonation is not ideal. therefore, it is very difficult to find homogeneous samples in the field. figure 4. plot of observed versus estimated fc of mangrove in karimunjawa island. the red-dashed line indicates the 1:1 relationship (analysis, 2016) in general, from the 1:1 line in figure 4, the estimated fc for lower values tends to be over-estimated, and under-estimated for the higher values. the over-estimated value was caused by the organic detritus of the mangrove trees found on the forest floor which add the total leaf reflectance recorder in the image. on the other hand, the under-estimated values are associated with high-density mangrove tree which commonly grow on the sea fringe where the tidal water frequently covers the forest floor. the tidal water absorbs the near-infrared spectrum used in evi calculation, thus reduce the value of evi. 3.3. mangrove degradation distribution the identification of degraded mangroves in the field and from the image resulted a threshold fc value of less than 0.48 to be considered as areas with degraded mangroves (red areas in figure 5a). as indicated previously, we can see from the figure 5a that degraded mangroves are located mostly in water-logged area, mangrove clearing for fishponds, and at the sea-fringe areas. based on the map calculation (figure 5b), there are 23.8 hectares (or 5.7%) of the study area categorised as degraded mangroves, indicated by the red polygons on the map. it is obvious that both natural and human disturbances caused the degradation of mangroves. figure 5c showed the dwarf ceriops tagal and avicennia marina community in a water-logged area. this area has low fc values due to some diebacks and low canopy density; therefore, it is identified as a naturally degraded mangroves. on the other hand, figure 5d shows the example of human induced degradation. the mangrove area has been clear cut for fish ponds development and coconut tree plantation. from the interview, we found this area has been abandoned for several years and remains to be an open area during the field visit. this type of land cover conversions are mostly found in the border between terrestrial and mangrove habitat and caused disturbance in mangrove forest. economic demands seem to be the underlying motivation of the local community in karimunjawa islands for converting mangrove forest to other land uses. http://dx.doi.org/10.14710/geoplanning.3.2.107-116 kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 114 | figure 5. (a) mangroves degradation status, (b) area comparison of degraded mangroves, (c) naturally degraded mangrove due to water-logged area, and (d) abandoned fish ponds in mangrove area (analysis, 2016) according to skidmore et al. (1997), sustainable land management refers to the attempts to balance the often conflicting ideals of economic growth of the society and at the same time maintaining environmental quality and viability. putting the map result into this context, the map in figure 5a provides a spatially-explicit physical information of the degraded mangroves in the study area. it contains locations and extent of the degraded mangroves. this information is one of the most important sources for conservation and management planning in this environment. by using this map, the stakeholders able to locate the distribution of the disturbances, identify their magnitude and their cause in the study area, and setup priority for sustainable management and planning. the results of this study show that alos avnir-2 data can efficiently be used as one of the sources in providing input information for planning and management activities in coastal area. information of mangrove degradation can be derived from remote sensing data through a semi-empirical approach. before any activities in environmental conservation and planning can be implemented, it requires a spatially-explicit information (or map) of the object of interests. thus, as mentioned by woodcock et al. (1983), the primary role of remote sensing in land management and planning is to provide information of the distribution of physical characteristics of the land that affecting the decision in location allocation or conservation in a study area. 4. conclusion this study found that fc estimation from alos avnir-2 data through hemispherical photos resulted in medium accuracy estimation. several factors might contribute to this issue, including positional error between samples in the field and image, inaccuracy in setting up parameters for hemispherical photos processing, field samples that were not well-distributed across the study site, and the heterogeneity of mangroves in the field. further efforts need to focus on studying the effects of sampling scheme and fc (a) (b) (c) (d) a b http://dx.doi.org/10.14710/geoplanning.3.2.107-116 kamal et al. / geoplanning: journal of geomatics and planning, vol 3, no 2, 2016, 107-116 doi: 10.14710/geoplanning.3.2.107-116 | 115 derivation parameters from hemispherical photos to the accuracy of fc estimation. mangrove degradation can be derived from fc by setting up a threshold based on field and image identification of the degraded mangroves. the final map shows the distribution of degraded mangroves in the study site. this information is needed to set up the priority for sustainable land management and planning in a coastal environment. the limitations of this study include (1) the use of medium spatial resolution alos avnir-2 only in the fc modelling, (2) single date observation and mapping, and (3) field samples that were not well-distributed due to the difficult access. nevertheless, this study provides a simple and robust method to estimate the degradation status of mangroves for environmental monitoring and assessment purposes. in the future, the similar approach needs to be undertaken at mangroves with different environmental setting and species composition to verify the finding. 5. acknowledgements access to image was provided by novi susetyo adi through the jaxa-kkp cooperation. field equipment and software were provided by the remote sensing laboratory of the faculty of geography, universitas gadjah mada, yogyakarta. fieldwork assistance was provided by tukiman, dimar wahyu anggara, and muhammad hafizt. 6. references asner, g. p. 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(2000). derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. journal of applied meteorology, 39(6), 826–839. http://doi.org/10.1175/15200450(2000)039<0826:daeogk>2.0.co;2 http://dx.doi.org/10.14710/geoplanning.3.2.107-116 geoplanning vol 2, no 1, 2015, 38-50 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning | 38 open access pemetaan perkembangan perhotelan di pusat perdagangan dan jasa kota semarang dengan sistem informasi geografis a. hermawana, j.a.syahbanab a universitas diponegoro, indonesia, email: andyhermawannn@gmail.com b universitas diponegoro, indonesia, email: yoesrona@yahoo.com abstract the expanded growth of the trade and services area of peterongan tawang – siliwangi (petawangi) in semarang city has resulted in a new growth of commercial center activity within the area. hotels as one of the potential trade and service components in the center of the area have an important role in the semarang economy. the growth of hotels is due to the growth of activities like meeting, incentives, conference, and exhibition (mice) as an effect of business activities in semarang city. the share of hotels in the petawangi area is around 70 percent of the total hotel tax in semarang city. the purpose of this research is to identify the characteristics of the growth of hotels in the petawangi area during the last 3 years (2011-2014). the research has used spatio-temporal gis and qualitative analysis, utilizing snowball sampling technique. the results show that the growth of hotels is excessive in the sense that the supply of hotel rooms exceeds the demand. the hotel growth is most intense in the urban villages of sekayu and pekunden. the emergence of the national government policy, which does not support the development of city hospitality through mice, may result in a decrease of the hotel tax in semarang for about 35 to 50 percent per year. to be able to keep the balance between supply and demand, better cooperation between the semarang city government and the hotels managements is needed. it is especially for developing the infrastructure such as integrated inter-modal transportation for easier access to and from airport, improvement of urban facilities, city tourism development, and organizing national events to increase the declining demand of mice. © 2015 gjgp undip. all rights reserved. abstract: adanya perkembangan pusat perdagangan dan jasa peterongan tawang siliwangi kota semarang yang lebih luas mengakibatkan tumbuhnya pusat-pusat kawasan komersial baru di dalamnya. hotel sebagai salah satu komponen perdagangan dan jasa yang potensial di pusat perdagangan dan jasa peterongan tawang siliwangi berperan penting sebagai penyumbang penerimaan pendapatan di kota semarang. hotel berkembang akibat adanya perkembangan aktivitas mice (meeting, incentives, conference, exhibition) dari efek aktivitas bisnis yang terjadi di kota semarang. hotel di kawasan petawangi memiliki 70% dari total pendapatan yang diterima dari seluruh kawasan yang ada di kota semarang. tujuan dari penelitian ini dimaksudkan untuk menelusuri bagaimana perkembangan yang terjadi di sektor perhotelan di pusat perdagangan dan jasa peterongan-tawang-siliwangi kota semarang. pendekatan penelitian yang digunakan yaitu dengan metode analisis spasial temporaldan analisis kualitatif dengan menggunakan metode pengumpulan data snowball sampling. hasil yang ditemukan bahwa saat ini pertumbuhan penyediaan perhotelan melampaui dari pertumbuhan permintaan kamar hotel. persebaran perhotelan terpadat terjadi di kelurahan sekayu dan kelurahan pekunden. munculnya kebijakan pemerintah pusat yang tidak mendukung perkembangan perhotelan pada kota mice mengakibatkan ancaman penurunan pendapatan hotel maupun pajak kota semarang 35-50% per tahunnya. untuk tetap dapat menjaga keseimbangan penyediaan dan permintaan maka diperlukan kerjasama antara pemerintah kota semarang dengan pengelola hotel untuk membangun sarana simpul transportasi bandara, pembenahan sarana prasarana perkotaan, pembuatan daya tarik wisata dalam kota maupun penyelenggaraan event nasional untuk menyeimbangkan penurunan permintaan mice. © 2015 gjgp undip. all rights reserved. article info; received: 29 march 2015 in revised form: 9 april 2015 accepted: 25 april 2015 available online: 30 april 2015 keywords: hotel’s mapping, gis, mice info artikel; diterima: 29 maret 2015 hasil revisi : 9 april 2015 disetujui: 25 april 2015 publikasi on-line: 30 april 2015 kata kunci: pemetaan hotel, sig, mice mailto:andyhermawannn@gmail.com mailto:yoesrona@yahoo.com geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 39 1. pendahuluan pertumbuhan dan perkembangan kota diwujudkan sebagai bentuk penerjemahan dari visi dan misi kota tersebut. visi adalah kondisi yang diinginkan pada akhir periode perencanaan yang direpresentasikan dalam sejumlah sasaran hasil pembangunan yang dicapai melalui program-program pembangunan dalam bentuk rencana kerja. kota semarang sebagai ibukota propinsi jawa tengah memiliki visi yang berlandaskan kondisi kota dan nilai historis yang dimilikinya. penentuan visi ini mendasarkan pada peraturan daerah nomor 6 tahun 2010 tentang rencana pembangunan jangka panjang daerah (rpjp) 2005 – 2025 dan penelusuran jejak historis kota semarang sebagai kota niaga di mana pada jaman dahulu pernah dinyatakan sebagai kota niaga terbesar kedua sesudah batavia. berdasar sejarah sebagai kota niaga tersebut dan didukung oleh analisis potensi, faktor-faktor strategis yang ada pada saat ini serta proyeksi pengembangan ke depan, maka dirumuskan visi kota semarang yaitu : “terwujudnya semarang kota perdagangan dan jasa, yang berbudaya menuju masyarakat sejahtera”. visi tersebut memiliki empat kunci pokok yakni kota perdagangan, kota jasa, kota berbudaya, dan masyarakat yang sejahtera. upaya yang dilakukan pemerintah dalam mengatur perkembangan kota semarang yang sesuai dengan visi misi serta untuk menghadapi pertumbuhan yang pesat adalah diberlakukannya pembangunan pada tingkatan bagian wilayah kota (bwk). sesuai dengan rencana tata ruang wilayah kota semarang tahun 2011-2031 dikatakan bahwa bwk adalah suatu kawasan fungsional atau kawasan yang memiliki kemiripan fungsi ruang. kota semarang terbagi menjadi 10 bagian wilayah kota dengan masing-masing bwk memiliki fungsi kawasannya tersendiri. sesuai dengan visi misi kota semarang sebagai kota perdagangan dan jasa, bwk 1 merupakan pusat kawasan yang memiliki fungsi perkantoran, perdagangan, dan jasa dengan kawsan segitiga pandama (pemuda, pandaran, gajahmada) serta simpang lima sebagai pusat kegiatannya atau cbd (central business district). bwk 1 ini mencakup kecamatan semarang tengah, semarang selatan, dan semarang timur dengan luasan 2.223 ha. saat ini kawasan cbd pandama telah berkembang menjadi pusat perdagangan dan jasa yang lebih luas menjadi kawasan petawangi (peterongan-tawang-siliwangi). kawasan petawangi sebagai pusat perdagangan dan jasa di kota semarang terus mengalami pertumbuhan yang pesat. saat ini pemerintah kota semarang sedang menggalakkan investasi skala nasional dan internasional dari penanaman modal dalam negeri maupun asing yang ditujukkan pada kawasan pusat perdagangan dan jasa petawangi. data yang berhasil dihimpun mencatat berbagai proyek besar perhotelan akan dibangun di kawsasan pusat kota tersebut. 2. data dan metode teori perkembangan kota diperlukan sebagai dasar teori yang mendasari perkembangan sektor perhotelan di pusat perdagangan dan jasa petawangi kota semarang. untuk mendalami teori tentang perkembangan kota sesuai yang terjadi di pusat kota semarang, maka sangat diperlukan teori perkembangan kota sebagai dasar. kota merupakan suatu kesatuan wilayah administratif yang memiliki tingkat kepadatan bangunan dan manusia yang tinggi di mana sebagian besar penduduknya bekerja bukan pada sektor pertanian. secara lebih kompleks seperti yang dijelaskan oleh yunus (2005:16-17) dalam sudut pandang morfologis, suatu kota dapat didefinisikan sebagai suatu daerah tertentu dengan karakteristik pemanfaatan lahan non pertanian, pemanfaatan lahan di mana sebagian besar tertutup oleh bangunan baik yang bersifat residensial maupun non residensial (secara umum tutupan bangunan / building coverage lebih besar dari komposisi vegetasi), kepadatan bangunan permukiman yang tinggi, memiliki pola jaringan jalan yang kompleks, dalam satuan permukiman yang kompak (contigous) dan relatif lebih besar dari satuan permukiman pedesaan di sekitarnya. berikut adalah penjelasan dari kedua bentuk perkembangan kota menurut yunus (2005:55-): 1. proses perkembangan spasial secara horizontal a. proses perkembangan spasial sentrifugal proses perkembangan ke luar inti perkembangan. b. proses perkembangan spasial sentripetal proses perkembangan kekotaan yang terjadi di bagian dalam kota (inner parts of the city) 2. proses perkembangan spasial secara vertikal gejala perkembangan vertikal ini adalah proses penambahan ruang kota dengan menambahkan jumlah lantai bangunan. perkembangan ini sering terjadi di area pusat kota (central business district) dikarenakan semakin minimnya lahan di pusat kota. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 40 sektor perhotelan erat kaitannya dengan kepariwisataan. kota semarang sebagai kota mice (meeting, incentives, conferencing, exhibition) sangat ditunjang dengan keberadaan layanan perhotelan untuk mendukung aktivitas mice tersebut. maka dari itu diperlukan perkembangan hotel yang optimal untuk mendukung aktivitas terkait pariwisata mice di kota semarang. 1. karakteristik jasa perhotelan kotler, dkk (2003) mengatakan bahwa pada dasarnya karakteristik pemasaran bidang jasa terbagi menjadi 4 hal yaitu: a. intangibility (tidak dapat didefinisikan) b. inseparability (tidak dapat dipisahkan) pelayanan yang tak terpisahkan berarti bahwa pengguna jasa juga merupakan bagian dari produk. implikasi lainnya adalah pelanggan dan pekerja harus memahami sistem pelayanan jasa. c. variability (berbagai macam bentuk) d. perishability (tidak dapat disimpan) peraturan tentang bagian wilayah kota i semarang diatur dalam perda nomor 6 tahun 2004. bwk i terdiri dari tiga kecamatan yaitu kecamatan semarang tengah, semarang timur, dan semarang selatan. fungsi kawasan ini adalah sebagai pusat kegiatan perdagangan, jasa, dan perkantoran di kota semarang. luasan masing-masing kecamatan di bwk 1 sebagai berikut: 1. kecamatan semarang tengah : 604, 997 ha; 2. kecamatan semarang timur : 770, 255 ha; 3. kecamatan semarang selatan : 848, 046 ha. sementara itu, secara administratif kawasan ini dibatasin oleh: 1. sebelah utara : kecamatan semarang utara. 2. sebelah selatan : kecamatan gajah mungkur dan kecamatan candisari. 3. sebelah timur : kecamatan gayamsari dan kecamatan genuk. 4. sebelah barat : kecamatan semarang barat. pada kawasan bwk i semarang ini dibagi menjadi 5 blok yang dikategorikan berdasarkan tata letak masing-masing kelurahan di dalamnya. berikut adalah data pembagian blok bwk 1 semarang: tabel 1. pembagian blok bwk i kota semarang (bappeda kota semarang, 2014) kecamatan kelurahan blok semarang tengah pindrikan lor, pindrikan kidul, sekayu, pandansari 1.1 kembangsari, bangunharjo, kauman, kranggan, purwodinatan 1.2 miroto, pekunden 1.3 grabahan, brumbungan, jagalan, karangkidul 1.4 semarang timur kemijen, rejomulyo 2.1 maltiharjo, mlatibaru 2.2 kebonagung, bugangan 2.3 semarang timur sarirejo, rejosari, 3.1 karangturi, karangtempel 3.2 semarang selatan bulustalan, barusari 4.1 randusari, mugasari 4.2 semarang tengah pleburan, wonodri 5.1 peterongan, lamper lor 5.2 lamper kidul, lamper tengah 5.3 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 41 untuk lebih jelas, di bawah ini adalah peta pembagian blok pada bwk i: gambar 1. pembagian blok bwk 1 (bappeda kota semarang, 2014) berdasarkan data yang dihimpun dari dinas pariwisata kota semarang tahun 2014, daftar hotel berbintang di kawasan pusat perdagangan dan jasa petawangi antara lain: tabel 2. hotel eksisting pusat perdagangan dan jasa petawangi dan sekitanya di kota semarang (dinas pariwisata kota semarang, 2014) no nama hotel alamat kelurahan 1 gumaya tower hotel jl gajah mada no 59-61 kelurahan kembangsari 2 ciputra simpang lima kelurahan pekunden 3 novotel jalan pemuda no 123 kelurahan sekayu 4 crowne plaza jalan pemuda no. 116-118 kelurahan sekayu 5 santika premiere jalan pandanaran no. 116-120 kelurahan pekunden 6 horison jl. kh ahmad dahlan no.2 kelurahan karang kidul 7 pandanaran jl pandanaran no. 58 kelurahan pekunden 8 siliwangi jl mgr soegijaprnoto no. 61 kelurahan pendrikan kidul 9 new metro jl kh agus salim no 2-4 kelurahan puwodinatan 10 quest jl plampitan no. 37-38 kelurahan bangunharjo 11 dafam jl imam bonjol kelurahan sekayu 12 semesta heritage jl kh wahid hasyim no 125-127 kelurahan bangunharjo 13 mg suites jl petempen malang no 294 kelurahan kembangsari 14 ibis jl. gajah mada no 172 kelurahan pekunden 15 holiday inn jl ahmad yani no 145 kelurahan pleburan 16 surya jl imam bonjol 28 kelurahan pandan sari 17 quirin jl gajah mada 44-52 kelurahan bangunharjo 18 amaris jl pemuda no 138 kelurahan sekayu 19 whiz jl kap. piere tendean no 9 kelurahan sekayu 20 ibis budget jl kap piere tendean kelurahan sekayu 21 citradream jl imam bonjol no 187 kelurahan pindrikan kidul 22 gajah mada 100 jl gajah mada no 100 kelurahan bangunharjo 23 @hom jl pandanaran no 119 kelurahan mugasari 24 merbabu jl pemuda no 122-124 kelurahan sekayu geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 42 no nama hotel alamat kelurahan 25 bali jl imam bonjol 144-148 kelurahan sekayu 26 aston jl mt haryono no 1 kelurahan purwodinatan masing-masing hotel tersebut jika ditampilkan pada area spasial, maka akan didapatkan sebaran hotel bintang seperti di bawah ini: gambar 2. hotel eksisting pusat perdagangan dan jasa petawangi (bappeda kota semarang, 2014) metode gis yang digunakan pada penelitian ini menggunakan beberapa tools analisis spasial yaitu: 1. analisis spasial temporal : bertujuan untuk mengetahui seberapa besar perubahan yang terjadi pada area perdagangan jasa yang terjadi di kawasan petawangi pada rtrw semarang tahun 2010 dan rtrw kota semarang tahun 2011-2031. 2. analisis clip : bertujuan untuk membuat irisan dan mengetahui wilayah baru pengembangan dari fungsi perdagangan dan jasa di petawangi. 3. analisis reclass : bertujuan untuk membuat kelompok-kelompok tingkatan/kelas wilayah secara spasial dengan menggunakan data kuantitatif/sekunder yang telah diinput pada data masing-masing kelurahan. selain itu, pendekatan non-gis yang digunakan dalam penelitian ini menggunakan pendekatan kualitatif dan analisis spasial temporal, untuk mengkaji perkembangan perhotelan di pusat perdagangan dan jasa peterongan tawangsiliwangi kota semarang. penelitian kualitatif merupakan penelitian yang menghasilkan analisis dengan menggunakan prosedur analisis yang memanfaatkan wawancara secara terbuka untuk mendapatkan informasi terkait objek yang diteliti, untuk memahami isu-isu yang dianggap sensitif dan tidak dapat diselesaikan dengan menggunakan metode penelitian kuantitatif (moleong, 2000:6). jenis analisis yang digunakan menggunakan metode deskriptif kualitatif. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 43 3. hasil dan pembahasan hasil temuan dan analisis diuraikan secara detail terhadap analisis analisis yang digunakan yaitu: 3.1 peluang perkembangan kawasan perdagangan dan jasa petawangi perkembangan sektor perhotelan di kawasan petawangi yang baru berjalan 3 tahun (2011-2014) masih membuka peluang yang terbuka lebar. peluang perkembangan sektor perhotelan di kawasan petawangi secara spesifik untuk saat ini masih terbuka lebar dikarenakan belum ada kajian pembatasan atau moratorium terhadap sektor tersebut. gambar 3. perluasan kawasan perdagangan dan jasa petawangi (hasil analisis, 2014) pada peta di atas telah digambarkan bahwa terdapat perluasan yang cukup siginfikan untuk kawasan campuran perdagangan dan jasa di sekitaran segitiga gajah mada-pemuda-pandanaran. kawasan yang dulunya murni permukiman saat ini telah berkembang menjadi kawasan campuran permukiman dan perdagangan jasa. perkembangan itu memunculkan kemungkinan besar perubahan fungsi lahan yang besar terhadap kawasan permukiman terutama pada kampung kota. beberapa kampung kota yang beralih fungsi lahan menjadi kawasan komersil perhotelan antara lain: a. kampung petempen : mg suites hotel dan semarang town square b. kampung sekayu : hotel crown dan mall paragon extension c. kampung jayenggaten : hotel gumaya geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 44 3.2 kebijakan pemerintah kota semarang 1. kebijakan perizinan perizinan merupakan pintu utama dari kebijakan yang diterbitkan oleh pemerintah. keberpihakan dan kinerja pemerintah kota semarang akan dilihat dari bagaimana kinerja dari pengurusan perizinan. saat ini bppt merupakan satu-satunya badan yang mengurusi perizinan pembangunan dan penanaman modal di kota semarang. dengan meningkatnya jumlah investasi maka diharapkan pertumbuhan ekonomi kota semarang juga meningkat dan multiplier efek dapat terjadi ke sektor lainnya. a. penyesuaian perda tata ruang b. tidak ada pembatasan izin perhotelan c. integrasi kegiatan umkm dengan perhotelan d. pengembangan pasar mice e. pelarangan kegiatan instansi pemerintah di hotel 2. dukungan pemerintah kota semarang terhadap perhotelan pemerintah kota semarang juga memberikan dukungan bagi sektor perhotelan yang tumnuh di kota semarang. dukungan dari pemerintah kota semarang sangat membantu bagi keberlanjutan sektor perhotelan khususnya di kawasan pusat perdagangan dan jasa petawangi. penjabaran dukungan dari pemerintah terhadap sektor perhotelan antara lain dukungan informasi perizinan dan peningkatan infrastruktur gambar 4. pengembangan infrastruktur petawangi (hasil analisis, 2014) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 45 3.3 analisis perkembangan perhotelan di pusat perdagangan dan jasa petawangi sebagai sektor potensial dan juga berada di pusat kawasan kota semarang, pertumbuhan sektor perhotelan di kawasan petawangi layak mendapatkan perhatian dan kajian akademik yang mendalam untuk pengembangan yang lebih optimal dan antisiapasi terhadap permasalahan yang akan muncul di kemudian hari. pada bahasan ini akan dijelaskan sebagai aspek yang berkaitan atas perkembangan perhotelan di kawasan petawangi kota semarang. merujuk pada informasi dan data yang didapatkan atas perkembangan perhotelan yang marak terjadi di tahun 2012, data instansi juga menunjukkan adanya lonjakan pengajuan pendirian hotel yang tinggi mulai tahun 2012 hingga akhir 2014 yang masuk di kota semarang. rekapitulasi perizinan perhotelan yang masuk di kota semarang pada tiga tahun terakhir dapat dilihat pada grafik di bawah ini: gambar 5. pertumbuhan izin pendirian hotel di semarang (hasil analisis, 2014) faktor lokasi menjadi faktor terpenting bagi penentuan sukses tidaknya usaha perhotelan. hal ini dikarenakan karena sektor perhotelan sangat berkaitan dengan layanan infastruktur lainnya seperti transportasi, perdagangan, pusat bisnis dan perkantoran. lokasi penetuan hotel berdekatan dengan area yang strategis dan mudah aksesbillitasnya bagi pengunjung. berdasarkan prinsip usaha yang disampaikan oleh sekretaris phri tentang faktor yang berpengaruh terdiri dari 4 komponen atau 4 p yaitu: place, product promotion, price. lokasi atau place merupakan faktor utama sebagai penentu keberhasilan usaha. gambar 6. persebaran hotel baru dan eksisting di petawangi (hasil analisis, 2014) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 46 berdasarakan kualifikasi data per masing-masing kelurahan, dikelompokkan menjadi tiga kategori berdasarkan banyaknya hotel yang ada. didapatkan tiga klasifikasi kelurahan dengan kepadatan hotel rendah (0-2), sedang (3-6) dan tinggi (7-12) yaitu: 1. kepadatan rendah : pindrikan lor, pindrikan kidul, pandansari, purwodinatan, kauman, kranggan, miroto, gabahan, brumbungan, jagalan. 2. kepadatan sedang : kembangsari, bangunharjo, pekunden, karang kidul. 3. kepadatan tinggi : sekayu. menelisik karakteristik kawasan mice, maka lokasi pusat layanan akan menjadi magnet utama permintaan akan perhotelan mice. untuk melihat secara keruangan, persebaran permintaan dapat dilihat pada peta di bawah ini: gambar 7. sebaran permintaan hotel mice (hasil analisis, 2014) sesuai dengan karakteristik mice, wilayah koridor jalan pandanaran simpang lima dan pemuda menjadi primadona sebagai preferensi lokasi bagi pengguna perhotelan di kota semarang. adanya kemudahan akses dan ketersediaan layanan dan sektor komersial menjadi daya tarik kedua lokasi tersebut menjadi lokasi favorit bagi pengguna mice. 3.4 karakteristik pasar mice 1. pasar mice kota semarang saat ini sektor perhotelan tengah berkembang untuk mendukung sektor basis kota semarang dengan pengembangan htel lama dan baru yang mendukung kegiatan mice. untuk saat ini fasilitas yang disediakan oleh pemerintah kota semarang belum mampu menampung segala jenis aktivitas bisnis dan pertemuan yang diadakan di semarang. 2. pengguna kegiatan mice kota semarang menurut cuplikan informasi wawancara menunjukkan bahwa pangsa pasar pengunjung dari kota semarang bukan berasal dari pendatang perorangan, hal ini kembali lagi terkait pada posisi kota semarang yang bukan merupakan kota yang dikunjungi untuk tujuan wisata seperti di jogja maupun bali. hampir semua kedatangan di kota semarang memiliki motovasi pekerjaan dan bisnis yang berasal dari pemeritah pusat dan pihak swasta. untuk melihat seberapa besar porsi untuk masing-masing pengguna mice, dapat dilihat pada tabel di bawah ini. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 47 tabel 3. presentase penggunaan mice petawangi (hasil analisis, 2014) hotel pengguna pemerintah swasta metro 30 60 grasia 35 35 quest 55 45 santika premiere 30 60 pandanaran 40 50 novotel 55 35 horison 40 30 rata-rata 40,714 45 total 85,71428571 data di atas menunjukkan bahwa pasar mice merupakan pasar yang potensial untuk pengembangan perekonomian di kota semarang. total dari hotel yang di survei menunjuukan bahwa pangsa pasar mice untuk swasta lebih tinggi 4-5% dari pangsa pasar mice pemerintah. hal ini bukan dikarenakan karena jumlah ketersediaan pasar dari pemerintah yang lebih rendah dibanding swasta. beberapa hotel memang membatasi kuota untuk pemerintah dikarenakan: i. pembayaran menggunakan ls: tidak dibayar secara langsung ii. antisipasi dari pelarangan yang dilakukan oleh pemerintah pusat 3.5 permasalahan terkait perkembangan sektor perhotelan di pusat perdagangan dan jasa petawangi semarang 1. permasalahan sudut pandang pemerintah permasalalahan atas perkembangan hotel ari sudut pemerintah lebih ditekankan kepada pelayanan dan kinerja dari perhotelan. permasalahan dilihat dari seberapa besar efek yang dimunculkan atas aktivitas perhotelan yang muncul dan menggangu palayanan publik ataupun mengurangi kualitas lingkungan. permasalahan yang diungkapkan oleh pemerintah antara lain: a. kurangnya ketersediaan parkir b. berkurangnya kualitas lingkungan c. kurangnya ruang terbuka hijau 2. permasalahan sudut pandang pelaku usaha di sisi lain, pelaku usaha melihat permasalahan yang terjadi dari aspek peluang usaha yang kian sulit dengan persaingan yang semakin ketat. maka menurut pelaku usaha, permasalahan yang muncul saat ini antara lain: a. pelarangan kegiatan instansi pemerintah di hotel. berdasarkan data dari phri kota semarang dapat disimpulkan bahwa terjadi penurunan yang signifikan terhadap ketidakberimbangan supply dan demand karena pelarangan kegiatan perhotelan terhadap pegawai negeri. penurunan terjadi dari tahun 2013 hingga awal tahun 2015 di mana jumlah hotel justru makin meningkat. penurunan terjadi hingga 33,6% antara tahun 2013 hingga 2015. untuk lebih jelasnya dapat dilihat pada table berikut ini. tabel 4. perhitungan tingkat hunian hotel petawangi (sumber: phri kota semarang) perkembangan perhotelan kawasan petawangi tahun jumlah hotel r. available r. sold occp arr total room rev 2013 15 816.473 560.942 70 496.035 285.834.617.463 2014 17 829.159 538.429 66 376.536 207.736.713.547 aprrox 2015 19 953.436 515.712 55 351.997 189.610.714.008 jan' 2015 19 79.453 42.976 55 351.997 15.800.892.834 pertumbuhan 4 136.963 -45.230 -15 -144.038 -96.223.903.455 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 48 analisis korelasi 0,90 -1,00 -0,97 -0,93 -0,94 korelasi terhadap penjualan kamar 0,97 0,93 0,94 b. keterbatasan bandara dan moda transportasi. c. ketidakberimbangan supply dan demand perhotelan. 3.6 analisis benchmark desain perkotaan lokasi pengembangan mice tabel 5. benchmark desain perkotaan pusat mice (hasil analisis, 2014) kondisi semarang singapura surabaya jalan di kota semarang masih di dominasi oleh kendaraan pribadi. namun pemerinah kota semarang telah berupaya untuk membangun layanan transportasi umum dengan bus rapid transit. orchard road singapura memberikan desain yang nyaman dan memberikan keseimbangan antara mobilisasi, pejalan kali dan angkutan umum. pembangunan desain yang moderen dan bersih memberikan kenyamanan bagi pengguna jalan untuk beralih menggunakan angkutan umum atau berjalan kaki. meskipun belum memilik layanan angkutan massal yang baik, kota surabaya merupakan kota yang bersih dan memiliki konsistensi yang baik terhadap penghijauan. pada jalan utama kota tersebut selalu dilengkapi dengan jalur hijau. jalan utama kota semarang berisikan berbagai aktivitas dari skala kecil, menengah, dan menengah ke atas. kota semarang masih memungkinkan adanya perpaduan antara komersial kecil hingga atas. penggunaan lahan di singapura terbagi menjadi sektor-sektor permukiman, perkantoran, dan pergadangan jasa. pada jalan orchard road memiliki fungsi komersial kelas dunia yang seragam persebaran sektor komersial dan perhotelan di surabaya tersebar di pusat kota surabaya dan terdapat di kawasan superblok yang tersebar di hampir seluruh kawasan kota surabaya. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 49 4. kesimpulan perhotelan perkotaan berbasis dagang dan bisnis memiliki perbedaan pada pengembangan perhotelan pada kawasan objek wisata konvensional. perhotelan pada kawasan perkotaan didorong oleh aktivitas ekonomi bisnis, perdagangan, dan jasa pada kota tersebut atau yang bisa disebut sebagai kegiatan mice (meeting, incentives, conference, and exhibition) yang tumbuh dan mengelompok pada pusat-pusat fasilitas dan layanan lainnya di pusat kota atau pusat kawasan. pasar mice kota semarang merupakan aspek potensial penyumbang pajak perhotelan hingga 70% dari total pendapatan pajak perhotelan kota semarang hingga tahun 2014. perkembangan hotel pada kota bisnis membentuk pola pengembangan kota secara vertikal dan intensif ke dalam bagian perkotaan. persebaran demand muncul pada pusat-pusat pelayanan kota seperti di sekitar simpang lima dan kawasan jalan pemuda sebagai bentuk kegiatan mice yang menginginkan mudahnya akses terhadap fasilitas penunjang lainnya. berkembangnya kegiatan bisnis memicu tumbuhnya perhotelan sebagai sektor akomodasi dalam menampung kegiatan rapat dan meeting yang dilakukan oleh instansi pemerintah maupun swasta. adanya pelarangan kegiatan pegawai negeri untuk mengadakan kegiatan di hotel mengakibatkan adanya penurunan pendapatan pengguna hotel. berkurangnya demand atas hotel ini jutru bersamaan dengan meningkatnya jumlah penyediaan hotel baru yang mengancam eksistensi hotel-hotel yang akan beroperasi di kota semarang maupun kota mice lainnya. diperlukan kajian terhadap peningkatan kuota dalam kebijakan pro-invetasi yang baik untuk menjaga stabilitas kegiatan ekonomi yang telah berjalan sebelumnya agar peningkatan supply tidak mengganggu dinamika pasar yang terjadi saat ini. penurunan penggunaan atas kamar hotel mengakibatkan adanya penurunan pendapatan dan penurunan pendapatan pajak hotel. jika tidak segera dibenahi dengan meperlebar jalur demand maka prospek pengembangan perhotelan tidak akan berkembang dikemudian hari. upaya dalam mengembangkan dan menjaga eksistensi sektor mice dalam perkotaan, pemerintah dan pelaku usaha perlu meningkatkan variasi layanan pada atraksi/daya tarik dan peningkatan layanan fasilitas dan infrastruktur. diperlukan revitalisasi objek wisata kota baik secara alamiah, buatan, maupun wisata sejarah yang dikembangkan sebagai daya tarik kunjungan wisata dalam kota. selain itu perkembangan perhotelan mice pada kota bisnis juga sangat bergantung pada simpul transportasi nasional dan internasional sebagai pintu utama bisnis, perdagangan, dan jasa di kota tersebut. di sisi lain, pelaku usaha perhotelan juga perlu memperhatikan permasalahan perhotelan pada aspek penyediaan parkir, penyediaan ruang terbuka hijau, dan pengolahan air limbah untuk menjaga kualitas lingkungan perkotaan. rekomendasi diberikan untuk peningkatan kualitas kawasan petawangi dan sektor perhotelan di dalamnya. rekomendasi dibagi menjadi dua bentuk yaitu rekomendasi kebijakan dan rekomendasi perencanaan. 1. rekomendasi kebijakan rekomendasi yang dapat diberikan kepada stakeholder terkait yang terkiat dalam pengembangan kawasan petawangi dan sektor perhotelan di dalam kawasan tersebut antara lain: a. rekomendasi pemerintah beberapa hal yang dapat dilakukan oleh pemerintah adalah: i. pengembangan transportasi bandar udara kota semarang ii. pembangunan objek wisata atau atraksi wisata baru di kota semarang iii. peningkatan kualitas infrastruktur perkotaan iv. penjagaan keamanan dan stabilitas politik di kota semarang v. pengkajian batas kuota pembangunan ijin perhotelan untuk menjaga dinamika pasar yang sehat b. rekomendasi pelaku usaha perhotelan diperlukan strategi yang perlu dilakukan oleh pelaku usaha dalam menciptakan sektor perhotelan yang nyaman, aman dan ideal. beberapa diantaranya berupa kebijakan dan pembenahan layanan dasar yang perlu ditingkatkan guna meningkatkan daya guna dan mengantisipasi permasalahan yang mungkin muncul akibat kegaiatn mice di hotel antara lain: i. pembangunan ruang parkir yang memadahi. ruang parkir dapat dilakukan secara masingmasing pengusaha hotel atau dengan gedung parkir komunal. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 38-50 hermawan dan syahbana | 50 ii. penambahan fasilitas pengolahan limbah untuk menjaga kualitas lingkungan yang tidak semakin memburuk iii. penyediaan ruang terbuka hijau (rth) sesuai dengan amanat uu penataan ruang yaitu 10% dari total luasan lahan. rth konvensional dapat dialihkan dengan menggunakan roof garden iv. antisipasi sasaran mice untuk menanggulangi dampak kerugian yang mungkin timbul v. komunikasi aktif dengan pemerintah kota semarang melaui wadah phri untuk penanganan permasalahan yang mungkin timbul dikemudian hari. 2. rekomendasi perencanaan sebagai kawasan padat dengan penggunaan lahan dengan tingkat diferensiasi yang tinggi, diperlukan metode perencaan yang komprehensif dan menyeluruh untuk menjaga kerbelanjutan pembangunan melalui konsep pembangunan berkelanjutan pada konsep kota kompak atau compact city. elkin (dalam zhou, 2011) mengtakan bahwa kota kompak adalah kota yang terkonsetrasi, memiliki kepadatan yang tinggi dan memiliki penggunaan lahan campuran yang intensif dan berdiri secara mandiri. hal ini sama yang terjadi dengan kawasan petawangi yang memiliki kepadatan yang tinggi dan campuran penggunaan lahan. untuk kondisi petawangi, perencanaan yang dapat dikembangkan antara lain: a. manajemen transportasi yang terintegrasi dan melayani semua ruang jalan di dalam kawasan petawangi. b. penyediaan layanan transportasi langsung dari petawangi ke simpul transportasi seperti bandara, stasiun dan terminal c. pembangunan bangunan permukiman baru secara vertikal pada segmen kelas menengah dan kebawah. d. pembenahan sistem saluran dan penyediaan ipal untuk menanggulangi degradasi lingkungan e. peningkatan kapasitas dan peran sosial terutama pada kampung-kampung kota yang eksistensinya terancam akibat perluasan lahan perdagangan dan jasa melalui regenarsi kawasan atau program kip pembangunan gedung parkir terpadu di kelurahan dengan kepadatan aktivitas hotel dan komersial tinggi, terutama sekayu, pekunden, dan karang kidul. 5. daftar pustaka bappeda. (2004). perda kota semarang nomor 6 tahun 2004. badan perencanaan pembangunan daerah kota sematang. bps. 2013 kota semarang dalam angka 2013. badan pusat statistik kota semarang. catanese, anthony. j dan j. james. c. snynder. (1986). teori perencanaan kota. jakarta. penerbit :erlangga. drucker, peter f. (2002). management challenges for the 21th century. us: perfect bound. kotler, philip, dkk. (2003). marketing for hospitality and tourism. new jersey: prentice hall. kotler, philip, dkk. (1993). marketing places. new york: the free press. moleng, lexy j. (2005). metodologi penelitian kualitatif. bandung: rosda. porter,michael e. (1990). the competitive advantage of nation. new york: the free press. wardiyanta. (2006). metode penelitian pariwisata. yogyakarta: penerbit andi. yunus, hadi sabari. (1982). pengarahan pemahaman pengertian kota. yogyakarta: fakultas geografi,ugm . yunus, hadi sabari. (2005). manajemen kota: perspektif spasial. yogyakarta: pustaka pelajar. yunus, hadi sabari. (2006). megapolitan: konsep, problematika, dan prospek. yogyakarta: pustaka pelajar. refleksi 5 tahun paska erupsi gunung merapi 2010: menaksir kerugian ekologis di kawasan taman nasional gunung merapi | 15 geoplanning vol 3, no 1, 2016, 15-22 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi:10.14710/geoplanning.3.1.15-22 gis-based analysis for assessing landslide and drought hazard in the corridor of mt. merapi and mt. merbabu national park, indonesia h. marhaentoa a gadjah mada university, indonesia abstract: a corridor is an area located between two or more protected areas that are important to support the sustainability of the protected areas. this study is aimed at assessing landslide and drought hazard in the corridor area between mt. merapi national park (mmnp) and mt. merbabu national park (mmbnp) as a part of the corridor management strategy. the corridor area of mmnp and mmbnp comprises four sub-districts in central java province, namely, sawangan, selo, ampel, and cepogo. a spatial analysis of arcgis 10.1 software was used to assess landslide hazard map and the thornthwaite & mather water balance approach was used to assess drought hazard map. the results have shown that three villages in cepogo sub-district and all villages in selo sub-district are highly prone to landslide hazard. furthermore, two villages in cepogo sub-district and four villages in selo sub-district are prone to drought hazard. this study suggests that these villages should initiate a program called conservation village model based on disaster mitigation for mitigating future landslide and drought disasters. copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): marhaento, h. (2016). gis-based analysis for assessing landslide and drought hazard in the corridor of mt. merapi and mt. merbabu national park, indonesia. geoplanning: journal of geomatics and planning, 3(1), 15-22. doi:10.14710/geoplanning.3.1.15-22 1. introduction the 5th world congress on national parks in durban, south africa in 2003 under the theme benefits beyond boundaries recommended that the principle of collaborative management between public bodies and local communities become a new model in the management of national parks (iucn, 2005). a manifestation of this new paradigm is the arrangement of the buffer zone of protected areas. conceptually, a buffer zone aims to enhance the conservation values of the buffered area (regulation of indonesian government no.68 of 1998). a buffer zone that connects two or more protected areas is known as a corridor area (beier & noss, 1998; indrawan et al., 2012). corridor area can be production forests and plantations, and even cultivated lands owned by communities, which, if it is designed properly, will be a valuable conservation tool (beier & noss, 1998). management of corridor area so far focused on its function as the expansion of protected areas to connect between biomes (joshi et al., 2013; wangchuk, 2007). numerous studies had been done to explain the function of the corridor area as wildlife migration path especially those with a broad range of habitat (douglas-hamilton et al., 2005; joshi et al., 2011; silveira et al., 2014). however, the presence of people who live in the corridor can be a threat to the survival of wildlife migration process (kushwaha & hazarika, 2004). the expansion of settlements and cultivation of seasonal crops became the most influential factor to disturb the existence of the corridor (joshi et al., 2011). one of the efforts to preserve the corridor area is through spatial planning and land management of it. in contrast to that of the other types of region, spatial planning of a corridor area has rarely got attention (pouzols & moilanen, 2014). moreover, mcrae et al., (2012) stated that there is a lack of spatial planning in a corridor area that focuses on protecting biological diversity in all levels (genetic, species, and landscapes). one of the obstacles is less of understanding between protected area managers and regional article info: received: 10 january 2016 in revised form: 12 march 2016 accepted: 25 april 2016 available online: 30 april 2016 keywords: corridor area, hazard analysis, gis, landslide, drought corresponding author: hero marhaento gadjah mada university, yogyakarta, indonesia email: marhaento@ugm.ac.id open access http://dx.doi.org/10.14710/geoplanning.3.1.15-22 mailto:marhaento@ugm.ac.id marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 16 | governments (anshari, 2006). in addition, people who live in corridor area are also lack of understanding about other functions of protected area, especially as a water and soil regulator to prevent natural disasters (qutni, 2004). the aspects of disaster vulnerability in the corridor management strategy are still new and challenging. we believe that putting the element of disaster management in corridor areas is one of the solutions to reach an understanding between protected area managers, local communities, and local governments. issues on disaster mitigation are more attractive than biodiversity protection so that it may gain more support from local community and local government. in addition, it is obligatory for local governments to protect communities from future disaster as stated in the indonesia law no. 24 of 2007 on disaster management. this study aims to analyze the two potential hazards in the corridor area of mt. merapi national park (mmnp) and mt. merbabu national park (mmbnp), namely landslides and drought. the corridor area of mmnp and mmbnp comprises administratively the sawangan sub-district (magelang district), selo subdistrict, cepogo sub-district and ampel sub-district (boyolali district) (see figure 1). according to infront (2008), these research areas are classified landslides prone caused by non-conservative tillage practice on the land use management. moreover, agricultural productivity in the research sites is threatened to decrease due to hydrometeorological conditions (putri, 2008). the results of this study will be useful as an advice for the conservation manager and local government to manage the corridor area based on disaster mitigation of landslides and drought hazard. figure 1. map of the study area (geospatial information agency of indonesia, 2015) 2. data and methods 2.1 research material all the materials used in the study were obtained from various sources, both institutional and public domains. the institutional data was acquired by examining the data and reports from relevant institutions, while the public domain data was acquired by online access from the web page of the data provider. table 1 describes the materials used in this study. table 1. research material (authors, 2015) name scale/resolution source administration map land use map altitude map slope map 1:25.000 indonesia topographic map nlp: 1408-244, 1408-333, 1408-522, 1408-611; geospatial information agency canopy density map 15 meter citra aster vnir on 07 august 2009 rainfall data monthly, 1997 – 2007 selo rain station, cepogo rain station, sawangan rain station and ampel rain station air temperature data monthly, 1997 – 2007 mt merapi observation station, center for volcanology and geological hazard mitigation (pvmbg) soil map 1:50,000 boyolali development planning agency (bappeda) marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 | 17 note: 1. the slope map is obtained by analyzing the digital elevation model (dem) map from the contour map of indonesia topographic map 2. canopy density map is obtained by analyzing the normalized difference vegetation index (ndvi) image of aster vnir and classified into 3 classes: high (ndvi ≥ 0.6), moderate (0.2 ≤ ndvi < 0.6) and low (ndvi < 0.2) 3. we performed all spatial analysis using arcgis 10.1 software. 2.2 landslide hazard analysis landslide hazard analysis was conducted by the spatial analysis of the factors that influence the occurrence of landslides in the area of research, namely: slope, land use, soil depth, soil type and rainfall. selection of these factors was based on field observations, previous studies from marhaento & sudibyakto (2007), subekti & hadmoko (2013), and interviews with the local communities. subsequently, all these factors were given a score for each class according to the level of importance on landslide occurrences (see table 2). we used overlay analysis using arcgis 10.1 software to combine all spatial information. the sum of scores from each class of each land unit was then used to determine the landslides hazard level. the results of the final score were proportionally classified into 5 classes, namely very low (vl), low (l), medium (m), high (h) and very high (vh). table 2. scores of each landslides triggering factor in the study area (marhaento & sudibyakto, 2007; subekti & hadmoko, 2013) factor class score land use hd : high density (ndvi ≥ 0.6) md: medium density (0.2 ≤ ndvi < 0.6) ld : low density ( ndvi < 0.2) hd mixed-plantation, md mixed-plantation 10 pasture and shrub 20 ld mixed-plantation 30 settlements, rice field 40 moor 50 mean annual rainfall (mm) < 2000 10 2000 < 2500 20 2500 < 3000 30 ≥ 3000 40 slope class (%) 0 < 8 10 8 – < 15 20 15 – < 25 30 25 – < 45 40 ≥ 45 50 soil depth (cm) < 90 10 90 – < 150 20 150 – < 300 30 ≥ 300 40 soil type grey andosol complex and litosols, brown andosols, brown latosols 30 grey regosol complex and latosols, litosols, brown litosols 40 2.3 drought hazard analysis drought hazard analysis was carried out by measuring the water balance and aridity index using the thornthwaite and matter water balance (tmwb) method (thornthwaite & mather, 1957). aridity index is a ratio between soil moisture deficiency and water demand for potential evapotranspiration to occur. the analysis calculates water balance in monthly basis and averages it in annual basis. there are three possible water balance conditions. first, a balance condition occurs when soil moisture meets water demand for potential evapotranspiration. second, a surplus condition occurs when soil moisture exceeds evapotranspiration needs and then contributes to runoff. third, a deficit condition occurs when soil moisture is not sufficient for fulfil evapotranspiration demand. tmwb aridity index (ai) is calculated using the following equation: n d ai *100  ; where the water deficiency d is calculated as the sum of the monthly differences between precipitation and potential evapotranspiration for those months when the normal precipitation is less than the normal evapotranspiration; and n stands for the sum of monthly values of potential evapotranspiration for the deficient months. based on the results of ai calculation and considering the agro-climate zones according to marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 18 | oldeman, we determined the drought hazard level based on the criteria on table 3. we performed ai calculation in each land mapping unit (lmu) that is formed by soil type map and elevation map. we note that drought hazard analysis in the present study continued the work of putri (2008). table 3. criteria for drought hazard classes (thornthwaite & mather, 1957) no. hazard class criteria 1. 2. 3. not risk (nr) risk (r) highly risk (hr) ia < 16,7 % 16,7 % < ia < 33,3 % ia ≥ 33,3 % three parameters are required to calculate d and n, namely monthly air temperature average, monthly rainfall and storage capacity. storage capacity parameter is determined by soil-texture and root-depth. in order to give an insight about the procedure to calculate the ai, steps to determine ai are as follows: 1. calculate monthly mean temperature (t) 2. calculate heat index value (i) monthly for each month, according to the formula 3. calculate annual heat index (i) and constant values (a) with the formula:    12 1j iji a = (675.10-9 *i3) (771.10-7 *i2) + (1792.10-5 *i) + 0.49239 4. calculate mean monthly potential evapotranspiration (pet) (mm / month), with the formula pet = 1.6 (i* t* 10) a 5. determine latitude correction factor (f) according to site study, which in the present study is at latitude 70 so that the value of f is: 7 0 s month 1 2 3 4 5 6 7 8 9 10 11 12 f 1.07 0.96 1.04 1.00 1.02 0.98 1.02 1.03 1.00 1.05 1.04 1.07 6. calculate corrected value of potential evapotranspiration (et) (mm / month), with the formula et = pet * f 7. calculate monthly rainfall data (p) 8. calculate difference between rainfall and potential evapotranspiration in monthly basis (p et) 9. calculate potential accumulation of water lost (apwl). if the result of the calculation no.8 is positive, then the value apwl is zero, whereas if the result of the calculation no.8 is negative, then apwl is calculated based on accumulative of negative values until reach positive. 10. calculate storage capacity (sto) that taking into account soil texture and root-depth. 11. calculate soil water consumption (st) st is in optimum condition when apwl is positive. if apwl is negative, st is calculated using the formula: st = sto * eapwl / sto e = 2, 718 12. calculate change in soil moisture (δ st) in monthly basis. change in soil moisture (mm / month) is the difference between usage of soil moisture in a month with usage of soil moisture in a previous month st δ = sti – sti-1 13. calculate the actual evapotranspiration (ea) in wet months (p > et) with the formula ea = et, while in dry months (p <et) we use formula, ea = p + |-δ st │ 14. calculate water deficiency (d) water deficiency is calculated in months when p < et using formula d = et – ea. marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 | 19 3. results and discussion 3.1 landslide hazard analysis the study area has nine land use classes, the three dominant classes of which are moor with the area of 3,552.8 ha (47.6%), settlement area of 1.158,2 ha (15.5%), and high density mixed-plantation area of 868.3 ha (11,6%). the moor areas consist of vegetables such as cabbage (brassica oleracea), prei (allium porum), carrot (daucus carota), onion (allium cepa), jipang (sechium edule), fennel (pimpinela anisium) and beans (phaseolus vulgaris). the mixed-plantation areas consist of perennial trees such as: sengon (paraserianthes falcataria), suren (toona sureni), leucaena (leucaena leucocephala), jackfruit (artocarpus sp), gmelina (gmelina arborea), acacia (acacia auriculiformis), cinnamon (cinamomum sp) and teak (tectona grandis). based on the digital elevation model (dem), medium slope and steep slope are dominant with the area of 2,664.2 ha (35.7%) and 1,783.5 ha (23.9%) respectively. the dominant soil type is andosol with the area of 3,513.9 ha (47.1%) and complex regosol and latosol type with the area of 2,840.3 ha (38.1%). soil with the depth of less than 90 cm and between 90-150 cm are dominant with the area of 2,632.4 ha (35.3%) and 4,823.4 ha (64.7%) respectively. using krigging analysis in the arcgis software to analyze monthly rainfall data from years 1997 2007 of four rainfall stations, we found that 6,540.9 ha (87.7%) area has a mean annual rainfall of 2500 3000 mm/year and 914.9 ha (12.3%) area has a mean annual rainfall above 3.000 mm, which mainly occurred in the cepogo sub-district. after overlaying all thematic maps and classifying the scoring results, we found that medium hazard class (m) is dominant in the study area with the area of 3,131.5 hectares (42.0%), followed by low class (l) with an area of 2,060.1 ha (27.6 %) and high class (h) with the area of 1,713.0 ha (23.0%). table 4 shows the detail results of the hazard analysis. table 4. landslide hazard classes in the study area (data processing, 2015) landslides vulnerability class size (ha) % very low (vl) 523.5 7.0 low (l) 2,060.1 27.6 medium (m) 3,131.5 42.0 high (h) 1,713.0 23.0 very high (vh) 27.6 0.4 total 7,455.8 100.0 we found that the area with high level of landslides hazard was the eastern part of selo and all villages in the cepogo sub-district. apparently, high rainfall occurrence and steep slope that dominant in these areas was the main cause of the high level of landslide hazard. in addition, we also found that landslides frequently occur on along the main road that connect selo sub-district with cepogo sub-districts. figure 2 shows the spatial distribution of landslide hazard in the study area. figure 2. landslide hazard map of the study area (analysis, 2015) marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 20 | 3.2 drought hazard analysis table 5 shows the calculation of aridity index (ai) in each land mapping unit. the results show that the entire area has ai value below 16.7%, which categorized as not risk (nr)—according thornthwaite and mather (1957). however, there are some areas that have a high value of ai, such as cepogo village and kembangkuning village at cepogo sub-district, and jeruk village, senden village, tarubatang villages, and selo villages at selo sub-district. figure 3 shows the spatial distribution of drought hazard in the study area. table 5. aridity index in each land mapping unit (data processing, 2015) no land mapping unit elevation (m dpl) ai (%) 1 ampel_ brown andosol 1000 – 1500 m 1267 4.0 2 ampel_ brown andosol > 1500 m 1664 3.3 3 ampel_ grey andosol complex and lithosol >1500 m 1664 1.9 4 ampel_ grey andosol complex and lithosol 1000 – 1500 m 1664 2.0 5 ampel_brown latosol 1000 – 1500 m 1245 2.8 6 cepogo_brown andosol < 1000 m 964 11.1 7 cepogo_brown andosol coklat 1000 – 1500 m 1102 7.1 8 cepogo_ grey complex regosol and lathosol < 1000 m 834 3.5 9 cepogo_ grey complex regosol and lathosol 1000 – 1500 m 1085 3.9 10 cepogo_brown latosol < 1000 m 744 3.1 11 cepogo_brown latosol 1000 – 1500 m 1110 4.1 12 cepogo_litosol coklat < 1000 m 812 3.2 13 cepogo_brown litosol 1000 – 1500 m 1039 3.8 14 selo_brown andosol < 1000 m 996 2.8 15 selo_ brown andosol 1000 – 1500 m 1326 2.6 16 selo_ brown andosol > 1500 m 1950 1.4 17 selo_ grey andosol complex and lithosol 1000 – 1500 m 1475 1.2 18 selo_ grey andosol complex and lithosol > 1500 m 1934 0.8 19 selo_ grey regosol complex and latosol < 1000 m 948 2.3 20 selo_ grey regosol complex and latosol 1000 – 1500 m 1276 1.6 21 selo_ grey regosol complex and latosol > 1500 m 1704 1.2 22 selo_brown latosol 1000 – 1500 m 1262 1.6 23 selo_brown litosol < 1000 m 966 2.4 24 selo_brown litosol 1000 – 1500 m 1032 4.4 note: land mapping unit consist of sub-district name, soil type and elevation class figure 3. drought hazard map of the study area (analysis, 2015) marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 | 21 3.3 discussion the new paradigm in management of protected area to emphasize collaborative management between public bodies and local communities brings consequence that the buffer zone of protected area should be managed. the importance of a buffer zone is even more when it connects more than one protected areas as a corridor area (joshi et al., 2011). in the present study, hazard analysis is used as one component of strategy to manage the corridor area between the mt. merbabu national park (mmbnp) and the mt. merapi national park (mmnp). numerous studies on the management of protected and corridor areas have focused on aspects of the protection of biodiversity (douglas-hamilton et al., 2005; kushwaha & hazarika, 2004; marhaento & kurnia, 2015; silveira et al., 2014). in fact, the role of corridor area as a protector from potential disasters for its surroundings is also significant and thus requires more attention. landslides and land drought are two kinds of natural disasters that can disrupt productivity of land and have a major impact on socio-economic of local communities—in the study area, they are mostly farmers. we found that approximately 23% of the area has a severe impact of landslide from high to very high grade. these areas are mainly in cepogo village, genting village, and sukabumi village at the cepogo subdistrict and in all villages at selo sub-district that is adjacent with mmnp and mmbnp. the landslides frequently occur in along the main road connecting selo sub-district with cepogo sub-districts. it shows that the cutting slope, which is often performed in the road-construction process, is as important factor in the landslide occurences. marhaento & sudibyakto (2007), priyono (2008), subekti & hadmoko (2013), and nirwansyah et al. (2015) also delivered the high contribution of cutting slopes in the landslide. using thornthwaite-mather water balance (twmb) method, we found that all areas have an aridity index (ai) value below 16.7%, or it includes in not risk criteria. it indicates that the water balance in the corridor area is sufficient to support crop productivity (thornthwaite & mather, 1957). however, some areas have a quite high ai value close to 16.7%, i.e. cepogo village and kembangkuning village at cepogo sub-district and jeruk village, senden village, tarubatang villages, and selo villages at selo sub-district. the areas detected to be prone to future landslides and drought would then become a prime target in the management strategy of mmbnp and mmnp corridor area. we suggest the scheme of rural conservation (dk) according to regulation of the forestry minister no.p-16 / menhut-ii / 2011 be implemented on villages with high level of hazards. furthermore, these selected villages should be designated as model desa konservasi berbasis mitigasi bencana or rural conservation model based on disaster mitigation effort (mdk-bmb). the selected villages in this scheme are pointed as a model to apply strategy on community empowerment with full respect to soil and land conservation tillage and disaster mitigation. 4. conclusion the results showed that several villages in the corridor area of mt. merapi national park (mmnp) and mt. merbabu national park (mmbnp) are prone to landslides and drought. the villages are cepogo, genting, and sukabumi villages at cepogo sub-district and all villages at selo sub-district that are located adjacent to the area of mmnp and mmbnp. the villages that are prone to drought hazard are cepogo village and kembangkuning village at cepogo sub-district and jeruk village, senden village, tarubatang village, and selo village at selo sub-district. we suggest these villages to be included in the national program called rural conservation model based on disaster mitigation effort (mdk-bmb), the implementation of which should be collaborative between the local governments and the conservation area managers. 5. acknowledgments the author acknowledges faculty of forestry, university of gadjah mada (ugm) for its support and fund for the research. the author would like to thank totok wahyu wibowo from the faculty of geography, ugm for his help in processing the spatial data, as well as prima nugroho and yanuar endro wicaksono for their assistance during the field work. marhaento / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 15-22 doi:10.14710/geoplanning.3.1.15-22 22 | 6. references anshari, g. z. 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(2007). maintaining ecological resilience by linking protected areas through biological corridors in bhutan. tropical ecology, 48(2), 177–187. 73 geoplanning journal of geomatics and planning vol. 9, no. 2, 2022 original research assessing urban development impacts in the padang coastline city, west sumatra indonesia; coastline changes and coastal vulnerability ulung j. wisha 1,2*, ruzana dhiauddin 3, koko ondara 2, wisnu a. gemilang 3, guntur a. rahmawan3 1. physical oceanography laboratory, department of physics and earth sciences, university of the ryukyus, japan 2. research center for oceanography, national research and innovation agency (brin), indonesia 3. research institute for coastal resources and vulnerability, ministry of marine affairs and fisheries, indonesia doi: 10.14710/geoplanning.9.2.73-88 abstract the capital coastline city of padang is intensively developed to enhance tourism attractions and protect the coastline from natural hazards and disasters. massive urban developments applied in the coastal area have not gone well, and several regions have been eroded and unstable. this study aimed to determine the significant change in padang city's coastline due to rapid urban development in the coastal area. spatial analyses are employed to determine the coastline changes and coastal vulnerability, such as a dsas (digital shoreline analysis system) and smartline-associated cvi (coastal vulnerability index) approach. a hydrodynamic and coastal model is also used to illustrate the transport mechanism and predict the level of coastal erosion. the result shows that substantial coastal changes and vulnerability have occurred. of particular concern, 77.21 % of padang's coastline is eroded with a rate of 0.21 49.4 m/year, 10.46% stable, and the rest, 12.32% experiencing accretion. more than 9% of coastal areas are categorized as highly vulnerable. the hydrodynamic-based model confirms the coastal erosion in several significant areas in padang city, proven by the relatively high value of bed-level change (ranging from 0.39 up to -4 m) and considerable variability of seasonal sediment transport and suspended materials. the erratic hydrodynamics and ineffective coastal building are the primary factors triggering padang city's coastal instability. copyright © 2022 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction padang coastline city is geographically positioned on the west coast of sumatra, indonesia, with a coastline of 84 km. the general city area is 694.96 km2; more than 60% of that area is protected forest, and the rest are functional urban areas. the topography state of padang city varies; 49.49% of the site is situated in the slope area, and around 23% is the declivous area. padang city faces the indian ocean in the west, below the equator, and between eurasian and indo-australian plates boundaries (ramdhan, 2021). this state makes padang city prone to coastal hazards and disasters (gemilang et al., 2017). that is why the government has rebuilt and rehabilitated the coastal protections since 2010. on the other hand, 25% of padang city is a built-up area. the developments in the coastal zone have been occurring because of tourism interest. the rapid urban development applied in padang city without any scientific assessments impacts the other area nearby on unstable coastal erosion and sedimentation. as a result, several regions are eroded, as reported e-issn: 2355-6544 received: 01 october 2022; accepted: 29 november 2022; published: 08 december 2022. keywords: coastal development, padang city, coastline instability, coastal erosion, and morphological alteration *corresponding author(s) email: ulun002@brin.go.id https://doi.org/10.14710/geoplanning.9.2.73-88 mailto:ulun002@brin.go.id wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 74 in padang beach, northern padang, and within the bungus teluk kabung area (figure 1). those areas are significant as the center for government buildings, department stores, tourism, port, schools, and universities. supported by unstable geological settings, coastal erosion is undoubtedly avoided. indeed, change in the coastal area is always undergone, and it will be stable naturally without any significant artificial interventions. therefore, making a substantial development without any scientific reason in the coastal area will worsen the condition. source: analysis, 2022 figure 1. the coastal areas impacted erosion throughout padang coastline city; red square denotes the area of study padang coastal area comprises alluvial properties arranged by coarse sand and mud throughout the coastline with a topography profile ranging from 0 to 10 m above sea level (fajrin et al., 2021). ocean wave exposure mainly triggers the dynamics of the padang coastline (rizal & ningsih, 2020). to date, the report of coastal erosion cases is still skyrocketing. according to haryani & syah (2018), erosion on the padang coastline is triggered by waves-induced longshore currents. at the same time, the accretion concentrated in the estuarine areas is caused by silting and river flow changes. determining the causal factors triggering coastal processes is crucial since coastal instability is undergoing in padang city. coastline change and vulnerability in padang city should be studied more. moreover, the local news published many reports regarding erosion occurring along the coastal area. the speed of coastal erosion in padang city has been reviewed by fajri & tanjung (2012), but this study only focused on the three observation stations in the padang barat sub-district. the effectiveness of coastal building in padang beach was also revealed by wardani et al. (2019). yuhendra et al. (2019) calculated the erosion in air manis beach by approximately 50 m. the effects and adaptation to coastal vulnerability in padang city have been reported by (ramdhan, 2021), describing the disaster and climate change threats and the adaptation adopted by the local government. many https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 75 kinds of coastal hazards and disasters threatening padang coastline city have also been described (gemilang et al., 2017; oktiari & manurung, 2010; putri et al., 2018; tohari et al., 2011). the previous studies only report erosion in one or two significant areas over the padang coastline. the coastline change identification needs to be updated accurately. a regional survey identifying coastline change throughout the coastline area of padang city is essential since rapid urban developments have been applied, and coastal erosion cases are still reported (hakam et al., 2019; yuhendra et al., 2019). this study analyzes the tenyear coastline changes by employing the dsasv5 and a modified smartline method to assess the 14 chosen physical factors triggering the susceptibility level in the coastal area. on the other hand, a hydrodynamic model simulating the longshore-current patterns and predicting the level of erosion and sedimentation should be carried out. no reports describe the coastal morphological alteration and modeling to date, and these aspects should be investigated. however, the information on coastal vulnerability is significant for the local and central government in future decision-making for early mitigation, physical support, and future regional development. this study aims to determine the coastline changes, assess the vulnerability level, and estimate the possible future alteration along the padang city coastline. 2. data and methods 2.1. study site the study area is focused on the padang city coastline, west sumatra, indonesia (figure 1). generally, the coastal area of padang city is structured by alluvial compositions arranged by coarse sand and mud sediment within a declivous slope (gemilang et al., 2017). the wave regimes from the open ocean provenance influence the dynamics of the coastal area. the erosion predominantly occurs along the coastline detected in the upstream regions, proven by the high sediment supply flowing through the river and settling in the surrounding estuaries (hakam et al., 2019). the variability of wave-driven current is believed to be the primary factor triggering coastal instability in padang city. 146 observation points within six subdistricts (koto tangah up to bungus teluk kabung) have been observed directly in the field (figure 2). this survey was conducted during april and august 2019. geologically, the coastal area of padang city is composed of igneous rock, sediment, and soft-type sediment. the formation of the coastal area is related to the beach constituent materials because the study area is composed of compacted sediment associated with cliff materials resulting in the declivous hilly formation. the coastal elevation in padang city is divided into three segments: elevation >25 meters observed along 4 km between lubuk begalung and bungus teluk kabung; elevation of 4-10 m found throughout the coastline, except for padang barat subdistrict with 0-3 m elevation. on the other hand, the slope of padang city ranged from 113% identified along 63 km from koto tangah up to bungus teluk kabung subdistrict (tanto et al., 2017). even though artificial coastal protection triggers unstable sediment transport, it might protect the coastal area from disasters and hazards. that is why along 29 km (39%) of coastline in koto tangah, padang utara, padang barat, padang selatan up to bungus teluk kabung subdistricts is protected by natural greenbelt and artificial coastal protection. on the other hand, almost 52.5 km from koto tangah up to padang selatan subdistrict is used as a settlement area. in contrast, the bungus teluk kabung subdistrict is commonly used as the center for industrial and fisheries interests (gemilang et al., 2017). 2.2. field data collection the field data were collected via direct field survey and secondary data modeling. we employed a smartline technique (lins-de-barros & muehe, 2013; sharples et al., 2009; thom et al., 2018) with several modified parameters according to the nature of the study area. direct observation of the eleven chosen parameters (beach materials, geomorphological states, wave exposure, beach slope, wave height, sediment grain size, distance between coastal to the prone area, coastal berm, beach face features, land use, and coastal building condition) was performed. on the other hand, the modeled parameters were digital elevation model (dem), https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 76 tidal range, and coastline changes. in addition to the observation station, all parameters were collected at specific points at 0.5 up to 1 km. the data collected were then assessed and classified into five scoring criteria (1 to 5). the given score shows the contribution level for each parameter observed to coastal vulnerability. the scoring classification is shown in table 1. parameters used in the scoring category are significant in determining coastal vulnerability. in addition to the field survey, these parameters consideration is based on the preliminary field observation previously performed. table 1. physical parameters and their classification in the smartline technique parameter very low low moderate high very high adapted from score 1 2 3 4 5 beach materials ice coral hard rock soft rock soft sediment sharples et al. (2009) geomorphology cliff stable beach with vegetation stable beach without vegetation beach delta, swamp, dune jadidi et al. (2013) wave exposures highly protected protected partly protected exposed fully exposed abuodha & woodroffe (2010) slope (%) 1-13 14-20 21-28 29-35 >36 jadidi et al. (2013) berm features forest, pond, swamp rural area mixed rural area urban zone mixed urban jadidi et al. (2013) grain size very fine fine moderate coarse very coarse wentworth, (1922) distance between coastal to the prone area (m) >61 31-60 21-30 11-20 0-10 jadidi et al, (2013) the height of berm >30.1 20.1-30 10.1-20 5.1-10 0-5 abuodha & woodroffe (2010) beachface features hard structure greenbelt unused area land-use protected area unclaimed settlement industrial agriculture gündogan et al. (2011) coastal building condition very good good moderate need maintenance damaged/ no building jadidi et al, (2013) elevation (m) >25 17-24 11-17 4-10 0-3 jadidi et al, (2013) tidal range (m) <1 1-1.9 2-4 4.1-6 >6 jadidi et al, (2013) coastline changes (m/year) accretion (>2.1) stable (1-2) stable (-1 +1) erosion (-1 -2) highly eroded (<-2) abuodha & woodroffe (2010) source: analysis, 2022 2.3. digital shoreline analysis system (dsas) a coastline changes analysis was performed to determine alterations resulting from the intense urban development level in padang city. many cases of coastal erosion have been reported and getting worse, especially in vital areas. digital shoreline analysis system (dsasv5) was employed to estimate the coastline changes throughout padang coastal area. dsas enables the user to calculate rate-of-change statistics from multiple time series shoreline positions (himmelstoss et al., 2018). the two main components, coastline and baseline are https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 77 preconditions to process the dsas analysis (wisha et al., 2021). additionally, automated measurement transects, and metadata are needed in the form of shapefiles recording the distance between the historical coastlines crossed by this transect line. by combining these components, we could calculate the coastline changes spatially. this study compared landsat 8 oli imagery recorded in 2009, 2014, and 2018. these images were digitized precisely following the coastline patterns. this stage was done manually by applying on-screen digitation. the baseline and coastline from north to south of padang city were digitized successively. the transect length was approximately 500 meters with 10 100 m intervals (figure 2). then, the linear regression rate (lrr) method was used to predict and classify the rate of coastline changes through the change statistics window in dsas (dada et al., 2019). this method is a statistical analysis for counting the level change applying linear regression that can be determined using a least-square regression line toward intersection points of coastline with transects (himmelstoss et al., 2018). however, the linear trend was estimated by fitting a regression line to the coastline positional data as follows: 𝑌 = 𝑚𝑋 + 𝐵 [1] where: 𝑌 = the predicted coastline positions 𝑚 = the rate of coastal movement 𝑋 = the date 𝐵 = the intercept source: analysis, 2022 figure 2. the transect of smartline observation and dsas calculations this expression was applied on all 146 transects to process the linear regression for each transect. the next stage was mapping the coastline change rate by attaching change statuses on the most recent data. the classes are erosion, accretion, and stability. in this step, cutting the latest data (2018) with an interval of 100 m given a status based on lrr was needed. these processes must adhere to the direction of baseline digitation. the level of coastline changes was classified into four classes as follows: accretion = if the rate is >2 m/year https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 78 stable = if the rate is between >1 and ≤ 2 m/year low erosion = if the rate is between >-1 and ≤ 1 m/year moderate erosion = if the rate is between >-2 and ≤ -1 m/year high erosion = if the rate is ≥ -2 m/year in this analysis, we applied the high-water line (hwl) as the preferred indicator for coastline delineation because of its ease of interpretation and field location (roy et al., 2018). hwl is the line attached to the land up to the line reached by the highest water level during the spring tides phase. even though many factors should be considered in determining the horizontal location of the mean high-water line due to the dynamics of beach and foreshore, the use of hwl is the best reference for locating the boundary between land and sea within an unstable environment (mandal et al., 2020). 2.4. coastal hydrodynamics and sediment modeling we employed a coupled model of mike21 with flexible mesh to simulate the mutual interaction between waves and currents using a dynamic coupling between the hydrodynamic and spectral wave models (wisha et al., 2018). hence, full feedback on the bed level changes calculated from the coupled model could be included. we sampled the result from the coupled model developed according to the significant eroded coastal zones shown in figure 1. the estimated erosion (sedimentation) was determined as the bed level changes resulting from the simulation. furthermore, the spatial distribution of estimated erosion was also mapped. the sediment transport model adopted in this study is based on the hydrodynamic models previously simulated (flow and spectral wave). the following equation defines the correlation between the wave energy flux component and the sediment transport: 𝑊𝑒𝑓 = 𝜌𝑔 8 𝐻𝑏 2𝐶𝑏𝑠𝑖𝑛𝛼𝑏𝑐𝑜𝑠𝛼𝑏 [2] where: 𝑊𝑒𝑓 = wave energy flux component along the coast at the time of wave breaking (𝑘𝑔. 𝑚. 𝑠−1) 𝜌 = specific gravity (seawater) (𝑘𝑔. 𝑚−3) 𝑔 = gravity acceleration (9.81 𝑚. 𝑠−2) 𝐻𝑏 = wave height at the time of breaking (𝑚) 𝐶𝑏 = breaking wave celerity (𝑚. 𝑠−1) 𝛼𝑏 = breaking wave angle a formula established by triatmodjo (2012) was used to estimate the annum sediment transport. this formula can be applied to the homogenous sandy beach with a grain diameter ranging from 0.175-1 mm. this formula is suitable to use in the padang coastal area, whereby the sand sediment predominates along the padang coastline (hakam et al., 2019). the formula is defined as follows: 𝑄𝑠 = 𝐾 (𝜌𝑠−𝜌)𝑔(1−𝑛) 𝑊𝑒𝑓 (3) where: 𝑄𝑠 = sediment transport along the coast (𝑚3. 𝑠−1) 𝐾 = 0.39 𝜌𝑠 = specific gravity (sand) (𝑘𝑔. 𝑚−3) 𝑛 = porosity (𝑛 ≈ 0.4) https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 79 a tss (total suspended sediment) modeling technique previously established for coastal and estuarine zones was simulated (wisha et al., 2022b). this simulation estimates the monthly averaged suspended sediment concentration along the padang coastline, particularly after the urban development has been applied. this simulation is based on the flow model previously simulated and validated by field measurement in 2019. this model included the bed and suspended loads calculated separately. in addition to the resume of research methodology, this study framework is shown in figure 3. source: analysis, 2022 figure 3. research frameworks of the spatial and hydrodynamic modeling 3. result and discussion 3.1. coastline changes of padang city based on 10-year compared data, the coastline changes in padang city were sufficiently dynamic, wherein the coastal erosion was predominant with almost 77.21 % of eroded areas and a rate of 0.21 up to 49.4 m/year (figure 4a). the erosion level is divided into three classes: highly eroded coastline 33.34%, moderately eroded coastline 20.58%, and lowly eroded 23.28%. the remnant classifications were accretion and stable, with 10.46% and 12.32%, respectively. we identified that 33.34% of padang city's coastline was highly eroded wherein the coastline retreated by more than two m/year over ten years. ironically, except for the padang utara subdistrict, only 9 km (12 %) of the stable coastal area was identified in all observed subdistricts. in contrast, it was only 7.7 km of the accreted area identified with an approximate rate of 2 m/year. https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 80 a significant coastal erosion in lubuk begalung subdistrict was detected with a rate of approximately 49.4 m/year (figure 4b), specifically between air manis beach and bayur bay. this result confirms the previous study (yuhendra et al., 2019) that the highest coastal erosion is observed in air manis beach, with a 50-m estimation of vanished coastline. the unclaimed coastline was observed in several parts of sungai pisang. on the other hand, the most nourished beaches were identified along lubuk begalung up to bungus teluk kabung. besides lubuk begalung, the most eroded areas were the padang barat and bungus teluk kabung subdistrict (sungai pisang and bungus selatan). many urban developments in the coastal area have been applied in the padang barat subdistrict, such as the merpati monument and masjid al-hakim, where overwhelming erosion is previously reported. on the other hand, sungai pisang and the bungus selatan village are on the same page, where a tendency of coastal erosion was also observed. the coastline instability and erosion have threatened the city’s vital infrastructures, where the center of economic activities, government offices, industries, and settlements are located. the existence of coastal building and protection probably plays a significant role in inducing coastal instability, which will be more addressed in the next section. source: analysis, 2022 figure 4. (a) coastline changes in padang coastline over ten years of observation and (b) the alteration rate at each transects https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 81 according to hakam et al. (2019), the coastal erosion issue in padang barat subdistrict commenced in the early 1800s because of the coastal barrier (mount padang) hampering the southerly sediment transport. since then, the development of the coastal groin had applied without any preliminary studies regarding ocean currents and waves. despite supporting marine tourism and coastal protection projects, the construction of coastal buildings triggers the alteration in sediment supply and demand transport patterns, resulting in erosion in nearby areas. besides experiencing extensive erosion, silting and sedimentation were also found in several areas, such as koto tangah, a tiny part of lubuk begalung, and some areas of bungus teluk kabung. the highest accreted area was in the surrounding pltu sirih bay (within bungus teluk kabung subdistrict), with a maximum rate of approximately 35.1 m/year (figure 3b). according to gemilang et al. (2017), the southern part of the bungus teluk kabung subdistrict is accreted at a rate of 3-9 m/year. 3.2. coastal vulnerability of padang coastline city based on the scoring on the considered parameters, the cvi index of padang coastline city ranged from 16.6 – 1096.3, divided into five classes of vulnerability: very low vulnerability (16.6-232.6), low vulnerability (232.7-448.6), moderate vulnerability (448.8-664.7), high vulnerability (664.8-880.7) and very high vulnerability (880.8). 40% of the coastal area of padang city is categorized into very low vulnerability, situated in the koto tangah and a tiny part of bungus teluk kabung (figure 5). source: analysis, 2022 figure 5. coastal vulnerability map of padang coastline overall, almost the northern coastline of padang city is predominated by very low up to moderate vulnerability, where the coastal formation and protection decrease the potency of being vulnerable. in contrast, high vulnerability is observed in the southern coast (a tiny part of lubuk begalung and bungus teluk kabung), https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 82 generally unclaimed and beachy areas. this result indicates that substantial land use changes could determine a region's vulnerability status. the more significant the alteration and development of a coastal area, the more arduous the nature to balance the changes as provenance, thereby substantially impacting the other nearby areas to get either eroded or vulnerable. instead of coastal protection in southern padang city, anthropogenic activities, such as industrial interest, port development, reclamation, tourism, and other coastal urban developments, are believed to trigger coastal vulnerability significantly (dada et al., 2019). according to zhu et al. (2017), besides the influence of human activities on triggering coastal susceptibility, climate change-induced hydrodynamics is the main factor controlling coastal dynamics and vulnerability. hence, this aspect will be addressed in the following subsection. the assessment of coastal vulnerability employing a smartline method is initially applied in padang city. the coastal vulnerability assessment aims to determine an initial state identifying the prone areas in padang city. the decision could be quickly made to prevent further planning that can endanger the significant areas. however, a shortcoming of several data employed and qualitative surveys are related to the clarity of the study results because the quality of data and information on several variables could affect the vulnerability scoring and ranking (dada et al., 2019). 3.3. model-based longshore current profiles and estimation of erosion-sedimentation the monthly average of longshore-current profiles shows a similar trend in all observation stations (figure 6a). it was generally higher during the northeast monsoon and getting lower during the southwest monsoon, with a deviation of about 0.02m/s. moreover, the dominant longshore currents flow was influenced by the monsoon system, whereby it tended to move southward during the northeast monsoon and vice versa for the remnant period. in the northern stations (pasir jambak, ulak karang, ulak karang utara), a similar pattern in velocity was observed, ranging from 0.046-0.06 m/s. in the padang barat subdistrict, the longshore-current fluctuations were sampled at merpati monument and masjid al-hakim, where an enormous coastal erosion occurs. the longshore current was more substantial than the northern stations (ranging from 0.05-0.0.07 m/s). source: analysis, 2022 figure 6. monthly average of longshore current profiles (a) and estimated sediment transport (b) in the eroded area of the padang coastline. black arrows denote the dominant current direction. https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 83 the current velocity in air manis beach ranged from 0.055-0.067 m/s. the moderate-very high erosion in this area is influenced by unstable sediment supply from nearby estuaries. since the urban development has been applied in the river's mouth, the erosion in another part commenced occurring. this area's relatively strong current also significantly triggers imbalanced sediment transport along the coast (burnette & dally, 2018). additionally, the pressures from anthropogenic activities also result in vulnerable coastal areas (gemilang et al., 2020). table 2. total sediment transport and the dominant transport direction observation station sediment flux qs (𝒎𝟑/𝒅𝒂𝒚) dominant sediment type sediment transport dominant direction pasir jambak beach 173.982 fine-medium sand north ulak karang utara 149.502 fine sand north ulak karang 142.570 fine sand north merpati monument 193.400 fine sand south masjid al-hakim 274.400 fine sand southwest air manis beach 217.404 fine sand northwest bungus selatan beach 222.920 fine-medium sand northeast sungai pisang beach 224.830 medium sand-silt southwest source: analysis, 2022 the remnant observation stations are bungus selatan and sungai pisang beach. the longshore currents’ profiles were considerably different due to the different morphological settings. in these areas, the gulf formation is supposed to weaken the current movements because of the presence of peninsula formations protecting the bay. the model result showed that the longshore current was relatively calm during the northeast monsoon and stronger during the southwest monsoon, ranging from 0.045-0.06 m/s and 0.047-0.063 m/s for the bungus selatan and sungai pisang stations. the symptoms of coastal erosion are currently reported because of the absence of coastal protection, substantial changes in land use, and robust hydrodynamic profiles. the estimated sediment transport in all stations varies considerably (figure 6b). except for pasir jambak, air manis, and bungus selatan stations, sediments transport peaked during the second transitional period season-northeast monsoon (september-february), ranging from 75.5-258.32 m3/day. at the pasir jambak station, the highest sediment transport was estimated during august at 230.43 m3/day, then significantly decreased during april (87.86 m3/day). compared to the other stations, the relatively high sediment distribution was observed at air manis station ranging from 95.89-280.83 m3/day, reaching its peak level during august, and getting tremendously low during april. moreover, an anomaly was identified during june, whereby the sediment predominantly moved southward. a complex coastal morphology in air manis beach could be the main factor causing this state. according to burnette & dally (2018), the deformed primary direction of windwave-driven current due to coastal bathymetry and morphology variation close to the coastal area could occur. at the teluk kabung selatan station, the sediment transport ranged from 98.83-258.32 m3/day. the high transport was identified during the second transitional season (august-september), while during the remnant periods, the transport was not too high, on average, 132.88 m3/day. in addition to coastal building developments in the study area, the coastal erosion and instability observed throughout padang coastline are the long-term impact of hydro-oceanographic variability wherein this aspect may be unconsidered while planning and during the construction. the relatively low sediment transport was identified at the ulak karang and ulak karang utara stations, where the attached coastal groins and revetment exist. the presence of coastal protection alters longshorecurrent features increasing the bed shear stress, thereby inducing a lower mixing and turbulence of nearshore bottom sediment (pascolo et al., 2018). on the other hand, the relatively high sediment transport observed at merpati monument and masjid al-hakim station is due to coastal building-induced longshore current deformation and the increased exposure to wind-wave-driven current (koropitan et al., 2021). even though the exposure to waves and currents was sufficiently substantial at sungai pisang beach, the sediment transport was not entirely high because of the cliff formation along the coastline of the southern bungus teluk kabung subdistrict. https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 84 based on the total sediment transport at the entire observation station, we found that the southern stations have a relatively higher annual sediment transport ranging from 217.404 to 274.400 m3/day (table 2). aside from the absence of coastal protection, the increased sediment flux is due to the high exposure of current, substantial urban development in the coastal area, the coastal morphology formation changes, and the dominance of sediment type at every observation station (khan et al., 2021). according to the field survey, the sediment type was generally river-sourced fine sand sediment. however, sediment mixing (fine and coarse) was identified during high tidal conditions. furthermore, the presence of coarse fractions indicates a robust current environment (gemilang et al., 2018). the total suspended sediment (tss) was modeled and validated by the field measurement (green dashed line in figure 7). overall, the highest suspended sediment concentration was observed at the southern stations (from air manis to sungai pisang), ranging from 121-157 mg/l, which was not considerably divergent during ebb and flood tides. at the sungai pisang station, the highest tss was identified at approximately 150 mg/l during ebb and flood tides. besides the fine cohesive sediment predomination, the relatively strong sea current features significantly control the sediment turbulence and mixing (wisha et al., 2022a). compared to the other stations dominated by fine-medium sediment, sungai pisang beach tends to have high turbidity resulting from the cohesive sediment resuspension, which requires longer to settle in the sea bottom (nurdjaman & putra., 2017). on the other hand, besides the fine sediment predomination, the low tss concentration (<90 mg/l) observed at the padang barat station indicates the weaker current flow. source: analysis, 2022 figure 7. estimated tss concentration during flood and ebb tides validated by the field measurement the predicted bed level change indicating coastal erosion (sedimentation) (figure 8) shows that the high value of bed level change is identified in almost all observed subdistricts, with the altered bed level ranging from 0 to -4 m. in contrast, the positive bed level value of 0 – 0.39 m is observed in the southern koto tangah, in the middle of padang barat, padang selatan, and several parts of bungus teluk kabung subdistrict (figure 8). this state is consistent with the spatial analysis results (figures 4 and 5), where coastal instability on padang coastline occurs. the changes in bed level reflect the wave-current-induced bed shear stress. spatially, the level of wave impact depends on the bed elevation (more significant in shallow water) and the existence of coastal protection and vegetation-dissipated wave energy (burnette & dally, 2018). the longshore current previously discussed elucidates the high erosion in several vital areas, proven by the high value of bed-level. integrating bed level changes, estimated sediment transport, and tss profiles could be a basis for examining the variability in erosion parameters caused by the changes in bed sediment properties (bayhaqi et al., 2022). https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 85 figure 8. estimated bed level change indicating coastal erosion and sedimentation in the padang coastal area because of the limited oceanographic data collected in this study, we only validate the simulation result using satellite and forecasted tidal data with less than 10% of rmse (root mean square error). however, we could compare the tss modeling result with the filed data collection in 2019 with an adequately close concentration between flood and ebb tides. long-term field measurement of oceanographical data could enhance the quality of simulation results. https://doi.org/10.14710/geoplanning.9.2.73-88 wisha et al. / geoplanning: journal of geomatics and planning, vol 9, no 2, 2022, 73-88 doi: 10.14710/geoplanning.9.2.73-88 86 4. conclusion padang city's coastline has been experiencing overwhelming coastal erosion with a coastal retreat of about two m/year. the very low to moderate vulnerability is predominant in the northern coastline of padang city due to the presence of coastal structures. in contrast, high and very high vulnerability is observed on the southern coast. the robust hydrodynamic profiles during september-march allow the higher transport mechanism near the beach. even though coastal urban development and artificial coastal protection could reduce the impact of hazards and disasters, it does not effectively trap the sediment to stabilize the eroded coastline, thereby inducing erosion in the other areas. the robust hydrodynamic features derived from the indian ocean erode the unprotected areas on the southern coast, proven by relatively high sediment transport and turbulence. it is recommended to reconsider and study the site's oceanographical conditions before constructing a coastal building and developing the coastal area. even though urban development offers benefits in terms of protection (from hazards and disasters) and tourism, based on this study, it influences the deformation of longshore current pattern-induced sediment transport, thereby inducing coastal instability and erosion. other nature-friendly solutions, such as a non-woven geotextile, hybrid engineering, and mangrove cultivation, could be a better option to protect the coastal area. this study only considers 14 parameters to be assessed using a smartline-associated cvi technique whereby other factors may have a significant influence on determining the coastal vulnerability. therefore, we recommend evaluating other physical parameters for further studies to get a better depiction of coastal vulnerability. on the other hand, long-term field measurement of tides, currents, and waves is required to understand the study area's real-time oceanographical variability and validate any modeling approaches employed in the future. 5. acknowledgments we would like to thank research institute for coastal resources and vulnerability (ricrv) for the research funding in 2019 in padang coastline city with grant number: 032.12.05.2428.003.001.053b and the head of ricrv, nia naelul hasanah ridwan. gratitude is also given to local governments (dkp dan balibangda) of west sumatra province for assistance during the field survey and those who have contributed to this work. 6. references abuodha, p. a. o., & woodroffe, c. d. 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[crossref] https://doi.org/10.14710/geoplanning.9.2.73-88 https://doi.org/10.1016/j.rsma.2017.10.006 https://doi.org/https:/doi.org/10.3390/w14162561 https://doi.org/10.1016/j.rsma.2022.102309 https://doi.org/10.1016/j.margeo.2017.01.003 | 89 geoplanning vol 6, no 2, 2019, 89-98 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.2.89-98 modelbuilder and unit hydrograph for flood prediction and watershed flow direction determination at the west branch of the little river, stowe, lamoille county, vermont, usa f. e. s. silalahia,b* , f. hidayatb a western michigan university, geological and environmental sciences, usa b geospatial information agency, indonesia abstract: the west branch of the little river in stowe, lamoille county, vermont has been widely studied, and this area is regularly subject to flooding. the west branch joins the little river, which flows into the winooski and drains into lake champlain. this area has undergone extensive development as an economic response to the ski resort industry over the past 50 years, and the recreational pathway is on the banks of the river. the little river is adjusting to the loss of historic floodplain area, channel modifications (straightening and gravel mining), and runoff changes. in this project, a dem with 10 and 30 meters resolution will be used to determine the watershed area for the outlet point at the south of stowe for hydrological analysis. this project intends to describe the watershed flow direction with a unit hydrograph that shows when water discharge at the outlet is at its height during a rainfall event and produce the floods prediction map by predicting the nature of flood events to help in planning and responding to flood events effectively using arcgis pro 2.0. the results show the time it takes water to flow to the outlet ranges from 0 seconds (rain that falls on the outlet itself) to over 8 hours and 46 minutes. the amount of water has accumulated, indicating that water will flow at its fastest when funneling toward the outlet point downstream of the town with no exception, indicating that water will flow at its fastest when funneling toward the outlet point downstream of the town. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): silalahi, f., & hidayat, f. (2020). modelbuilder and unit hydrograph for flood prediction and watershed flow direction determination at the west branch of the little river, stowe, lamoille county, vermont, usa. geoplanning: journal of geomatics and planning, 6(2), 89-98. doi: 10.14710/geoplanning.6.2.89-98 1. introduction hydrological processes are complex (khan, yufeng, & ahmad, 2009). a proportion of the precipitation falls on the stream and river network directly and contributes to runoff. the rest of the precipitation reaches the ground, which infiltrates through the soils, directly contributing to surface runoff (guéro, 2006). runoff occurring on uplands flows downstream in various patterns affected by spatial and temporal distribution of rainfall, rate of snowmelt, hydraulics of streams, watershed and channel storage, geology, and soil characteristics, watershed surface and cover conditions (the u.s. department of agriculture, 2007). understanding the effect of sediment and changing water input to streamflow is crucial, so it needs to know hydraulic resistance by understanding step-pool formations (sulebakk, 2017; maxwell & papanicolaou, 2001). the west branch of the little river in stowe, lamoille county, vermont has been widely studied because it is regularly subject to flooding. high flows that would normally access the floodplain are causing extensive bank erosion, channel widening, loss of aquatic habitat, and general channel instability (the lamoille county planning commission, 2006). a previous study about peak discharges estimation and unit hydrographs was developed for streams in charlotte and mecklenburg county in 2003. estimating unit article info: received: 11 july 2019 in revised form: 11 september 2019 accepted: 11 november 2019 available online: 30 december 2019 keywords: little river, flood, unit hydrograph, geographic information systems (gis), arcgis pro *corresponding author: florence silalahi western michigan university, geological and environmental sciences, usa email: florenceelfriede@gmail.com open access https://doi.org/10.14710/geoplanning.6.2.89-98 mailto:florenceelfriede@gmail.com silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 90 | hydrographs is an important component used in watershed modeling and (or) design of stormwatermanagement structures. the unit hydrograph includes three statistical relations, i.e., storm peak discharge, unit-hydrograph peak discharge, and unit-hydrograph lag time. the statistical relation to estimating the storm peak discharge is based on analyses of observed peak discharges regressed against rainfall and basin characteristics (weaver, 2003). flood prediction involves the rainfall-runoff transformation processes based on empirical or combined conceptual physically-based descriptions of the processes involved, i.e., rainfall into a flood hydrograph and the translation of that hydrograph throughout a watershed or any other hydrologic system (ramírez, 2000). the unit hydrograph is the surface runoff hydrograph resulting from one unit of rainfall excess, spatially and temporally distributed over a watershed uniformly for a specified duration, which was applied at the outlet of the sub-catchment(s) along the river (guéro, 2006). despite it categorization as a conservative method, the unit hydrograph remains a useful and practical approach to dealing with operational hydrological forecasting and rainfall-runoff modelling. besides, this method can compute the predicted time to peak of runoff more accurately for time less than one hour (kusumastuti & jokowinarno, 2012). in this project, a digital elevation model (dem) will be used to determine the watershed area for the outlet point at the south of stowe and make it ready for hydrological analysis. it has 10 meters resolution and was derived from the united states geological survey (usgs). besides, a pour point feature that depicts the outlet downstream of the little river where a unit hydrograph will be created, a polygon boundary that depicts the boundaries of stowe from vermont center for geographic information (vcgi), and a raster layer with 30 meters resolution that depicts the surface water bodies in the area derived from features in the nhdplus version 2 dataset are needed. this project intends to describe the watershed flow direction with a unit hydrograph that shows when water discharge at the outlet is at its height during a rainfall event and predicts flooding that will help plan and respond to flood events effectively. 2. data and methods as the study area (figure 1), the west branch joins the little river, which flows into the winooski and drains into lake champlain. at the little river's confluence in the village of stowe, the elevation is about 695 feet above sea level. the mountain road and the stowe recreation path parallel the west branch for most of its length. upstream of ranch brook, to where the main channel leaves route 108 (north of big spring), the channel slope averages approximately 4.5% with a step-pool/cascade-pool morphology. figure 1. map location (the lamoille county planning commission, 2006) https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 | 91 land use adjacent to the river is predominately agricultural, commercial, residential, and recreational. the valley post-glaciation contained a glacial lake that left well-drained, highly permeable soils up to high elevations in the modern valley. the glacial lake(s) also left behind extremely deep silt, sand, and gravel (the lamoille county planning commission, 2006). this area has undergone extensive development as an economic response to the ski resort industry over the past 50 years, and the recreation path is adjacent to most of the river, so the river is adjusting to the loss of historic floodplain, channel modifications (straightening and gravel mining), and changes in runoff. between 1995 and 1998, vermonters suffered nearly $60,000,000 in flood damage. the majority of large twentieth-century floods have occurred during the summer (june through august) and are associated with intense cloudbursts, which stay in the mountains producing high rainfall amounts. the remainder is divided evenly between fall floods and winter/spring floods. the fall floods (september through november) are often associated with hurricanes. in contrast, the winter/spring floods (january through april) are associated with rain events, snow events, or snowmelt (the lamoille county planning commission, 2006). the study will begin with the precondition of the elevation model that requires dem preparation to get accurate hydrological analysis for the area around stowe. precondition the elevation model steps, i.e., assessing the flow directions, identifying and fulfilling the sinks. figure 2. result of precondition (sinks locate as orange dot or area) the second process is to delineate the watershed. watershed delineation requires a flow direction of the raster layer and an outlet point. the watershed delineating process includes assessing flow direction, assessing flow accumulation, and measuring the outlet point's distance. the pour point as an outlet may differ slightly due to the dem's resolution or other inaccuracies. from the stream's actual location, the outlet must be precisely located on the stream as rendered in the dem. so, in measuring the distance to the outlet point, the outlet point's location must be mapped to match the stream exactly and get an accurate watershed. delineating a watershed requires two components, i.e., a flow direction raster layer and an accurate outlet point. a watershed area is an area in which all flowing water will flow toward an outlet point using verified-dem. the watershed represents all areas that flow to the specified outlet (environmental systems research institute, 2018). a watershed is mapped by determining the watershed area upstream of the outlet using the watershed tool in arcgis pro. the watershed comprises almost the entirety of stowe's town boundaries. this indicates rather than draining away, almost all rainfall that lands in stowe will go rushing through the actual town. https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 92 | along with data processing, it is necessary to build geoprocessing workflows. modelbuilder will be used to develop flood hazard models for the study area (see figure 3). modelbuilder is a visual programming language and it can be exported to python script (kraemer & hale, 2014; zandbergen, 2015). geoprocessing models help spatial analysis process and documentation of data management automatically. modelbuilder represents a process of geoprocessing tools as diagram chain sequences, using the output of one process as the input to another process. modelbuilder needs geoprocessing tools, map layers, datasets, and other data types, and connect all of it into a process by running the model step-by-step, up to a selected step, or run the entire model (armstrong, 2009; allen, 2011; kraemer & hale, 2014; environmental systems research institute, 2018). modelbuilders obtained from gis processing can be used to predict conditions related to simulation, evaluation, and discovery because it is able to explain the solutions to various problems in the spatial context. this model must answer at least six questions, which are related to identification of events, locations, trends, optimal paths, patterns, and models, for example damage to settlements due to lava flooding (kumalawati et al., 2013), soil erosion in small forested catchment (csáfordi et al., 2012), an earthquake-induce landslides risk assessment (zhu, 2010), and flood hazard assessment based on basin morphometry (omran et al., 2011). the modelbuilder of this research was created using arcgis pro 2.0. figure 3. modelbuilder of flood prediction and watershed flow direction determination at the west branch of the little river, vermont, usa https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 | 93 the third step is creating a velocity field and isochrones map. the process that needs to be completed from the data collection, processing process, and map creations is shown in figure 2. creating steps on a velocity field, i.e., creating the slope raster, calculating the slope-area term, calculating the velocity field, and determining the minimum and maximum velocities limit. for the better prediction of flooding events, planes will need to know how long water flow will be and when the flow will reach the outlet during a hypothetical rainfall event. the speed of flowing water must be estimated with a velocity field. construction of a velocity field requires several assumptions, i.e., 1) velocity is spatially variant, which means it affected by slope and flow accumulation; 2) velocity is uniform at a given location at a given time, which means it does not change over time; and 3) velocity is discharging invariant at a given location, which means it does not depend on the location's rate of water (environmental systems research institute, 2018; the u.s. department of agriculture, 2007). these assumptions will provide a generally accurate velocity field as an approximation of observed phenomena. the last step is creating a unit hydrograph. there are five classifications of hydrographs, i.e., natural hydrograph, hydrograph unit, dimensionless unit hydrograph, synthetic hydrograph, and dam breach hydrograph (indarto, 2015). in this paper, the unit hydrograph (hydrograph unit) is used in flood prediction, which is obtained through the presentation of an isochrone map, which will show the relationship between the time and area of water flowing into the outlet. the unit hydrograph (uh) of a watershed is defined as the direct runoff hydrograph as a result of a constant intensity of excess rainfall’s volume that is distributed uniformly over the drainage area for a specific duration of effective rainfall. when dealing with a rainfall of different duration, a new unit hydrograph must be derived for the new duration. the fundamental assumptions for modeling hydrologic systems using unit hydrographs are a) watersheds respond as linear systems, which implies that the proportionality principle that is scaled accordingly; b) the effective rainfall intensity is uniformly distributed over the entire river basin; c) the rainfall excess is of constant intensity throughout the rainfall duration; and d) the duration of the direct runoff hydrograph (time base) is independent of the effective rainfall intensity and depends only on the effective rainfall duration (ramírez, 2000). the unit volume is usually considered to be associated with 1 cm (1 inch) of effective rainfall distributed uniformly over the basin area (environmental systems research institute, 2018; ramírez, 2000). figure 4. flowchart of a research process https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 94 | 3. result and discussion isochrone maps are one way of analyzing a spatiotemporal phenomenon such as accessibility that changes through space over time, so in order to determine the number of potential users in a study area, we need to know the location of these people through time (van den berg et al., 2018). for the same reason, isochrones need to be mapped to determining how long it takes water to reach the outlet, how fast the water flows (velocity), and the extent of the watershed (by calculating the slope and flow accumulation area). to determine the watershed, there are two components that are needed, i.e., accurate flow direction and outlet point (environmental systems research institute, 2018). the following equation is used to calculate a velocity field: v = vm (sb ac) / (sb ac m) vm is the average velocity of all cells in the watershed, assumed with average value of vm = 0.1 (environmental systems research institute, 2018). the quantity sb ac m is the average slope-area term across the watershed, i.e., the number of cells that flow into that cell, or flow accumulation. each cell in the velocity field is assigned a velocity based on the local slope generated from the dem layer, and the upstream contributing area generated from the stowe fill flow accumulation layer. unit hydrograph analysis refers only to direct runoff and base flow, which is composed of contributions from delayed interflow (the portion of the streamflow contributed by infiltrated water that moves laterally in the subsurface until it reaches a channel) and groundwater runoff (the flow component contributed to the channel by groundwater). groundwater runoff is extremely slow as compared to surface runoff (ramírez, 2000). in this case uh model structure is assumed to be appropriate to represent catchment behavior. figure 5. watershed flow direction with unit hydrograph with 10 meters dem’s resolution total streamflow hydrographs are usually conceptualized as being composed of: a) direct runoff, which is composed of contributions from surface runoff (the main contributor to the peak discharge, includes all overland flow as well as all precipitation falling directly to the stream channels) and quick interflow (slower https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 | 95 than surface runoff, includes quick interflow that contributes to direct runoff, and delayed interflow, which contributes to base flow) (ramírez, 2000). the cell values in a flow direction raster layer can normally be one of only eight integers (see figure 6) : 1, 2, 4, 8, 16, 32, 64, and 128, which corresponds to the eight possible flow directions (environmental systems research institute, 2018). figure 5 shows stowe watershed flow direction is to estimate for each cell, the adjacent cell into which water would flow, i.e., 1 and 2 towards the east and southeast. water that falls in the western mountains takes longer than water that falls on the low-lying stream beds closest to the outlet. figure 6. flow direction coding flow time is calculated as the length that water must flow divided by the velocity at which it flows (the first equation). there are two variables to determine flow length, i.e., flow direction layer and weight layer (the third equation), which is represent an impedance, example water flowing through forested land takes longer than water flowing over smooth rock because it's impeded by terrain). an equation to calculate flow time (environmental systems research institute, 2018): flow time [t] = flow length [l] / velocity [lt-1] ……… (1) flow time [t] = flow length [l] * weight [l-1t] ………. (2) by combining these equations: weight [l-1t] = 1 / velocity [lt-1] ………. (3) figure 7. stowe_slope_areaterm and stowe_time which has been processed the stowe_time layer shows the time it takes water to flow to the outlet ranges from 0 seconds (rain that falls on the outlet itself) to about 31612 seconds or ± 8 hours and 46 minutes. the last map as an output of this project is an isochrones map (figure 9) shows contour lines of locations where the water flow exhibits equal travel time to the outlet of the watershed. this time interval should be appropriate for the stowe watershed with a time interval of 1800 seconds for every cell per isochrone zone (environmental systems research institute, 2018). these time intervals as the ordinate of the unit hydrograph. a dem with a resolution of 30 meters is also used for additional analysis. the results of dem processing with a resolution of 30 meters also show relatively the same time and water flow trends. however, it can be seen in figure 8 that the hydrograph unit has a better curve because the dem 10 meter resolution more accurately displays fluctuating differences (discharge of outlets). the stowe_time layer shows the time it takes water to flow to the outlet ranges from 0 seconds (rain that falls on the outlet itself) to about 48,600 seconds or ± 13 hours 30 minutes. the three hours different have a big impact on rescue time when the hazard happens. https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 96 | based on the amount of water has accumulated, the area around stowe is no exception, indicating that water will flow at its fastest when funneling toward the outlet point downstream of the town. figure 8. watershed flow direction with unit hydrograph with 30 meters dem’s resolution figure 9. flood prediction of the west branch of little river, stowe with 10 meters dem’s resolution https://doi.org/10.14710/geoplanning.6.2.89-98 silalahi and hidayat / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 89-98 doi: 10.14710/geoplanning.6.2.89-98 | 97 4. conclusion this paper shows contour lines of locations where the water flow exhibits equal travel time to the outlet of the watershed, which is an adjacent cell into integer 1 and 2 towards east and southeast that falls on the low-lying stream beds closest to the outlet. the water that falls in the northeast of the study area takes the most prolonged (in time) than the other direction because the water flowing through forested land takes longer than water flowing over a smooth rock because the plants impede it. from data processing, the stowe_time layer shows the time it takes water to flow to the outlet ranges from 0 seconds (rain that falls on the outlet itself) to about 31612 seconds or ± 8 hours and 46 minutes, using dem with 10 meters resolution, and about 48,600 seconds or ± 13 hours 30 minutes, using dem with 30 meters resolution. both of them show the same unit hydrograph and the trend for water flow relatively. but, the three hours different have a big impact on rescue time when the hazard happens. based on the amount of water has accumulated, the area around stowe is no exception, indicating that water will flow at its fastest when funneling toward the outlet point downstream of the town. 5. acknowledgments the writers would like to thank esri, usgs, vermont center for geographic information (vcgi), and the lamoille county planning commission, who provided data, related document, tutorial, insight, and expert input that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper. the writers would like to thank dr. peter j. voice as an advisor for this research. the writers also thank the ministry of research, technology, and higher education of indonesia through the riset-pro scholarship 2017 (nr. 264/riset-pro/fgs/viii/2017). 6. references allen, d. w. 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[crossref] https://doi.org/10.14710/geoplanning.6.2.89-98 https://doi.org/10.1017/cbo9780511529412.012 https://doi.org/https:/doi.org/10.1007/978-3-319-71470-7_10 https://doi.org/10.3133/wri20034108 https://doi.org/10.1109/geoinformatics.2010.5567701 61 geoplanning journal of geomatics and planning vol. 8, no. 1, 2021 original research temperature acquisition system for real time application of first velocity correction by edm (electronic distance measurement) felipe a. c. rodriguez 1*, luis a. k. veiga 1, wilson a. soares 1 1. federal university of paraná doi: 10.14710/geoplanning.8.1.61-74 abstract the first velocity correction is used to correct the measured distance affected by the velocity variation of the electromagnetic wave propagation in a medium. this correction depends on the refractive index of the propagation medium and reference refractive index. the influence of the temperature in the medium refractive index is critical; some estimates establish that variation 1°c causes 1ppm of error in distances. in the measuring processes with total stations, the temperature is usually collected at only one point, for example, in the position where the measuring instrument is setup. however, the wave propagates in a medium of non-constant temperature, where the extremes of the line can present variations and thus this measurement in only one point could be non-representative. in this context, it was developed a low-cost real-time temperature acquisition system. this system provides the temperature values in different locations allowing their monitoring through the time. experiments realized during the geodetic monitoring of a dam, show variations up to 8°c among geodetic points on the dam and around it. an analysis was development to evaluate the influence of temperature variations on monitoring distances and geodetic coordinate of a 2d network with different approaches (temperature modeling). the results shows different values for distances (1.0 mm) and coordinates (0.5 mm) depending of the approach choose. copyright © 2021 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction the accuracy of the distances obtained by electronic distances measurement (edm) technique is a critical factor to some applications as geodetic network monitoring or geodetic control of structures (brunner, 1984; ussisoo, 1969; scaioni et al., 2014). the accuracy of edm depends of two factors; the first, known as internal, is related with the manufacturing characteristics own of each instrument. on second case found the external factors that are more complex since depend mainly of the environmental conditions of the medium in which the electromagnetic wave propagates (rüeger, 1990). according brunner (1984); rüeger (1990); torge & müller (2012) and ogundare (2015) the principal effect that generate the medium is the variation of propagation velocity of the wave due to mainly density changes in their composition. this situation affect directly the distance compute and therefore their accuracy. according to brunner (1984), the ideal situation to geodetic calculations it would be when the wave propagated in the vacuum, this supposes a homogeneous and constant medium however, the troposphere, medium of propagation associated to the terrestrial measurements, is constantly changing their composition (density). rüeger (1990), define that the principal components of the troposphere that change their composition and affect the velocity propagations of wave in edm instruments are the pressure, humidity and temperature. the correction of the effects which the atmospheric parameters of pressure, humidity and temperature cause on the wave velocity propagation are modeled through the refractive index, this is a factor that related the velocity e-issn: 2355-6544 received: 13 march 2020; accepted: 1 january 2021; published: 30 july 2021. keywords: edm, refraction, sensors *corresponding author(s) email: felipe.carvajalro@gmail.com https://doi.org/10.14710/geoplanning.8.1.61-74 mailto:felipe.carvajalro@gmail.com rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 62 of light of propagation in a medium and in the vacuum. for edm corrections, the refractive index is used to compute the first velocity correction, the last one provide the distance bias generated by the variation of the velocity of the electromagnetic wave due to the medium composition (rüeger, 1990). the equation 1 presented the first velocity correction: 𝐾 = (𝑛𝑓 − 𝑛𝐿) ∙ 𝑑 (1) where: 𝐾 = the first velocity correction 𝑛𝑓 = the reference refractive index 𝑛𝐿 = group refractive index valid for atmospheric conditions described by t, p, e 𝑑 = electronic distance or distance influenced by atmospheric parameters the refractive index of the medium depends on the frequency of the electromagnetic radiation propagating through it and the composition of the medium. in 1963, the international union of geodesy and geophysics (iugg) decided, in the xiiith general assembly, that the refractive index for light and nir waves (employed in the electronic distance measurement) could be reduced to ambient conditions through the simplification of the formula proposed by barrel e sears 1939 (rüeger, 1990). the equation 2 shows how to obtain the refractive index. 𝑛𝐿 = 1 + 𝑛𝑔 − 1 1 + 𝛼 ∙ 𝑡 ∙ 𝑝 1013.25 − 4.125 ∙ 10−8 1 + 𝛼 ∙ 𝑡 ∙ 𝑒 (2) where: 𝑛𝐿 = group refractive index valid for atmospheric conditions described by t, p, e 𝑛𝑔= group refractive index 𝑡 = dry bulb temperature of air (°c) 𝑝 = atmospheric pressure (mb) 𝛼 = coefficient of expansion of air (= 0.003661 per °c) 𝑒 = partial water vapor pressure (mb) other simplification was proposed by kohlrausch (1955), cited by rüeger (1990). in this case the coefficient of the air expansion varies slightly doing α = 1/273.15. with this modification, through equation 3 the refractive index is obtained: (𝑛𝐿 − 1) = (𝑛𝑔 − 1) + 273.15 ∙ 𝑝 (273,15 + t) ∙ 1013.25 ∙ 11.27 ∙ 10−6 (273.5 + t) ∙ 𝑒 (3) the error in the refractive index due to pressure, temperature and humidity parameters can be estimated by the partial differentials of the kohlrausch formulate. in rüeger (1990) and ogundare (2015) are presented examples that shows that, for the refractive index of light, the temperature is critical for the determination of the refractive index. according to the authors, for a temperature of 15°c, a pressure of 1007 mb, a partial water vapor pressure of 13 mb and a group refractive index of 1.0003045, a pressure variation of 1.0mb generates an error of 0.3 ppm in the distance determination, humidity variation of 1mb produces 0.04 ppm of error in the distance, meanwhile, the temperature variation of 1°c results in an error of 1ppm in the distance determination. https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 63 the measurement of atmospheric parameters is a typical procedure to land surveys engineers before to staring a measurement. torge & müller (2012) present gradients of -0.0065°c/m, -0.12hpa/m, -0.0035hpa/m, to temperature, pressure e humidity respectly. from these values is posibile conluding that the temperature presents more variation in function to changes in the altitude. rüeger (1990) and angus-leppan & brunner (1980), postulate that the average between the measurement of the pressure and humidity parameters at both terminal of a line are sufficient to achieve the representative value, however for the temperature parameter the average is not always representative, since the temperature gradients in the lower atmosphere are unstable. the principal cause for this is the different heat absorption and emission capacities of the earth's surfaces. in this context brunner (1984), postulate that the development of instrument solutions to measure the atmospheric parameters can be provide information that permit the modeling of this parameters, therefore obtain a better estimation of refractive index and of the first velocity correction. actually, the total stations have a microprocessor to automatic compute of the refractive index and first velocity correction, for this, the user should be to insert the atmospheric values, and the correction is made. in this case, the use of external atmospheric sensors are necessary. in addition, some total stations have internal sensors to measure the atmospheric parameters. for both cases, the measure is made in one place; therefore, this not considered the variations of atmospheric parameters around it, mainly the temperature. the problem of the representative of these values has led to the development of an atmospheric monitoring system as well as estimation models from the data provide by these systems. an example is a sensor development by solarić et al. (2012), this sensor provides atmospheric data to calculate the atmospheric corrections on the edm calibration baseline, this system permits measure the data on the each pillar of the baseline. artese & perrelli (2018), proposed a method using the climatic data and a digital terrain model (dtm) of a landslide area, in this case the model is done to entire area. the next step after collected of data, it is the modeling of parameters, in this context robertson (1977); brunner & fraser (1977); angus-leppan & brunner (1980); fraser (1981) and brunner & rüeger (1992), presented mathematical models to atmospheric parameters estimation. to apply each of these models, the measurements of atmospheric parameters are necessary. currently, low-cost technology has allowed the development of programmable sensors, microcontrollers like arduino or raspberry, communication modules like xbee or lora. in this context, we present a temperature acquisition system to monitoring and collect the temperature data, critical parameter to compute the refraction index. this system was developed through the concept of real time wireless sensor network (rwsn), with opensource hardware and software and low-cost sensors. two experiment was presented with objective of evaluate the operation of this system and the temperature monitoring, also was presented an example to evaluate the incidence of the variations of this parameter on a 2d topographic network. 2. data and methods for the proposed temperature acquisition system was selected different devices, and in some cases evaluated and configured them as it will be shown later. for the communication, the zigbee protocol was chosen; through radio frequency xbee s2c devices, this protocol provides wireless communication. in the case of the control system, the microcontroller arduino uno allowed the configuration of parameters used to send data and temperature measurements and with which frequency. finally, it was evaluated the digital temperature sensor ds18b20 (classic and waterproof version) and the tmp36 analog temperature sensor. this allowed choosing the one that was more suitable for this work. below each step developed is detailed. 2.1. ds18b20 and tmp36 temperature sensors the evaluation applied to ds18b20 and tmp36 sensor seeks to choose the more suitable sensor for this work. the tmp36 sensor is analog and provides voltage differences that can be converted in temperature through a scale factor (devices, 2020). the d18b20 is a digital sensor with two versions; classical, not https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 64 encapsulated, and waterproof, both versions provide directly temperature values (maxim, 2020). the main characteristics are presented in the table 1. table 1. main characteristics of both sensors. sensor tmp36 ds18b20 operating system analog digital precision ±2°c ±0,5°c operation range 40°c to 125°c 10°c to 85°c 2.2. comparison between ds18b20, tmp36 temperature sensors and the standard thermometer it was compared both ds18b20 (classical version) and tmp36 sensors, related to a standard glass mercury thermometer of 0.1°c nominal precision, defined as the reference of the response in temperature range. this experiment was developed in the physics laboratory at federal university of paraná. the experiment consisted in inducing temperature values through a thermostatic bath that permits to heat one solution from the 7°c to 100°c range. figure 1, shows that the thermometer and sensors were submerged in water. figure 1. thermostatic bath and experiment configuration the circuit shown in figure 2 is composed by an arduino uno microcontroller, ds18b20 and tmp36 sensors, and provides automatically the temperature. the temperature values from the thermometer correspond to analogic measurements. figure 2. ds18b20 and tmp36 circuit for comparative experiment https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 65 the results of this experiment are shown in the figure 3, in this case it were realized 73 temperature measurements. the approximate range of these values is 10°c to 45° c. figure 3. evolution temperature during of experiment. for evaluating the differences between temperature series of ds18b20, tmp36 sensors and the reference thermometer, it was used the pearson’s chi-squared statistical test with a 95% confidence interval (agresti, 2007). in this case, the expected values (e) correspond to the reading of the glass reference thermometer; the observed values (o) correspond to the measurements of the sensors tmp36, ds18b20, and n correspond to the number of sample elements. 𝜒𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 2 = ∑ (𝑂𝑖 − 𝐸𝑖)2 𝐸𝑖 𝑛 𝑖 (4) the degrees of freedom are defined by: 𝑣 = (𝑟 − 1) ∙ (𝑘 − 1) (5) where: v: degrees of freedom r: number of rows k: number of columns two hypotheses were proposed, h0 the samples are homogeneous and h1 the samples are not homogeneous. table 2 shows the results: table 2. the pearson’s chi-squared statistical test for tmp36 and ds18b20 sensors sensor v 𝝌𝒆𝒔𝒕𝒊𝒎𝒂𝒕𝒆𝒅 𝟐 𝝌𝒑−𝒗𝒂𝒍𝒖𝒆 𝟐 tmp36 72 114.908 47.857 ds18b20 72 3.488 47.857 considering the results obtained in the experiment, it is possible to conclude that the better adherence of the results were obtained with the ds18b20 sensor in comparison with the results obtained by the tmp36 sensor, so the hypothesis h0, for the sensor ds18b20, was accepted, however for the sensor tmp36 it was not. based on the results obtained in this statistical test it was chosen as the best option for the development of this work the ds18b20 sensor (soares, 2006; de rubeis et al., 2017). https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 66 2.3. ds18b20 sensor correction the next step was the calibration of ds18b20 sensor, in this case it was used the waterproof version because this has a more resistant structure. it was applied the same test of the previous experiment. the figure 4 shown the temperature curve of the ds18b20 sensor and thermometer figure 4. thermometer and ds18b20 temperature evolution for this experiment, it was realized 79 temperature measurements. the approximate range of these values is 22 °c to 60°c, maximum thermometer graduation. two hypotheses were proposed, ho the samples are homogeneous and h1 the samples are not homogeneous. table 3 shown the results: table 3. the pearson’s chi-squared statistical test for ds18b20 waterproof sensor sensor v 𝝌𝒆𝒔𝒕𝒊𝒎𝒂𝒕𝒆𝒅 𝟐 𝝌𝒑−𝒗𝒂𝒍𝒖𝒆 𝟐 ds18b20 78 0.346 51.910 the figure 5, shows the temperature difference between ds18b20 sensor and the reference thermometer. in addition, a trend line adjusted by a second-degree polynomial, calculated from difference values between the thermometer and ds18b20 sensor, provides the correction function for temperature sensor measurements, the calibration function is: figure 5. temperature differences between ds18b20 sensor and thermometer, trend-line and correction function https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 67 the estimated temperature for the ds18b20 sensor used is: 𝑡𝑐 = 𝑡𝑠 + (−0.001177 ∙ 𝑡𝑠 2 + 0.096833 ∙ 𝑡𝑠 − 1.470066) (6) where: 𝑡𝑐=correction temperature values 𝑡𝑠= ds18b20 temperature values 2.4. arduino and xbee configuration the xbee devices were configured with the api (application programming interface) operating mode; this mode is available for the operation of the zigbee protocol, used in this case, and permits the xbee coordinator to receive wireless data packets from multiple xbee devices, identifying each remote device. this characteristic is fundamental to associate each xbee device with a geographic location. we used the star topology in this zigbee network, configuring a coordinator and router devices. with an arduino uno board, it was possible to control the data to be sent to the network and the temperature measurement frequency, for this, two sketches control the transmission and reception of data in nodes and in coordinator respectively. for those arduino uno boards integrated with the remote devices, the format of the data is the principal function since the sensor measurement must be sent as well as its identification. in the case of coordinator, the arduino uno board must interpret the data packet received. the network is composed of five nodes and one coordinator. each node has a ds18b20 temperature sensor, xbee s2c module, and an arduino uno microcontroller. for the coordinator, the configuration is the same, but the ds18b20 is not used. figure 6 shows the configuration of the coordinator and the node. figure 6. coordinator and node structure for the wireless network. for the coordinator, the energy power was provided by the usb port connection. for the nodes, due to their characteristic of being positioned in different positions on the ground, it was used a solar panel-based power and batteries, the latter being used as a reserve, if there are any problems in the power supply from the solar panels. figure 7 shows the configuration of node energy system https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 68 figure 7. configuration of node energy system the configuration of this power source considers a 5.5-volt solar panel, a one-way diode; a lithium battery charger, a lithium battery, and a voltage lift to keep this parameter constant during its use. the node and coordinator are shown in figure 8. figure 8. coordinator and nodes of network the upper part of the module has two functions: the first is protecting the temperature sensor from the direct incidence of the sunrays and, secondly, it supports the solar panel. this part can be rotated for better positioning of the solar panel. figure 9 shows the wireless network diagram, in this case, the nodes were configured to send the temperature values each 1 minute to the coordinator. the coordinator controls the data flow and a sketch in python language save the information figure 9. network operation diagram https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 69 the collected temperature data, received by the coordinator node, were transmitted to the computer that was connected to the total station. one program developed in c # language receives this information and, using a user-selected interpolation model, determined the temperature value to be used for the correction of the first velocity. this program automatically sets the temperature and pressure values for the total station. 3. result and discussion the system was designed to be used primarily in monitoring works of large engineering structures. thus, it was tested in a dam 67 meters (220 ft) high, 1,100 meters (3,600 ft) long, that has a geodetic network built for monitoring purposes. the salto caxias hydroelectric power plant is located between the municipalities of capitão leônidas marques and nova prata do iguaçu, 650 km far from curitiba, brazil (figure 10). figure 10. the location of salto caxias hydroelectric plant in this dam there is an external monitoring network (figure 11) composed of six forced centering pillars (fixed points) and several prisms fixed on the upstream face of the dam (object points). in addition, there is an internal monitoring network, used to measure points in the inspection-galleries of the dam figure 11. external control geodetic network https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 70 the observation of external network provides the geodetic control for the galleries. figure 12, the position of the point ege05 allows the connection of both networks through observation to the point eg21 that correspond to the inner geodesic station closest to the outside of the gallery. for this connection, the total station was set up at point ege05 and is realized the back sight at point ege04 (inclined distance of approximately 116 meters) and foresight at point egi21 (approximately 37 meters). figure 12. gallery geodetic network, adapted (zocollotii filho, 2005) the sensors were installed at the ege04 point (sensor s5455), ege05 (sensor s5451) and egi21 (sensor s6579). figure 13, 14 and 15 show the localization of the sensors figure 13. ege05 temperature sensor installation figure 14. ege04 temperature sensor installation figure 15. egi21 temperature sensor installation https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 71 the sensors provided a temporal temperature series of two hours approximately. figure 16 shows the temperature variations during the experiment. figure 16. the temperature variation in egi21, ege05 and ege04 this experiment seeks to present the behavior of the temperature around the dam. for this, in ege03 pillar one sensor provides temperature at the total station position. near the downstream prisms line, the other four sensors provided the temperature near these object points. these sensors were placed at different heights. figure 17 shows the sensors distribution. the distance from the ege03 point to the prisms and the sensors was approximately 280 meters. figure 17. sensors distribution in the dam for the second test in this experiment, the five sensors provided a temporal series about 50 minutes of collected temperature values. figure 18 shows the temperature variations during the experiment figure 18. temperature values of five sensors located in salto caxias dam https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 72 the figure 18 show variations around 8°c between the different places where the temperature sensor was installed. an analysis was made comparing the precision of electronic distances and the refractive index, the last one was computed using the maximum difference of temperature during the second experiment (8°c). for the refractive index was used equation 3, with pressure and humidity measured on where the total station was installed. for electronic distance precision was used the equation 7 where 𝜎𝑖 2 and 𝜎𝑡 2 miscentering error for instrument and reflector respectively, 𝑎 and b are specific accuracies and d is distance (ghilani, 2017). to perform the analysis we choose a high precision electronic distance meter (1 + 0.5ppm) and the ege03 – dam distance (280m approximately). for the compute the distance precision, we inconsiderate miscentering error for instrument and reflector since both are fixed in the pillar for monitoring and in the body of dam respectively. results are presented in table 4. table 4. comparison between index refraction and electronic distance precision approximate distance index refraction influence on distance (first velocity correction) (m) distance precision (68% interval confidence) (m) ege03 – dam (280m) 0.002 0.001 the results show that the variation in meters generated when considering the difference between the refractive indices is greater than the precision provided by the instrument for the distance used. considering that the pressure and humidity parameters were considered fixed, the temperature and its variations affect the performance of the instrument. for evaluation the influence of temperature on coordinate precision, we simulated a 2d topographic network (figure 19) with two-control point (a, d), two unknown points (b, c) and five distances (continuous lines). to obtain the coordinates and their precisions we used trilateration technique and parametric least squared method (ghilani, 2017). the refraction index was compute with the temperature data set from the acquisition system presented in the section 3.5 during the second experiment. with these values, we developed two mathematical models to temperature determination. in the first approach, we used the temperature value measured in the place where the total station was installed (classic approach), while that the second approach was obtained through the mean temperature value from the five sensors used in the previous experiment. for the pressure and humidity parameters, we used the values measured with external sensors on the total station position during the experiment presents in the section 4.1, for this simulation these parameters are considering invariants. figure 19. the simulated network 𝜎𝐷 = √(𝜎𝑖 2 + 𝜎𝑡 2 + 𝑎2 + (𝐷 ∙ 𝑏 𝑝𝑝𝑚)2) (7) https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 73 the refraction index values for the first and second approaches are 18.49 ppm and 17.38 ppm respectively. with these values, the first velocity and their distance precision were computed to each distance on the simulated network. the precision of b and c points are presented in table 5: table 5. precision coordinates b and c temperature approach b (68% interval confidence) c (68% interval confidence) x (m) y (m) x (m) y (m) one sensor 0.0052 0.0086 0.0072 0.0054 mean (5 sensors) 0.0049 0.0081 0.0068 0.0051 for the confidence interval used, the precisions reached through the use of an mean of temperature are better than those obtained in the traditional way. this may be because the second approach provides a more representative temperature value of the region. for the first experiment, the temperature values presented differences in the egi21, egi21, egi21 control points (maximum value of 12°c). these differences could be originated by different factors such as the temperature dissipated by the structure or environmental conditions around the dam. both factors can create different thermal conditions in each control point, with irregular variations. in the geodetic monitoring context, the external network is used as reference for the displacement analysis of internal network, the latter, provides relevant information about the health of the structure. for the deformation analysis of internal network it is necessary the connection between networks, this is made through angles and distance measurement, consequently the temperature values provides by the developed system and the differences found should be considered in the distance measurement. in the second experiment, the temperature sensors were installed on the top and around the dam. were collected temperature values during the geodetic monitoring of points on the downstream. the results show that temperature values decreasing with increasing the height, however the decreasing it is not regular. some of the possible factors that influence the temperature values are the heat dissipation of the structure and the water mass influence on the points that are on the dam top. in both experiments, the temperature values have an irregular comportment during the geodetic monitoring. these variations can be affecting the correct determination of first velocity correction, thus the distances obtained by edm. the uncertainties generated by the temperature differences were obtained thought the first velocity correction, for this, the highest temperature difference found in these experiments (8°c) was used. this difference was obtained between the pillar ege03 and a monitoring point on the top of the dam. the approximate distance between them is 280 meters and uncertainties are 2.0 mm approximately. the comparison between the uncertainties generated by the refractive index variations and the distance precision (1 mm) show that the temperature value is determinant to obtain precise distances by electromagnetic wave propagation. for the coordinate precision values, the 2d simulate network show that the precisions of coordinates provide by the least square method are better where the refractive index was calculated with the mean temperature of sensors. thus, the modeling of temperature is necessary for the geodetic monitoring of structures, where the correct determination of distances is relevant to the coordinate compute and deformation analysis. 4. conclusion the system development for temperature acquisition provides in real-time values in multiple locations. this characteristic permits temperature monitoring during the observation of a geodetic network or in other related activities. the temperature values obtained in the dam experience and their variations in different locations around to dam can affect the calculation of the first velocity correction. considering that variation on 1°c affect distances in 1ppm and that it was found in both experiments, differences up to 8°c, to work in https://doi.org/10.14710/geoplanning.8.1.61-74 rodriguez et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 61-74 doi: 10.14710/geoplanning.8.1.61-74 74 temperature modeling for this correction is necessary. the experiments carried out show the importance of temperature both in determining distances and in coordinate precision of a topographic network, in this context, the developed system allows to collect data for modeling and provide a more representative temperature value. 5. acknowledgments the authors would like to thank copel companhia paranaense de energia for allowing the tests to be carried out at the salto caxias plant. the authors also thank for the scholarship given. 6. references agresti, a. (2007). an introduction to categorical data analysis. john wiley & sons. angus-leppan, p. v, & brunner, f. k. (1980). atmospheric temperature models for short-range edm. the canadian surveyor, 34(2), 153–165. artese, s., & perrelli, m. (2018). monitoring a landslide with high accuracy by total station: a dtm-based model to correct for the atmospheric effects. geosciences, 8(2), 46. brunner, f. k. (1984). geodetic refraction. berlin: shringer, 216. brunner, f. k., & fraser, c. s. (1977). an atmospheric turbulent transfer model for edm reduction. proc. iag symp., wageningen. brunner, f. k., & rüeger, j. m. (1992). theory of the local scale parameter method for edm. bulletin géodésique, 66(4), 355. de rubeis, t., nardi, i., & muttillo, m. (2017). development of a low-cost temperature data monitoring. an upgrade for hot box apparatus. journal of physics: conference series, 923(1), 12039. devices, a. (2020). low voltage temperature sensors tmp35/tmp36/tmp37. analog devices: norwood, ma, usa. fraser, c. s. (1981). a simple atmospheric model for electro-optical edm reduction. australian surveyor, 30(6), 352–362. ghilani, c. d. (2017). adjustment computations: spatial data analysis. john wiley & sons. maxim, i. (2020). programmable resolution 1-wire digital thermometer. data sheet ds18b20. ogundare, j. o. (2015). precision surveying: the principles and geomatics practice. john wiley & sons. robertson, k. d. (1977). the use of atmospheric models with trilateration. survey review, 24(186), 179–188. rüeger, j. m. (1990). electronic distance measurement: an introduction. springer science & business media. scaioni, m., barazzetti, l., giussani, a., previtali, m., roncoroni, f., & alba, m. i. (2014). photogrammetric techniques for monitoring tunnel deformation. earth science informatics, 7(2), 83–95. soares, w. a. (2006). investigação de uma modelagem matemática como alternativa para aumento da área de cobertura de estações de referência dgps. boletim de ciências geodésicas, 12(1). solarić , n., barković , djuro, & zrinjski, m. (2012). automation of the measurement of atmospheric parameters in precise distance measurement. geodetski list, 66(3), 165–186. torge, w., & müller, j. (2012). geodesy. de gruyter. ussisoo, i. (1969). correction problems in electronic distance measurements. tellus, 21(4), 549–567. zocollotii filho, c. a. (2005). utilização de técnicas de poligonação de precisão para o monitoramento de pontos localizados em galerias de inspeção: estudo de caso da usina hidrelétrica de salto caxias. boletim de ciências geodésicas, 11(2), 287–288. https://doi.org/10.14710/geoplanning.8.1.61-74 | 47 geoplanning vol 7, no 1, 2020, 47-56 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.7.1.47-56 mapping of waste management planning based on society and geographic conditions n. widyaningsiha*, s. sasakib a urban planning, esa unggul university, indonesia b department of sociology, teikyo university, japan abstract: household solid waste is a major environmental issue, not only in big cities but also in suburban areas. setia asih village location is in kecamatan tarumajaya, kabupaten bekasi. bekasi is closed with jakarta, and it has unique characteristics, such as the dual market economy, administration system, and the local people's social culture. setia asih village has tremendous unmanaged household solid waste. it covers the land and the river. this research paper used a qualitative approach due to the limited statistical data on the village level in indonesia. the preliminary observation showed that local people do not know about managing their household solid waste. their local government does not have any responsibility to manage the household solid waste on the village level. it becomes the local people's responsibility. there is a lack of coordination among stakeholders in household solid waste management at setia asih village. i conducted the solid waste management training for 300 people and focus group discussion (fgd) with each dusun (lower level than a village) representative. i found that local people have solutions to solve their household solid waste problem. the local government builds a new waste bank as corporate social responsibility (csr) from state-owned enterprises. copyright © 2020 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): widyaningsih, n., & sasaki, s. (2020). mapping of waste management planning based on society and geographic conditions. geoplanning: journal of geomatics and planning, 7(1), 47-56. doi: 10.14710/geoplanning.7.1.47-56 1. introduction recent studies related to waste management in the regional scope have become the focus of research in this decade (giovanis, 2015; ko et al., 2020; prajapati & patel, 2018; ramandey, 2016). several discussions highlighted the involvement of stakeholders in independent waste management in each region (indrianti, 2016; lederer et al., 2015; suthar et al., 2016). the trend in parts of the world is household solid waste (babaei et al., 2015; dai et al., 2015; dhokhikah et al., 2015). household solid waste is still the biggest issue in most developing countries in asia, one of which is indonesia. indonesia had more than 260 million people in 2019. they concentrate in big cities, such as jakarta, surabaya, and medan. as a capital city, jakarta has more than 10 million people live there (a.k.a. megacity). jakarta has supporting areas, such as bogor, depok, tangerang, and bekasi (jabodetabek). bekasi has the biggest number of housing sales last year compared to the other supporting areas. bekasi also has the biggest number of population growth rate than the other supporting areas. they do not have household solid waste composition data for inorganic and organic waste. the phenomena show us that the bekasi area can transform into a fast-growing city based on its population and its easy access to jakarta. the periurban area of jakarta metropolitan area is still growing faster than the core area, dki jakarta. the population growth in the peri-urban has converted land uses from mostly agriculture to urban (winarso et al., 2015). article info: received: 30 july 2018 in revised form: january 2019 accepted: january 2020 available online: 7 july 2020 keywords: household solid waste, focus group discussion, corporate social responsibility, mapping of waste management *corresponding author: niluh widyaningsih urban planning, esa unggul university, indonesia email: nhwidya@yahoo.com open access http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.7.1.47-56 mailto:nhwidya@yahoo.com widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 48 | the increasing number of populations relate to the increasing number of household needs, such as the demand for housing, clothes, and food. people cannot live without their basic needs. since the industrial revolution, there is a huge development in the transportation, health service, manufacturing, and communication sectors. in the 19th and 20th centuries, all of those sectors' development is faster and bigger with information and technology (it). we called it the globalization era, where people can connect without border, space, or time limitations. technological advancements, which have risen above simply being a development, affect generation procedures and day by day life of consumers by changing their habits and behaviors (alim, 2019). since the industrial revolution, the use of plastic has increased manifold without improving its adequate management as a waste. most of the plastic waste produced in the world is mainly from packaging industry followed by building and construction (hameed et al., 2019). people engaged in more daily routines and need more practical food serving (a.k.a. packaging for food products). in ancient times, we know only natural packaging from leaves or dried animal's skin. for example, if we go to a different place or travel and we need to carry our food, we wrap it with a big leaf or use dried goatskin. all-natural packaging materials changed into plastic, which has specific characteristics that can beat other packaging materials. plastic packaging materials are easy to fold, low price, waterproof, and light to carry out. people would do more consumption activities if the advertisements on non-food and food products were intense on the television (widyaningsih, 2015). besides that, people in big cities live close to each other due to the scarcity of land for housing. so, it makes people easy to communicate with each other during their free time, and it becomes unpaid advertisement media among people. after we do the consumption activities, we create waste and pollution to the environment. most people do not know any knowledge and information to manage their household solid waste from their house before they throw it into the temporary waste area. they let the local government and the scavengers are managing their household solid waste. in developing countries, we can easily find scavengers as an informal, hidden market economy. in the jakarta area, the percentage of inorganic waste is higher than organic waste. jakarta has a high regional minimum wage compares to other areas. this income level makes people in jakarta can shift the consumption pattern. their consumption is also higher on non-food products than on food products. bekasi has almost the same conditions as in jakarta, such as population number is big, high mobility of the people, and close distance between jakarta bekasi. the only difference is that the local government does not manage their local household solid waste on the village level. from my preliminary observation at setia asih village last january 2018, i could see plastic waste covers almost the soil or land and river. local people do not pay much attention to land pollution everywhere around the bekasi area. they do not bother with their solid waste around their house area. they do not have any penalty from the local government if they ignore their household solid waste. most local people throw their household solid waste into unmanaged areas or the closest vacant/empty area. some previous research only focused on how to manage waste, but did not pay attention to regional aspects that could help facilitate waste management (eriksson et al., 2005; johnson, 2017; khandelwal et al., 2019; liikanen et al., 2018). seeing the existing gaps, this paper aims to map the area in determining service zones in waste management. this research paper used a mix-methods approach to gather more information from the field because there is no data or updated data statistically from the local government, especially household solid waste at setia asih village. i combined the secondary data and the primary data to support the analysis and results. in the end, the research paper will be used for the local government and local people to set up a new mechanism for their household solid waste management based on their local characteristics (social and geography conditions) through a waste bank system. widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 | 49 2. data and methods the research started at the end of the last year, 2017, where i helped on corporate social responsibility (csr) program from one of the state-owned enterprises in jakarta to build a waste bank in one region of jakarta's supporting areas. the main idea is not on the physical new waste bank building but especially on the household solid waste management system based on the local absolute advantage. for example, if a local region has a household solid waste problem and does not get any non/institution funding, the stateowned enterprise is willing to help society. my first observation is that setia asih village has a good financial track record with the state-owned enterprise. the local people do not have any household solid waste management system yet. based on the secondary data for their administration level, we found that setia asih village location is in kecamatan tarumajaya (kecamatan is higher level than village) and the location is in kabupaten bekasi (kabupaten is higher level than kecamatan). there are eight villages in kecamatan tarumajaya, such as: (a) pusaka rakyat village; (b) setia asih village; (c) pahlawan setia village; (d) setia mulya village; (e) segara makmur village; (e) pantai makmur village; (f) segarajaya village; and (g) samudra jaya village. setia asih village has the biggest number of rukun tetangga or rt (around 90-100 households in one rt). this village has 110 rt, 32 rukun warga or rw (rw is higher level than rt) and seven dusun (dusun is higer level than rw). setia asih village has the biggest population, which is 41,548 people (20,879 males and 20,669 females) in 2016. their household number is 10,065, which is still the biggest number compared to the other villages and has an average of 4-5 persons per household. the number is higher than on my previous research in kecamatan duren sawit, east jakarta, which has 3-4 persons per household. it means the demand for food and non-food products for basic needs will be higher at setia asih village than in kecamatan duren sawit, east jakarta. if the demand increases, so do the household consumption. it means more waste on the environment. setia asih village has heterogenic people with an open-minded society that can accept the socio-culture changes dynamically. they have a dual market economy, where i found the traditional market in front of the local government office (kantor kepala desa) and minimarkets, such as alfamart or indomaret. these markets have different characteristics, but both give plastic shopping bags to the customers. in indonesia, we called the plastic shopping bag plastic kresek. i interviewed with semi-structured questionnaires to gather the primary data. i randomly used samples or respondents (in school, traditional market, minimarket, local bank, local business area, or government officers). people live unevenly distributed among the sub-region of setia asih village area. we will see the used area spreading on the local map later. almost 50% of the area is for regular housing areas, markets, and schools. the rest of the areas are for housing complex projects, and they are still vacant/empty land. they do not have the setia asih village map administratively. so, i used the online google map to spot the areas and the new waste bank potential location at setia asih village. the local people work in the agriculture sector, such as farming (chicken, duck, and catfish) and independent farming for their household needs (vegetable and fruit). i found that each household could develop their own small farm/pond in their own backyard. they do not need a big area. for example, they do farming in a portable big pond for the catfish. it is space efficiency farming with a bigger number on their harvesting than traditional farming. to build a household solid waste management system at setia asih village, i divided this research framework into three sections, such as: a. mapping of social condition from their population number, housing types, and work fields. b. mapping of environment condition from the local map – housing area or vacant/empty area. c. mapping of economic condition from their number of savings book accounts through the state-owned enterprise. i used a qualitative approach by conducting: firstly, the general training on household solid waste on april 21, 2018, that divided into two sections – in the morning from 9 am-12 pm and in the afternoon from widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 50 | 1 pm-4 pm. more than 300 people attended the event. the event collaborated with the state-owned enterprise for its csr program (figure 1). secondly, i gathered the information deeply about the household solid waste issues and their local solutions based on their capability and capacity. the fgd was conducted on april 25, 2018, from 1 pm-4 pm with each dusun representative (two people). the total number who attended the second event were eight dusun multiples by two people is the same as 16 people. still, only 10 people attended the fgd because of the bad rain weather in setia asih village (figure 2). we had a oneperson representative from tabungan desa (village savings bank), the representative for the csr program from the state-owned enterprise, and the representative from the local government officials who became the household collector leader. figure 1. training on household solid waste figure 2. focus group discussion on the household solid waste management widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 | 51 3. results and discussion setia asih village location is a very attractive area because it closes with the capital city with easy access (transportation); the local people have some home-industries that have the export quality of products (food products and batik linen to improve their economic condition); and many potential areas for a housing complex in the future (environment aspect). the important thing is they are willing to learn how to manage their household solid waste. the solid waste comes from the household. i find it floating on the river and almost in every corner of the road, especially for the vacant/empty area. it around 55% of the household solid waste is organic waste, and the rest is an inorganic waste. from the first event training on the introduction about household solid waste, the representative from all levels of community associations attended the morning session. they are representative from each rt (two people) and community leaders, such as housewife group, a home industry group, youth group, nongovernment organization, religious group, local school, and local market community). they were very interested in listening to the training and the explanation about household solid waste. here are some semi-structured interview responses during that event: “basically, we do not know how to manage our household solid waste. so far, we just throw it to the closest vacant/empty area. we used the vacant/empty area behind one of the housing complex walls. now, after listening to the information about household solid waste and solid waste management, we know that our household solid waste can have economic value and can be recycled products.” (alias mrs. yuni) “our villagers can support the new waste bank, but not all of us live close with the waste collector positions. so far, we collected our plastic bottles from house to house into a big empty house/room, and sometimes there are waste collectors who helped to sell it.” (alias mr. anto) “we thought that inorganic and organic waste would be degradable by nature together whenever we put it. we do not know that we can use organic waste into liquid organic fertilizer and inorganic waste into the recycled product." (alias mr. udin) first, there is no statistical data regarding the household solid waste composition (plastic, paper, glass, and metal) at setia asih village. i used an approximate number (after discussing with the local government officials, the state-owned enterprise, and the community leader). each person creates around 0.5 – 1 kilogram (kg) of household solid waste every day. the average number of people per household at setia asih village is 4 – 5 people. the total population is 41,548 people multiple by 0.7 kg (the average value) for each person from the demography data. the total number of unmanaged household solid waste every day is 29,084 kg per day without any support from the local government at the village level to manage it properly. second, i searched for information at setia asih village for the waste collectors who buy the household solid waste, such as plastic, paper, glass, and metal. the informal, hidden market economy system for the scavengers on the solid waste is not open access data than any other job fields. it made the research for the solid waste price was difficult. my observation along the river showed that almost all of the soil blended with plastic waste for years. it is a tremendous solid waste. there are used plastic wrap, dirty bottles or paper, and used styrofoam packaging. so, when the building construction officers built the new waste bank building (march 2018), it was hard to dig the soil blended with plastic waste (figure 5). it took a long time to finish the job. besides that, from statistical data, setia asih village has the biggest store (397) in 2016 compared to other villages at kecamatan tarumajaya. the second biggest store is pusaka rakyat village (250) and pantai makmur village (146). of course, these stores will add easier access for the local people to buy more stuff (food and non-food products) whether they do the consumption with rational thinking (buy the products only if they need them) or they buy the products to satisfy their other thought (buy the products because they want to have it or for their prestige only). third, the housing distance at setia asih village is distributed unevenly. no administrative local map, and i used the online search engine (google) to plot the area, including the new waste bank position. in the future, the new waste bank position is in the corner of the river at setia asih village. they will build a playground for the kids and a public park also. below are the local maps for kabupaten bekasi (figure 3) widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 52 | and setia asih village (figure 4). in figure 4, i divided the region into four quadrants to make it easier to analyze the region: a. quadrant 1 has a vacant/empty area, and there will be a new housing complex in the future. b. quadrant 2 has two housing complex areas (green ara and cluster somerset), and part of the area is vacant/empty area. c. quadrant 3 has two housing complex areas (perumahan wahana harapan and perum pesona bumi insani), and there are local schools, minimarkets, and mosques. d. quadrant 4 has two housing complex areas (de residence and perum puri harapan), local banks, and public utilities. this quadrant is close with harapan indah bekasi for business. figure 3. map of bekasi, west java widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 | 53 figure 4. map of quadrant analysis for setia asih waste management after the household solid waste training, the local government chose the people involved in the household solid waste management and people to manage the new waste bank at setia asih village. from each dusun, they have one person to be their preventatives or as a waste collector who brings the household solid waste to the new waste bank. i explained how to manage the household solid waste from their house before throwing it into the temporary waste area and the new waste bank mechanism for the household solid waste based on the conditions. widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 54 | figure 5. the new waste bank location at setia asih village the local government, the local people (through their representatives), the state-owned enterprise (under the csr program manager), and i agreed that we are going to implement the household solid waste to clean up the region from the household solid waste (figure 6): a. the waste collector officer will collect the household solid waste (twice a week) from each household, and s/he will bring it to the new waste bank for people who live far from the new waste bank location. b. people who live close to the new waste bank will directly bring their household solid waste to the new waste bank location. the new waste bank will open every day with regular office hours. c. all the household solid waste must be separated into plastic, paper, glass, and metal because we considered the solid waste market. d. the household solid waste must be cleaned to be measure at the new waste bank. if it is clean, the price is higher on the solid waste market. e. each household must have a savings book account to record all the prices of the household solid waste. the informant informed me that around 2,500 – 3,000 people already have a savings book account (individually or each household) at setia asih village. before introducing the new waste bank mechanism, the scavenger around setia asih village sells the household solid waste (without being recycled) for a very low price. now, the local people can have extra money from their household solid waste. table 1. setia asih village waste bank (price per kg) type of solid waste price per kilogram plastic aqua plastic bottles aqua plastic cups 7,000 idr 5,000 idr paper newspaper white paper 1,600 idr 1,500 idr glass metal misc. syrup bottles regular glass aluminum wooden stuff 3,000 idr for each bottle 5,000 idr 7,500 idr unidentified widyaningsih and sasaki / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 47-56 doi: 10.14710/geoplanning.7.1.47-56 | 55 actually, qualitative research can gather more information if we can deeply listen to local people's opinions. i can describe it as extra knowledge based on local capability and capacity from the unstructured interview. the local people and the local farmers said they could transform the organic household solid waste into organic liquid fertilizer. the farmers bought that to use in their farming. here is one of the inputs from the community leader: “we could transform the organic household solid waste using the composter tools, and the organic liquid fertilizer price is only rp. 10,000 each bottle. the farmers used it, and the vegetable/fruit is of good quality. we just need funding to buy composter tools." (alias mr. toni) figure 6. the new waste bank mechanism finally, setia asih village is one example of how local condition (socio-cultural and their geography condition) can solve their household solid waste by their own capacity and capability. they need to build a bridge to do the household solid waste management system comprehensively from local government, nongovernment institutions, community groups, academic, and all stakeholders (including the waste picker and the small home-industry for the recycled products) who involve strengthening the waste management system through a waste bank. 4. conclusion this research shows that the regional aspect and the role of stakeholders are very important in the management of household waste. this research also produces a new mechanism for household waste management based on local characteristics. this can be a reference for the government and local communities to implement a waste bank system so that waste management can be carried out effectively and efficiently. 5. acknowledgments i would like to thank all my colleagues at urban planning, esa unggul university, for their endless encouragement and mr. rujito at setia asih village for his expert advice on local community development. for those who touched my life anyway since i started my post-graduate school at environmental science, university of indonesia, you all know who you are. i am truly grateful for all you have done throughout all my academic researches. 6. references alim, l. d. 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[crossref] https://doi.org/10.1016/j.resconrec.2015.06.013 https://doi.org/10.1016/j.jclepro.2004.02.018 https://doi.org/10.1016/j.wasman.2015.03.006 https://doi.org/10.4018/978-1-7998-0031-6.ch004 https://doi.org/10.1016/j.sbspro.2016.05.431 https://doi.org/10.4324/9781315736761 https://doi.org/10.1016/j.biortech.2019.121515 https://doi.org/10.1016/j.wasman.2020.01.020 https://doi.org/10.1016/j.habitatint.2015.08.015 https://doi.org/10.1016/j.jclepro.2018.06.005 https://doi.org/10.12777/wastech.4.1.13-15 https://doi.org/10.1016/j.scs.2016.01.013 https://doi.org/10.1016/j.habitatint.2015.05.024 1 geoplanning journal of geomatics and planning vol. 10, no. 1, 2023 original research temporal analysis of land use and land cover changes in vizianagaram district, andhra pradesh, india using remote sensing and gis techniques yenda padmini1, mallula srinivasa rao2, gara raja rao2* 1. department of geosciences, dr. b.r. ambedkar university, srikakulam, india 2. department of geology, andhra university, visakhapatnam, india doi: 10.14710/geoplanning.10.1.1-10 abstract land use and land cover change (lulcc) has become a significant global concern due to its wide-ranging environmental, social and economic impacts. this literature review aims to provide a comprehensive overview of the key ideas, drivers, consequences and approaches to studying lulcc. by synthesizing various research articles, this review offers insights into the causes and impacts of lulcc, as well as the methods used to analyze and monitor these changes. the review also highlights the importance of understanding lulcc dynamics for sustainable land management and policy making. between 2017 and 2022, the lulc categories underwent several changes. data acquisition process for satellite imagery combining sentinel-2 digital remote sensing data digital remote sensing data through the copernicus open access hub. the spectral resolution is 10, 20, and 30 meters respectively, while the spatial resolution is 10 meters which was used for the lulc analysis of the study area. this analysis underscores the importance of lulcc monitoring to inform sustainable land management practices and conservation efforts. the trends identified provide a basis for further investigation into the underlying drivers of these changes and their potential impacts on ecosystems, water resources and human well-being. continued monitoring and proactive measures are essential to mitigate adverse impacts and promote sustainable land use in the future. copyright © 2023 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction land use and land cover changes (lulcc) refer to the transformation of the earth's surface due to human activities, which change the physical and biological appearances of the land. these changes encompass various processes such as deforestation, urbanization, agricultural expansion, industrialization, and infrastructure development (aneesha satya et al., 2020; krishna & reddy, 2017; reddy et al., 2019; singh et al., 2019). lulcc is a global phenomenon that has profound implications for the environment, society, and economy. the significance of studying lulcc lies in its far-reaching impacts on both natural and human systems. understanding the drivers, consequences, and patterns of lulcc is crucial for effective land management, conservation, and sustainable development (pattanaik et al., 2011). environmental implications: lulcc significantly affects ecosystems, biodiversity, and natural resources. deforestation and habitat fragmentation, for example, lead to the loss of species and disruption of ecological processes. changes in land cover also impact carbon sequestration, water resources, soil quality, and climate regulation, contributing to global environmental encounters for instance climate change and loss of ecosystem services (kandrika & roy, 2008). e-issn: 2355-6544 received: 06 may 2023; accepted: 20 august 2023; published: 25 september 2023. keywords: landuse, landcover, remote sensing, gis, change detection and vegetation *corresponding author(s) email: rajaraogeo@gmail.com https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 mailto:rajaraogeo@gmail.com padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 2 social and economic impacts: lulcc directly influences human societies and economies. urbanization and industrialization drive economic growth but often result in land degradation, pollution, and social inequalities. changes in land use can displace communities, trigger land conflicts, and affect traditional livelihoods, thereby impacting social stability and human well-being (kandrika & roy, 2008; sethi et al., 2014). understanding these social and economic consequences is crucial for equitable and sustainable development. policy and decision-making: lulcc research provides valuable insights for policymakers, land managers, and urban planners. comprehensive understanding of the drivers and consequences of lulcc can inform the formulation of land-use policies, zoning regulations, and resource management strategies. by integrating scientific knowledge and evidence-based approaches, decision-makers can address environmental and socio-economic challenges, promote sustainable land use practices, and mitigate the negative influences of lulcc (buyadi et al., 2014; kandrika & roy, 2008; langat et al., 2021; sethi et al., 2014; srivastava et al., 2020). climate change mitigation and adaptation: lulcc play a significant role in climate change mitigation and adaptation. forest conservation and restoration efforts, for instance, contribute to carbon sequestration and the decrease of greenhouse gas releases (sudhakar et al., 2006). recognizing the potential of land-based solutions is essential for achieving climate goals and enhancing resilience to climate change impacts. conservation and biodiversity protection: lulcc research contributes to the conservation and protection of biodiversity and ecosystems. identifying areas of high ecological value, understanding habitat connectivity, and assessing the impacts of lulc decisions on biodiversity are critical for prioritizing conservation efforts and designing effective protected area networks (cihlar & jansen, 2001; jaiswal et al., 1999; yadav et al., 2012). the importance of studying the lulcc in india cannot be overstated due to the country's unique socio-economic and environmental context. here are some key reasons why studying lulcc in india is crucial: rapid urbanization: india is undergoing significant urbanization, with a rapidly growing population and increasing migration from rural to urban areas. this urban expansion leads to the conversion of agricultural land and natural habitats into built-up areas, impacting ecosystems, water resources, and biodiversity (jiang & tian, 2010; wolch et al., 2014). understanding the patterns and consequences of urbanization is vital for sustainable urban planning, resource management, and mitigating the associated environmental and social challenges. agricultural expansion and intensification: agriculture is a vital sector for india's economy and sustenance of its large population. however, the expansion and intensification of agriculture, driven by factors such as population growth and changing consumption patterns, result in the conversion of forests, grasslands, and wetlands into agricultural lands (areendran et al., 2013). studying lulcc related to agriculture helps identify sustainable farming practices, balance food security with environmental conservation, and address issues such as soil erosion, water scarcity, and pesticide use. forest conservation and biodiversity: india is home to diverse and ecologically significant forest ecosystems, including tropical rainforests, mangroves, and dry deciduous forests. lulcc studies play a crucial role in monitoring deforestation rates, identifying areas of high biodiversity value, and understanding the drivers of forest loss, such as logging, encroachment, and infrastructure development (butt et al., 2015). this knowledge supports conservation efforts, restoration initiatives, and the protection of endangered species and their habitats. water resource management: india faces significant challenges related to water scarcity, pollution, and unsustainable water management practices. lulcc studies provide insights into land cover changes affecting watersheds, rivers, and aquifers. understanding the impacts of lulcc on water availability, quality, and hydrological processes helps inform water resource management strategies, groundwater recharge initiatives, and sustainable irrigation practices (rajasekhar et al., 2019; rajasekhar et al., 2020). climate change adaptation and mitigation: india is vulnerable to the impacts of climate change, including increased frequency and intensity of extreme weather events, rising temperatures, and changing rainfall patterns. lulcc research is essential for assessing the contribution of land-based activities to greenhouse gas emissions, identifying carbon sinks, and developing climate change adaptation strategies. sustainable land management practices, such as afforestation, agroforestry, and sustainable land-use planning, can play a crucial role in climate change mitigation and adaptation (kudnar & rajasekhar, 2019; pradesh, 2018; rajasekhar et al., 2019; rajasekhar, 2019; siddi raju et al., 2018). https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 3 policy formulation and planning: comprehensive studies on lulcc provide valuable inputs for policy formulation and planning processes. evidence-based research helps policymakers understand the drivers and consequences of land-use changes, assess the effectiveness of existing policies, and design strategies to promote sustainable land use practices (rajasekhar et al., 2018; rajasekhar et al., 2019; rajasekhar et al., 2019). this includes formulating land-use zoning regulations, protecting ecologically sensitive areas, and integrating socioenvironmental considerations into urban and regional planning. the lulcc research in india is essential for addressing urgent environmental, social, and economic issues. india can develop and implement effective policies and strategies that promote sustainable development, safeguard biodiversity, ensure water security, and contribute to climate change mitigation and adaptation by comprehending the drivers, consequences, and patterns of land-use changes. in conclusion, the primary goals of lulccs include understanding the drivers, assessing environmental and socio-economic impacts, devising sustainable land use strategies, enhancing climate change resilience, and supporting informed policy and decision making. researchers can contribute to the preservation of natural resources, the promotion of sustainable development, and the health of ecosystems and human societies by pursuing these goals. 2. data and methods 2.1. study area the study focuses on analysing land use and land cover changes in vizianagaram district, located in the state of andhra pradesh, india. vizianagaram district is situated in the north-eastern part of the state, between 17° 49' 42" n and 18° 43' 21" n latitude and 82° 59' 51" e and 83° 50' 55" e longitude. it covers an area of approximately 3846 square kilometres (fig 1). the district is characterized by a diverse landscape, encompassing various land use types such as agricultural fields, forests, urban areas, water bodies, and barren land. it is known for its agricultural productivity and is a key contributor to the state's economy. vizianagaram district is subject to ongoing development and urbanization pressures, which have led to significant lulcc over time. source: analysis, 2022 figure 1. location map of the vizianagaram district, andhra pradesh, india the analysis aims to understand the extent and patterns of these changes, identifying areas of conversion, expansion, and degradation of different land cover types. the study utilizes spatial approaches to analyze multitemporal satellite imagery, including data from different sensors such as landsat, sentinel, or similar sources. various spatial analysis tools and classification algorithms will be employed to delineate and quantify land use and land cover classes accurately. the findings of this study will provide valuable insights into the changing aspects of lulc in vizianagaram district, facilitating better land management and planning decisions for sustainable development in the region. https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 4 2.2. methodology we primarily employed two categories of data in the current study. these are remote sensing and topographic map data. sentinel 2's georeferenced and combined digital remote sensing data are obtainable through the copernicus open access hub. the spectral resolutions are 10, 20, and 30 meters, respectively, while the spatial resolution is 10 meters used for the analysis of the lulc in the study area. 2.2.1. data acquisition the data acquisition process for sentinel-2 satellite imagery involves several steps. here is a detailed overview of the data acquisition process for sentinel-2 imagery. the sentinel-2 mission is part of the european space agency's (esa) copernicus program, which aims to provide earth observation data for various applications. sentinel-2 satellites are equipped with multispectral sensors that capture high-resolution imagery of the earth's surface. sentinel-2 satellites have a global coverage and provide regular and systematic acquisition of data over specific regions (buyadi et al., 2014; perea-ardila et al., 2022; singh et al., 2019). the imagery is freely available to users worldwide through various data access portals, including the copernicus open access hub and commercial providers. the hub provides access to the complete archive of sentinel-2 data, allowing users to search, browse, and download imagery for their desired location and time period. sentinel-2 data is available in different product types, including orthorectified top-of-atmosphere reflectance values, while level-2a products include atmospherically corrected surface reflectance values. the choice of product type depends on the specific analysis requirements. sentinel-2 imagery has a spatial resolution of 10 meters (for visible and near-infrared bands) and 20 meters (for red-edge and shortwave infrared bands). the sensors onboard sentinel-2 capture data in 13 spectral bands, ranging from visible to shortwave infrared. sentinel-2 imagery undergoes rigorous calibration and validation processes to ensure its quality and accuracy (ayele et al., 2018; buyadi et al., 2014; falcucci & maiorano, 2007; hegazy & kaloop, 2015; pereaardila et al., 2022; singh et al., 2019; yang & lo, 2002). calibration parameters and metadata accompany the imagery, allowing users to assess the quality and make any necessary adjustments during subsequent analysis. it is important to note that the availability and access to sentinel-2 imagery may vary based on the specific user's location, data access agreements, and any limitations or restrictions imposed by the data providers. 2.2.2. image pre-processing the pre-processing of sentinel-2 data involves several steps to prepare the imagery for further analysis. sentinel-2 imagery undergoes radiometric calibration to convert the raw digital numbers (dn) acquired by the satellite sensors into calibrated at-sensor radiance values. this step corrects for sensor-specific characteristics, such as detector variations and radiometric response. atmospheric correction is performed to remove the effects of atmospheric scattering and absorption on the satellite imagery (ayele et al., 2018; buyadi et al., 2014; cihlar & jansen, 2001; falcucci & maiorano, 2007; hegazy & kaloop, 2015; park & lee, 2016; perea-ardila et al., 2022; scroll & for, n.d.; singh et al., 2019; yang & lo, 2002). this step is crucial for obtaining accurate and comparable surface reflectance values across different time periods and locations. various algorithms, such as the sen2cor algorithm, can be applied for atmospheric correction. geometric correction, also known as orthorectification, is carried out to remove geometric distortions caused by the sensor viewing geometry and earth's terrain. it involves aligning the imagery to a geodetic reference system and correcting for distortions such as terrain relief, tilt, and rotation. ground control points (gcps) from accurate reference data sources, such as high-resolution orthophotos or digital elevation models (dems), are used for accurate georeferencing. sentinel-2 data is acquired in tiles, and for larger areas of interest, it may be necessary to mosaic multiple tiles together to create a seamless composite image. mosaicking involves aligning and blending adjacent tiles to create a continuous image. additionally, if multiple acquisitions of the same area are available, they can be temporally assembled to create composite images representing a specific time period. https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 5 sentinel-2 imagery has different spatial resolutions for different spectral bands. if needed, the imagery can be resampled to a common spatial resolution to ensure consistency in subsequent analysis steps. each of these pre-processing steps is essential to ensure accurate and reliable analysis results using sentinel-2 imagery. the specific pre-processing workflow may vary based on the analysis objectives, software tools, and user requirements. 2.2.3. image classification erdas imagine software provides a range of tools and capabilities for image classification of sentinel data. start by importing the sentinel imagery into erdas imagine. this can be done by accessing the "data manager" and selecting the appropriate data format for sentinel data, such as geotiff. the sentinel imagery to enhance its quality and prepare it for accurate classification. this may involve radiometric calibration, atmospheric correction, geometric correction, and noise reduction techniques. erdas imagine provides a range of pre-processing tools to perform these tasks (division & road, 2013; munahar et al., 2022). to perform supervised classification, you need to collect training data representing different land cover classes within the sentinel image. this involves selecting representative sample areas on the imagery and assigning them to specific classes. erdas imagine provides tools for interactive digitizing, polygon creation, or importing training data from external sources (boori & voženílek, 2014; fahad et al., 2020; keerthi & nehru, n.d.). extract relevant features from the sentinel imagery that can discriminate between different land cover classes. erdas imagine offers various spectral, textural, and contextual feature extraction methods. these features can include band values, vegetation indices (e.g., ndvi), texture measures (e.g., glcm), or spatial attributes. erdas imagine provides a range of classification algorithms that can be applied to the extracted features and training data. these include maximum likelihood, support vector machines (svm), random trees, neural networks, and decision trees. each algorithm has its own advantages and limitations, and the choice depends on the specific analysis requirements. once the model is trained, it can be applied to classify the entire sentinel image. erdas imagine provides tools for applying the classification algorithm to the image and generating a classified image or a thematic map (civco et al., 2002; malaviya et al., 2010; schmid, 2017; shalaby & tateishi, 2007). the classified image assigns each pixel to a specific land cover class based on the model's classification results. erdas imagine offers a comprehensive set of tools and functionalities for image classification of sentinel data, allowing users to extract meaningful information about land cover and land use patterns from the imagery. 2.2.4. accuracy assessment assess the accuracy of the classification results by comparing them with reference data or ground truth information. erdas imagine provides tools for accuracy assessment, such as error matrices, kappa coefficient calculation, and class-level or pixel-level accuracy metrics. after classification, you can perform postclassification processing to refine the results and generate thematic maps. this may include techniques like majority filtering, sieve filtering, or object-based classification to remove small or isolated classification errors and improve map accuracy. it is important to note that the specific methodology adopted for land use/land cover analysis and change detection analysis may vary depending on the study objectives, available data, and the chosen software or tools. 3. result and discussion change detection analysis examined land use/land cover variability. lulc photos for 2017–2022. these lulc pictures show land use land cover variations in vizianagaram district, andhra pradesh, india, over the research period. this range matches the 2017–2022 natural vegetation range across puliyeru river basin (gandhi et al., 2015). the employed spectral data offers land use landcover vizianagaram at low spatial resolution (10 m). https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 6 source: analysis, 2022 figure 2. land use land cover analysis 2017, 2018 and 2019 years of the study area source: analysis, 2022 figure 3. land use land cover analysis 2020, 2021 and 2022 years of the study area the lulc changed from 2017 to 2022 in the research region (fig 2 and fig 3). in the dry zone, october to february is moist and may to september is dry. lulc decreased in the dry season and increased in the rain season in both dry and intermediate zones. the late dry season (july–october) has the lowest vegetation values in the dry zone. thus, lulc readings may track dry zone dry and wet episodes throughout the year. table 1. percentage of area under different lulc 2017 to 2022 of vizianagaram, andhra pradesh, india lulc categories area of percentage 2017 2018 2019 2020 2021 2022 waterbodies 2.05 1.59 1.23 2.19 2.00 3.02 dense vegetation 18.40 15.39 13.05 15.37 15.53 16.66 flooded vegetation 0.30 0.29 0.18 0.32 0.15 0.23 agriculture, crop lands 67.42 69.99 70.69 68.42 67.76 65.05 built up lands 5.18 5.17 5.57 5.90 5.76 6.29 scrub/vegetation 0.09 0.06 0.05 0.07 0.06 0.07 degraded lands 6.56 7.51 9.22 7.71 8.74 8.67 source: analysis, 2022 https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 7 source: analysis, 2022 figure 4. percentage based land use/land cover trends (2017-2022) of vizianagaram, andhra pradesh, india based on the results of the land use/land cover change detection analysis (table. 1 and fig 4), the following results were obtained: 1. waterbodies encompass natural and artificial bodies of water such as lakes, rivers, ponds, and reservoirs. in 2017, waterbodies covered 2.05% of the total land area, which decreased slightly to 1.59% in 2018. however, there was a subsequent increase to 1.23% in 2019 and a significant jump to 2.19% in 2020. in 2021, it slightly decreased to 2.00%, but then rose again to 3.02% in 2022; 2. dense vegetation category represents areas with dense vegetation, including forests, woodlands, and regions with a high concentration of trees and plants. in 2017, dense vegetation covered 18.40% of the land area, which decreased to 15.39% in 2018. the percentage continued to decline to 13.05% in 2019 but showed a slight increase to 15.37% in 2020. in 2021 and 2022, there was a further rise to 15.53% and 16.66%, respectively; 3. flooded vegetation refers to areas that are temporarily flooded or inundated with water. this category includes marshes, swamps, and locations prone to seasonal flooding. the percentage of flooded vegetation was relatively low throughout the years, ranging from 0.18% in 2019 to 0.32% in 2020 (fig 4). in 2022, it reached its lowest point at 0.15% but slightly increased to 0.23% in the same year; 4. agriculture, crop lands comprise areas utilized for cultivating crops such as farmlands, plantations, and fields. in 2017, agriculture occupied 67.42% of the land area, which increased to 69.99% in 2018 (table 1). the percentage continued to rise, reaching its highest point at 70.69% in 2019. however, there was a decline in subsequent years, with values of 68.42% in 2020, 67.76% in 2021, and 65.05% in 2022; 5. built-up lands pertain to areas transformed by human activities, encompassing structures like buildings, roads, and urban developments. the percentage of built-up lands remained relatively stable over the years, ranging from 5.17% to 6.29%. the highest value was observed in 2022 (table 1), indicating a slight increase in urbanization and infrastructure development; 6. scrub vegetation refers to low-lying vegetation characterized by shrubs, bushes, and sparse plant cover. the percentage of scrub/vegetation was consistently minimal, ranging from 0.05% to 0.09% throughout the years; 7. degraded lands represent areas that have undergone ecological deterioration, often due to human activities or natural factors. the percentage of degraded lands varied from 6.56% in 2017 to 9.22% in 2019. although there were fluctuations, the values remained relatively consistent in subsequent years, with a range of 7.51% to 8.74% from 2018 to 2021. in 2022 (table 1), the percentage of degraded lands decreased slightly to 8.67%. these percentages provide insights into the spatial distribution and changes in land cover categories over time. the fluctuations in each category reflect the dynamics of land use and can be indicative of environmental changes, urbanization, and shifts in agricultural practices (el-kawy et al., 2011; mishra et al., 2020; poyatos et al., 2003). the land use and land cover (lulc) categories underwent several changes between 2017 and 2022. the waterbodies category experienced fluctuations throughout the period, with a decrease from 2.05% in 2017 to 1.59% in 2018 and a further decline to 1.23% in 2019. however, there was a significant increase in 2020, reaching https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 8 2.19%, followed by a slight decrease to 2.00% in 2021 (table 2). the most notable change occurred in 2022, with the waterbodies category expanding to cover 3.02% of the area. dense vegetation showed a general decreasing trend, starting at 18.40% in 2017 and gradually declining to 13.05% in 2019. however, there was a recovery in 2020, with coverage increasing to 15.37%. in 2022, dense vegetation covered 16.66% of the area, resulting in an overall decrease of 1.73% (table 2). the flooded vegetation category remained relatively small, ranging from 0.18% in 2019 to 0.32% in 2020, with a decrease to 0.23% in 2022. agriculture, crop lands represented a significant portion of the land area, but there was a decrease from 67.42% in 2017 to 65.05% in 2022. built-up lands showed a slight increase, from 5.18% in 2017 to 6.29% in 2022 (table 2). scrub/vegetation and degraded lands experienced minimal changes, with slight fluctuations over the years. overall, these changes reflect the dynamic nature of lulc patterns over the specified period (arévalo et al., 2020; erener et al., 2012; singh et al., 2019; suneela & mamatha, 2016; treitz & rogan, 2004). table 2. percentage of area changes under different lulc 2017 to 2022 of vizianagaram, andhra pradesh, india lulc categories 2017 2018 2019 2020 2021 2022 changes waterbodies 2.05 1.59 1.23 2.19 2.00 3.02 0.97 dense vegetation 18.40 15.39 13.05 15.37 15.53 16.66 -1.73 flooded vegetation 0.30 0.29 0.18 0.32 0.15 0.23 -0.07 agriculture, crop lands 67.42 69.99 70.69 68.42 67.76 65.05 -2.37 built-up lands 5.18 5.17 5.57 5.90 5.76 6.29 1.11 scrub/vegetation 0.09 0.06 0.05 0.07 0.06 0.07 -0.01 degraded lands 6.56 7.51 9.22 7.71 8.74 8.67 2.11 source: analysis, 2023 4. conclusion in conclusion, the analysis of land use and land cover changes across different categories from 2017 to 2022 provides valuable insights into the dynamic nature of the landscape. waterbodies experienced a fluctuating pattern, with a decrease in 2018 and 2019, followed by an increase in 2020 and a slight decrease in 2021, before experiencing a significant rise in 2022. this suggests potential shifts in water resources and the need for further investigation into the factors driving these changes. dense vegetation exhibited a gradual decline over the years, indicating potential deforestation or land clearing activities. while the decline was relatively small, it raises concerns about the preservation of biodiversity and ecosystem health in the area. flooded vegetation remained relatively stable throughout the period, with only minor fluctuations. this category may be influenced by seasonal variations or specific hydrological patterns in the region. agriculture, crop lands maintained a dominant presence, although a gradual decline was observed. this decrease may indicate a shift in land use practices, potentially driven by factors such as urbanization, changing agricultural practices, or the conversion of agricultural lands to other uses. built-up lands experienced a steady increase over the years, indicating urban expansion and infrastructure development in the area. this trend highlights the need for sustainable urban planning and land management strategies to ensure efficient use of resources and minimize environmental impacts. scrub/vegetation and degraded lands showed relatively minor changes during the analyzed period. however, these categories are still important in terms of ecological balance and the restoration of degraded ecosystems. efforts to preserve and rehabilitate these lands should be considered to maintain biodiversity and ecosystem services. overall, the analysis underscores the importance of monitoring lulccs to inform sustainable land management practices and conservation efforts. the identified trends provide a basis for further investigation into the underlying drivers of these changes and the potential impacts on ecosystems, water resources, and human well-being. continued monitoring and proactive measures are crucial to mitigate adverse effects and promote sustainable land use in the future. https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 padmini et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 1-10 doi: 10.14710/geoplanning.10.1.1-10 9 5. references abd el-kawy, o. r., rød, j. k., ismail, h. a., & suliman, a. s. 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[crossref] https://ejournal.undip.ac.id/index.php/geoplanning/editor/viewmetadata/56337 https://doi.org/10.17163/lgr.n34.2021.02 https://doi.org/10.1016/j.dib.2018.04.050 https://doi.org/10.1007/s40808-019-00680-1 https://doi.org/10.1080/014311601750038866 https://doi.org/10.12944/cwe.9.2.26 https://doi.org/10.35940/ijeat.b5127.129219 https://doi.org/10.37398/jsr.2020.640219 https://doi.org/10.1016/s0305-9006(03)00064-3 https://doi.org/10.1016/j.landurbplan.2014.01.017 https://doi.org/10.1080/01431160110075802 45 geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 45 56 original research modeling the water quality of lake itasy by long short-term memory (lstm) using landsat 8 data randrianiaina jerry jean christien frederick1,2*, rakotonirina rija itokiana1, jean robertin rasoloariniaina², fils lahatra razafindramisa1 1. laboratory of matter and radiation physics (lpmr), university of antananarivo, madagascar 2. institut d’enseignement supérieur d’antsirabe-vakinankaratra, university of antananarivo, madagascar doi: 10.14710/geoplanning.12.1.45-56 abstract modeling lake water quality is very important to preserve and protect this resource. several algorithms can be used to model lake water quality using in-situ measurement data. this work used the long short-term memory (lstm) deep learning (dl) architecture to obtain models for modeling and predicting water quality parameters of lake itasy depending on the reflectance of landsat8 oli. the main purpose of this study was to identify the appropriate lstm model in function of the optimization algorithms: adagrad, rmsprop and adam, in order to do the estimation on the date provided, according to the date of satellite image acquisition. the obtained results showed the performance of the developed lstm model, with an adaptive moment estimation (adam) optimization algorithm that provided an excellent concordance between the collected and simulated water quality parameters. moreover, the correlation coefficient (r²) was 0.993 for the conductivity and 0.977 for the dissolved oxygen concentration. the root mean square error (rmse) values for conductivity and dissolved oxygen concentration were 0.898 and 0.228 respectively. after choosing the best model, the water quality parameters of the lake itasy were estimated on may 25th 2020. the conductivity ranged from 46.8 µs.cm-1 to 66.5 µs.cm-1, and the dissolved oxygen concentration from 6.5 mg/l to 9.1 mg/l. these values indicate that the water from lake itasy respects the malagasy norms in terms of conductivity and dissolved oxygen concentration copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction in surface waters, such as the sea, rivers and lakes are important for long-term economic development and environmental sustainability (al-shaibah et al., 2021; nagy-kovács et al., 2019). however, the prediction of lake water quality has attracted the attention of researchers due to its effect on the life of biodiversity. therefore, modeling lake water quality is necessary to monitor, preserve and manage this resource. modeling water quality with traditional methods, such as in situ-measurements, sample collection from the site and laboratory analyses is impractical. these methods are very expensive, time consuming and limit the assessment of spatial and temporal trends of the water quality (jerry et al., 2019). in this study, the conductivity of lake itasy water and its dissolved oxygen concentration were modeled. lake itasy is the third largest lake in madagascar (approximately 35 km²) and plays an important role in the economic development of the itasy region, thanks to tourism, which brings benefits to the local population through related activities, and fish farming (i. et al., 2020). therefore, it is necessary to seek a new approach to e-issn: 2355-6544 received: 05 december 2023; revised: 13 september 2024; accepted: 09 may 2025; available online: 14 may 2025; published: 26 may 2025. keywords: lstm, landsat8, water quality, itasy lake *corresponding author(s) email: r2jcfjerry@gmail.com https://doi.org/10.14710/geoplanning.12.1.45-56 mailto:r2jcfjerry@gmail.com frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 46 avoid the limitations of the traditional monitoring method, as mentioned above, in order to preserve and protect this resource. remote sensing techniques and machine learning offer opportunities and approaches for modeling and monitoring lake water quality. remote sensing modeling of lake water quality requires the establishment of a reliable relationship between surface reflectance measured by remote sensing and water quality parameters collected in situ (barrett & frazier, 2016). previous research on water quality such as srivastava et al. (2024), are still focused on utilizing the visible, infrared (nir), and thermal infrared (tir) spectra to detect water characteristics such as suspended sediment and algae growth. then ness et al. (2025), the development of mathematical models based on remote sensing data has been carried out extensively to analyze more than 26,000 water samples to understand the dynamics of environmental parameters. as well as research conducted by gao (2024) which focused on monitoring industrial and agricultural waste pollution to see the quality of the aquatic environment. however, most of the research that has been done in the past is limited to spectral analysis approaches and the development of conventional statistical or regression models. another studies, which have been conducted in lake itasy, utilized deep learning to analyze water quality including parameters such as ph and turbidity (jerry et al., 2019), as well as the use of landsat 8 in water surface temperature monitoring which showed results that normal temperatures lie in the range of 18.1°c to 22.6°c (jerry et al., 2018). based on previous research, the application of long short-term memory (lstm) on environmental research has become more and more extensive (huang & kuo, 2018) due to its good performance in time-series prediction , but no one of that using this model to water quality assessment. the application of deep learning models such as lstm in the use of machine learning techniques, is still relatively rare in quality modeling, especially in lake itasy. based on that gap, it found that there has been no research that specifically utilizes the use of deep learning lstm model in itasy lake. the use of lstms can provide a deeper understanding of temporal patterns in environmental data that cannot be fully captured by static models or traditional approaches. to enrich the knowledge about implementation of lstm model, this study aimed to convey the improvement of water quality model using lstm to show the new development of lstm based on deep neural networks, branches of machine learning. in detail, the model utilized landsat 8 satellite imagery to get some advantages of free and open-source data. 2. data and methods 2.1. study area lake itasy is located in the itasy region, the miarinarivo district and the rural commune of ampefy. it is sited within the volcanic field of itasy and geographically located between 19° 04’ latitude south and 46° 47’ longitude east (figure 1). figure 1. location of the study area https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 47 2.2. data used in this study, images from the landsat8 satellite were used. they were obtained the same day as the insitu measurements of the water quality parameters of lake itasy. the images can be freely downloaded from the united states geological survey (usgs) website. landsat8 carries two sensors: oli (operational land imager) and tirs (thermal infrared sensors). the characteristics of landsat 8 oli/tirs are given in table 1. table 1. landsat 8 characteristics band designations wavelength (µm) spatial resolution (m) band 1 (coastal aerosol) 0.43 – 0.45 30 band 2 (blue) 0.45 – 0.51 30 band 3 (green) 0.53 – 0.59 30 band 4 (red) 0.64 – 0.67 30 band 5 (near infrared) 0.85 – 0.88 30 band 6 (short wave infrared) 1.57 – 1.65 30 band 7 (short wave infrared) 2.11 – 2.29 30 band 8 (panchromatic) 0.50 – 0.68 15 band 9 (cirrus) 1.36 – 1.39 30 band 10 (thermal infrared) 10.6 – 11.19 100 band 11 (thermal infrared) 11.50 – 12.51 100 source: landsat 8 data users handbook, 2015 2.3. water quality parameters 2.3.1. conductivity conductivity is the capability of the water to carry electric current and serves as a tool to assess the purity of water (murugesan et al., 2006). the ions like chloride −( )cl , sodium +( )na , calcium +2( )ca , phosphate −3 4( )po etc. are responsible for electric current conduction. 2.3.2. dissolved oxygen dissolved oxygen is one of the key parameters in water quality analysis. it is used by most aquatic organisms to survive (gholizadeh et al., 2016). dissolved oxygen concentration is influenced by the temperature, the rate of photosynthesis, the turbidity, and the concentration of organic matters, such as industrial waste (arief, 2017). 2.4. method 2.4.1. image pre-processing the dark object subtraction (dos1) atmospheric correction method was used in this work to reduce the atmospheric effects and to calculate the values of surface reflectance. this method can provide an accurate mapping for wetland areas and is well accepted by the geospatial community (song et al., 2001). the quantum gis (qgis) software was used to carry out the dos1 atmospheric correction and to draw the maps shown in this article. 2.4.2. long short-term memory (lstm) the deep neural network is the branch of machine learning introduced to approach the objective of automatic learning. these methods were developed in the 1980s but were quickly left because they were considered as not promising (blier, 2017). with the introduction of computers, which can make quick calculations, new big data databases, and the progress in the optimization techniques, deep learning has become the most competitive algorithm for different tasks (lecun et al., 2015), such as voice recognition and face recognition. in deep learning, there are many architectures that can be used, such as convolutional neural https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 48 network (cnn), recurrent neural network (rnn), and long short-term memory (lstm) which was introduced by hochreiter and schmid huber in 1997 (hochreiter & schmidhuber, 1997) to remedy the vanishing and exploding gradient problem that is encountered in rnn. lstm was the architecture used in this study. a. the lstm architecture as an improvement of recurrent neural network, lstms are capable of learning long-term dependencies and remembering past information, while predicting future values and taking them into account (hochreiter & schmidhuber, 1997) lstms that use purpose-built the memory cells to store information also have this chained in similar structure, but the repeating module is structured differently (liu et al., 2019). there are four interacting layers in an lstm cell (olah, 2015) as depicted in figure 2. source: ping liu et al. (2019) figure 2. lstm unit structure forget gate ft (equation 1) (aslam et al., 2019): it decides what kind of information will be removed and how much information will be kept (hafezparast mavadat & marabi, 2021). this process was carried out by the application of the sigmoid function σ which takes the preceding output value −1th at −1t and the input value tx att . the output value of ft varies from 0 to 1.  1( , )t f t t ff w h x b−=  +σ ………… (eq. 1) input gate ti (equation 2) (liu et al., 2019): it acts as a filter because it decides which information will be added to the memory cell. it takes the values 1th − and tx , and its value is also included between 0 and 1 by using the sigmoid function .  1( , )t i t t ii w h x b −=  + ………… (eq. 2) memory cell: it contains two elements, which are the new memory cell tc (equation 3) and the final memory cell tc (equation 4). the new memory cell is used to carry the information which comes being’s updates and the final memory cell is used to update the state of the new memory cell.  1tanh( , )t c t t cc w h x b−=  + ………. (eq. 3) 1t t t t tc f c i c−=  +  …………. (eq. 4) here, tc and 1tc − are the cell states at times t and −1t . the output gate ot (equation 5) (liu et al., 2019):’it decides which information can be sent to the output of the cell state. finally, the output of the lstm unit was obtained by multiplying the output gate by the hyperbolic tangent of the cell state (equation 6). https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 49  ( )1 ,t o t t oo w h x b −=  + ………….. (eq. 5) tanh( )t t th o c=  …………. (eq. 6) note that fw , iw , cw , ow are the matrix weights associated with f , i ,c ,o . the terms fb , ib , cb , ob are the biases that were obtained during the training and testing process. the sign * represents the matrix multiplication and  indicates the multiplication element by element. b. optimization algorithm three optimization algorithms based on the gradient descent were used to optimize the deep neural network parameters, i.e. to update network weights iterative in training data: adagrad (adaptive gradient method) (wang et al., 2019), rmsprop (root mean square propagation) (hinton et al., 2011) and adam (adaptive moment estimation) (kingma, 2014). c. activation function the rectified linear unit (relu) proposed by nair and hinton in 2010 was the activation function used in this work. relu is the most widely used activation function in the deep learning network (nair v. & hinton, g.e., 2010). it has proved to be the most successful and offers the best performance (dahl et al., 2013; zeiler et al., 2013). it is given by the following equation 7: ……. (eq. 7) 2.4.3. model evaluation a model without validation criteria is not a model. the performance of the models used in this study was evaluated according to two common evaluation indicators: the correlation coefficient ( 2r ) and the root mean square error ( )rmse . a. the correlation coefficient the correlation coefficient measures the relationship degree between two variables. its value ranges between -1 and +1. if it is near -1 or close to +1, it indicates that the correlation between two variables is strong. it is given by (equation 8). 2 1 2 2 1 1 ( )( ) ( ) ( ) n i i i n n i i i i t t y y r t t y y = = = − − = −  −    …………….. (eq. 8) where it and t represent the in-situ measurements and their average, iy and y represent the obtained values from the model and their average, n is the observation number. b. root mean square error (rmse) rmse is often used to calculate the difference between the predicted value and the observed value. the smaller the rmse value, the smaller the distance between the two values (prediction and observation). rmse can be calculated using equation 9. , 0 ( ) max(0, ) , 0 x if x f x x and x if x   = =    https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 50 2 1 ( ) n i i i t y rmse n = − =  ………………………… (eq. 9) where it is the in-situ measurement of the value of the water quality parameter, iy is the prediction from the models and n is the observation number. 2.4.4. research diagram a summary of the methodology used to conduct this research is shown in figure 3. the images used in this study were landsat8 imagery, downloaded from usgs. when the data were obtained, the pre-processing included the atmospheric correction, and the extraction of the study area was performed on the qgis software. then, the bands 2,3,4,5 were selected and these pixels were extracted because the spectral response for these bands is related to water quality (jerry et al., 2019). the pixels extracted from the in-situ measurements data were integrated into the lstm model to train the network. the following step was to evaluate the performance of the lstm model using rmse and r2 to choose the appropriate model that will be used to estimate the conductivity and the dissolved oxygen in order to create the map distribution of these parameters. figure 3. research flow chart of the study 3. result and discussion this section presents the findings derived from the application of the lstm model to predict water quality parameters, specifically conductivity and dissolved oxygen, based on remote sensing data. the discussion also covers the interpretation of the results in relation to the objectives of the study. the performance of the model is evaluated using rmse and r² metrics to assess the accuracy and reliability of the predictions. the results are organized in subsections to clearly explain the model output and its implications. 3.1. result 3.1.1. conductivity the lstm architecture deep neural network used for modeling the conductivity of lake itasy water was constituted of 20 inputs, 3 lstm layers containing 15, 24 and 16 elements respectively, followed by 2 dense layers of 20 and 10 elements, and by 20 output layers. the number of epochs was 10000. https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 51 figure 4. in-situ measurements and conductivity predicted using the adagrad optimization figure 5. in-situ measurements and conductivity predicted using the rmsprop optimization the curves in the figure 4 show the in-situ measurement conductivity and the predicted conductivity from this architecture with the adagrad optimization. the correlation coefficient between the two values was 0.500. it can be concluded that the model is inadequate. the figure 5 shows the measured conductivity and the predicted conductivity when the rmsprop optimization was used. this is an acceptable model with a correspondence rate of 85.9% between the in-situ values and the predicted values. figure 6. in-situ measurements and conductivity predicted using the adam optimization after changing the optimization algorithm to adam, the similarity between measured conductivity and predicted conductivity was obtained as shown in the figure 6. with a 99.3% correspondence between two values, the model is appropriate. the performance of the model according to the optimization algorithm is given in table 2. according to this table, the best optimization algorithm that can be used to model the conductivity of lake itasy is the adam optimization algorithm since the correlation between collected values and estimated values is high (r²=0.993) and the rmse value is weak (rmse=0.898). table 2. model performance optimization algorithm r² rmse adagrad 0.500 6.940 rmsprop 0.859 4.432 adam 0.993 0.898 https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 52 the figure 7 shows the estimation of the conductivity of lake itasy using lstm with the adam optimization. the spatial distribution of water conductivity ranged from 46.8 µs.cm-1 to 66.5 µs.cm-1. it indicates that the lake itasy water respects the malagasy norm which is inferior to 250 µs.cm-1, and that it is of good quality (class a) (water surface classifications according to decree n°2003/464 in 15/04/03). figure 7. map distribution of the conductivity of lake itasy (05/25/2020) 3.1.2. dissolved oxygen on the other hand, for the modeling of the lake itasy dissolved oxygen concentration, an architecture composed of 18 inputs, 3 lstm hidden layers of 33, 23 and 15 elements, 2 dense layers of 17 and 15 elements and one output layer of 18 elements was constructed. the number of iterations was 10000. figure 8. in-situ measurements and dissolved oxygen concentration predicted using the adagrad optimization figure 9. in-situ measurements and dissolved oxygen concentration predicted using the rmsprop optimization the dissolved oxygen concentration measured in situ and the dissolved oxygen concentration estimated using the adagrad optimization are represented in figure 8. it is an acceptable model as the correlation coefficient between measured values and predicted values is 0.775. the figure 9 shows the measurements of the dissolved oxygen concentration collected in situ and the concentrations predicted by replacing the adagrad https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 53 optimization by rmsprop. according to this figure, the concentrations measured are almost similar to the concentrations estimated, with a 92.5% correspondence. thus, this combination can be considered to be a good model. the curves in the figure 10 show the collected data on the dissolved oxygen concentration and the estimations when the optimization is adam. they are in good concordance, with a correlation coefficient of 0.997, the model is then adequate. figure 10. in-situ measurements and dissolved oxygen concentration predicted using the adam optimization table 3. model performance optimization algorithm r² rmse adagrad 0.775 0.683 rmsprop 0.925 0.417 adam 0.977 0.228 table 3 depicts the performance of the model according to the optimization algorithm. this table indicates that the lstm architecture utilizing the adam optimization is the most adapted model to estimate the dissolved oxygen concentrations of lake itasy. figure 11. map distribution of dissolved oxygen of lake itasy (05/25/2020) https://doi.org/10.14710/geoplanning.12.1.45-56 frederick et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 45 56 doi: 10.14710/geoplanning.12.1.45-56 54 the spatial distribution of surface water dissolved oxygen of lake itasy is shown in the figure 11 after applying the best model, dated may 25th 2020. regarding the lake itasy water class, it is good because the concentration of dissolved oxygen is superior to 5 mg/l everywhere (water surface classifications according to the decree n°2003/464 in 15/04/03). 3.2. discussion the focus of the present study was to compare the performances of the lstm model when the optimization algorithm was changed while keeping the numbers of lstm layers and the epoch number. the results showed that the lstm model using a rectified linear unit (relu) activation function and the adam optimizer was the best model with a high-level r². this result is in accordance with the research of dheda & cheng (2020) based on both multivariate single and multiple step lstm models using relu activation and rmsprop optimizer, and confirmed the abilities of the lstm model in water quality prediction (a et al., 2021; liu et al., 2019; wang et al., 2023; wang et al., 2017). one weakness of this work was that the number of data collections was little. this study is a further response to various studies that have been conducted previously by gao (2024); jerry et al. (2018, 2019); ness et al. (2025); and srivastava et al. (2024). although these studies have made important contributions to water quality assessment through the use of remote sensing and deep learning models, none have specifically utilized the use of lstm in temporally modeling water quality. the use of lstm in this study provides a new perspective due to its ability to capture the patterns and dynamics of time series data that are relevant in monitoring changes in water quality over time. as such, this approach not only complements previous research, but also offers the potential to improve and understand long-term fluctuations in water quality. based on the results of the research that has been done, it can be seen that the advantages of the results obtained using lstm show results that are close to real conditions. possible future works include improving the number of in situ measurements and expanding the study to incorporate ensemble modeling techniques, combining lstm with another deep learning architecture, such as cnn (barzegar et al., 2020), rnn, to further improve the accuracy and robustness of the predictions. this is not only useful for environmental management, but can be maximized for infrastructure provision planning in supporting the availability of water resources. 4. conclusion lake biodiversity plays an important role for aquatic life, for humans and for appropriate development. in madagascar, surface waters are used for human consumption after treatment and for other activities, such as fish farming. to protect this resource, modeling about the quality of the water is necessary. this work showed that the long short-term memory (lstm) with the adam optimization was the most appropriate model for modeling and predicting the quality of the lake itasy water using remote sensing (landsat 8). this model reached a high level of correlation coefficient r²>0.95 for the conductivity and the dissolved oxygen concentration, which is trustworthy. the obtained results indicate that lake itasy respects the malagasy norms regarding conductivity and dissolved oxygen concentrations. this work is a contribution to avoiding the problems associated with the traditional method of monitoring water quality, and to enabling the monitoring and evaluation of changes in conductivity and dissolved oxygen in the surface water of lake itasy. the two parameters that were modeled in this work are among the key parameters for determining surface water quality. therefore, it is highly recommended that future research model other water quality parameters such as turbidity, ph, temperature etc. to gather more knowledge about this lake. 5. references a, o. c., a, b. w., mayowa, a., & olajide, o. m. 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[crossref] https://doi.org/10.14710/geoplanning.12.1.45-56 https://doi.org/10.3390/e25081186 https://doi.org/10.1109/iske.2017.8258814 https://doi.org/10.1109/icassp.2013.6638312 volume 1,no 1,2014, 13-20 http://ejournal.undip.ac.id/index.php/geoplanning | 13 open access geoplanning e-issn: 2355-6544 kajian perubahan ketersediaan ruang terbuka hijau di kecamatan tembalang, kota semarang, berbasis interpretasi citra satelit r. nugrahaa, s. rahayub a universitas diponegoro, indonesia, email: anakpakam@gmail.com b universitas diponegoro, indonesia, email: sri.yksmg@yahoo.com abstract: increasing the number of population in the district tembalang, semarang in 19912010 has implications for the increase of space for settlement and infrastructure that impact on the reduced number of green space. district tembalang needs special attention, especially in the provision of green space because it is one of the fast developing area in the semarang. based on the above problems, the research question arises, how changes in the availability of green space in the district tembalang? the purpose of this study is to assess the changes in availability of green space in the district tembalang, semarang during the period of 12 years i.e from 1999 till 2011. this study uses remote sensing techniques by performing image interpretation using arcgis 9.3 software and using data satellite imagery of district tembalang 1999 and ikonos satellite imagery 2011. based on the analysis, an area of green space in the district tembalang in 1999 was 3214, 86 ha and in 2011 was 3017,73 ha. the decrease of green space area during the period of 1999-2011 is 197.13 ha. one of the recommendations of this study is the need to control the development activity, according to the land use planning based on rdtrk (urban planning documents) semarang to maintain the availability of green space abstrak: peningkatan jumlah penduduk di kecamatan tembalang kota semarang pada tahun 1991-2010 berimplikasi terhadap peningkatan kebutuhan ruang untuk permukiman dan sarana prasarana yang berdampak pada berkurangnya jumlah rth (ruang terbuka hijau). kecamatan tembalang perlu mendapat perhatian khusus, terutama dalam penyediaan rth karena merupakan salah satu kawasan cepat berkembang di kota semarang. berdasarkan permasalahan diatas, maka muncul pertanyaan penelitian, bagaimana perubahan ketersediaan rth di kecamatan tembalang?“. tujuan penelitian ini adalah untuk mengetahui perubahan ketersediaan rth di kecamatan tembalang, kota semarang selama kurun waktu 12 tahun yaitu dari tahun 1999 s.d tahun 2011. metode penelitian yang digunakan adalah metode kuantitatif. penelitian ini menggunakan teknik penginderaan jauh dengan cara melakukan interpretasi citra menggunakan software arcgis 9.3 dengan menggunakan data citra satelit kecamatan tembalang tahun 1999 dan citra satelit ikonos kota semarang tahun 2011. berdasarkan hasil analisis, luas area rth di kecamatan tembalang pada tahun 1999 adalah 3.214,86 ha dan pada tahun 2011 adalah 3.017,73 ha. penurunan luas area rth selama kurun waktu tahun 1999-2011 adalah 197,13 ha. salah satu rekomendasi dari penelitian ini adalah perlunya pengendalian aktivitas pembangunan yang sesuai dengan rencana guna lahan berdasarkan rdtrk kota semarang untuk menjaga ketersediaan rth. 1. pendahuluan ruang terbuka hijau adalah sebentang lahan terbuka tanpa bangunan yang mempunyai ukuran, bentuk dan batas geografis tertentu dengan status penguasaan apapun, yang di dalamnya terdapat tetumbuhan hijau berkayu dan tahunan (perennial woody plants), dengan pepohonan sebagai tumbuhan penciri utama dan tumbuhan lainnya (perdu, semak, rerumputan, dan tumbuhan penutup tanah lainnya), sebagai tumbuhan pelengkap, serta benda-benda lain yang juga sebagai pelengkap info artikel; diterima: 3 maret 2014 hasil revisi : 10 maret 2014 disetujui: 26 maret 2014 publikasi on-line: 1 april 2014 kata kunci: sistem informasi geografis, citra satelit, rth article info; received: 3 march 2014 received in revised form: 10 march 2014 accepted: 26 march 2014 available online: 1 april 2014 keywords: gis, satellite imagery, green space mailto:anakpakam@gmail.com mailto:sri.yksmg@yahoo.com geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 14 dan penunjang fungsi rth yang bersangkutan oleh purnomohadi (2006). pada 30 tahun terakhir luas rth diperkotaan berkurang dengan sangat signifikan. berkurangnya luas rth di perkotaan salah satunya disebabkan oleh jumlah penduduk diperkotaan yang terus meningkat. untuk lebih jelas melihat peningkatan jumlah penduduk di kecamatan tembalang dapat dilihat pada gambar 1 berikut. gambar 1. grafik peningkatan jumlah penduduk tembalang (bps, 2011) peningkatan jumlah penduduk yang terjadi di kecamatan tembalang dikarenakan terdapat kecendrungan pekembangan kota semarang ke arah selatan yang menjangkau kawasan tembalang dan sekitarnya. faktor pendorong berasal dari kawasan pusat kota semarang yang semakin padat dan beban yang ditanggungnya pun semakin berat. sedangkan faktor penarik berasal dari kawasan tembalang berupa lahan-lahan yang masih dapat dimanfaatkan untuk kegiatan budidaya tanpa meninggalkan aspek pelestarian lingkungan (rdtrk semarang bwk vi 2000-2010). beragamnya aktifitas dan semakin bertambahnya penduduk pendatang di kecamatan tembalang akan memberi implikasi terhadap meningkatnya kebutuhan ruang untuk permukiman serta sarana prasarana penunjangnya. hal ini berarti bahwa dengan bertambahnya jumlah penduduk kecamatan tembalang akan bertambah pula ruang terbangun yang akan semakin menggeser rth di kecamatan tembalang jika pembangunan tidak dilakukan dengan perencanaan yang baik. oleh sebab itu, sebagai kecamatan yang sedang berkembang, kecamatan tembalang perlu mendapat perhatian khusus terutama dalam penyediaan rth agar dapat terwujud keseimbangan dalam pembangunan, hal ini sangat beralasan karena berdasarkan rdtrk semarang bwk vi tahun 2000-2010 kecamatan tembalang difungsikan untuk perumahan yaitu sebagai penampung limpahan penduduk dari pusat kota semarang. pengembangan permukiman kota berupa perumahan baru (real estate) di kecamatan tembalang terdapat di kelurahan meteseh, kelurahan sendangmulyo, sebagian kelurahan jangli, sebagian kelurahan kedungmundu, sebagian kelurahan sambiroto, dan sebagian kelurahan mangunharjo. berdasarkan dari uraian permasalahan di atas, dapat diketahui bahwa rth sangat penting bagi keberlanjutan suatu kota dalam hal ini khususnya di kecamatan tembalang kota semarang, maka dari itu timbul pertanyaan penelitian (research question) yang mendasari kegiatan penelitian ini, yaitu “bagaimana perubahan ketersediaan rth di kecamatan tembalang kota semarang selama kurun waktu 12 tahun yaitu tahun 1999 sampai dengan tahun 2011?“. untuk menjawab pertanyaan tersebut maka perlu dilakukan penelitian tentang ketersediaan rth di kecamatan tembalang kota semarang selama periode 12 tahun yaitu dari tahun 1999 sampai tahun 2011 di kecamatan tembalang kota semarang. tujuan yang ingin dicapai dalam penelitian ini adalah untuk mengkaji perubahan ketersediaan rth di kecamatan tembalang kota semarang selama periode 12 tahun yaitu dari tahun 1999 sampai tahun 2011. 2. data dan metode ruang terbuka hijau merupakan ruang yang direncanakan karena kebutuhan akan tempat-tempat pertemuan dan aktivitas bersama di udara terbuka. ruang terbuka adalah ruang yang bisa diakses oleh masyarakat baik secara langsung dalam kurun waktu terbatas maupun secara tidak langsung dalam kurun waktu tidak tertentu. ruang terbuka itu sendiri bisa berbentuk jalan, trotoar, ruang terbuka hijau seperti taman kota, hutan dan sebagainya oleh hakim dan utomo (2004). manfaat rth diantaranya adalah untuk identitas kota, nilai estetika, penyerap karbondioksida, pelestarian air tanah, penahan angin, ameliorasi iklim, habitat dan kehidupan liar. berdasarkan peraturan daerah kota semarang no.7 tahun 2010 tentang penataan ruang terbuka hijau (rth) jenis penataan rth di kota semarang terdiri atas rth kawasan hutan geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 15 lindung, rth kawasan taman hutan raya, rth kawasan rawan bencana, rth kawasan pantai berhutan bakau, rth kawasan sempadan pantai, rth kawasan sempadan sungai, rth kawasan sempadan mata air, rth kawasan sempadan waduk, rth kawasan pertanian lahan basah, rth kawasan pertanian lahan kering, rth kawasan perikanan/ tambak, rth kawasan hutan produksi, rth kawasan permukiman, rth kawasan perkantoran dan fasilitas umum, rth kawasan perdagangan dan jasa komersial, rth kawasan pendidikan, rth kawasan industri, rth kawasan wisata, rekreasi dan olah raga, rth kawasan pemakaman, rth pertamanan dan lapangan, rth kawasan khusus militer, rth kawasan terminal, rth kawasan stasiun kereta api, rth kawasan pelabuhan laut, rth kawasan bandar udara, rth jalur jalan, rth jalur sempadan rel kereta api, rth jalur sambungan udara tegangan tinggi (sutt) dan sambungan udara tegangan ekstra tinggi (sutet); dan rth taman atap (roof garden). metode yang digunakan dalam studi ini adalah metode kuantitatif dengan menggunakan penginderaan jauh. metode ini sebagai metode ilmiah yang konkrit/empiris, obyektif, terukur, rasional, dan sistematis. metode ini menggunakan data-data penelitian berupa angka-angka dan analisis menggunakan statistik (sugiyono, 2008). penginderaan jauh disingkat inderaja, berasal dari bahasa inggris yaitu remote sensing. pada awal perkembangannya, inderaja hanya merupakan teknik yang dikembangkan untuk memperoleh data di permukaan bumi. akan tetapi, seiring dengan perkembangan iptek, ternyata inderaja seringkali berfungsi sebagai suatu ilmu karena sangat besar kemanfaatannya. menurut lillesand dan kiefer (1998), penginderaan jauh adalah ilmu dan seni untuk memperoleh informasi tentang obyek, daerah, atau gejala dengan jalan menganalisis data yang diperoleh dengan menggunakan alat tanpa kontak langsung terhadap obyek, daerah, atau gejala yang dikaji. penginderaan jauh akan bergantung pada panjang energi gelombang elektromagnetik sedangkan gelombang elektromagnetik bervariasi membentuk panjang gelombangnya. cahaya matahari berperan dalam penggunaan sensor padapengideraan jauh. sebagian sensor dapat mendeteksi energi yang diemisikan bumi oleh janssen dan huurneman (2001). data yang digunakan adalah data citra ikonos dan foto udara. sejak diluncurkan pada september 1999, citra satelit bumi, space imaging ikonos menyediakan data citra yang akurat, dimana menjadi standar untuk produk-produk data satelit komersil yang beresolusi tinggi. ikonos memproduksi citra 1-meter hitam dan putih (pankromatik) dan citra 4-meter multispektral (red, blue, green dan near-infrared) yang dapat dikombinasikan dengan berbagai cara untuk mengakomodasikan secara luas aplikasi citra beresolusi tinggi oleh space imaging (2004). disamping mempunyai kemampuan merekam citra multispetral pada resolusi 4 meter, ikonos dapat juga merekam obyek-obyek sekecil satu meter pada hitam dan putih. sebagai salah satu data penginderaan jauh citra foto udara mampu menyajikan gambaran mirip wujud dan letak sebenarnya di lapangan dan dapat dilihat pola keruangannya (sutanto, 1987). segala hasil perekaman foto udara ini berpuluh hingga beribu pasang foto udara tergantung dari tujuan pemetaan dan perekaman selalu disimpan dalarn media penyimpanan. hal ini selalu dilakukan karena pemotretan obyek, daerah atau fenomena yang dikaji itu selalu dilakukan berkala dan tidak saal itu juga. menurut estes dan simonett (1975) interpretasi citra merupakan perbuatan mengkaji foto udara atau citra dengan maksud untuk mengidentifikasi objek dan menilal arti pentingnya objek tersebut. untuk melakukan interpretasi citra maupun foto udara digunakan kreteria interpretasi, yaitu terdiri atas rona atau warna, ukuran, bentuk, tekstur, pola, bayangan, situs, dan asosiasi. selain dengan penginderaan jauh, interpretasi juga dibantu dengan perangkat lunak sig. aronoff menjelaskan dalam prahasta (2009: 116) sistem informasi geografis (geographic information system) adalah sistem yang berbasis komputer yang digunakan untuk menyimpan dan memanipulasi data mengenai informasi geografis. sig dirancang untuk mengumpulkan, menyimpan, dan menganalisis objekobjek dan fenomena di suatu lokasi geografis merupakan kerakteristik yang penting untuk dianalisis. dengan demikian, sig merupakan sistem komputer yang memiliki empat kemampuan dalam menangani data yang bereferensi geografis seperti masukan, manajemen data (penyimpanan dan pemanggilan data), analisis, dan manipulasi serta keluaran. pengumpulan data primer dilakukan dengan cara melakukan tinjauan dan pengumpulan data secara langsung dari kondisi yang ada di lapangan. perolehan data primer dalam penelitian ini adalah berupa observasi di lapangan. pelaksanaan teknik pengumpulan data dengan melalui observasi ini yaitu dengan melakukan pengamatan secara langsung terhadap obyek wilayah studi yaitu rth kecamatan tembalang. geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 16 3. hasil dan pembahasan berdasarkan hasil interpretasi citra menggunakan citra foto udara kecamatan tembalang tahun 1999, rth kecamatan tembalang memilki luas 3.214,86 ha, rth mendominasi luas persentase penggunaan lahan di kecamatan tembalang, yaitu sebesar 80,55%. kemudian dari sisa penggunaan lahan selain rth yaitu seluas 264,43 ha atau 6,63% digunakan untuk penggunaan lahan bangunan dan 511,73 atau 12,82% digunakan untuk penggunaan lahan lainnya (tanah longsor, tanah kosong, fasilitas umum, perkantoran, pabrik, dan jalan raya). untuk lebih jelas luas tata guna lahan kecamatan tembalang tahun 1999 (ha) dapat di lihat pada gambar 2 dan hasil interpretasi citra satelit di gambar 3 dan tabel 1. gambar 2.diagram luasan rth di kecamatan tembalang tahun 1999 (analisis, 2013) gambar 3. hasil interpretasi dan jenis rth tahun 1999, ( analisis, 2013) (tanah kosong, fasilitas umum, perkantoran, dan pabrik dan jalan raya) geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 17 tabel 1. luas rth hasil interpretasi dan jenis rth tahun 1999 (analisis, 2013) dari hasil tersebut diperoleh rth terluas adalah rth kawasan pertanian lahan kering seluas 1.510,8 ha, kemudian terluas kedua adalah rth kawasan permukiman seluas 1.033,21 ha, terluas ketiga adalah rth kawasan pertanian lahan basah seluas 378,05 ha, terluas keempat adalah rth kawasan pendidikan seluas 112,67 ha, terluas kelima adalah rth kawasan rekerasi dan olah raga seluas 90,10 ha, dan terluas keenam atau terluas terkecil adalah rth kawasan pemakaman seluas 90,03 ha. sementara itu, untuk rth kawasan hutan lindung pada tahun 1999 di kecamatan tembalang tidak ditemukan karena penggunaan lahan pada kawasan lindung telah beralih fungsi menjadi penggunaan lahan non rth kawasan hutan lindung seperti pertanian lahan kering dan pertanian lahan basah. selanjutnya, berdasarkan hasil interpretasi citra menggunakan citra ikonos kecamatan tembalang tahun 2011, rth kecamatan tembalang memiliki luas 3.017,73 ha dari total luas kecamatan tembalang yaitu 3.991,02 ha, rth mendominasi luas persentase penggunaan lahan di kecamatan tembalang pada tahun 2011 yaitu sebesar 75,62%. kemudian dari sisa penggunaan lahan selain rth yaitu seluas 419,81 ha atau 10,52% digunakan untuk penggunaan lahan bangunan dan 553,48 ha atau 13,86% digunakan untuk penggunaan lahan lainnya (tanah longsor, tanah kosong, fasilitas umum, perkantoran, dan pabrik, dan jalan raya). untuk lebih jelas luas tata guna lahan kecamatan tembalang tahun 2011 dapat di lihat pada gambar 4 dan hasil interpretasi pada gambar 5 dan tabel 2. gambar 4. diagram luas rth di kecamatan tembalang tahun 2011 (analisis, 2013) no. jenis rth tahun 1999 luas rth persentase (%) 1 rth kawasan pertanian lahan basah 378.05 9,47% 2 rth kawasan pertanian lahan kering 1.510,8 37,84% 3 rth kawasan permukiman 1.033,21 25,91% 4 rth kawasan pendidikan 112,67 2,82% 5 rth kawasan rekreasi dan olah raga 90.1 2,26% 6 rth kawasan pemakaman 90.03 2,25% jumlah 3.214,86 80,55% (tanah kosong, fasilitas umum, perkantoran, dan pabrikdan jalan raya) geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 18 gambar 5 . hasil interpretasi citra satelit tahun 2011 (analisis, 2013) tabel 2. luas rth hasil interpretasi dan jenis rth tahun 2011 (analisis, 2013) no. jenis rth tahun 2011 luas rth persentase 1 rth kawasan pertanian lahan basah 265,35 6,65% 2 rth kawasan pertanian lahan kering 1.271,08 31,86% 3 rth kawasan permukiman 1.187,08 29,74% 4 rth kawasan pendidikan 107,24 2,69% 5 rth kawasan rekreasi dan olah raga 96,38 2,41% 6 rth kawasan pemakaman 90,6 2,27% jumlah 3.017,73 75,62% berdasarkan hasil tersebut, rth terluas adalah rth kawasan pertanian lahan kering seluas 1.271,08 ha, kemudian terluas kedua adalah rth kawasan permukiman seluas 1.187,08 ha, terluas ketiga adalah rth kawasan pertanian lahan basah seluas 265,35 ha, terluas keempat adalah rth kawasan pendidikan seluas 107,24 ha, terluas kelima adalah rth kawasan permukiman seluas 90,6 ha, dan terluas keenam atau terluas terkecil adalah rth kawasan rekreasi dan olah raga seluas 90,38 ha. sementara itu, untuk rth kawasan hutan lindung pada tahun 2011 di kecamatan tembalang tidak ditemukan karena penggunaan pada kawasan lindung telah beralih fungsi menjadi penggunaan lahan non rth kawasan hutan lindung seperti pertanian lahan kering dan pertanian lahan basah. setelah interpretasi data citra dilakukan, selanjutnya dilakukan analisis overlay raster untuk mengetahui perubahan luasan rth. dari hasil analisis tersebut, rth di kecamatan tembalang pada analisis tahun 1999 adalah 80 % (3.214,86) ha dari luas kecamatan tembalang dan rth kecamatan tembalang pada tahun 2011 memiliki luas 75 % (3.017,73 ha) dari luas kecamatan tembalang. hal ini menunjukkan selama kurun waktu 1999-2011 rth di kecamatan tembalang mengalami penurunan seluas 197,13 ha atau geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 19 berkurang 4,93% dari luas rth tahun 1999. untuk lebih jelasnya mengenai perubahan ketersediaan setiap rth di kecamatan tembalang selama kurun waktu tahun 1999-2011 dapat di lihat pada tabel 3 berikut. tabel 3. perubahan luas rth dari 1999-2011 (analisis, 2013) no. jenis rth hasil overlay luas perubahan rth persentase (%) 1 rth kawasan pertanian lahan basah -112,7 -2,82% 2 rth kawasan pertanian lahan kering -239,72 -5,98% 3 rth kawasan permukiman 153,87 3,83% 4 rth kawasan pendidikan -5,43 -0,13% 5 rth kawasan rekreasi dan olah raga 6,28 0,15% 6 rth kawasan pemakaman 0,57 0,02% jumlah -197,13 -4,93% dari hasil tersebut dapat diketahui bahwa secara keseluruhan rth di kecamatan tembalang selama kurun waktu tahun 1999-2011 mengalami penurunan luas 197,13 ha dengan rth yang mengalami penurunan luas adalah rth kawasan pertanian lahan basah, dan rth yang mengalami peningkatan luas adalah rth kawasan permukiman. untuk rth yang mengalami penurunan paling luas adalah rth kawasan pertanian lahan kering yaitu seluas 239,72 ha, sedangkan untuk rth yang mengalami peningkatan paling luas adalah rth kawasan permukiman yaitu seluas 153,87 ha. berkurangnya luas rth tersebut dikarenakan alih fungsi rth lebih luas dibandingkan dengan luas pertambahan rthnya, sedangkan meningkatnya luas rth tersebut dikarenakan sebaliknya yaitu alih fungsi rth yang lebih kecil dibandingkan dengan luas pertambahan rth. untuk lebih jelas, hasil analisis alih fungsi rth di kecamatan tembalang tahun 1999-2011dapat dilihat pada gambar 6. gambar 6 . hasil analisis alih fungsi rth tahun 2011 (analisis, 2013) geoplanning 2014,vol: 1, no: 1, 13-20 nugraha dan rahayu | 20 4. kesimpulan selama kurun waktu tahun 1999-2011 rth di kecamatan tembalang didominasi oleh rth kawasan pertanian lahan kering dengan luas 1.510,80 ha pada tahun 1999 dan seluas 1271,08 ha pada tahun 2011 yang sebagian besar terdapat di kelurahan rowosari. terjadi penurunan luas rth selama kurun waktu tahun 1999-2011 di kecamatan tembalang seluas 197,13 ha yang sebagian besar terdapat di kelurahan meteseh dengan luas 59 ha. sementara itu, pertambahan rth paling luas adalah rth kawasan permukiman yang mengalami pertambahan seluas 262,86 ha yang sebagian besar terdapat di kelurahan sendangmulyo dengan luas 69,46 ha. rth di kecamatan tembalang yang tidak mengalami perubahan selama kurun waktu tahun 1999-2011 adalah seluas 2.607,63 ha yang sebagian besar terdapat di kelurahan rowosari dengan luas 375,69 ha. dari hasil tersebut, penataan ruang kecamatan tembalang pada masa mendatang harus lebih memprioritaskan atau memperketat aturan terhadap kawasan lindung di kecamatan tembalang agar kawasan lindung di kecamatan tembalang dapat terwujud, karena dengan adanya kawasan lindung maka akan ada vegetasi yang mampu menjaga kepentinngan hidrologi, yaitu tata air dan dapat menghilangkan pengaruh topografi terhadap erosi dapat mencegah terjadinya banjir besar. kegiatan pembangunan di kecamatan tembalang harus lebih mengarah pada usaha menjaga kelestarian rth agar dapat terwujud keseimbangan dalam pembangunan, hal ini sangat beralasan karena berdasarkan rdtrk semarang bwk vi kecamatan tembalang difungsikan untuk perumahan yaitu sebagai penampung limpahan penduduk dari pusat kota semarang. 5. daftar pustaka estes, je, simonett, ds. 1975. chapter 14: fundamentals of image interpretation, in r.g. reeves (ed.), manual of remote sensing, vol. ii, falls church: american society of photogrammetry, pp. 869– 1076. janssen, l.f.l and huurneman c.g. 2001. principles of remote sensing. itc educational texbooks series. itc, enshede, netherlands. lillesand dan kiefer. 1998. penginderaan jauh dan interpretasi citra penginderaan jauh, yogyakarta: gadjah mada university, terjemahan peraturan menteri dalam negeri nomor 1 tahun 2007 tentang penataan ruang terbuka hijau kawasan perkotaan. perda kota semarang no.7 tahun 2010 tentang ruang terbuka hijau. prahasta, eddy. 2009. sistem informasi geografis konsep-konsep dasar (perspektif geodesi & geomatika),informatika: bandung. purnomohadi, ning. 2006. ruang terbuka hijau sebagai unsur utama tata ruang kota, jakarta, direktorat jendral penataan ruang rencana detail tata ruang (rdtr) kota semarang tahun 2000-2010. badan perencanaan pembangunan daerah kota semarang. space imaging. 2004. http://www.spaceimaging.com/products/ikonos/index.htm [22 mei 2013] sugiyono. 2008. statistika untuk penelitian, alfabeta, bandung. sutanto. 1987. metode penelitian penginderaan jauh untuk geografi. makalah ceramah untuk staf pengajar ums surakarta. kajian perubahan ketersediaan ruang terbuka hijau di kecamatan tembalang, kota semarang, berbasis interpretasi citra satelit abstract: increasing the number of population in the district tembalang, semarang in 1991-2010 has implications for the increase of space for settlement and infrastructure that impact on the reduced number of green space. district tembalang needs spec... abstrak: peningkatan jumlah penduduk di kecamatan tembalang kota semarang pada tahun 1991-2010 berimplikasi terhadap peningkatan kebutuhan ruang untuk permukiman dan sarana prasarana yang berdampak pada berkurangnya jumlah rth (ruang terbuka hijau). k... 1. pendahuluan keywords: gis, satellite imagery, green space gambar 1. grafik peningkatan jumlah penduduk tembalang (bps, 2011) 2. data dan metode selain dengan penginderaan jauh, interpretasi juga dibantu dengan perangkat lunak sig. aronoff menjelaskan dalam prahasta (2009: 116) sistem informasi geografis (geographic information system) adalah sistem yang berbasis komputer yang digunakan untuk... 3. hasil dan pembahasan gambar 2.diagram luasan rth di kecamatan tembalang tahun 1999 (analisis, 2013) gambar 3. hasil interpretasi dan jenis rth tahun 1999, ( analisis, 2013) gambar 4. diagram luas rth di kecamatan tembalang tahun 2011 (analisis, 2013) gambar 5 . hasil interpretasi citra satelit tahun 2011 (analisis, 2013) gambar 6 . hasil analisis alih fungsi rth tahun 2011 (analisis, 2013) 4. kesimpulan 5. daftar pustaka | 37 geoplanning vol 7, no 1, 2020, 37-46 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.7.1.25-36 utilization of modis surface reflectance to generate air temperature information in east java indonesia a. faisola*, b. budiyonoa, i. indartob*, e. novitab a ps thp, fapertek, papua university, manokwari, indonesia b ps teknik pertanian, ftp, university of jember, east java, indonesia abstract: ambient air temperature is the main variable in the climatological and hydrological analysis. however, indonesia's limited number of meteorological stations was becoming a problem to provide air temperature data for large areas. this study aims to generate air temperature using the relationship of land surface temperature and vegetation index. a total of 6 climatological stations and 84 modis images for three years (2015 to 2017) were used for the analysis. research methods include image georeferencing, band extraction from modis, derivation of ndvi, generating ambient air temperature, calibrating using the local meteorological station, and image interpretation. results show that the modis surface reflectance product's accuracy in generating ambient air temperature in east java at any period is 86,37%. so modis surface reflectance product can be used as an alternative solution to generate ambient air temperature. copyright © 2020 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): al amin, m., ilmiaty, r., & marlina, a. (2020). flood hazard mapping in residential area using hydrodynamic model hec-ras 5.0. geoplanning: journal of geomatics and planning, 7(1), 25-36. doi: 10.14710/geoplanning.7.1.25-36 1. introduction land air surface temperature or ambient-temperature is the main variable in the climatological and environmental-related analysis. usually, air temperature used as input for calculating evapotranspiration, crop water requirement, and water allocation for irrigation purposes, such as the work of allen et al. (1998), huntington & allen (2009), bachour (2013), paparrizos et al. (2014), faisol (2015), faisol (2016), and faisol et al. (2017). conventionally, this data is obtained from a ground based climatological station. based on the meteorology, climatology, and geophysics agency (bmkg), indonesia's number of climatological stations in 2018 is 1.197 stations (the meteorology climatology and geophysics agency, 2018). however, most stations were observed manually, and some of them are no longer operated because the equipment was destroyed and/or no replacement tool. therefore, measurement of land-air surface temperature by other methods promise one of the possible solutions to provide more data availability. with the advance of remote sensing technology, some sensors (landsat, aster, modis, sentinel) provide spectral bands to record heat energy reflected or emitted from earth surface features. for example, landsat 8 is equipped with an oli sensor that capable of recording land surface temperature maximum and minimum (usgs, 2016 and laosuwan et al., 2017). aster provides a specifics channel to record heat energy refracted from the land surface (jiménez-muñoz & sobrino, 2009). to generate air temperature information, the remote sensing data must have a visible electromagnetic spectrum (0.4 μm 0.7 m), near-infrared (0.7 μm 1.3 m), and thermal infrared (8.0 μm 14.0 m) (faisol, 2015). moderate resolution imaging spectroradiometer (modis) is one satellite remote sensing data equipped with those electromagnetic spectra (national aeronautic and space administration, 2018a) modis is the first interdisciplinary instrument that can be used to monitor land, ocean, and atmosphere. its instrument operates on both the terra and aqua spacecraft with a viewing swath width of article info: received: 9 may 2018 in revised form: january 2019 accepted: january 2020 available online: 7 july 2020 keywords: modis, air temperature, vegetation index *corresponding author: arif faisol papua university, manokwari, indonesia email: arif.unipa@gmail.com *corresponding author: indarto ps tep, ftp, universitas jember email: indarto.ftp@unej.ac.id open access http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.7.1.37-46 mailto:arif.unipa@gmail.com faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 38 | 2,330 km and views the entire surface of the earth every one to two days. its detectors measure 36 spectral bands between 0.405 and 14.385 µm, and it acquires data at three spatial resolutions; 250m, 500m, and 1.000m (national aeronautic and space administration, 2018b). modis provides more than 150 science data products to support various studies in agriculture, climatology, marine, forestry, and others (savtchenko et al., 2004). modis surface reflectance (mod09) is one of modis science data product that computed from modis level 1b land bands 1 (620-670 nm), 2 (841-876 nm), 3 (459-479), 4 (545-565 nm), 5 (1230-1250 nm), 6 (1628-1652 nm), and 7 (2105-2155 nm). the product estimates the surface spectral reflectance for each band as it would have been measured at ground level as if there were no atmospheric scattering or absorption. it corrects for the effects of atmospheric gases and aerosols (national aeronautic and space administration, 2018a). besides, modis surface reflectance has been equipped with the thermal band that is band 31 (10,78 – 11,284 µm) and band 32 (11,77 – 12,27 µm) (vermote et al., 2015). a lot of studies have been using modis to generate air temperature. flores & lillo (2010) using modis to estimate air temperature on a regional scale in chile, yao & zhang (2012) estimating air temperature in the southeastern tibetan plateau based on modis satellite image, shen & leptoukh (2011) using modis land surface temperature to estimate surface air temperature in eastern eurasia, zeng et al. (2015) using modis land surface temperature product to estimate daily air temperature in the us, and noi et al. (2016) using modis land surface temperature product to estimating daily maximum and minimum air surface temperature in vietnam. those studies show that the accuracy of modis to generate air temperature up to 92% compared with measured and air temperature records from meteorological stations. after seeing the gap, this study contributes to generate air temperature information in east java, indonesia. 2. data and methods 2.1. data the data that used in this research is modis surface reflectance (mod09) image as many 84 scenes for three years (2015 to 2017), modis geolocation (mod03) that corresponding with mod09 image, and daily air temperature data from 6 meteorological stations. mod09 and mod03 accessed from nasa website and daily temperature accessed from the meteorology, climatology and geophysics agency (bmkg) website. figure 1. the landuse maps of east java (indonesian geospatial information agency, 2018) faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 | 39 figure 2. the topography maps of east java (shuttle radar topography mission, 2018) 2.2. methods generally, the main stages of data processing to generate air temperature information from modis surface reflectance is: 1. image georeferencing image georeferencing is to georeferencing modis surface reflectance by associated with modis geolocation. modis geolocation is the modis data equipped with information about geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. this information is included in the header to enable calculating the approximate location of the center of the detectors of any of the 36 modis bands. 2. modis bands extraction the purpose of this stage is to extract the modis bands that are used for generating air temperature. there is surface reflectance red bands (ρ band 1), surface reflectance near-infrared bands (ρ band 2), brightness temperature (band 31 and band 32), and solar zenith. this stage is done simultaneously with image georeferencing. 3. derivation of normalized difference vegetation index (ndvi) ndvi is a measure of the degree of vegetation cover for an area. ndvi of modis imagery calculated with the following equation (vermote & vermeulen, 1999): [1] where: ndvi = normalized difference vegetation index ρband 1 = surface reflectance band 1 (red) ρband 2 = surface reflectance band 2 (near-infrared) 4. generating air temperature the air temperature was generated using the relationship of land surface temperature and vegetation index with the following equation (hong, 2008): faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 40 | ta = 0,9731 ts + 7,5878. 𝑁𝐷𝑉𝐼 − 5,0638.cos 𝜃 − 2,7233 [2] where: ta = air temperature (ok) ts = land surface temperature (ok) ndvi = normalized difference vegetation index 𝜃 = solar zenith angle (radians) solar zenith angle (𝜃) is the angle between the zenith and the center of the sun's disc and extracted from modis geolocation. land surface temperature (ts) calculated with the following equation (hong, 2008): [3] where: ts = land surface temperature (ok) tb = brightness temperature (ok) ε0 = surface emissivity brightness temperature extracted from modis surface reflectance. surface emissivity (ε0) calculated with the following equation (bastiaanssen et al., 2002): [4] where: ε0 = surface emissivity ndvi = normalized difference vegetation index 5. calibrating using local meteorological station this process is to know the accuracy of air temperature generated from modis surface reflectance by comparing it with local meteorological station data. the methods used in calibrating is root mean square error (rmse) with the following equation: [5] where: rmse = root mean square error xi = air temperature data from meteorological stations (oc) yi = air temperature that generated from modis surface reflectance (oc) n = amount of data 6. image interpretation this stage aims to create air temperature distribution maps at any period. flowchart to generating air temperature using modis surface reflectance product shown in figure 3. faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 | 41 figure 3. flowchart to generate air temperature from modis surface reflectance (authors, 2018) 3. results and discussion from modis surface reflectance interpretation, air temperature in the study area during 2015 – 2017 is 17.37 oc – 37.09 oc. generally, air temperature in residential and lowland areas higher than in agriculture and plateau area. air temperature distribution in the study area shows in figure 4 to figure 6. the comparison between air temperature generated from modis surface reflectance and meteorological stations data recording is shown in figure 7 to figure 12. figure 4. air temperature distribution in east java on 29 march 2015 (analysis, 2018) faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 42 | figure 5. air temperature distribution in east java on 12 june 2016 (analysis, 2018) figure 6. air temperature distribution in east java on 24 may 2017 (analysis, 2018) figure 7. air temperature comparison between modis surface reflectance processing with banyuwangi meteorological stations (analysis, 2018) faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 | 43 figure 8. air temperature comparison between modis surface reflectance processing with malang meteorological stations (analysis, 2018) figure 9. air temperature comparison between modis surface reflectance processing with pasuruan meteorological stations (analysis, 2018) figure 10. air temperature comparison between modis surface reflectance processing with nganjuk meteorological stations (analysis, 2018) faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 44 | figure 11. air temperature comparison between modis surface reflectance processing with surabaya meteorological stations (analysis, 2018) figure 12. air temperature comparison between modis surface reflectance processing with sidoarjo meteorological stations (analysis, 2018) previous research has shown modis accuracy for generating air temperatures up to 92% compared to air temperature records and measurements from meteorological stations (flores & lillo, 2010; noi et al., 2016; shen & leptoukh, 2011; yao & zhang, 2012; zeng et al., 2015). in this study, air temperature generated from modis surface reflectance is higher than data recording from meteorological stations with an average accuracy of 86.37%. several factors cause the difference in air temperature generated from modis surface reflectance and meteorological stations data recording, e.g., (1) the air temperature generated from modis surface reflectance is the air temperature at imagery acquisition time. in contrast, air temperature from meteorological stations is the average temperature recording in the morning, daytime, and afternoon; (2) air temperature generated from modis surface reflectance depends on weather conditions during recording, while air temperature from meteorological stations is based on weather conditions throughout the day; (3) modis acquisition time for east – java is 09.10 am – 10.20 am, so it caused air temperature higher than data recording from meteorological stations. faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 | 45 4. conclusion this study has successfully modeled air temperature with modis so that it provides the view that modis images can be used for air temperature in fast growing regions on a regional scale. generally, modis surface reflectance can be used to generate air temperature. with 86.37% accuracy, modis surface reflectance can be used as an alternative solution to get air temperature data due to limited climatological stations. for detailing the analysis, future work needs to be done by using better satellite resolution. if it possible, the results of this study would be better by combining some of satellite imagery and data about air temperature. 5. acknowledgments the authors would like thanks to ristekdikti – ministry of research, technology and higher education of the republic of indonesia that financed this research by penelitian kerjasama antar perguruan tinggi (pkpt) grant. 6. references allen, r. g., pereira, l. s., raes, d., & smith, m. 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(2009). a single-channel algorithm for land-surface temperature retrieval from aster data. ieee geoscience and remote sensing letters, 7(1), 176–179. [crossref] laosuwan, t., gomasathit, t., & rotjanakusol, t. (2017). application of remote sensing for temperature monitoring: the technique for land surface temperature analysis. journal of ecological engineering, 18(3). [crossref] national aeronautic and space administration. (2018a). retrieved march 30, 2018, from https://modis.gsfc.nasa.gov/data/dataprod/dataproducts.php?mod_number=09 national aeronautic and space administration. (2018b). retrieved march 9, 2018, from https://lpdaac.usgs.gov/ https://doi.org/10.4067/s0718-58392010000300011 https://doi.org/10.1061/41036(342)420 https://doi.org/10.1109/lgrs.2009.2029534 https://doi.org/10.12911/22998993/69358 faisol et al. / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 37-46 doi: 10.14710/geoplanning.7.1.25-36 46 | noi, p. t., kappas, m., & degener, j. 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(2018). no title. retrieved march 5, 2018, from https://earthexplorer.usgs.gov/ the meteorology climatology and geophysics agency. (2018). retrieved march 5, 2018, from http://www.bmkg.go.id/ u.s. geological survey. (2016). data users handbook version 2.0. earth resources observation and sciences, greenbelt, maryland. vermote, e. f., kotchenova, s. y., & ray, j. p. (2015). modis surface reflectance user’s guide, version 1.4. usa: nasa. vermote, e. f., & vermeulen, a. (1999). atmospheric correction algorithm: spectral reflectances (mod09). atbd version, 4, 1–107. yao, y., & zhang, b. (2012). modis-based air temperature estimation in the southeastern tibetan plateau and neighboring areas. journal of geographical sciences, 22(1), 152–166. [crossref] zeng, l., wardlow, b. d., tadesse, t., shan, j., hayes, m. j., li, d., & xiang, d. (2015). estimation of daily air temperature based on modis land surface temperature products over the corn belt in the us. remote sensing, 7(1), 951–970. [crossref] https://doi.org/10.3390/rs8121002 https://doi.org/10.30955/gnj.001221 https://doi.org/10.1016/j.asr.2004.03.012 https://doi.org/10.1088/1748-9326/6/4/045206 https://doi.org/10.1007/s11442-012-0918-1 https://doi.org/10.3390/rs70100951 | 41 geoplanning vol 4, no. 1, 2017, 41-52 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.1.41-52 the development of marine spatial planning and its application for floating fish net culture d. sutrisno a a geospatial information agency (big), cibinong, indonesia abstract: marine spatial planning has become the crucial issues for an archipelagic state such as indonesia. the global market demand on marine economic species has been initiated the exploitation of the marine species which will become the hindrance in maintaining the sustainable marine biodiversity. besides that, the degradation of marine species will also become the problem for traditional fishermen. therefore, a model has to be employed to spatially manage the coastal waters as the alternative for fishermen activities during closed seasons, such as floating fish net culture. the aim of this study was to develop marine spatial planning model based on ecological approach in order to identify the potentiality of marine waters for marine culture such as floating fish net culture. the method for the model consisted of social assessment using the delphi for developing the rule of marine planning for floating fish net culture and the spatial analysis technique for determining the model of marine spatial planning for floating fish net culture. the area of kupang bay waters, east nusa tenggara was used as the study area. the result indicated that the model can be used to sustainable marine spatial planning, especially for floating fish net culture. the model considered the aspects of potential area for marine culture, the management of zonation and transportation lanes, the conservation and protected area and the strategic area. application in kupang bay illustrated the aspect of technology input such as raceways since the majority of the area of kupang bay waters is classified as medium potential. further research still needs to optimum the application of model to others marine area. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): sutrisno, d. (2017). the development of marine spatial planning and its application for floating fish net culture. geoplanning: journal of geomatics and planning, 4(1), 41-52. doi:10.14710/geoplanning.4.1.41-52 1. introduction the development paradigm of fishery sub sector in indonesia is currently focused on activities in captured fisheries of high economic species such as groupers, snappers etc. groupers and snappers are types of demersal fish generally living in the coral reef environment. the sustainable potency of indonesia marine fisheries resources reaches 6.4 million tons per year, including demersal fish and coral fish for 1.36 million and 145 thousand tons, respectively (mukuan et al., 2014). the global market demand of the mentioned species has threaten the sustainability of the coral reef associated species, and equally to the coral reefs due to the usage of destructive fishing gears such as bomb or poison for exploitation by the fishermen are usually. considering the sustainability of the capture fisheries prospect, especially for high economic fish species, the need to open access for capture fisheries activities to cultivation becomes crucial. wide-ranging cultivating activities are possible since indonesia is supported by coastal waters with many closed and semi-closed coastal areas with relatively calm water conditions. this condition is significantly prospective for the development of marine culture, such as floating fish net culture. the world-wide intensive culture of floating net cages is indicated as one of the major methods for intense fish production in the tropical areas (liao et al., 2004; ouattara et al., 2003). article info: received: 3 october 2016 in revised form: 7 january 2017 accepted: 20 february 2017 available online: 26 march 2017 keywords: marine biodiversity, spatial planning, ecological and economic model, floating fish net culture corresponding author: dewayany sutrisno geospatial information agency (big), cibinong, indonesia email: dewayany@gmail.com open access http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 42 | in indonesia, the high economic fish product reaches only 15.45% of the floating fish net culture (utama, 2008). this fact indicates the opportunity of floating fish net culture to improve the national product of high economic species such as groupers, snappers etc. however, the floating fish net culture deals with several problems, such as seedlings, capital, infrastructure, technological aspect, deleterious effects on the water quality and lack of integrated spatial planning of the coastal area (de silva & phillips, 2007; gorlach-lira et al., 2013). hence, marine spatial planning model should be beforehand used to determine the location of the floating fish net culture and its impact to the environment. the farmed fish are type of demersal fish such as groupers. this can simply be done if the model is developed based on ecological aspects (oladokun et al., 2013). there have been limited studies concerned on integrated spatial planning in coastal area. a few researchers focused on sea use management and marine fauna (douvere, 2008; douvere & ehler, 2009; gilliland & laffoley, 2008; hartoko & kangkan, 2009; hegland, raakjær, & van tatenhove, 2015; murray & salama, 2016). therefore, this research intends to develop the integrated marine spatial planning model. the aim of this study was to develop marine spatial planning model based on ecological approach to identify the potentiality of marine waters for marine culture specifically floating fish net culture. 2. data and methods 2.1. study area and general method by taking into account the eastern part of indonesia having clear water and high visibility, the coastal waters of kupang bay east nusa tenggara province was selected as the study area. kupang bay is located in the western part of kupang city, the capital of east nusa tenggara province. this bay has the blue clear water rich on biodiversity such as coral reefs and its associated fishes. regarding the marine culture activities, the oyster pearl culture based on floating nets cages has been developed in the southern part of the area (figure 1). figure 1. the map of study area (modified from google earth) http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 | 43 the method for developing the marine spatial planning model for floating fish net culture was based on the multi criteria analysis, which combine social assessment and technical spatial analysis. the social assessment consists of delphi survey to identify the contributing parameters to the marine spatial planning for floating fish net culture model. meanwhile, the technical spatial analysis consists of development of formula, scoring and weighting based on the result of social assessment and union overlay method. taken together, these results will finally contribute to the development of marine spatial planning model. a literature review was done prior to the delphi survey in order to obtain the most reliable opinion consensus on a group of experts on those parameters. the model should be developed based on ecological approach and sustainable marine spatial planning. the steps of developing the marine spatial planning for fish net culture were described in figure 2. figure 2. flowchart of the study 2.2. determining marine spatial planning model assessing the relevant contributing parameters to a model is the important part to be completed prior the model development. in this case, a literature review collecting parameters carrying significant impacts on marine spatial planning for floating fish net culture has been carried out. the literature study should consider physical, chemical and biological characteristics of the water as well as technical aspects, infrastructure and environment for the best living environment of a marine species (affan, 2012). the delphi method was used to select the parameters, the rank of parameters in each influence on sustainability marine species life and the rank for weighting and scoring of each parameter. ten to fifteen marine management and spatial planning experts were participated in this survey as respondent. the method consisted of collecting each expert best opinion for the variables within a questionnaire and interview, coding the knowledge and the expert’s evaluation on the knowledge based system (rosnelly & utama, 2012). the survey was performed in three rounds. the first round aimed to identify the relevant parameters to the model. the second round aimed to rank the parameters due to the relevancy to achieve the optimal product. the third round aimed to rank the parameters for weighting and scoring (figure 3). http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 44 | figure 3. the delphi survey steps the weighting and scoring were carried out using sutrisno (2006) concept that ranks the parameters into three requirement classes. (a) minimum requirement parameters: the parameters should be existed to establish perpetuity life of the object (marine species) of marine culture. the absence of these parameters may result in the failure of the marine culture activities. (b) optimal pre-requirement of marine culture objects: these parameters are the secondary requirement to have an optimum life of the marine culture objects. some of water qualities parameters may be included in this requirement. (c) ideal/ supporting requirement of marine culture objects: these parameters need to be existed for the better product of the marine culture. some water quality parameters such as phosphate and ph, security, distance marketing may become part of these aspects. the result of this step is the potential matrix for the marine spatial planning focusing on floating fish net culture. the result of the delphi analysis resulted in the eleven critical parameters to the spatial model of the floating fish net culture, i.e: protection, pollutant, primary productivity, bathymetry, sea temperature, salinity, visibility, seabed material, dissolved oxygen, ph, and phosphate. nothing like land area, the criteria of assessing the suitability of the coastal waters for marine culture are dependable to management instead of substrate. therefore, the rank of the parameters within a matrix should follow the management approach classifying the parameters into minimum, optimum and supporting requirements and can be expressed as:  ininininis zilpsff x ,,,,  [1] whereas marine spatial planning for floating fish net culture ( xsf ) should be dependent on; a. s = supporting environment condition for marine culture, consist of i – n parameters such as; relatively calm waters area and pollutant free area. (a) relatively calm waters: marine culture needs a closed or semi closed coastal area, a safety coastal area from the storm, wave, current and any natural destruction. the absence of this aspect may result in the destruction of the marine culture infrastructure. (b) pollutant free area: the marine culture area should be free of polluted matters. the presence of this aspect may decrease the quantity and quality even the extinction of the object of marine culture. the pollutant data can be assessed from pollutant data (secondary data form other research), remote sensing analysis that indicates extreme sedimentation, oil spill and eutrophication or through the distance from the residential, river mouth, port, fabrics or others nearby. b. p = primary healthy living environment for the cultured, consist of i – n parameters such as clear water, primary productivity, water depth, etc. (a) primary productivity: depending on the nature feeding, the primary productivity has to be considered as important since it may affect the mortality of the species’ object of the culture. (b) clear water: the clearer the water, the healthier the environment available for marine culture. the clear water is indicated by the depth of visibility up to the bottom of the sea. (c) depth: this aspect was importance due to the feeding and feces aspects. because less depth may cause pollution from feeding activities as well as nearby area and even from the seabed itself. c. l = supporting living environment for culture consist of i – n parameters such as water quality. round 3 http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 | 45 d. i = supporting facilities and infrastructure consist of i – n parameters such as transportation lanes, cold storage, and other infrastructure e. z = determining national management zone for regency or province at i mile from coastal based point. (si-n), (pi-n), i and z may be considered as minimum requirement parameters, (li-n) as optimum or supporting requirement parameters. thus following the above rule, the rank of parameters for developing the potential waters for floating fish net culture simply consider point (a) to (c), while points (d) and (e) are required for the whole model of marine spatial planning approach (table 1). the parameters are; a. minimum requirement parameters • protected area, such as bay, strait and other protected area. the spatial data of these parameters can be obtained from remote sensing and base map analysis. • pollution: under the assumption that no chemical pollutant exists in the area, the distance from ports, residential, river mouth and industrial areas can be assumed as the criteria for pollutant free and environmental management approach. • primary productivity: under the assumption that the feedings depend more on the nature, the primary productivity can be represented by the spatial chlorophyll distribution. in this study, the primary productivity was obtained from big. • water depth: need to support the floating net infrastructure and sustainability of environmental. for this study, the water depth data was obtained from coastal environmental map (lpi) big. the mapping of water depth using remote sensing data has its limitation since either landsat tm, landsat 8 olie or spot 6 were simply able to map 18 to 25 meter of water depth (arief et al., 2013; setiawan, osawa, & nuarsa, 2014). therefore, the bathymetry data from lpi map is more applicable. • sea temperature: the life span of specific species depends on the sea water temperature. accordingly, the spatial information of sea surface temperature is important for developing the model. • salinity: maintaining the degree of water salinity is important for specific culture to be species. the fresh water impact may cause mortality as well as more saline water. • nearby land use system: these parameters may affect the marine culture area due to the pollution or introduction of destructive objects such as diseases or germs. therefore, the development of the model essentially requires the land use information. b. optimal pre-requirement of marine culture objects: visibility and seabed material. c. ideal/ supporting requirement of marine culture objects: phosphate and ph may become part of these aspects. the data can be obtained from field observation table 1. modeling matrix of potential area for floating fish net culture http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 46 | the result of the survey states the weighting criteria assessed based on the scale of 1 to 10, whereas the highest value is ten (10) and the lowest value is two (2). meanwhile, the scoring method was assessed based on the scale of 1 to 20, whereas the highest value is twenty and the lowest values are two (2). the above matrix model modified the bakosurtanal (2009), a suitability model to a potential area for floating fish net model, as shown as: 2 )*(..)*( 11 1 nparnscoreparscore i bobbobbobbob p     [2] whereas p1-i is the potential area classes 1 to i, bobscore is the weighed parameters, bobpar is the scored for each parameters, i n are the parameters for floating fish net model. so, the potential classes (p1 to n) can be classified as: p1 if weighting scored is ≥ a p2 if else weighting scored is among b – a p3 if weighting scored is among c – b and n or not any if weighting scored is ≤ c whereas and a= 1300, b = 1026 and c = 540, those were calculated from the potential matrix. 2.3. mathematical model development a. determining the potential area for floating fish net sub-model the mathematical model for determining spatial prospect for floating fish net culture was modified from bakosurtanal (2009). bakosurtanal model is a suitability model for floating fish net culture. meanwhile, this study developed the potential model for floating fish net culture. the parameters for bakosurtanal model are only water qualities, while this model employed environment and marine regulations on spatial planning. the bakosurtanal model is explained as: nparpar nparnkesparkes score bobbob bobbobbobbob bob      1 11 )*()*( [3] whereas bobscore is the suitability of coastal waters for floating fish net culture, bobkes is the suitability weight, bobpar is the scored for each parameters, i n are the parameters for floating fish net model. the output of the model is classified into four classes according to its potential for floating fish net culture, there is high potential (p1), moderately potential (p2), low potential (p3) and not having any potential (n). union overlay method were employed for the implementation of the model, while the query was using the mathematical model of potential area for floating fish net culture. b. determining the sea lanes area and regulation sub-model regulation on zonation and transportation lanes are the crucial issues that should be implemented in marine culture management as it is stated in the law no 1/2014 regarding the zonation for marine spatial planning. the marine spatial planning should act upon the rule of management zone, such as located four or twelve miles from the coastline based on lowest water level (lwl) as well as the transportation lanes. the sub-model simply excluded the management zone and transportation lanes from potential area for floating fish net sub-model and explained as: iniscore zibobfs   [4] whereas, fs is the floating fish net culture on zonation and lanes rules, bobscore is the potential area for floating fish net culture, i-n are the transportation lanes and other infrastructure and z is the national management zonation at i miles from coastal based points. union overlay method were employed for this steps of assessment while the query was using the mathematical model of floating fish net culture on zonation and lanes rules http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 | 47 c. analyzing the conservation zone implementation of the floating fish net culture should consider its aspect to the environment. these criteria have been embedded in the model matrix but quite often ignoring the coastal or upland land utilization. for the best result of the floating fish net culture, the coastal waters area must be free from the influences of ports, dense population, industries, intensive agriculture and the river mouth related to upstream activities. indeed, the numbers or the density of floating fish net per km square and distance from conservation or unique coastal ecosystem has also become part of conservation area assessment. not any mathematical model was developed for this sub-model due to the complicated objects. union overlay method was employed for this sub-model, following by query for ignoring the potential floating fish net culture area from nearby sensitive environment. 2.4. data sources for the application of the model, the secondary spatial data were collected based on the requirement. these data were (a) base map from geospatial information agency (big) (road, coast line, administrative boundary, river, annotation, and settlement); (b) water quality data such as dissolved oxygen, salinity, ph, phosphate, pollutants, seabed material and primary productivity from big (c) secondary remote sensing derived data such as land use from big and (d) socio-economic data derived from district statistical agency (bps, 2008) and coastal zone plan (bappeda ntt, 2007). 3. results and discussion 3.1. application of floating fish net culture model applying potential area for floating fish net model to the study area indicates that the majority of the waters in kupang bay has medium prospect to be developed as floating fish net culture (figure 4). the highest potential (p1) class individually covered the small area locates between the semau island the strait. this area has calm waters, free from pollutant, rich in primary productivity, and supported by suitable water depth, sea temperature and salinity. meanwhile, the majority of the sea waters of kupang bay were considered as the moderately potential area (p2). this class has limitation in protection (current) and primary productivity. since the primary productivity becomes the problem of these classes, the floating fish net planning should consider the intensive marine culture system that depends on nutritionally complete diet. the nutrition added to the system either fresh, wild, or form of dry pellet can be functional if the ecological aspect has been previously applied in the management system (widiastuti, 2014). the small zone of low potential (p3) spreads nearby the coastal area indicat the depth problems beside primary productivity, protection and pollutant. considering the depth, a vertical raceways culture system should be in consideration into the system since it has self-cleaning characteristic supported by medium to low current exchange rate (beveridge, 2008; black, 2001; heard & martin, 1979). related to the depth, the sedimentation from deforestation, industrial effluents are often associated with unsatisfactory quality of water (chapman, 1996) that should be considered as well. the input to reduce the impact such as the net infrastructure is definitely needed. the application of the model to the study area in kupang bay was slightly different with hartoko and kangkan (2009). hartoko and kangkan (2009) assessed the kupang bay coastal waters resulting into two classes’ i.e highly suitable-s1 and moderately suitable-s2. the limiting factors were dissolved oxygen (do), water current, depth, and type of bottom substrate (hartoko & kangkan, 2009). hartoko and kangkan (2009) study differed with the study in this paper in the consideration of the suitability analysis developed using spatial interaction rgb model, a remote sensing raster model. as opposed, this study was developed based on potential/ prospect zone by considering management approach that not only considered the suitability, but correspondingly regulation, management and environment. 3.2. the sea lanes area and zonation regulation model sea lanes are assigned by the law of the republic of indonesia no 1/2014 as important part of marine spatial planning (sub section 2.2-point d and e). related to the marine spatial planning model for floating http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 48 | fish net culture, the model for sea lane and zonation should exclude the lanes and limit the culture within the administrative zone for floating fish net culture area. the model can be modified into: ))(( 11 ini zalpfs   [5] whereas, fs is the floating fish net culture on zonation and lanes rules, p1-i is the potential area for floating fish net culture classes 1 to i, l is the transportation lanes and other infrastructure from 1 – n, a is the distance of exposure for study area from coastal points and z is the national management zonation at i miles from coastal based points. within the study area, there are two commercial ports, i.e. tenau and balok, and fisheries port namely as oeba that should not include in the model. figure 4. floating fish net prospect spatial data (own analysis, 2016) 3.3. analyze of the conservation zone the environment assessment indicates the potential zone for floating net culture having minor problems regarding the environment condition. marine spatial planning assigning the conservation area should be part of the planning (law no 4/2014). it means that these areas should not be included in the spatial planning for floating fish net culture area. therefore, the development of the culture should consider the distance from social economic activity and protected marine resources such as distance from the coral reefs area, ports, rivers and residential area (figure 5). applying these criteria to the marine spatial planning model for floating fish net culture can be seen in figure 6. in general, implementation of the model illustrates the less area available for floating fish net culture within the kupang bay. marine spatial planning considers the marine culture, conservation, national strategic, and lanes areas as it has been explored in this study. in the case of kupang bay, two classes (p2 and p3) were defined to the development of the floating fish net culture. it means, more technology input is needed for developing the floating fish net culture. in this case due to the limitation in depth, primary productivity, protection and pollutant. considering the numbers or the density of floating fish nets per km square and its relation to the sustainable product and environment is the critical object to discuss, especially in detail marine spatial planning considering the detail information of in site planning. the intensive floating fish net culture can lead to the eutrophication of water bodies and to the emergence of deleterious effects on the water http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 | 49 quality, such as the blooms of toxic cyanobacteria harmful for wildlife and humans (gorlach-lira et al., 2013). there are several works on the effect of fish culture in cages on water quality (see jahani et al., 2012; mente et al., 2006; ntengwe & edema, 2008; price et al., 2015; price & morris jr, 2013; schenone, vackova, & cirelli, 2011; wu, 1995; yin, harrison, & black, 2008). therefore, the waters can be divided into single kilometers square sea boxes (1 km2) not including the transportation lanes. for marine culture, less than 40 % of the potential area can be developed as the floating fish net culture if the water area is designed for marine culture purposes. this limitation hopefully will decline the pollutant or disturbance to the environment. however, these numbers should be further analyzed since there is not any exact carrying capacities model to define the sea boxes concept in marine spatial planning. figure 5. (a) coral reefs condition and (b) environment consideration (own analysis, 2016) a b http://dx.doi.org/10.14710/geoplanning.4.1.41-52 sutrisno / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 41-52 doi: 10.14710/geoplanning.4.1.41-52 50 | figure 6. enable areas for marine spatial planning for floating fish net culture (own analysis, 2016) 4. conclusion marine spatial planning is the most important part of policy for obtaining sustainable marine development. the model of marine spatial planning for floating fish net culture has followed this policy . the model was developed based on management approach considering the potential area for marine culture, in this case for floating fish net culture, the zonation management and lanes, the conservation and protective area and so does the strategic area. the application of the model to the study area, kupang bay waters, indicates that the majority of the area was generally classified as medium potential. several technological inputs should be added to the system to achieve the optimum result of sustainable floating fish net culture. the technological input should consider the artificial feedings due to the problem of primary productivity, relocation the marine culture or development of infrastructure technology due to the pollutant or water depth such as vertical raceways. besides that, the coastal waters problems were varied from one to other location. this condition may not change the model of potential area for floating fish net culture, the model of zonation and lanes management and the model of conservation. but will change the constants and the rank of parameters within the model matrix. therefore, a further study considering other area should be implemented using this model. 5. acknowledgments the author thanks to the geospatial information agency that has provide us the base and thematic geospatial data information. the author thanks equally the central bureau of statistics (bps) and the regional governments that have provide the data for this study. 6. references affan, j. m. 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(2008). analisis kelayakan usaha budidaya ikan kerapu macan di pulau panggang, kabupaten administratif kepulauan seribu, dki jakarta. phd thesis. widiastuti, i. (2014). small-scale freshwater aquaculture practices in indonesia: an application of sustainable livelihood approach to nile tilapia farmer in west sumatera. phd thesis. kagoshima university. wu, r. s. s. (1995). the environmental impact of marine fish culture: towards a sustainable future. marine pollution bulletin, 31(4–12), 159–166. [crossref] yin, k., harrison, p. j., & black, e. (2008). risk analysis of coastal aquaculture : potential effects on algal blooms. assessment and communication of environmental risks in coastal aquaculture, 76, 175–199. http://dx.doi.org/10.14710/geoplanning.4.1.41-52 https://doi.org/10.1016/0025-326x(95)00100-2 volume 1, no 2, 2014, 93-101 http://ejournal.undip.ac.id/index.php/geoplanning | 93 open access geoplanning e-issn: 2355-6544 pemetaan potensi bencana longsor di kelurahan kembang arum widjonarkoa, h.b.wijayab a universitas diponegoro, indonesia, email: widjonarko39@gmail.com b universitas diponegoro, indonesia, email: holibinawijaya@yahoo.com abstract: disaster is a real fact that sometimes less get the full attention by both government and society. this is reflected from the hustle bustle of activity that occurs after the disaster compared with efforts to minimize the impact of disasters. coexist with a culture of disaster has not become either by the government or the public. and if you are able mendudukan proportionally about the disaster in everyday life, the risk of a disaster will be minimized by itself. one push to institutionalize a culture coexist with disaster is to drive the community to better identify more closely the potential disaster that is around them, one of which is to encourage people to be able to map out the potential disaster in the environment around them. kembang arum society in general does not fully know and understand the potential and landslide conditions in their neighborhood. community knew after the collapse of the of a cliff retaining embankments. people were not so attentive to activities pengeprasan hills that surround them. this situation in the long run will further add to the high risk of landslides in the area of the village of flower arum, especially in areas with morphology hills and steep slopes. abstrak: kebencanaan merupakan satu fakta riil yang terkadang kurang mendapatkan perhatian penuh baik oleh pemerintah maupun masyarakat. kondisi ini tercermin dari begitu hiruk pikuknya kegiatan pasca bencana terjadi dibanding dengan upaya untuk meminimasi dampak akibat bencana. hidup berdampingan dengan bencana belum menjadi satu budaya baik oleh pemerintah ataupun masyarakat. padahal jika mampu mendudukan secara proporsional tentang kebencanaan dalam kehidupan sehari-hari maka resiko bencana akan dapat diminimalisir secara sendirinya. salah satu mendorong melembagakan budaya hidup berdampingan dengan bencana adalah mengajak masyarakat untuk lebih mengenali secara lebih dekat potensi kebencanaan yang ada di sekitar mereka, salah satunya adalah mendorong masyarakat untuk dapat memetakan potensi kebencanaan yang ada di lingkungan sekitar mereka. masyarakat kembang arum secara umum tidak sepenuhnya mengetahui dan paham potensi dan kondisi bencana longsor yang ada di sekitar tempat tinggal mereka. masyarakat tahu setelah ada kejadian longsor seperti runtuhnya talud penahan tebing. masyarakat juga tidak begitu perhatian terhadap kegiatan-kegiatan pengeprasan bukit yang ada di sekitar mereka. keadaan ini dalam jangka panjang akan semakin menambah tinggi resiko bencana longsor di wilayah kelurahan kembang arum, khususnya pada wilayah dengan morfologi perbukitan dan lereng yang curam. 1. pendahuluan kondisi fisik suatu wilayah akan mempengaruhi karakteristik lahan yang ada. indonesia sebagai wilayah yang mempunyai topografi beragam mempunyai potensi kebencanaan yang berbeda-beda pula. berdasarkan data yang terdapat pada badan nasional penanggulangan bencana (bnpb), selama bulan januari 2014 telah terjadi sekitar 182 kejadian bencana di berbagai wilayah di indonesia, mulai dari banjir, tanah longsor, angin puting beliung, hingga letusan gunung api. hal ini membuktikan begitu besarnya ancaman bencana bagi kehidupan masyarakat di indonesia terutama yang tinggal di daerah rawan info artikel; diterima: 22 september 2014 hasil revisi : 24 september 2014 disetujui: 26 september 2014 publikasi on-line: 1 october 2014 kata kunci: pemetaan, bencana longsor article info; received: 22 september 2014 in revised form: 24 september 2014 accepted: 26 september 2014 available online: 1 oktober 2014 keywords: mapping, landslide hazard . mailto:widjonarko39@gmail.com mailto:holibinawijaya@yahoo.com geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 94 bencana. sebanyak 95% lebih merupakan bencana hidrometeorologi seperti banjir, tanah longsor, dan angin puting beliung, dengan korban jiwa banyak disebabkan oleh bencana banjir dan tanah longsor. bencana alam yang terjadi hampir di setiap di wilayah indonesia adalah bencana banjir dan tanah longsor, dan diperkirakan ancaman banjir dan longsor masih akan terus berlanjut hingga maret 2014. bencana longsor banyak terjadi di berbagai wilayah karena sekitar 45% luas lahan di indonesia adalah lahan pegunungan berlereng yang peka terhadap longsor dan erosi. namun demikian faktor kelerengan bukanlah satu-satunya penyebab longsor, karena selain faktor alam yang juga dipengaruhi oleh curah hujan dan geologi, laju infiltrasi, dan penutup lahan, faktor manusia juga mempunyai andil dalam terjadinya longsor. tanah longsor adalah suatu peristiwa geologi dimana terjadi pergerakan permukaan tanah (crozier, 1999) seperti jatuhnya bebatuan atau gumpalan besar tanah. peristiwa tanah longsor atau dikenal sebagai gerakan masa tanah, bebatuan, atau kombinasinya, sering terjadi pada lereng-lereng alam maupun buatan, dan sebenarnya merupakan suatu fenomena alam. terjadinya longsor merupakan suatu kondisi dimana alam mencari keseimbangan baru akibat adanya gangguan atau faktor-faktor yang mempengaruhinya dan menyebabkan terjadinya pengurangan gaya geser serta peningkatan tegangan geser. dengan terjadinya longsor tersebut tentunya dapat mengancam dan mengganggu kehidupan dan penghidupan masyarakat, dan dapat mengakibatkan timbulnya korban jiwa manusia, kerusakan lingkungan, kerugian harta benda, dan dampak psikologis. berkaitan dengan hal tersebut diperlukan adanya mitigasi bencana yang merupakanserangkaian upaya untuk mengurangi risikobencana, baik melalui pembangunan fisik maupunpenyadaran dan peningkatan kemampuan menghadapiancaman bencana. untuk mendukung upaya tersebut tentunya diperlukan informasi mengenai daerah atau lokasi yang rawan longsor yang dapat dibangun melalui sistem informasi geografis dengan melibatkan peran serta masyarakat sebagai pihak yang terkena dampak dalam memberikan informasi terkait dengan lokasi-lokasi longsor serta sejauhmana dampak longsor mereka rasakan. kecamatan semarang barat kota semarang, merupakan daerah yang memiliki kondisi tanah dengan kestabilan yang bervariasi dan topografi yang beragam. salah satu wilayah kelurahan di kecamatan semarang barat yang mempunyai potensi cukup besar terhadap terjadinya bencana longsor adalah kelurahan kembangarum. kelurahan kembangarum memiliki kelerengan datar hingga curam. wilayah kelurahan kembangarum yang bertopografi datar (0-8%) yaitu pada rw.ii, iii, iv, untuk wilayah yang bertopografi landai (2-15%) berada pada rw.i. v, vi, vii, ix, dan xiii, sedangkan wilayah yang bertopografi bergelombang/agak curam (15-25%) berada di wilayah rw.viii, xi, dan xii, dan wilayah yang bertopografi curam (25-40%) rw x dan sebagian kecil rw xiii. kondisi tersebut mengakibatkan sebagian wilayahnya rentan terhadap bahaya longsor. dengan adanya kemungkinan terjadinya bencana longsor tersebut maka perlu adanya pemetaan terhadap lokasi yang rawan longsor sehingga sedapat mungkin dapat meminimalkan dampak yang nantinya mungkin terjadi. masyarakat sebagai pihak yang paling besar terkena dampak, dilibatkan dalam penentuan lokasilokasi yang paling sering terjadi longsor. selain itu juga penggunaan sistem informasi geografis dapat digunakan untuk mengidentifikasi kawasan rawan bencana longsor, terutama terkait dengan kondisi fisik wilayah yang ada. 2. data dan metode dalam penelitian ini digunakan data yang sifatnya kuantitatif yaitu berupa informasi mengenai karakteristik fisik kawasan yang diperkirakan akan memberikan kontribusi terhadap kejadian longsor di kelurahan kembang arum. selain itu dibutuhkan juga data yang bersifat kualitatif yang merupakan hasil pemetaan terhadap pemahaman dan pengetahuan masyarakat kembang arum terkait bencana longsor di kelurahan kembang arum. gambaran lebih jelas mengenai kondisi fisik kelurahan kembang arum dapat diikuti pada uraian berikut. kelurahan kembangarum, kecamatan semarang memiliki luas lahan 17.464,25 ha, berbatasan dengan kelurahan kalipancur, purwoyoso, krapyak (ngaliyan) dan kelurahan kalibanteng kidul, kalibanteng kulon, manyaran semarang barat (lihat gambar). sebagian besar lahan pada wilayah kelurahan kembang geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 95 arum merupakan kawasan permukiman, baik kawasan perumahan, aktivitas perdagangan dan jasa skala lingkungan. aktivitas yang terjadi di kelurahan kembangarum sudah cukup maju, hal tersebut terlihat dari adanya perkantoran, pendidikan, dan aktivitas perindustrian. jenis penggunaan lahan di kelurahan kembang arum terdiri dari permukiman, fasilitas kesehatan, fasilitas pendidikan, industri, perkantoran, kawasan militer dan campuran. dari luas lahan total kelurahan ini, tata guna lahan yang mendominasi adalah permukiman. adapun jenis penggunaan lahan di kelurahan kemban arum selengkapnya dapat diikuti pada tabel berikut. tabel 1. jenis penggunaan lahan kelurahan kembang arum (peta citra kota semarang, 2012) no jenis guna lahan luas (ha) persentase (%) 1 permukiman 16055,38 91,93283 2 fasilitas kesehatan 36,70 0,210144 3 fasilitas pendidikan 159,06 0,910775 4 industri 43,70 0,250225 5 perkantoran 24,47 0,140115 6 markas satbrimob 952,66 5,454915 7 campuran 192,28 1,100992 jumlah 17464,25 100 morfologi kawasan merupakan kombinasi antara perbukitan dan dataran dengan ketinggian antara 0-200mdpl. dari hasil observasi terhadap bentuk muka bumi, kelurahan kembangarum terdiri atas dataran dengan klasifikasi relief berdasarkan beda elevasinya yang bervariasi mulai dari ketinggian 0-50 m (hampir datar) berada di rw. rw.ii, iii, iv, ketinggian 5-50 m (bergelompang lemah/landai) berada di rw.i. v, vi, vii, ix, dan xiii. ketingian 25-75 m ( bergelombang kuat/miring) rw.viii, xi, dan xii, ketinggian 50-200m (berbukit bergelombang) berada di rw x dan sebagian kecil rw xiii. pola morfologi berupa perbukitan dan dataran berimplikasi pada pola kelerengan di wilayah kelurahan kembang arum. sebagian besar wilayah kelurahan kembang arum memiliki kelerengan landai. tipe lereng curam pada wilayah kelurahan kembang arum terdapat pada sebagian kecil wilayah kelurahan kembang arum. gambar 1. kenampakan permukaan bumi (morfologi) kelurahan kembang arum (interpolasi peta kontur kota semarang dengan menggunakan global mapper, 2014) purwoyoso kembang arum geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 96 kelurahan kembangarum terletak pada kelerengan yang bervariasi diantaranya lereng datar (2-15%), landai (15-25%), dan lereng curam (25-40%). kelerengan curam (25-40%) menyebabkan wilayah kelurahan kembang arum temasuk kedalam wilayah yang rawan dengan bahaya longsoran.berdasarkan kondisi kelerengan kelurahan kembangarum tersebut maka kelurahan ini termasuk dalam wilayah yang rawan bencana alam berupa gerakan tanah menengah dan gerakan tanah rendah. selain itu kelurahan ini juga memiliki curah hujan yang cukup tinggi yaitu 27,7-34,8mm/hari yang menjadi salah satu faktor terjadinya bahaya geologi. sedangkan jenis tanah yang untuk kelurahan kembangarum sendiri terdiri dari jenis tanah asosiasi aluvial kelabu, dan mediteran coklat tua. gambar 2. jenis tanah dan kelerengan di kelurahan kembang arum (bappeda kota semarang dan interpolasi kontur 2m) 3. hasil dan pembahasan 3.1 lokasi rawan longsor dan faktor penyebabnya berdasarkan pada hasil survai lapang dan penggalian informasi melalui penduduk yang tinggal di kelurahan kembang arum didapat satu fakta bahwa pada beberapa lokasi di kelurahan kembang arum memiliki satu kerawanan yang cukup tinggi akan bahaya tanah longsor. berdasarkan pada hasil verifikasi lapang, terdapat beberapa daerah di kelurahan kembang arum yang mengalami longsor diantaranya adalah rw i, ix, xi dan xii (lihat gambar 3). dari ke empat lokasi yang rawan longsor di kelurahan kembang arum ke empatnya memiliki karakteristik longsor yang berbeda-beda. seperti contoh di rw ix, bencana longsor lebih diakibatkan kepada jenis konstruksi dan pondasi bangunan yang kurang kuat, sehingga frekuensi kejadian longsornya pun tidak dapat diperkirakan dalam rentang waktu tertentu. sedangkan dibeberapa tempat lebih dikarenakan oleh curah hujan yang terlalu tinggi sehingga frekuensi longsor pun dapat diprediksikan terjadi dalam waktu-waktu tertentu (musim penghujan).dampak yang diakibatkan juga berbeda-beda dari setiap rw yang diidentifikasi rawan akan longsor. dampak longsor di rw ix tidak begitu parah apalagi sampai menimbulkan korban jiwa, kebanyakan dampak dari longsor yang berada di rw ix sifatnya lebih ke personal yaitu, dampak hanya dirasakan kepada pemilik rumah yang mengalami longsor tersebut. geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 97 bencana longsor di kelurahan kembang arum merupakan peristiwa geologi yang terjadi karena pergerakan masa batuan atau tanah dengan berbagai tipe dan jenis seperti jatuhnya bebatuan atau gumpalan besar tanah. longsor yang ada di kelurahan kembang arum memiliki 2 faktor penyebab, yaitu faktor internal dan faktor eksternal. yang merupakan faktor internal meliputi kondisi fisik alam seperti topografi yang curam yaitu 25-45% atau > 45%, kondisi tanah yang peka terhadap erosi dan rawan longsor yaitu jenis tanah alluvium dan mediteran (lihat gambar 2), beserta curah hujan yang tinggi yaitu rata rata 37mm/jam. faktor eksternal terjadinya longsor di kelurahan kembang arum meliputi kontruksi bangunan yang buruk dan juga cara pengaliran air hujan atau jaringan drainase yang kurang memadai. seperti yang diketahui daerah dengan topografi tinggi yaitu 25-45% dan > 45% tidak diperkenankan sebagai lahan terbangun. adapun yang tetap digunakan sebagai lahan terbangun seharusnya menggunakan konstruksi dan pondasi yang menyesuaikan ketinggian tanah, akan tetapi di kelurahan kembang arum masih banyak dijumpai bangunan yang berada pada lokasi tersebut yang belum menggunakan konstruksi dan pondasi yang sesuai, sehingga tidak mampu menopang bangunan secara kuat. gambar 3. lokasi rawan longsor kelurahan kembang arum ( hasil wawancara dan observasi lapang, 2014) geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 98 3.2 pengetahuan masyarakat tentang bencana longsor pengetahuan masyarakat terhadap karakteristik lingkungan yang dijadikan tempat tinggal erat kaitannya dengan pemahaman masyarakat terhadap lingkungan tempat tinggalnya. seperti yang diungkapkan yunus (2010), bahwa perilaku manusia (behavior) dipengaruhi yang melekat pada dirinya atau factor internal seperti pengetahuan, pengalaman, dan pendidikan. dari definisi pengetahuan terhadap lingkungan di atas dapat dijadikan dasar untuk mengidentifikasi tingkat pengetahuan masyarakat kembang arum akan bencana longsor. tingkat pengetahuan masyarakat akan bencana longsor, terutama bagi masyarakat yang tinggal dilokasi rawan longsor masih tergolong rendah. hal tersebut dapat diketahui dari sebagian besar masyarakat yang tidak mengetahui akan bencana longsor yang ada di daerah yang mereka tinggali. rata-rata masyarakat belum begitu menyadari bahwa daerah yang mereka tempati sebenarnya rawan akan longsor karena mayoritas dari mereka meyakini bahwa daerah yang mereka tempati tidak rawan longsor. hal yang melatar belakangi keyakinan masyarakat sekitar bahwa daerah yang mereka jadikan tempat tinggal tidak rawan longsor adalah melihat kondisi jenis tanah padas yang memiliki struktur kuat, sehingga warga berkeyakinan bahwa daerah yang mereka tempati aman dan bebas dari longsor. gambar 4. pengeprasan bukit pada wilayah kembang arum (hasil wawancara dan observasi lapang, 2014) namun ada pula masyarakat yang belum mengetahui apa definisi longsor. masyarakat tidak mengetahui bahwa daerah yang mereka tempati rawan akan longsor. masyarakat hanya mengetahui bahwa ada waktu-waktu tertentu dimana sebagian tanah turun ke permukaan yang lebih rendah yang merupakan dampak dari air hujan. meskipun demikian terdapat pula beberapa masyarakat yang sebenarnya mengetahui bahwa daerah yang mereka tempati merupakan rawan longsor, akan tetapi mereka lebih memilih tinggal di daerah tersebut lebih dikarenakan tidak ada pilihan lain. hal ini merupakan salah satu dampak dari keterbatasan lahan sehingga membuat harga lahan yang semakin tinggi. sehingga masyarakat tetap bertahan untuk tinggal, walaupun dengan resiko keselamatan yang membahayakan. secara umum persepsi merupakan pandangan sesorang terhadap suatu hal. terdapat dua cara pendekatan untuk memahami suatu presepsi. pertama adalah pandangan konvensional. pandangan konvensional menganggap presepsi sebagai kumpulan penginderaan, aktivitas kognisi, member penilaian dan pemaknaan. pendekatan kedua adalah pandangan holistic. pandangan kedua berpendapat bahwa presepsi muncul secara spontan dan langsung. hal ini dikarenakan orgsanisme selalu mengeksplorasi linbgkungan dan melibatkan setiap objek yang ada di lingkungannya (fisher dkk, 1984; sarwono, 1992). berdasarkan hasil wawancara kepada masyarakat di sekitar rawan longsor, diketahui masyarakat sekitar rawan longsor sebagian besar tidak berkenan untuk berpindah dari tempat yang telah mereka tempati. hal ini seperti yang disampaikan salah seorang informan yang berada di sekitar rawan longsor: geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 99 “tidak ada rencana mau pindah kecuali anak-anak. kalaupun ada longsor ya biarkan saja itu kan kehendak tuhan” lebih lanjut, persepsi yang muncul mengenai longsor oleh seorang warga yang bertempat tinggal disekitar daerah rawan longsor seperti yang diungkapkan pada kalimat di bawah ini : “di sini masalah longsor itu gak ada ya mbak adapun terjadi longsor itu kesalahan manusianya yang kurang tepat mengatur airnya, seperti rumah atas itu sebenarnya kalu menata airnya lewat jalur sebenarnya gak longsor itu air dari genteng atas langsung ke bawah dan dipondasinya itu kan gak diplester. belum ada longsor kalau dari alam ya itu tadi terjadi longsor karena kesalahan manusia sendiri.” dari beberapa argumentasi yang disampaikan masyarakat dan korban longsor, mereka beranggapan bahwa mereka tidak mengetahui jika wilayah yang mereka tempati adalah daerah rawan longsor. yang mereka ketahui adalah ketika terjadi longsoran tanah itu merupakan akibat dari pondasi rumah yang kurang kuat dan gerusan air hujan. berdasarkan pengetahuan dan persepsi masyarakat pada kawasan rawan longsor dapat pula diidentifikasi pola-pola adaptasi masyarakat kelurahan kembang arum dalam mengantisipasi kejadian longsor. bentuk-bentuk adaptasi masyarakat adalah dalam bentuk adaptasi secara fisik, yaitu melakukan perbaikan konstruksi bangunan dan juga melakukan perkuatan tebing pada wilayah perbukitan yang rentan longsor. kemampuan adaptasi masyarakat kembang arum sangat dipengaruhi oleh kemampuan finansial masyarakat. secara kebetulah pada kawasan-kawasan yang sering mengalami kejadian longsor kemampuan finansial masyarakat cenderung rendah, sehingga bentuk adaptasi untuk meminimalkan resiko longsor dapat dikatakan rendah. 3.3 analisis resiko bencana longsor pada kelurahan kembang arum secara fisik, kelurahan kembang arum memiliki potensi yang cukup tinggi terhadap bencana longsor. perilaku masyarakat yang mengubah bentang alam dengan melakukan pengeprasan bukit dan bermukim pada daerah dengan kelerengan curam menambah tinggi potensi bencana longsor di kelurahan kembang arum. beberapa kawasan yang memiliki potensi longsor yang tinggi antara lain wilayah rw viii, ix, x, xii dan xiii (warna merah). sedangkan lokasi yang memiliki potensi longsor sedang adalah wilayah rw i, iv, v, vii, viii, ix, x, xii dan xiii. hasil perhitungan dengan menggunakan metode pembobotan ini tidak jauh berbeda dengan hasil observasi dan wawancara yang dilakukan pada masyarakat di kelurahan kembang arum. hasil simulasi ini mengkonfirmasikan bahwa informasi yang disaring dari masyarakat berkaitan dengan longsor benar adanya. gambar 5. potensi longsor akibat kondisi fisik kawasan (data diperoleh dari bappeda kota semarang diolah dan dianalisis tim penyusun, 2014) geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 100 potensi longsor akan semakin tinggi pada lokasi-lokasi tersebut seiring dengan lemahya kemampuan adaptasi masyarakat kembang arum terhadap bencana longsor. lokasi-lokasi yang berada pada tingkat kerawanan sedang akan menjadi memiliki resiko tinggi karena kemampuan adaptasi masyarakat untuk membuat bangunan pengaman berupa talud tebing adalah rendah. keadaan ini tentu tidak dapat didiamkan begitu saja oleh pemerintah kota semarang. sosialisasi dan peningkatan pemahaman masyarakat akan bencana longsor perlu dilakukan secara intensif untuk meningkatkan pengetahuan masyarakat. selain itu perlu ada upaya bantuan pemerintah untuk melakukan kegiatan perkuatan tebing sesuai dengan kaidah konstruksi agar masyarakat kembang arum tidak semakin tentan terhadap resiko bencana. tingkat kerentanan ini akan semakin tinggi apabila dilihat dari sudut pandang kepadatan penduduk. secara kebetulan kawasan yang memiliki potensi longsor tinggi adalah kawasan dengan tingkat hunian yang padat dan sebagian besar adalah bangunan yang tidak/belum memiliki bangunan pengaman talud tebing. gambar 6. kerentanan longsor akibat faktor kepadatan (analisis tim penyusun, 2012) tingkat kerentanan tinggi ini diakibatkan potensi dampak yang akan semakin besar apabila kejadian longsor terjadi pada kawasan dengan kepadatan penduduk tinggi. potensi korban akan semakin banyak seiring dengan kepadatan yang tinggi serta pengetahuan serta kemampuan adapatasi yang rendah. 4. kesimpulan berdasarkan pada hasil dapat disimpulkan bahwa masyarakat kelurahan kembang arum belum memiliki pengetahuan yang memadai terhadap potensi bencana longsor di sekitar lingkungan permukiman mereka. pengetahuan tentang longsor muncul saat ada kejadian bencana longsor di sekitar lingkungan permukiman mereka. kondisi ini tercipta karena minimnya informasi kebencanaan yang dipublikasikan oleh pemerintah kota semarang. pengetahuan yang kurang memadai ini harus dihadapkan pada fakta bahwa mereka tidak memiliki kemampuan yang cukup untuk menghindarinya, dikarenakan faktor ekonomi masyarakat. sebagian besar masyarakat karena kondisi ekonominya pasrah terhadap keadaan. keinginan untuk terhindar dari bencana dihadapkan pada ketidakmampuan untuk membiayai upaya mitigasi terhadap bencana. keadaan ini tergambar jelas dari konstruksi rumah pada daerah dengan lereng yang cukup curam yang tidak dilengkapi dengan talud penahan tebih yang memadai. keadaan ini akan semakin buruk di masa mendatang apabila upaya untuk mengurangi resiko baik secara individual maupun secara kolektif tidak pernah dapat diwujudkan. untuk itu pemerintah kota semarang perlu pro aktif untuk mengurangi resiko bencana, melalui upaya sosialisasi dan peningkatan pengetahuan masyarakat terhadap bencana longsor di kelurahan kembang arum. selain itu pemerintah geoplanning 2014,vol: 1, no: 2, 93-101 widjonarko dan wijaya | 101 kota semarang juga dibutuhkan untuk memberikan fasilitasi peningkatan kapasitas adaptasi masyarakat kelurahan kembang arum terhadap bencana longsor melalui bantuan teknis dan dana untuk pembangunan pengaman tebing. upaya ini penting mengingat banyak kejadian longsor di kota semarang yang terjadi sebagai akibat faktor kelalaian para pihak terkait, sebagai contoh longsor di lempong sari. kawasan ini secara geomorfologi dan fisik mirip dengan kelurahan kembang arum. faktor pembeda adalah pada wilayah tersebut cukup banyak masyarakat yang memiliki kemampuan 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gis and participatory mapping in community development planning. paper for the esri international user conference, sustainable development andhumanitarian affairs track, san diego, ca. --------, 2007, undang-undang negara republik indonesia no 24/2007 tentang penanggulangan bencana --------, 2007, undang-undang negara republik indonesia no 26/2007 tentang penataan ruang. pemetaan potensi bencana longsor di kelurahan kembang arum abstract: disaster is a real fact that sometimes less get the full attention by both government and society. this is reflected from the hustle bustle of activity that occurs after the disaster compared with efforts to minimize the impact of disasters.... abstrak: kebencanaan merupakan satu fakta riil yang terkadang kurang mendapatkan perhatian penuh baik oleh pemerintah maupun masyarakat. kondisi ini tercermin dari begitu hiruk pikuknya kegiatan pasca bencana terjadi dibanding dengan upaya untuk memin... masyarakat kembang arum secara umum tidak sepenuhnya mengetahui dan paham potensi dan kondisi bencana longsor yang ada di sekitar tempat tinggal mereka. masyarakat tahu setelah ada kejadian longsor seperti runtuhnya talud penahan tebing. masyarakat ju... 1. pendahuluan keywords: mapping, landslide hazard 2. data dan metode 3. hasil dan pembahasan 3.1 lokasi rawan longsor dan faktor penyebabnya 3.2 pengetahuan masyarakat tentang bencana longsor 3.3 analisis resiko bencana longsor pada kelurahan kembang arum 4. kesimpulan 5. daftar pustaka | 259 geoplanning vol 5, no. 2, 2018, 259-268 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi :10.14710/geoplanning.5.2. 259-268 time travel estimations using mac addresses of bus, passengers: a point to path-qgis analysis a. hidayata,b , s. terabea , h. yaginumaa a urban and transportation planning laboratory, department of civil engineering b department of civil engineering, universitas teknologi sulawesi, jalan talasalapang no.51 makassar 90221, indonesia abstract: currently, the development of wi-fi is proliferating. especially in the field of transportation and smart cities. at the same time, wi-fi is a low-cost technology, which offers a longer survey time and is able to support the big data era. this paper describes our study, which first uses a wi-fi scanner to capture media access control (mac) address data of bus passengers wi-fi devices and then identifies each mac address travel time to confirm the bus passengers. the mac address is a unique id for aech device used suchh as moble phones, smartphones, laptops, tablets, and other wi-fi-enabled equipment. the wi-fi scanner was placed inside the bus to capture all tthe mac addresses inside and around the bus. the survey was conducted for one day (eight hours). the paper describes the procedure of the time travel estimation for each mac address using the “point to path” analysis in qgis open source software. this procedure, using point to path-gis, produced 70.000-80.000 raw data points cleaned into 100-130 new data point. the procedure determined how many passengers traveled and explained which bus passengers used based on travel time. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): hidayat, a, terabe ,s., & yaginuma, h. (2018). time travel estimations using mac addresses of bus, passengers: a point to path-qgis analysis. geoplanning: journal of geomatics and planning, 5(2), 259-268. doi:10.14710/geoplanning.5.2.259-268. 1. introduction technology development is growing every day. this development also applies to information technology systems such as wi-fi. wi-fi is a network connection system that is currently used almost universally. the wi-fi connection is built into devices such as smartphones, laptops, tablets, and other devices that receive internet signals or data (hidayat et al., 2017a). the development of wi-fi and information technology systems has penetrated the transportation engineering sector. the use of wi-fi in transportation is currently advancing, especially in terms of the development of travel data related to origin-destination (od), speed estimation, travel time, and the estimation of passengers. this advancement pertains to transportation sectors such as a pedestrian, motor vehicle, and others (abedi, 2014; al-husainy & fadhil, 2013; xia et al., 2014). wi-fi thrives because of its low cost, accessibility, energy efficiency, and mobile scanner capacity (non-static scanner). furthermore, almost everyone uses wi-fi daily due to the easy data retrieval process. wi-fi technology is based on ieee 802.11 standards (including 802.11a, 802.11b, 802.11g, and 802.11n) (cisco, 2008; najafi et al., 2014). it is a popular method to provide internet access for wireless users (xu et al., 2013). the more common wi-fi mode of operation is 802.11, called the infrastructure mode, where stations communicate with other wireless stations and wired networks (typically ethernet) through an access point. the access point bridges traffic between wireless stations through the lookup of the destination address in the 802.11 frame (sridhar, 2008). the infrastructure mode supports smartphones, tablets, routers, and laptop, among others. a smartphone can be identified by its unique id such as its international mobile equipment identity (imei) number or the media access control (mac) address. the imei is received when the mobile device is registered on a network, whereas the mac address is on every data packet sent by the wi-fi-enabled mobile handset. mac addresses are designed to be persistent and open access article info: received: 26 july 2018 in revised form: 29 august 2018 accepted: 20 sept 2018 available online: 25 oct 2018 keywords: wifi scanner, point to path, gis, travel time, procedure, mac address corresponding author: arief hidayat urban and transportation planning laboratory-department of civil engineering-tokyo university of science email: ariefhidayat06@hotmail.com http://doi.org/10.14710/geoplanning.5.2.259-268 https://orcid.org/0000-0001-8845-6747 mailto:ariefhidayat06@hotmail.com hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 260 | globally unique (martin et al., 2017). a mac address is a 48-bit number used to identify a network interface (cunche, 2014). the wi-fi connection for smartphones is designed to periodically transmit a probe-requestframe to determine a known access point (matte, 2017; yaik et al., 2016). probe requests are the active scans by the mobile device (sun et al., 2017; verbree et al., 2013). the probe request content includes the sender's mac address (musa & eriksson, 2012). the wi-fi scanner probe request can load all mac address data into a single log file. this system accesses the mac address without connecting to the internet and is a passive scanning activity that collects data. the wi-fi scanner as a probe request mode was developed to collect mac addresses included in the infrastructure mode. this study uses a wi-fi scanner on a bus to collect mac address data from bus passengers and non-passengers. wi-fi systems capture mac address data from wi-fi device users (abedi et al., 2015; dunlap et al., 2016; jackson et al., 2014). the mac address is the same on each device that is wi-fi-enabled. mac addresses are unique numbers and letters for each device and no device has two mac address. in addition, one mac address cannot be assigned to two devices (asija, 2016; hidayat et al., 2017a; hidayat et al., 2018b; sapiezynski et al., 2015; shiravi, et al., 2016). in intersection estimation research today, there is a relationship between wi-fi data and travel time. such intersection estimation research seeks to confirm the accuracy of the bluetooth and wi-fi data on urban roads against reliable travel time results (shiravi et al., 2016). the research that detects human movement uses high wi-fi frequencies, connected with gps so that the position of the mac addresses or access points can be identified (sapiezynski et al., 2015). the use of wi-fi and bluetooth in public terminal transportation has also been applied in a high and wide frequency to capture mac addresses so the travel behavior of pedestrian patterns can be identified and understood in terms of seconds and minutes (shlayan et al., 2016). this public terminal transportation research considers high-frequency wi-fi detected data compared with bluetooth data. the reliability of travel time using bluetooth has been investigated to identify the bluetooth ability to detect mac addresses (araghi, et al., 2015). in such studies, data processing was conducted by dividing the detection zone and detection time. another empirical evaluation of wi-fi was conducted on road transport. the method used was to detect “exit to exit” with a procedure filtering the data with time as the main variable (abbott-jard et al., 2013). “exit to exit” is intended to be the “beginning” and the “end” of each mac address identification time. other travel research using static equipment has been conducted for bicycle users with time and speed filtering data processing to confirm the penetration rate of wi-fi data (böhm et al., 2016; ryeng et al., 2016). research on the time travel estimation process is important in confirming the accuracy of the wi-fi data (hidayat et al., 2018b). gis software explains spatial data distribution, in the case of transportation, this is through the use of open-source qgis software. there are several studies related to the use of gis for transportation and travel time. the distribution of wi-fi spatialized data can be identified if the data have specific xy coordinates captured through the wi-fi scanner and gps (feng & liu, 2012; odiyo, 2014). the gis is also more efficient for wireless data deployment in the development of trade and services or urban planning (aldasouqi & salameh, 2014). furthermore, research on travel diary data has been conducted based on travel time using gis. in such research, the analyses use “starting” and “ending” person-trip data in combination with spatiotemporal data (yu & shaw, 2004). an analysis of “day-to-day” variations in travel time using gps connected to a notebook pc can capture vehicle movement over multiple days. one study describes the tracking of vehicle movements using a gps device based on travel time and travel speed (ohmori et al., 2002). path gis research uses spatial trajectory analysis on all of the gis data points obtained for vehicle movements (zambrano et al., 2016). more specifically, examining the logical path of tourist movements using gps data from tourism spots, the travel time data for each tourist (meng-lung lin et al., 2009) or the fastest and shortest travel times can be classified using gis modelling (abousaeidi et al., 2016; ilayaraja, 2013). this research uses a wi-fi scanner to retrieve data from wi-fi users. for the study, the wi-fi scanner was placed on a bus to capture the mac addresses of the bus users and non-bus users. the difference between this study and previous research is that this study uses non-static wi-fi placed on a moving vehicle. the data results are analyzed using a gis procedure. an important variable in this research is “time,” which measures how long each mac address is traveling. travel time is essential for the identification and confirmation of the bus versus non-bus passengers. this study conducts a “point to path” procedure to https://doi.org/10.14710/geoplanning.5.2.259-268 hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 | 261 analyze the origin of destination or “beginning” and “end” of mac address identification based on the travel time variable. the aim of a “point to path” analysis with a travel time variable is to estimate bus passenger usage. 2. data and methods 2.1. location a survey was conducted in obuse in the nagano prefecture in japan (figure 1). the obuse bus is a tour bus that delivers passengers to a tourism spot in the town of obuse. obuse was selected for the wi-fi field test because obuse is a tourism area with a hop-on-off bus system. this system is usually characterized as a per day, time pay system. this field test was done by using a wi-fi scanner placed on the obuse bus from 09:50 to 17:10 (hidayat et al., 2017a; hidayat et al., 2017b; hidayat at al., 2018a; hidayat et al., 2018b; terabe et al., 2017). the obuse bus makes nine stops and the distance between each bus stop is about 500 meters or three minutes. the obuse bus also makes seven loops, each called circulation time (ct), from bus stop one to bus stop nine and then starts back at one. thus, the nine bus stops can categorize obuse as a bus stop tourism spot. figure 1. obuse orientation map and bus route map 2.2. wi-fi scanner equipment and installation as stated, the wi-fi scanner equipment captures mac addresses of devices such as smartphones, laptops, tablets, computers, and other wi-fi-enabled devices. the mac address only shows a unique identification for each device and it does not display personal data. the wi-fi scanner equipment includes an antenna, gps, and a mobile battery (figure 2). this wi-fi scanner has an approximate range of about 200-300 meters and it detects bus passenger wi-fi-enabled devices, buildings, vehicles, and pedestrians (hidayat et al., 2017a, hidayat et al., 2017b, hidayat et al., 2018b). the scanner uses a mini raspberry pi computer. this is a quad-core processor-powered single board computer running at 900mhz and the system has 1 gb ram capacity. it also has a usb port, a pole stereo output, a video port, and an hdmi port, plus a micro sd port for loading the operating system and storing data. the scanner includes gps tracking bu-353 with high frequency. in terms of electricity, it is powered by a mobile battery 30,000 mah, so it can be active up to 12 hours. the wi-fi scanner was placed inside the bus near the driver. after the survey ended, the scanner was turned off and the mac address recorded data was downloaded for further analysis. the data became raw data that would be confirmed for each travel time. https://doi.org/10.14710/geoplanning.5.2.259-268 hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 262 | figure 2. obuse bus, wi-fi scanner equipment, and wi-fi scanner approximate range 3. results and discussion this section describes the processing of the raw data into travel time data. this procedure is the cleaning of the wi-fi data. the wi-fi log data are in the form of time and mac addresses, while the gps log data are in the form of time, latitude, and longitude. there are seven processing steps for the wi-fi data, which start with the raw data and include combining the gps and wi-fi data, converting the coordinates, inserting “point to path” qgis analysis, analyzing travel time, confirming bus passengers based on circulation time, and validating or comparing driver data and wi-fi confirmed data. figure 3 presents a chart of the wi-fi cleaning process converting the data into travel times. this procedure is performed using python and qgis open-source software. the python software makes it easier to analyze the amount of wi-fi data. all analyses use open-source anaconda python 3.0 and qgis 3.0 girona applications. figure 3. flowchart of data processing 3.1. the first step combines data the wi-fi scanner provides the wi-fi log data and the gps provides the gps log data. these data are still distinct, so they need to be merged so that each mac address has a position or coordinate. the position and coordinate easily track the mac address. to merge these data, the pandas python package and the concept command are used, with the mac address as the merging "key." the screenshot in figure 4 shows the structure of combining the wi-fi and gps log data. figure 4. screenshot python combining data https://doi.org/10.14710/geoplanning.5.2.259-268 hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 | 263 3.2. the second step converts the coordinate system the data that have been merged need to be converted to utm from decimal degrees. this change is done so that it is easier to calculate the distance the mac addresses travel in the next analysis. the coordinate data transformation uses the python’s geo-pandas module. the figure 5 screenshot shows the structure of the data once they are converted. these data should appear in the qgis interface. the data are entered as csv-shaped so that the "add" data is a “delimited” file and the set of x and y coordinates are the spatial positions. the utm reference used is utm wgs 84 zone 54n. the mac address data appear on the qgis interface as data points (figure 6). figure 5. screenshot python – utm conversion figure 6. wi-fi data points 3.3. the third step is “point to path (ptp)” analysis point to path (ptp) is one tool in qgis that "connects the dots" based on a common attribute and a sequence field. the attribute field determines which points should be grouped together into a line (qgis, 2011; sherman, 2011). the sequence field determines the order in which the points will be connected. before the analysis, ptp plugins must be installed first on the menu qgis managed plugin. this tool has three variables: “group,” “begin,” and “end” (sherman, 2011). • group the name of the id/mac address taken from the field, we chose as the group field • begin the time value of the first point order field used to create the path • end the time value of the last point order field used to create the path in ptp analysis, two crucial factors must be considered when entering “group” and “time.” “group” represents specific data based on the merged data and “time” is the time input. the “group” data capture the mac addresses, while the “time” data capture the time-shaped column: “hour,” “minute,” and “second.” the results show the od line of the movement of each mac address. new attributes of the output data include the “begin” (time journey begins) and “end” times (time end of trip). “begin” and “end” for each mac address are used to analyze the travel time of each mac address (figure 7 and 8). https://doi.org/10.14710/geoplanning.5.2.259-268 hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 264 | figure 7. screenshot python – ptp analysis figure 8. ptp results analysis 3.4. the fourth steo is time travel analysis time data were analyzed in python with the equation “travel time = end – begin,” and show the number of seconds and minutes for each mac address. furthermore, the mac addresses are divided by using the time classification as shown in table 1. once divided by time classification, the numbers of mac addresses are shown that can be confirmed as passengers. this study uses three minutes because the distance between each bus stop is about three minutes and the distance for one loop (from bus stop one to nine and back again) is about 40 minutes (figure 9a). tabel 1. time classification no travel time (minutes) classification 1 >40 non-passenger 2 < 3 non-passenger 3 3-40 passenger 3.5. the fifth step confirms the passengers this stage confirms the passengers who use the bus that travels seven cts from 9:50 to 17:10. in this process, the mac address travel times are shown as well as the bus circulation times. the mac addresses outside the ct can be removed. mac addresses can be defined as passengers who are in the zone of ct. the bus circulation times are taken from the obuse bus timetable or the bus stops (table 2) (figure 9b). tabel 2. bus circulation time bus circulation time cn begin cn end ct 1 9:50 10:40 ct 2 10:50 11:40 ct 3 11:50 12:40 ct 4 13:20 14:10 ct 5 14:20 15:10 ct 6 15:20 16:10 ct 7 16:20 17:10 https://doi.org/10.14710/geoplanning.5.2.259-268 hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 | 265 3.6. discussion the raw travel time result shows the amount of travel time in the range of 0-400 minutes. this result still has to be reclassified based on the bus circulation time. there are 2,000 mac addresses selected from about 75,000 raw data points for the ct analysis. the figure 9 histogram shows the frequency of the number of mac addresses with their travel times (sridhar, 2008). after confirmation, based on the bus ct, the mac addresses that are estimated as passengers equate to almost 120 mac addresses with their frequency distribution as in figure 9. from the histogram, we can see that more than 60% of the travel time is from 3-15 minutes, with the rest in the range of 15-40 minutes. a ground count and analysis test the data validity. furthermore, the ground “truth” results are taken from driver data. the ptp result compared with driver data show that the difference trend is not too significant between the ptp data and ground data. between 10:50 am and 11:40 am and 11:50 am and 12:40 pm passenger data tends to be high. this is because the morning before noon is a better time to travel. at other times, the circulation tends to decrease such as in early morning and late afternoon. this analysis is in line with some previous results that provides an illustration of how the ptp procedure can be used to calculate the number of passengers on a bus using wi-fi data (see abousaeidi et al., 2016; ilayaraja, 2013). figure 9. travel time histogram of differences between before and after the process figure 10. comparison between driver data with a ptp procedure a) 1st classification raw data – justify travel time ct: circulation time dd: driver data/ground truth wj: wifi justification ddw: difference between dd and wj b) 2nd classification justify circulation time https://doi.org/10.14710/geoplanning.5.2.259-268 hidayat et al/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 259-268 doi :10.14710/geoplanning.5.2. 259-268 266 | in figure 10, the comparison between the driver field data and the wi-fi procedure results shows that ct1, ct2, ct5, ct5, and ct7 have insignificant value differences compared with ct3 and ct4. during ct3 and ct4, there is a significant number of visitors to obuse using the bus or detected around the bus (hidayat et al., 2017a, hidayat et al., 2017b, hidayat et al., 2018b). these time frames (circulation times) capture the highest number of passengers, which tends to be very significant from morning until noon. passengers begin to decrease from noon into the afternoon. the difference in the number of passengers between the driver and wi-fi data is in the 5-10 passenger range. 4. conclusion the results imply that ptp analysis is advantageous (easy, instantaneous, intuitive) when used to process wi-fi scanner data, in particular, and big data in general. travel time data can be categorized into two types: namely, passenger and non-passenger data. the analysis can be used as part of the development of smart city-based transportation and big data projects. based on the comparison between driver data and wi-fi confirmed data, there was not a significant difference in the number value between these. the data cleaning process still needs to be developed with various additional analyses to get the confirmed wi-fi data closer to the field data. further research can be fine-tuned to cover the non-passenger parts such as pedestrians, vehicles, and buildings and the making of an od matrix for passenger data. the origin of the movement is still a straight line, which should be based on the route, so the distance calculations still include errors. 5. acknowledgments the authors would like to thank all those who have contributed to this paper. thank you to members of the transportation planning laboratory, the department of civil engineering, and the tokyo university of science, which provided surveys, data, and extensive assistance in support of this paper. thank you to my workplace universities technology sulawesi-indonesia, my scholarship from the ministry of research, technology and higher education and the indonesia endowment fund for education (lpdp) ministry of finance, the republic of indonesia. thank you also to all the reviewers who provided corrections for this paper. 6. references abbott-jard, m., shah, h., & bhaskar, a. 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(2016). visualization and analysis of vehicle paths with qgis and weka. international journal of innovation and applied studies, 18(4), 9324. https://doi.org/10.14710/geoplanning.5.2.259-268 https://doi.org/10.1109/itsc.2016.7795559 https://doi.org/10.1155/2017/7821585 https://doi.org/10.5194/isprsarchives-xl-4-w4-31-2013 https://doi.org/10.3390/s141120843 https://doi.org/10.5121/ijwmn.2013.5402 | 23 geoplanning vol 3, no 1, 2016, 23-32 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.1.23-32. land use change in suburban area: a case of malang city, east java province s. n. rukmanaa, i. rudiartob a pgri adibuana university, surabaya, indonesia b diponegoro university, indonesia abstract: the development of suburban areas of malang city has developed an expansion of built-up areas between urban and suburban areas. there has been a great phenomenon that mostly occurs along the suburban areas where industrial activities took place. this study aims to determine what factors have influenced the land use change in the suburban areas of malang city by employing “geoda” application. it is one of the geographical information system applications that particularly deals with statistical analysis. to achieve this purpose, the objectives are: delineating the study area, analyzing the characteristics of land use change, assessing and analyzing the variable influencing the land use change. the results have shown that the characteristics of land use change, such as population, distance, migration, and occupation transformation are directly proportional to the land use change. it has also been identified that the high level of density is only located in the surrounding areas of industries. from the assessed variables through the statistical model, population (x1), density (x2) and migration (x3) are found as the influencing factors of land use change. copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): rukmana, s. n. & rudiarto, i. (2016). land use change in suburban area: a case of malang city, east java province. geoplanning: journal of geomatics and planning, 3(1), 23-32. doi:10.14710/geoplanning.3.1.23-32 1. introduction land use change in peri-urban area is one of the impacts of urbanization process where urban activities expand (arta & pigawati, 2015; ives & kendal, 2013; paül & mckenzie, 2013; shi, sun, zhu, li, & mei, 2012). this issue has become one of the most interesting subjects to be studied particularly in developing countries. one of the prominent factors in land use change is population growth. un-habitat (2005) reported that in 1950 asia’s urban population was about 232 million or about 17% of the total population, and in 2005 it increased up to 40%. the distribution of population within and between cities, regions and nations is influenced further by certain migration patterns because the city as a generator region provides complete facilities, vacancies, and other attracting forces such as industry, education, etc. therefore, the high rate of migration living in urban area can lead to uncontrolled land needs, and it will in turn affect the land use change in the future. in the context of land use change, many of it were caused by urban development located in suburban areas. as webster & muller (2009) mentioned, peri-urban zone begins just beyond the contiguous built up area and sometimes extends as far as 150 km from the core city. it has positive and negative impacts. one of the positive impacts is the creation of new job for people living in suburban areas, for example as industrial labors. on the other hand, the negative impacts can be related to the quality of land use in terms of the economic aspect (phuc, van westen, & zoomers, 2014; zhang et al., 2015). it means that when the quality of agriculture decreases, the tendency of farmers to sell their agriculture land is high (kangalawe, christiansson, & östberg, 2008). through the process of land conversion, agricultural land is converted into article info: received: 21 march 2016 in revised form: 1 april 2016 accepted: 25 april 2016 available online: 30 april 2016 keywords: landuse change, suburban area,spatial regression, malang corresponding author: siti nuurlaily rukmana pgri adibuana university, surabaya, indonesia email: nuurlaily_rukmana@unipasby.ac.id open access http://dx.doi.org/10.14710/geoplanning.3.1.23-32 mailto:nuurlaily_rukmana@unipasby.ac.id rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 24 | more productive land such as settlements, industry, etc., and creates transformation of the occupation from farmers to labors (mcgee, 1991). malang is one of the regions that has such experience from 1990 to 2011. malang has promoted expansion urban areas that caused land use change. in addition, the presence of industrial activities also creates a high in-migration flow. thus, the impact of this phenomenon is occupation transformation. this article elaborates some influencing factors that contributed to land use change and its impacts on occupation transformation. some studies have been conducted to assess land use change from different perspectives and approaches such as physical and socio-economic aspects. the urbanization process also has an impact on the reduction of agricultural land use through the enlargement of residential and industrial areas (guan et al., 2011; su, jiang, zhang, & zhang, 2011). they used physical aspects to measure the relationships between land use change and urbanization process. these aspects are total area, patch density, parameter area ratio distribution, euclidian nearest neighbor distance, and aggregation index. in addition, patch numbers and patch areas of a built-up land are still increasing and showing the diffused distribution patterns from an urban center to suburban region (guan et al., 2011). both studies measured transformation of agricultural landscapes under rapid urbanization by applying global moran’s i statistics and local indicators of spatial association (lisa) analysis. 2. data and methods 2.1 data this research used data collected from different sources. the data is related to the physical and social aspects and divided into two purposes as shown in table 1. table 1. the data (authors, 2015) data and information sources delineation of study area: initial urban and rural status, density, accessibility and built up area. basic village data (podes), cbs (2010-2012), spatial planning of malang city (2010-2012) assessing the influence of land use change;  population  density  migration  distance  occupation transformation cbs (2010-2012), development planning agency of malang city 2.2 research methods this research used quantitative approach in analyzing the factors influencing land use change as well as scoring method to delineate the study area. scoring method was applied in order to delineate study area into a more realistic condition. the analysis itself was conducted into two types, i.e., descriptive and spatial statistical analyses. a) descriptive analysis was applied for: (1) delineating the study area based on scoring in each criteria (i.e. the percentage of built up area and road level) (2) analyzing the characteristics of land use change in suburban area of malang city by using an exploratory data analysis (eda). the first step to analyze spatial regression is eda (l. anselin & getis, 1993; anselin et al., 2006). the function of eda is to determine the outlier or extreme value through the tools of boxplot and box map. moreover, to assess the relationship between dependent variable and independent variable can be conducted by scatter plot tool. http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 | 25 n range b) spatial statistical analysis spatial multiple regressions analysis was employed through open geoda and arcgis 9.3 software. the regression analysis was divided into two processes, i.e., simple linear regression and spatial regression. the spatial regression analysis can be continued if the value of lagrange multiplier (lm) lag and lm error in simple linear regression is less than 0.05. spatial regression in this analysis was based on the following rules. spatial weight describes the model of spatial interaction between a polygon and another polygon. it can be used to analyze total villages of the study area that have been influenced and to be included in the model formula. the output of this model can be visualized as follows: y = a.w+b+ a.x1+ b.x2+ c.x3+d.x3+ e.x4+ f.x5+… where: y = land use changes a = lambda w = spatial weight (queen contiguity) b = constants a-z = variable coefficients x1 = population x2 = density x3 = total migration x4 = accessibility x5 = occupations transformation 3. results and discussion 3.1 delineation of study area the process of delineating suburban area was done by collecting initial data. there are two criteria to assess suburban area, i.e., total built up area and road level or accessibility. these criteria were applied to each village in the study area by using scoring and then summarizing them into the final weights (see table 2). table 2.total of delineation area (analysis, 2015) parameter weight indicator score classification sub district a b c d e f g h i built up area 50 the percentage of built up area 1 rural (0 %25%) 50 (1) 50 (1) 50 (1) 150 (3) 50 (1) 150 (3) 150 (3) 150 (3) 50 (1) 3 sub urban (25% 75%) accessibility 50 road level 1 local 100 (2) 50 (1) 150 (3) 50 (1) 50 (1) 100 (2) 100 (2) 100 (2) 150 (3) 2 collector 3 main road total 150 100 200 200 100 250 250 250 200 information: a = bululawang sub district f = pakisaji sub district b = tajinan sub district g = pakis sub district c = singosari sub district h = dau sub district d = wagir sub district i = karangploso sub district e = tumpang sub district the range of the class can be calculated as follows: p = range = the highest data – the lowest data note : r = range p = interval class n = total class http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 26 | p = 250 100 = 75 2 based on the formula, the class intervals in terms of classification zone were (1) rural = 100-175; (2) suburban = 176 – 250. based on the overall analysis above, the delineation area is located in the northern part of malang city (singosari, karangploso, and pakis) as shown in figure 1. it is reasonable since those three sub districts are industrial area for different products such as tobacco and furniture. in addition, it is also recognized from the physical appearance that these three sub districts have strategic location and accessibility (malangsurabaya) and hence, the tendency of land conversion is quite high. figure 1. delineation map (analysis, 2015) 3.2 characteristics of land use change as mentioned before, in order to analyze the land use change, five variables were selected, i.e., population, density, migration, distance, and occupation transformation. those variables are then divided into dependent and independent variables. total built up area was determined as the dependent variable and the five variables mentioned above as the independent variables. http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 | 27 a) population lynch (2005) mentioned that the rapid population of third world cities raises concerns on the changing nature of the relationships between urban and rural area. malang is one of the cities that have such experience of population growth. based on the analysis, the highest population percentage was found in pangentan village-singosari sub-districts. high population concentration in these areas is merely because of two factors, i.e., location of furniture industries and major access from malang to surabaya. box map analysis was applied to show the relation between population and land use change. as shown in figure 2, the population variable is directly correlated to land use change, which means high population level will affect land use change in the suburban of malang. figure 2. scatter plot and box map analysis of population (analysis, 2015) b) density density is one of the drivers of urbanization process where it may indicate that the built up area is growing (knox & mccarthy, 1994). it is also relevant to malang city. high density areas are found in some parts of the city particularly in the industrial area. population and density are two aspects directly correlated to the urbanization process. high population concentration will influence the level of density. based on the analysis, it is indicated that the highest density is located in pangentan village-singosari subdistricts as well as sekarpuro village of pakis sub-district. in those sub-districts, agriculture and furniture industries are dominant. figure 3 shows the box map and scatter plot analysis that indicates the relationship between land use change and density. the density variable is in contrast with the land use change in the suburban area of malang city. this is because the settlements and facilities are just located in surrounding workplaces and shows that not all villages have high density as population density is always followed by the location of jobs. this is similar with what has been mentioned by (bergstrom et al., 2013) that people and jobs often move together. http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 28 | figure 3. scatter plot and box map analysis of density (analysis, 2015) c) migration in this research, the migration is only about the number of entrants in each village people who work in surrounding their workplace and people who do not have the ability to live in urban area. the highest migration number was found in purwoasri village-singosari sub-district. this village is located just on the side of the major road of malang-surabaya. it is found that the migration variable is in line with land use change and therefore high migration number will also affect land use change in malang’s suburban (see figure 4). migration in malang’s suburban is very much related to the pull factor of the region, i.e., industrial area. figure 4. scatter plot and box map analysis of migration (analysis, 2015) http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 | 29 d) distance distance variable was selected to compare village’s location to the city center where distances among villages were assessed. the results show that there were no villages became outliers in the model of box map analysis. it means that the average of distance between each village to the urban area is not so far. moreover, the distance variable is directly correlated to the land use change; it means that land use change occurs on a far distance from the city center. from the current condition, land use change was not found in the whole location but only in several places where industrial and commercial activities were located (see figure 5). figure 5. scatter plot and box map analysis of distance (analysis, 2015) e) occupation transformation the occupation transformation variable was focused on the total labors in each village. it was found that the highest occupation transformation is located in banjararum village-singosari sub-district. the occupation transformation in this village tends to follow the development of the area where a large tobacco industry is located. as the conclusion from the statistical test, the occupation transformation variable is in line with the land use change in the suburban area of malang (see figure 6). figure 6. scatter plot and box map analysis of occupation transformation (analysis, 2015) 3.3 model of land use change http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 30 | previous analysis by using box map and scatter plot only shows the preliminary relation of each variable in correspondent with land use change. the challenge is how to assess all the variables into a single model and based on the model the factors that influence land use change can then be identified. two approaches have been done in order to assess the best land use change model, as follows: a) correlation analysis; from this analysis, it was found that variables of density and occupation transformation have a strong relationship to land use change. therefore, simple regression analysis was necessary to compare the model resulted from each approach. b) regression analysis was employed to see the correlation between variables by reducing the outliers through one by one outlier of independent variables. if the value of lagrange multiplier (lm) error is appropriate (less than 0.05) then spatial regression analysis is possible to be applied. to determine the best model of land use change, these criteria need to be fullfilled: a) the model should has a high coefficient of determination value (r2), where this value range from 0 to 1 (closer to 1 considered as the best), b) total of independent variables; the independent variables which are entered in the formula can be identified as the best model. based on the whole model process done in linear regression analysis, two models were identified that can further be processed into spatial regression analysis, as shown in table 3. table 3. the models (analysis, 2015) dependent variable model of land use change r2 value total of independent variables spatial probability land use change using correlation independent variable: y = 172,678.9 – 0.2139184 .w + 164.8632 x1 – 336.3252 x2 0.35 2  reducing one by one outlier in term of population variable: y = 3,733.646 – 0.140907 .w + 98.51888 x1 – 341.7208 x2 + 3,579.966 x3 0.40 3  the best model of land use change in malang suburban area must have two criteria as mentioned in the previous explanation. therefore, the best model in this study is chosen by eliminating village used as population variable and reducing one by one of the outliers with the equation as follows; y = 3,733.646 – 0.140907 .w + 98.51888 x1 – 341.7208 x2 + 3,579.966 x3 where, y = land use change (km2) x1= population (inhabitants) x2 = density (inhabitants/km2) x3 = migration (inhabitants) from the model and statistical results, it can be inferred that the urbanization process that implies to land use change in suburban area of malang city is influenced by migration aspect followed by population growth. the higher the level of population growth may create more demand on the land for different http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 | 31 purposes such as settlement and facilities. the result also shows that most of the land use change in suburban area occurred because of the development of industrial area. this has been the pull factor for the surrounding areas to be developed. the existence of industrial area as the pull factor then determines the density level of its area where more people would like to stay around. it is also found out that the distance from city center is not one of the influence factors in land use change. in malang city, the development of suburban areas was mostly caused by the growth of the industrial areas instead of the city center expansion. 4. conclusion the development of industrial areas has contributed greatly to the total migration of suburban area of malang city with the average rate of 7.69% in 2010-2012. this phenomenon creates occupation transformation from agricultural activities to non-agricultural ones such as residential, commercial, service, labor, etc. as this study focused on the labor activities with the average level of 3.04%, therefore, the people particularly those working as labors prefer to choose their dwelling around the workplace for two reasons, i.e., the location close to their workplace to minimize their transportation cost and cheaper land price for housing and ownership purposes. the two reasons have made malang urban region grows to the areas where industries are located, and this phenomenon creates unbalance development. consequently, the growth of malang urban region has only been in particular location, which may cause inefficiency in managing the city. 5. references anselin, l., & getis, a. (1993). spatial statistical analysis and geographic information system. in m. m. fischer & p. nijkamp (eds.), geographic information systems, spatial modeling, and policy evaluations. berlin heidelberg: springer-verlag. anselin, l., et. al. (2006). geoda: an introduction to spatial data analysis. geographical analysis, 38(1), 5– 22. arta, f., & pigawati, b. (2015). the patterns and characteristics of peri-urban settlement in east ungaran district, semarang regency. geoplanning: journal of geomatics and planning, 2(2), 103–115. http://doi.org/10.14710/geoplanning.2.2.103-115 bergstrom, j. c., et. al. (2013). land use problems and conflicts: causes, consequences and solutions. routledge. guan, d., et. al. (2011). modeling urban land use change by the integration of cellular automaton and markov model. ecological modelling, 222(20-22), 3761–3772. http://doi.org/10.1016/j.ecolmodel.2011.09.009 ives, c. d., & kendal, d. 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(2014). agricultural land for urban development: the process of land conversion in central vietnam. habitat international, 41, 1–7. shi, y., et. al. (2012). landscape and urban planning characterizing growth types and analyzing growth density distribution in response to urban growth patterns in peri-urban areas of lianyungang city. landscape and urban planning, 105(4), 425–433. http://doi.org/10.1016/j.landurbplan.2012.01.017 su, s., et. al. (2011). transformation of agricultural landscapes under rapid urbanization: a threat to sustainability in hang-jia-hu region, china. applied geography, 31(2), 439–449. http://dx.doi.org/10.14710/geoplanning.3.1.23-32 rukmana and rudiarto / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 23-32 doi: 10.14710/geoplanning.3.1.23-32 32 | un-habitat. (2005). housing the poor city in asia. project report. nairobi. webster, d., & muller, l. (2009). peri-urbanization: zones of rural-urban transition. human settlement development-volume i, 280. zhang, y., et. al. (2015). responses of soil respiration to land use conversions in degraded ecosystem of the semi-arid loess plateau. ecological engineering, 74, 196–205. http://dx.doi.org/10.14710/geoplanning.3.1.23-32 | 143 geoplanning vol 4, no. 2, 2017, 143-156 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.143-156 a gis-based tsunami evacuation model considering land cover and spatial configuration (case of purworejo regency, indonesia) f. f. hakim a,b, w. t. de vries a, f. siegert a, j. a. sjahbana c a technische universität münchen, germany b ministry of public works and housing, indonesia c diponegoro university, indonesia abstract: in indonesia, several programs have dealt with tsunami mitigation, such as the german-indonesian tsunami early warning system (gitews) project (2005-2011). despite the success of these projects, many coastal areas in indonesia are still vulnerable to tsunamis, due to the variety of land cover and spatial configuration characteristics. one of such vulnerable areas includes purworejo regency. this paper evaluated the degree to which land cover and spatial configuration characteristics influence the tsunami evacuation process, and thus influence tsunami hazard mitigation. the evaluation drawn on data from a low to medium density populated coastal area of purworejo regency. the analysis relied on a quantitative approach, using a cross-sectional field survey, followed by a gis-based analysis. this is complemented by a raster-based analysis to incorporate the land cover and spatial configuration aspects. the combined analysis derived which buildings could act as evacuation buildings in case of a tsunami. the associated tsunami evacuation routes were calculated using a least cost path (lcp) analysis method. the results suggested that several public facility buildings are likely to be used as tsunami evacuation buildings. yet, even though the overall capacity of these buildings is adequate to accommodate the estimated number of evacuees in a larger area, the specific demand at certain locations in the study area is much higher than these localities can handle. this disproportionate spatial variation in required capacity needs further attention. moreover, the survey responses indicated that the majority of the respondents was not well informed regarding the tsunami evacuation procedures. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): hakim, f. f, et. al. (2017). a gis-based tsunami evacuation model considering land cover and spatial configuration (case of purworejo regency, indonesia). geoplanning: journal of geomatics and planning, 4(2), 143-156. doi:10.14710/geoplanning.4.3.143-156 1. introduction a tsunami is a particularly disastrous natural hazard. the threat of this natural disaster remains hidden until it is triggered by an earthquake on the seabed. the degree of devastation of tsunamis has been shown by the aceh tsunami on 26th december 2004, which shattered 413 km2 in the aceh province’s coastal area alone (samek, skole, & chomentowski, 2004), whilst also affecting many other parts of coastal areas around the indian ocean. the aceh tsunami disaster initiated several programs and activities to support tsunami mitigation in indonesia. one of the most noticeable examples concerned gitews, the german-indonesian tsunami early warning system project (2005-2011), later known as inatews (indonesia tsunami early warning system). despite the proclaimed success of the project (münch, rudloff, & lauterjung, 2011), its implementation is not considered completed (agency for meteorology climatology and geophysic, 2010). at local level, gitews has enabled tsunami hazard zone mapping through detailed tsunami inundation modelling in three different locations: kuta (bali), padang (west-sumatra), and cilacap (central java) (gayer et al., 2010). meanwhile, many other areas were assessed using empirical methods to classify coastal area hazard classes article info: received: 20 november 2016 in revised form: 11 july 2017 accepted: 26 august 2017 available online: 30 august 2017 keywords: tsunami evacuation, land cover, spatial configuration, least cost path corresponding author: febri fahmi hakim ministry of public works and housing, jakarta, indonesia email: febrifahmi@pu.go.id open access https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 144 | to be used as a basis for developing a tsunami hazard risk map (sturnz et al., 2011). as of 2009 the purworejo regency became part of the gitews project area, which resulted in a tsunami hazard risk maps for purworejo as well. however, a closer look at the tsunami hazard risk map and the tsunami evacuation map produced for purworejo regency revealed that the level of detail is limited. moreover, detailed studies on tsunami hazard risk in indonesia mostly focus on areas which include either critical infrastructures or highly developed urban areas (budiarjo, 2006; dewi, 2010; fakhrurrazi & nes, 2012; gayer et al., 2010; sturnz et al., 2011). as a consequence, regular settlement areas often receive less attention in these risk mapping endeavours, although most coastal areas of west sumatera and the southern part of java are particularly prone to tsunamis. therefore, many villages in tsunami-prone coastal areas in indonesia with different land cover and spatial configuration characteristics are still vulnerable, including the purworejo regency. the suggestion that land cover and spatial configuration characteristics influencing tsunami evacuation process is not a novel idea on its own. gayer et al. (2010) connected land cover of densely populated coastal areas to tsunami inundation extents. wood (2009) related land cover with community assets, implying that more developed urban areas would have higher and more significant capacity for evacuees. wood & schmidtlein (2012) analyzed the tsunami evacuation of the pedestrian on the different land cover by incorporating the speed conservation value of each land cover classes. fakhrurrazi & nes (2012) examined the influence of the spatial configuration in a tsunami evacuation process regarding aceh tsunami in 2004, from an architectural perspective. both studies suggested that a different land cover and spatial configuration characteristics of the tsunami-prone coastal areas could influence the tsunami evacuation process to the safe zone. nevertheless, there are limited studies which examine the land cover aspect and directly relate it to the spatial configuration issue on a tsunami evacuation context of the low to medium density populated tsunami-prone coastal area. previous studies examined either impact of the land cover or the impact of spatial configuration, as two independent aspects. gayer et al. (2010) focused on the use of a detailed roughness map to develop tsunami inundation model for three pilot areas (i.e. padang, cilacap, and kuta). kaiser et al. (2011) studied the influence of land cover roughness to the tsunami inundation which shows the influence of dense vegetation and the built environment on tsunami flow velocities. romer et al. (2012) investigated the use of remote sensing techniques and data for determining the ground elevation and land cover information for tsunami hazard assessment. kaiser et al. (2013) examined the result of tsunami wave flows on land cover and the corresponding ecosystem. meanwhile, among the limited study, lonergan (2011) focused on the spatial configuration aspect by employing visibility analysis considering topographical elevation and land cover for an optimum placement of tsunami evacuation signs. the aim of this paper was to evaluate the degree to which land cover and spatial configuration together influence the options for the tsunami evacuation process, and the decisions in relation to tsunami hazard mitigation in a low to medium density populated coastal area in purworejo regency. the paper has the following sequence: the subsequent section introduces the profile of the study area, followed by the research method. the next section discusses the gis-based tsunami evacuation model considering land cover and spatial configuration aspects for purworejo regency, which comprises the identification of potential tsunami evacuation buildings (pteb) and potential tsunami evacuation routes (pter) in the study area. the last section provides general conclusions and a number of practical recommendations. 2. data and methods 2.1. study area the study area is located in the southern part of java island, facing the java trench in the indian ocean. the study area consisted of 6 (six) villages in the coastal regions of purworejo regency, namely: harjobinangun (grabag sub-district), keburuhan, awu-awu, depokrejo, kumpulsari, and kaliwungu kidul (ngombol sub-district). the study area was selected for its geographical features as compared to other coastal areas along the coastline of purworejo regency, and the historical occurrences of tsunamis in the region. figure 1 illustrates the study area. https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 | 145 figure 1. the study area (base map source from big and srtm 1-arc second (left). bathymetry data etopo2 is from gebco (bottom-right). google earth imagery (top-right)) the coast of the study area is plain beach, with a slight elevation from mean sea level (msl). the beach is the lowest part which has an elevation of about 1-2 meters above msl. around 500 meters away from the coastline, there is a long sand dune formation parallel to the shore. but since 2000’s, the villagers started to convert many parts of the beach as shrimp farms, nowadays one can find shrimp farms everywhere near the beach, both in the east and western part of the river. mangrove trees exist in some spots along the beach on the east of jali river, but these are limited in number. on the western part of jali river, there is the formation of tall trees parallel to the coastline with a strip width of around 50 meters. the land cover and land use in the study area is dominated by the agriculture and residential area (pemerintah kabupaten purworejo, 2011). close to the coastline, the dominating land use is shrimp farms, and rain-fed agriculture. near the rain-fed agricultural area and shrimp farms in the eastern part of the river, the land cover is characterized by the presence of a built environment cluster, which is part of keburuhan village. since the study area is not a downtown area, the spatial configuration of the study area is not as dense as the nearby urban area, kutoarjo, in the north direction. the distribution of buildings in the study area mostly follows the road networks; either the primary road network passes the area or the road network in the village. in between buildings there still vast areas of greeneries with big trees. 2.2. methods and dataset this study focused on the use of mostly free and open source software to support the analysis stage, to encourage the local disaster management stakeholders with a tight operational budget to use the methods described in this study. in the tsunami evacuation modeling stage, five data types were used: numerical data, raster data, vector data, qualitative data (including photos and non-numerical information collected during the field visits) and interview responses. the numerical data consisted of demographic data of the study area; the time series data of the tsunami wave propagation (result of the tsunami propagation simulation), and the data resulted from the semi-structured interviews using a questionnaire. the interviews were conducted to support the building inventory process, to examine understanding of respondents of spatial configuration (i.e. by giving questions which contain a pair of satellite images with five marks to be selected according to its corresponding environment in the form of perspective sketches), https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 146 | and to assess their vehicle ownership and evacuation transport preferences. the population data was collected in 10 participatory gis (p-gis) mapping sessions with 13 key respondents who were village officers in the study area. the time series data of the tsunami wave propagation were obtained from the tsunami simulation in hakim (2016). meanwhile, the tsunami source model used in the tsunami simulation stage was derived and adapted from kongko and hidayat (2014). the raster data consisted of landsat 7 etm+ slc-off, which is used in the land cover analysis stage; the gridded bathymetry data (30-arc second) which was downloaded from gebco website (http://www.gebco.net); and, the srtm (1-arc second) dataset which was downloaded from (http://eros.usgs.gov). the latter datasets were used in the tsunami propagation, run-up, and inundation simulation stage. meanwhile, the vector data were obtained from the indonesian geospatial information agency and the local development agency of purworejo regency. the tsunami simulations stage was carried out in easywave and anuga using the method described in babeyko (2012) and roberts et al. (2015). the landsat imageries merged following the gap filling method described in usgs (2004). then, the land cover classification (supervised) was carried out in monteverdi before the tsunami run-up and inundation simulation conducted in anuga, to determine the friction values applied for each land cover class in the model. the tsunami evacuation modeling was carried out in qgis 2.82 wien. first, the potential tsunami evacuation buildings (pteb) were identified, by overlaying the tsunami prone area map resulted from the tsunami simulation stage. the service area of each pteb was also examined by incorporating the cell-cross time (cct) concept (juliao, 1999) by considering the tsunami evacuation time constraint resulted from the tsunami simulation. after this process, the potential tsunami evacuation routes (pter) was identified using the least cost path (lcp) method. the inverse speed conservation value (scv) (wood & schmidtlein, 2012) and the inverse sky view factor (svf) (grimmond et al., 2001) were used as a proxy for developing a cost raster layer. the spatial configuration analysis was carried out by calculating two-dimensional cumulative isovist of each potential tsunami evacuation route. the least cost paths resulted were simplified to one evacuation path for each group of paths to certain potential tsunami evacuation building, which represents the longest and the most used evacuation paths used by the evacuees. the line was then converted into points in a regular interval (100 m) as a base for generating the individual isovist for each point. the resulted isovist were merged to calculate the cumulative isovist for each potential tsunami evacuation route. 3. result and discussion 3.1. a gis-based tsunami evacuation model for purworejo regency there are several available tsunami evacuation models which could be applied in the study area. these models are often grouped into several types, such as traffic model, evacuation behaviour model, and timeline/critical path management model. in this study, we considered two approaches for gis-based tsunami evacuation modeling: a vector-based approach (budiarjo, 2006; dewi, 2010), and a raster-based approach (mück, 2008; wood & schmidtlein, 2012). the raster-based model was selected as the basis for tsunami evacuation modeling in this study, to be able to accommodate the land cover and the spatial configuration consideration into the model. since the slope is not considered in this model, the accumulated cost (isotropic) geo-algorithm was used to represent the same weight in all directions. the cct concept was combined with the scv concept in order to derive the service area map of each pteb in the study area. the lcp method was the used to identify the pter for the tsunami evacuation. in addition, the spatial configuration analysis was done by calculating the cumulative isovist of each pter to assess the performance of each pter regarding the visibility in a tsunami evacuation context. 3.2. the incorporation of land cover and spatial configuration in the tsunami evacuation model the land cover factor was incorporated into the tsunami evacuation model by applying the cct concept combined with the inverse scv concept. the spatial configuration aspects are integrated into the https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 | 147 model by using the inverse svf concept. the cct map derived from the evacuee walking speed standard described in fema (2008) (see table 1). this walking speed standard was then used to calculate what juliao (1999) calls the “cell crossing time” (cct). the cell crossing time (cct) is the time needed to pass through one raster cell in a gis environment, which represents the time required to travel a particular distance in a real environment. in a gis environment, this particular range is the same as the cell size (e.g. raster layer in qgis with a cell size of 1 unit on the map which is projected in utm projection, will have 1-meter distance for each cell). by utilizing the cct in combination with the scv concept, we could develop a cost surface raster layer containing value regarding the time spent to cross one raster pixel in different land cover classes. only then we could generate the accumulated cost surface layer to analyze the catchment/service area of each pteb, based on the total time needed to go to the pteb from any location in the tsunami prone area. table 1. the evacuee walking speed standard (fema, 2008) warning time walking speed travel distance teb spacing 2 hours 2 mph (0.89 m/s) 4 miles (6.4 km) 8 miles (12.8 km) 30 minutes 2 mph (0.89 m/s) 1 mile (1.6 km) 2 miles (3.2 km) 15 minutes 2 mph (0.89 m/s) ½ mile (0.8 km) 1 miles (1.6 km) the least cost path analysis focuses on land cover constraints with two costs aspects: “speed conservation value (scv)”, and “sky view factor (svf)”. speed conservation value represents the amount of speed conserved during a movement in a different land cover classes (wood & schmidtlein, 2012). the higher the scv, the higher the evacuee movement speeds on that particular land cover class. meanwhile, the sky view factor represents the amount of the sky opening above the observer in different surrounding environments (grimmond et al., 2001), which could also imply the amount of natural light (e.g. daylight from the sun, or natural light during the night from the moon) which penetrates the different land cover classes. the higher the svf in one location, the greater the amount of natural light penetrates this place. both factors share the same numeric scale, between 0 and 1, which is suitable to be used in the least cost path analysis. however, in this research, the value will be inverted to represent the inverse scv and inverse svf value, to be able to compute the least-cost path correctly. it means that the higher the inverse scv and the inverse svf value in certain pixels, the higher the cost to pass through those pixels. the scv value used in this research is based on the scv value developed in soule & goldman (1972), and further used by wood and schmidtlein (2012), as an interpretation of national land cover database (nlcd) into a set of coefficients of energy cost prediction. the nlcd itself is a land cover map product of the us government developed by the multi-resolution land cover characteristics (mlrc) consortium (usgs, 2015). in this study, we used the inverse scv value to represent the movement cost/impedance of different land cover classes. similarly, we also used the svf value indirectly, by changing the svf value to reflect the opposite condition using the similar method used for scv. the svf value utilized in this analysis is based on the work of grimmond et al. (2001), who conducted an observation of the svf in a small city bloomington in the united states. the inverse scv and inverse svf values used in this analysis, and the corresponding scv and svf value of the original research, are described in table 2 and table 3. 3.3. the pteb and the corresponding pter in the study area the nominee of the pteb is selected from the public facility and social facility buildings in the study area. these structures are chosen since public service building usually has several advantage compared to the private housing. first, the public facility buildings usually have wider floor area with an open plan space which is suitable to be used as temporary evacuation space. furthermore, public service buildings such as school often equipped with a large yard where the temporary shelter can be build. this building is also often equipped with the required service areas (e.g. clean water, sanitation, kitchen, and so on). twenty public facility or social facility buildings are nominated as the pteb. however, after these building’s location (which is represented by point layer in qgis) overlaid with the tsunami prone area map, https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 148 | there are only nine buildings located in the safe areas. therefore, only these nine buildings are potentially to be functioned as tsunami evacuation buildings. figure 2 shows the selected pteb in the study area. meanwhile, the service area for each pteb was calculated using the 30 minutes’ tsunami evacuation time constraints resulted from the tsunami simulation in easywave (hakim, 2016). since the service area map of each pteb derived from the cct and scv alone is overlapping each other, the voronoi polygon is used to divide the service area within the 30 minutes’ evacuation timeframe. the voronoi diagram represents the area where all points within this field are closer to the point which is predefined as its center, compared to other center points (menke et al., 2015). that is why the voronoi diagram is often used to define the service area of particular center points such as a public facility. table 2. the inverse scv value used in the gis analysis (soule & goldman, 1972; wood & schmidtlein, 2012) nlcd categories in wood & schmidtlein (2012) soule and goldman’s surface categories scv value in wood & schmidtlein (2012) land cover category in this research inverse scv roads blacktop 1 road network 0 open water none 0 river 1 developed, open space dirt road 0.9091 developed, low intensity dirt road 0.9091 developed, medium intensity dirt road 0.9091 settlement, medium density 0.0909 developed, high intensity dirt road 0.9091 settlement, high density 0.0909 barren land hard sand 0.5556 deciduous forest light brush 0.8333 evergreen forest light brush 0.8333 vegetation, medium density 0.1667 mixed forest light brush 0.8333 shrub/scrub heavy brush 0.6667 vegetation, high density 0.3333 grassland/herbaceous light brush 0.8333 beach 0.1667 pasture/hay light brush 0.8333 cultivated crops light brush 0.8333 agriculture (irrigated/rain fed) 0.1667 woody wetlands swampy bog 0.5556 emergent herbaceous wetland swampy bog 0.5556 table 3. the inverse svf used in this study (adapted from grimmond et al., 2001) land use categories in grimmond et al. (2001) mean svf value in grimmond et al. (2001) land cover types in this research inverse svf none river assumed to be 1.0 downtown 0.83 settlement, high density; vegetation (medium/high density 0.17 single-family residential 0.85 settlement, medium density 0.15 estate residential 0.87 institutional 0.88 multi-family residential 0.91 parks/open space 0.92 agriculture (irrigated/rain fed); cropland 0.08 manufactured housing 0.93 vacant 0.93 beach 0.07 commercial 0.94 industrial 0.97 https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 | 149 figure 2. the potential tsunami evacuation buildings (pteb) in the study area (analysis, 2016) the calculation results of the capacity of each pteb compared to the potential evacuees from each service area shows that the capacity of six ptebs could meet the demand while three others are over capacity. meanwhile, for the night time scenario, four ptebs are adequate in accommodating the evacuees, but five others are over capacity. however, the calculation of the total capacity of the ptebs and the total evacuees shows that the capacity of the ptebs is adequate to shelter the evacuees. the result indicates a disproportion of tsunami evacuation capacity in the study area, which needs further attention. therefore, some ptebs are needed to be improved to be able to meet the number of the potential evacuees within its service area. the calculation results are presented in table 4. table 4. the number of evacuees served by each pteb (analysis, 2016) pteb adjusted pteb service area (sq. km) potential evacuees within service area the capacity of the pteb (after adjustment) the evacuees that are not accommodated day night day night day night pteb 6 1.54 771 910 357 357 414 553 pteb 7 1.53 247 324 108 88 139 236 pteb 12 1.3 513 657 142 122 371 535 pteb 13 0.22 19 57 294 294 (-275)* (-237)* pteb 14 0.86 111 272 726 856 (-615)* (-584)* pteb 15 0.39 32 90 53 68 (-21)* 22 pteb 18 0.38 40 95 361 361 (-321)* (-266)* pteb 19 0.43 99 161 756 886 (-657)* (-725)* pteb 20 1.23 143 385 307 287 (-164)* 98 t o t a l 7.88 2007 2951 3104 3319 924 1444 notes: * indicates a surplus in capacity. the surplus of pteb’s capacity for the day time scenario is 2053 while the night time scenario is 1812. compared to the unaccommodated evacuees, the surplus is 1129 (daytime scenario) and 368 (night time scenario). however, this result suggests the disproportion of pteb capacity versus the demand. https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 150 | the corresponding pter was then identified using the lcp method as previously described. the additional spatial configuration analysis was carried out by applying the view shed analysis on each pter, which is resulted in the lcp analysis. after this process, the performance of each pter was then examined regarding the spatial configuration aspects, from the architectural perspectives. the least cost path analysis relied on the shortest path algorithm developed by dijkstra (1959), finding the shortest path between a source point and a destination point (schmidtlein & wood, 2015). in a gis environment, dijkstra’s algorithm could be carried out on both vector and raster layer. in the raster-based analysis, like in lcp analysis, the calculation is conducted for each raster cell, from the source raster cell to the destination cell by continuously moving around the moore neighborhood. a moore neighborhood is the area of eight raster cells, which are the nearest neighbor to the center cell (schiff, 2011). in the lcp analysis, the algorithm iteratively evaluates the value of the cells in a moore neighborhood and selects the cells which have the lowest cost in that cells neighborhood, in the predefined accumulated costs raster layer. the lcp analysis computes the least cost distance between the destination points and the source points (figure 3). by incorporating the land cover and spatial configuration aspects into the cost surface layer, we can model the influence of land cover and spatial configuration to the selection of the potentially the most preferable path to travel through during a tsunami evacuation. the hexagonal tessellation is adapted from budiarjo (2006) and dewi (2010) to simplify the evacuee source points in the model. the analysis result shows there are 221 least cost paths resulted from the source points (hexagon centroids) to the destination ptebs. the result is then exported into a spreadsheet file and analyzed. in a spreadsheet program, these paths are categorized into two different categories regarding the travel time, namely: “safe” path, and “not safe” path. the safe path category represents the paths which have less than 30 minutes travel time, based on the standard evacuee speed described in fema (2008). figure 3. the least cost path between the evacuee source points and the pteb (analysis, 2016) https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 | 151 figure 4. the travel time attribute of each least cost path (analysis, 2016) the result shows 28 paths categorized as “not safe”, and 193 paths categorized as “safe” (figure 4). however, these 28 paths share a common line and direction to the ptebs. the difference is that the former are longer to travel through by the evacuees than the latter. this difference is usually due to some building blocks which are located closer to the coastline and has a greater distance to the pteb. the road space utilization within the 30 minutes evacuation timeframe is presented in figure 5. figure 5. maximum ratio of road space used at the road segment passed by the maximum number of evacuees (analysis, 2016) the view shed analysis is carried out to examine the performance of each pter. this process was done by analyzing the cumulative binary view shed or isovist to model how much the observer (i.e. the evacuee) would see the surrounding environment when he/she moves along the particular pter. the model is the simplified version of the reality, considering only the position of the evacuee on some points with 100 meters spacing along the path. by knowing the cumulative isovist of each pter, we could examine the location along the pter which potentially raise confusion among the evacuees during the tsunami evacuation process, due to the spatial configuration aspect. figure 6. the visibility profile (cumulative binary viewshed) of each pter (analysis, 2016) https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 152 | figure 6 shows the influence of the spatial configuration in the surroundings of each pter. the displayed routes represent a ratio value of cumulative pter isovist to full cumulative isovist of the same path but without obstruction of more than 0.8. this means that the evacuees would have quite a good visibility when they proceed for the tsunami evacuation through these pter (see table 5). however, these are the calculation of the cumulative isovist and not the individual isovist. although the overall visibility of the pter can thus be considered quite well, there is also a possibility that in some locations or intersections, the visibility drops with almost 50 percent. this number implies that in assessing the pter, a disaster manager should also determine the presence of such spatial configuration along the pter, in order to ensure the appropriate intervention is done in this particular area. table 5. the comparison of pter isovist with the full cumulative isovist on the same path but without obstruction (analysis, 2016) pter length of path (m) isovist area (cumulative) (pter isovist) (m2) cumulative isovist area of the same length without obstruction ratio of pter isovist to cumulative isovist of the same length without obstruction pter 1 2200 375973.796 413002.715 0.91 pter 2 1180 189328.2131 229051.0061 0.83 pter 3 1500 266260.4612 298929.1825 0.89 pter 4 1700 279790.8475 327655.906 0.85 pter 5 1400 258468.1716 277937.5665 0.93 pter 6 300 77606.5676 86455.2637 0.90 pter 7 1600 319254.0109 339612.4514 0.94 3.4. field validation the field survey enabled the collection of demographic data, which derived the building inventory as well. alongside this information the responses revealed is the ability of people in how each interprets and connects the spatial information contained in a map or in a satellite images, and how they relate it to the corresponding actual environment (the example of images used in the question regarding spatial configuration is illustrated in figure 7). this crucial insight shows how people understand maps in the context of a tsunami evacuation process, and this helps how disaster managers can deal with this aspect. figure 7. the example of pair image used in the question regarding spatial configuration aspect (analysis, 2016) https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 | 153 the field survey yielded a variety of insights in spatial understanding and associated choices in behavior. most of the respondents had a basic degree of spatial awareness. however, around 70 percent of the respondents correctly answered only three out of ten questions related to this aspect, while only 2.1 percent respondents can answer 80% of these questions correctly. these results suggest that spatial awareness is clearly different from working consistently with maps and deriving decisions on this. a further look into the response time of the interviewees to the spatial configuration questions (figure 8) shows an interesting trend. after answering several questions, the trend shows the decreasing average response time in answering the questions in spatial configuration aspect. however, since the number of correct answers is very low compared to the wrong answer in this type of question, there is a possibility that the small response time does not result from the better respondent’s understanding of the questions. figure 8. the response time of the respondents on spatial configuration questions (analysis, 2016) there are several reasons which might explain the result of the survey. first, the resolution of the satellite imagery which is use in the question is not high. consequently, respondents could have difficulties in differentiating between buildings and other features such as road, shrimp ponds, or difficulties in differentiating the road intersections due to the limited sharpness of the satellite imagery. the second possibility is that the questions in this aspect could be too difficult to grasp by the respondents, either due to the nature of the questions which only present images and symbols with the minimum description, or due to the unfamiliarity of the respondent to the map and satellite imagery. regardless, this finding suggests that there is a difficulty among the respondents in creating a connection between a map or satellite imagery and the corresponding environment. this difficulty could create a problem since map or satellite imagery is used extensively by the government in the dissemination of disaster management planning information to the general public. the use of map or satellite imagery as a tool for communicating the disaster management planning information should present in a way that is easy to read and understand by the general public. besides that, the use of these tools should accompany by the increasing level of people’s understanding of the tools itself, especially if the tools are used for the local villagers. the field survey result on the different aspect also indicates the low level of people’s understanding on disaster mitigation (figure 9). based on the field survey results, around 65 percent of the respondents seems to have limited understanding regarding the tsunami mitigation planning at the local government level, especially the information related to the location of the tsunami evacuation building/shelter, even though the general information regarding this aspect is already adopted in the local development planning document, while the more detailed information is already adopted in the draft of local contingency planning documents. the other 35 percent of the respondents answered the question but give various answers. this finding could suggest either the lack of people’s curiosity towards the local disaster https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 154 | management planning information or the lack of local government efforts in disseminating the issue of disaster preparedness to the smallest unit in the community. figure 9. result of survey regarding spatial configuration understanding and tsunami evacuation aspect (analysis, 2016) in the context of disaster management planning, the result implies a need for a new method of communicating the disaster-related information to the villagers. less formal dissemination materials such as comic, graphic booklet, photo book, or audio book (i.e. for people with disability), instead of a map full of symbols could be easier to read and comprehend by the local villagers. other than that, the dissemination of disaster management planning information on a regular basis is essential to build public awareness on disaster mitigation continuously. integrating the disaster management knowledge as a part of the course at the school could also be an option. 4. conclusion current methods of tsunami-related disaster evacuation preparation predominantly focus on infrastructure and urban centers. in order to include more rural areas and smaller cities, alternative methods are needed. the selected gis-based tsunami evacuation model in this research relies on a least cost path method, which is a raster-based model. the model is selected for its flexibility, especially when used to represent tsunami evacuation for different land cover classes and spatial configuration. the rasterbased approach is considered more appropriate for modeling tsunami evacuation processes in a less to the medium developed urban area such as the study area. it is in any case better than the vector-based approach, which is developed for highly developed urban areas. the land cover aspect and spatial configuration aspect is shown to have influence on the tsunami evacuation process. land cover aspect influences especially the accessibility of each pteb in terms of evacuation time whilst the spatial configuration aspect influences the visibility profile along the pter. even though the general visibility of the tsunami evacuation route is good, there is a possibility of a two dimensional visibility drop in certain junctions or nodes. the test results also show that there is a disproportionate distribution of the tsunami evacuation capacity in the study area, which needs to be 3% 24% 27% 44% 1% vehicle ownership have no vehicle have 1 vehicle have 2 vehicles have 3 vehicles have 4 vehicles 4% 19% 33% 15% 15% 8% 2%1%2% spatial understanding 0% correct answer 10% correct answer 20% correct answer 30% correct answer 40% correct answer 50% correct answer 60% correct answer 70% correct answer 80% correct answer 9% 5% 53% 33% selected evacuation transport bicycle car motorcycle running 65% 1% 21% 3% 3% 7% information on evacuation shelter doesn't know know/closest town know/higher ground know/mosque know/village field know/village office https://doi.org/10.14710/geoplanning.4.2.143-156 hakim et al. / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 143-156 doi: 10.14710/geoplanning.4.2.143-156 | 155 redressed. some ptebs need to be improved in order to cater for the number of the potential evacuees within its service area. given these results, there are several recommendations both for the local government side, the disaster manager working closely with the people, and the community which is living in the tsunami-prone coastal areas. the recommendations include (1) the adapted gis-based tsunami evacuation model could be used by the local disaster managers to enrich the information regarding the tsunami threat in the purworejo regency in a spatial planning document. it is valuable material for a capacity assessment, both for local government officers, disaster managers, and the community. (2) local governments which include tsunami mitigation as a priority in their development planning document have the opportunity to replicate the gisbased tsunami evacuation model applied in this work, by following the workflow provided in this study. (3) the government could improve the existing public facility buildings which are targeted to be used as a vertical tsunami evacuation building in tsunami-prone coastal areas to anticipate future tsunami threats. (4) disaster managers could use the new approach to disseminate information about tsunami hazard risks. this is possible for example by using more graphic materials such as comic books, graphic booklets, photo books, or audio books (i.e. for people with disability), instead of relying on a map full of symbols. this variety could be easier to read and comprehended by the local villagers. (5) there is an urgent need to involve the community in disaster mitigation efforts, in order to build community awareness about disaster risks. 5. acknowledgments i would like to express my gratitude especially to the ministry of public works and housing of the republic of indonesia, for the scholarships and funding support for this research. 6. references agency for meteorology climatology and geophysic. (2010). inatews: indonesia tsunami early warning system concept and implementation. jakarta. babeyko, a. (2012). easywave: fast tsunami simulation tool for early warning. budiarjo, a. (2006). evacuation shelter building planning for tsunami-prone area; a case study of meulaboh city, indonesia. itc enschede. dewi, r. s. (2010). a gis-based approach to the selection of evacuation shelter building and routes for tsunami risk reductiona case study of cilacap coastal area, indonesia. university of twente. dijkstra, e. w. 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(2011). two-dimensional automata. in cellular automata: a discrete view of the world (p. 272). john wiley & sons. schmidtlein, m. c., & wood, n. j. (2015). sensitivity of tsunami evacuation modeling to direction and land cover assumptions. applied geography, 56, 154–163. [crossref] soule, r. g., & goldman, r. f. (1972). terrain coefficients for energy cost prediction. journal of applied physiology, 32(5), 706–708. sturnz, g., et. al. (2011). tsunami risk assessment in indonesia. natural hazards and earth system sciences, 11, 67–82. [crossref] usgs. (2004). filling the gaps to use in scientific analysis. usgs. (2015). us land cover. wood, n. (2009). tsunami exposure estimation with land-cover data: oregon and the cascadia subduction zone. applied geography, 29(2), 158–170. [crossref] wood, n. j., & schmidtlein, m. c. (2012). anisotropic path modeling to assess pedestrian-evacuation potential from cascadia-related tsunamis in the us pacific northwest. natural hazards, 62(2), 275– 300. [crossref] https://doi.org/10.14710/geoplanning.4.2.143-156 https://doi.org/10.5194/nhess-11-765-2011 https://doi.org/10.5194/nhess-12-2103-2012 https://doi.org/10.1016/j.apgeog.2014.11.014 https://doi.org/10.5194/nhess-11-67-2011 https://doi.org/10.1016/j.apgeog.2008.08.009 https://doi.org/10.1007/s11069-011-9994-2 doi: 10.14710/geoplanning.4.2.143-156 copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): hakim, f. f, et. al. (2017). a gis-based tsunami evacuation model considering land cover and spatial configuration (case of purworejo regency, indonesia). geoplanning: journal of geomatics and planning, 4(2), 143-156. doi:10.14710/geoplanning.4.3.143-156 1. introduction a tsunami is a particularly disastrous natural hazard. the threat of this natural disaster remains hidden until it is triggered by an earthquake on the seabed. the degree of devastation of tsunamis has been shown by the aceh tsunami on 26th december ... the aceh tsunami disaster initiated several programs and activities to support tsunami mitigation in indonesia. one of the most noticeable examples concerned gitews, the german-indonesian tsunami early warning system project (2005-2011), later known a... keywords: tsunami evacuation, land cover, spatial configuration, least cost path corresponding author: febri fahmi hakim ministry of public works and housing, jakarta, indonesia email: febrifahmi@pu.go.id however, a closer look at the tsunami hazard risk map and the tsunami evacuation map produced for purworejo regency revealed that the level of detail is limited. moreover, detailed studies on tsunami hazard risk in indonesia mostly focus on areas whic... the suggestion that land cover and spatial configuration characteristics influencing tsunami evacuation process is not a novel idea on its own. gayer et al. (2010) connected land cover of densely populated coastal areas to tsunami inundation extents. ... nevertheless, there are limited studies which examine the land cover aspect and directly relate it to the spatial configuration issue on a tsunami evacuation context of the low to medium density populated tsunami-prone coastal area. previous studies e... the aim of this paper was to evaluate the degree to which land cover and spatial configuration together influence the options for the tsunami evacuation process, and the decisions in relation to tsunami hazard mitigation in a low to medium density pop... 2. data and methods the coast of the study area is plain beach, with a slight elevation from mean sea level (msl). the beach is the lowest part which has an elevation of about 1-2 meters above msl. around 500 meters away from the coastline, there is a long sand dune form... the land cover and land use in the study area is dominated by the agriculture and residential area (pemerintah kabupaten purworejo, 2011). close to the coastline, the dominating land use is shrimp farms, and rain-fed agriculture. near the rain-fed agr... this study focused on the use of mostly free and open source software to support the analysis stage, to encourage the local disaster management stakeholders with a tight operational budget to use the methods described in this study. in the tsunami eva... the raster data consisted of landsat 7 etm+ slc-off, which is used in the land cover analysis stage; the gridded bathymetry data (30-arc second) which was downloaded from gebco website (http://www.gebco.net); and, the srtm (1-arc second) dataset which... the tsunami simulations stage was carried out in easywave and anuga using the method described in babeyko (2012) and roberts et al. (2015). the landsat imageries merged following the gap filling method described in usgs (2004). then, the land cover cl... the tsunami evacuation modeling was carried out in qgis 2.82 wien. first, the potential tsunami evacuation buildings (pteb) were identified, by overlaying the tsunami prone area map resulted from the tsunami simulation stage. the service area of each ... 3. result and discussion 4. conclusion 5. acknowledgments 6. references | 1 geoplanning vol. 6, no. 1, 2019, 1-12 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.1.1-12 evaluation of modis-derived lst products with air temperature measurements in cyprus a. m. georgiou a , s. t. varnava b a enalia physis environmental research center (enalia), acropoleos 2, 2101 aglanzia, nicosia, cyprus b department of geography, university of the aegean university hill, mytilene 81100, greece abstract: air temperature data is usually obtained from measurements made in meteorological stations, providing only limited information about spatial patterns over wide areas. the use of remote sensing data can help overcome this problem, particularly in areas with low station density, having the potential to improve the estimation of air surface temperature at both regional and global scales. land surface (skin) temperatures (lst) derived from moderate resolution imaging spectroradiometer (modis) sensor aboard the terra and aqua satellite platforms provide spatial estimates of near-surface temperature values. in this study, lst values from modis are compared to ground-based near surface air (tair) measurements obtained from 4 observational stations during 2011 to 2015, covering coastal, mountainous and urban areas over cyprus. combining terra and aqua lst-8 day and night acquisitions into a mean 8-day value, provide a large number of lst observations and a better overall agreement with tair. comparison between mean monthly lsts and mean monthly tair for all sites and all seasons pooled together yields a very high correlations (r > 0.96) and biases ranging from 1.9oc to 4.1oc. modis capture overall variability with a slightly systematic overestimation of seasonal fluctuations of surface temperature. for the evaluation of intra-seasonal temperature variability, modis showed biases up to 6.7oc in summer with a tendency to overestimate the variability while in cold seasons, limited biases were presented (0.10oc ± 0.50oc) with a tendency to underestimate the variability. finally, there was no indication of tendency for modis to systematically underor overestimate the amplitude of the inter-annual variability analysis. the presented high standard deviation can be explained by the influence of surface heterogeneity within modis 1km2 grid cells, the presence of undetected clouds and the inherent difference between lst and tair. overall, modis lst data proved to be a reliable proxy for surface temperature and mostly for studies requiring temperature reconstruction in areas with lack of observational stations. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite: georgiou, a., & varnava, s.t. (2019). evaluation of modis-derived lst products with air temperature measurements in cyprus. geoplanning: journal of geomatics and planning, 6 (1), 1-12. doi:10.14710/geoplanning.6.1.1-12. 1. introduction land surface temperature (lst) consist a key parameter in the physics of land surface processes as controls the exchange of long wave radiation between surface and atmosphere at local through global scales (li et al., 2013; anderson et al., 2008). it is an important climatological variable (ermida et al., 2014) as well as diagnostic parameter of land surface conditions. it plays a crucial role in the surface energy balance, and as such it has been used in evapotranspiration (anderson et al., 2008; kustas and norman, 1996), to infer surface heat fluxes (caparrini et al., 2004), to desertification processes (wan et al., 2004), soil moisture (nemani et al., 1993) vegetation properties (ermida et al., 2014), near-surface meteorology (anderson et al., 2008; crosson et al., 2012) and weather forecasting (vancutsem et al., 2010). meteorological measurements provide accurate temporally discrete temperature information but have limited ability to describe its spatial heterogeneity over large areas. compensation for this paucity of information may be obtained my various methods of interpolation between known sites. even these methods proved to be successful in estimating temperatures near meteorological stations with low errors (florio et al., 2004; anderson, 2002; mostovoy et al., 2006), however they suffer from arbitrary location of weather stations (caparrini et al., 2004) and often lack of near real-time data accessibility. regardless of the method, interpolation accuracy is highly dependent on station network density and the scale of spatial and temporal article info: received: 19 sept 2018 in revised form: 10 april 2019 accepted: 30 april 2019 available online: 30 august 2019 keywords: modis, land surface temperature, air temperature, regression analysis, cyprus corresponding author: andreas georgiou enalia physis environmental research centre, acropoleos 2, 2101 aglanzia, nicosia, cyprus email: a.georgiou@enaliaphysis.org.cy open access http://doi.org/10.14710/geoplanning.6.1.1-12 http://doi.org/10.14710/geoplanning.6.1.1-12 https://orcid.org/0000-0002-0989-4309 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 2 | variability of the parameter (caparrini et al., 2004). moreover, the measurements are frequently interpolated with significant errors associated and often lead to unrepresentative spatial patterns (benali, et.all 2012). the use of remote sensing data can greatly improve the estimation of lst, fill the gaps on spatial and temporal information thus improving the knowledge of both climate and terrestrial biological processes at regional and global spatial scales (benali et al., 2012). in regions where the density of meteorological stations is sparse and the data is unavailable, remote sensing can be an important and valuable source information that uniquely resolve these difficulties (georgiou & akçit, 2016). the launch of the moderate resolution imaging spectroradiometer (modis) on board aqua and terra has made it possible to get spatial estimates of land surface temperature at high temporal (daily) and spatial resolution (1 km) across the world. however, satellite data first need to be validated with in situ measurements in order to determine their suitability as a proxy for ambient surface temperature in large scale environments. such validation has been carried out in several studies worldwide. yu et al. (2014) evaluates modis lst products using longwave radiation measurement data from observation sites in the northern arid region of china, while vancutsem et al. (2010) explore different approaches to retrieve high-resolution temperature from modis data over different ecosystems. some others validate satellite derived data using long-term night-time ground measurements (zhu et al., 2013) or compare modis derived land surface temperatures with ground surface and air temperatures measurements in a continuous permafrost terrain (benali et al., 2012; zhu et al., 2013; hachem et al., 2012). bernardello et al. (2016) use similar method approach for comparison remote-sensing and in situ data in near shore habitats. finally, hengl et al. (2011) present a procedure to interpolate daily mean temperature over a period by using time series of auxiliary predictors. however, up to now there is still considerable lack of relevant scientific works regarding the greater area of cyprus. the main purpose of this paper is to utilize lst level-3 8-day records from the moderate image spectrometer (modis) on-board both terra and aqua satellites to investigate the land surface temperature regime over the cyprus island and their comparison with ground-based near surface air (tair) measurements obtained from observational stations. the study will thus contribute to the assessment of annual and seasonal surface temperature variations and the identifications of any anomalies. the framework comprises a combination of already established methods and tools for lst calibration and estimation, statistics and visualization techniques. geographical information systems (gis) has been chosen as an integrated platform for analysis, modelling and presentation of the results in a combination with visual basic programming language as a maintainable and formulation tool. the rest of paper is organized as follows. section 2 is devoted to the description of the studied area and the methodological approach, the description of in situ data from the observational stations and the satellite imagery that used. in addition, the numerical model configuration and simulation set-up are presented. a presentation and illustration of the model results is given in section 3 and finally, the paper concludes with section 4 were the main results are summarized and future work aspects are discussed. 2. data and methods 2.1. study area the island of cyprus is located in the eastern part of the mediterranean sea and particular in the levantine basin. cyprus has a typical eastern mediterranean climate: the combined temperature-rainfall regime is characterized by cool-to-mild wet winters and warm-to-hot dry summers (michaelides et al., 2009). the island can be divided into five main morphological regions: (i) the mountainous complex of troodos located at the center of cyprus; (ii) the mountain range of pentadaktylos at the northern part; (iii) the central plain of mesaoria which is located between of these two mountainous ranges; (iv) the hilly areas around the mountainous complex of troodos; and (v) the coastal plains (figure 2). 2.2. methodological framework the presented methodological framework involves a coherence of several stages. more specific, the first step is to define and gather both satellite imagery and ground measurements that needed for the analysis in order to build the digital geo-database. the volume of the data must be organized, filtered and renamed in order to set up a sequence of data accordingly the derived satellite and the time that data gathered corresponding to in situ data. the next step is to calculate the statistics of the data and apply the analysis procedure and, the final step is to visualize the final results. http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 | 3 figure 1 provides a schematic diagram of the proposed gis based methodology with the operational functions that performed through it. formats for data in this figure are presented with different symbols, and are denoted at the bottom of the figure. double-lined rectangles represent the two main functions that developed: (a) the regression analysis function where the relationship among modis-derived lst and tair measurements investigated in the terms of overall and inter-seasonal variability, and (b) the regression analysis in terms of inter-annual variability in addition with the production of anomaly land surface temperatures (alst) maps. a more detailed methodology description is illustrated in next sub-section. figure 1. schematic diagram of processes performed with the developed tool the arcgistm software system was used for the process, analysis, display and quality control of the modis data. in addition, visual basic programming language was used to develop supporting tools in order to deliver robust, maintainable and scalable solutions in the analysis. the innovation of this work derives from the proposed modelling of the entire methodology, which combines different tools providing a versatile platform of analysis and semi-automation of the operation, which might also extended into fully automated. additionally, there are no similar studies over the study area that investigate the lst and compare in situ data with satellite derived imagery thus the results would be very helpful for the local science community. 2.3. ground measurements the daily tmin, tmax and tavg data measured at 1.2m above the ground for 4 meteorological stations (table 1) between 2011 and 2015. it has to be noted that only these 4 stations provide coherence measurements over study area for the examined time period. in addition, some stations are missing data due to cloudy days or malfunction of the data loggers and removed from further analysis. the tair range from -5.0 to 33.0oc with a data logger accuracy of ±0.50oc as reported by the manufacturer. the data source of the data is the department of meteorology of cyprus which is part of the organizational structure of the ministry of agriculture, rural development and environment. daily tair logger data were aggregated to obtain mean 8-day series period surface temperature in order to have comparable time steps between data loggers and satellite derived data. however, several factors http://doi.org/10.14710/geoplanning.6.1.1-12 http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 4 | may influence measurement bias; vogt et al. (1997) stated to the changes on instrumentation, station location and the surrounding environment. moreover, according to benali et al. (2012), the heterogeneity related with the observation time and the differences of data aggregation methodology at different temporal scales (e.g. hourly to daily) can have a significant impact on the quality and inter comparability of measured data. table 1. information of validation stations no station name code st.no elev. (m) days lat. long. characteristics 1 pafos (airport) pf 82 10 1815 34ο 43' 32ο 30' coastal area 2 athalassa ath 666 162 1776 35ο 09' 33ο 24' urban area 3 larnaca (airport) lca 731 1 1826 34ο 53' 33ο 38' coastal area 4 prodromos pr 225 1380 1763 34ο 57' 33ο 38' high vegetation the distribution of the meteorological stations that used highlight two coastal regions (pafos and larnaca stations), one mountainous region at an elevation of 1380m a.s.l. (prodromos station) with high vegetation that exposed to snow during winter period and one with low vegetation in urban area (athalassa station). although station density influences the spatial distribution of uncertainty it highlights the regions where the tair estimation based on remote sensing can potentially provide a higher added value. figure 2 presents the distribution of the observational stations over the island in an averaged lst base map as derived from modis/terra and modis/aqua data of 2015. figure 2. distribution of observational meteorological stations over cyprus with average lst for 2015 as a base map. information about stations can be found in table 1 2.4. modis lst data and process the lst dataset that used was derived from moderate-resolution imaging spectroradiometer (modis) instrument on board both aqua and terra sun synchronous satellites. the modis/terra and modis/aqua land surface temperature products provide per-pixel temperature and emissivity values in a sequence of swath-based to grid-based global products. the modis/terra and modis/aqua lst thermal 8-day l3 (mod11a2 and myd11a2) of spatial resolution of 1km which configured on a 0.05o latitude/longitude climate modeling grid (cmg) are used. this version of modis lst has a significant improved spatial coverage, http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 | 5 stability and accuracy when compared with previous versions (wan, 1999). this product retrieves lst with an error lower than 1oc (±0.70oc standard deviation) in the range of -10 to 50oc, assuming that the surface emissivity is known (wan, 2008). according to benali et al. (2012), the validation of the lst algorithm showed errors lower than 1oc over homogeneous surface such as water, crop and grassland surfaces. the satellite image database consists of lst 8-day data obtained from nasa ocean color web servers (ocean color web) in hierarchical data format (hdf). in order to eliminate low quality data because remote sensing based tair estimations are strongly influenced by errors on the lst retrievals, a good quality flag (ql0) was chosen which include sun glint mask and sea contaminations. the application of this flag increases the reliability of lst estimation (georgiou & akçit, 2016). a total of 920 images corresponded of a 5-year period (2011 – 2015) for day and night were used. hachem et al. (2012) suggest this combination of all modis lsts available in order to produce accurate lst maps. note that for terra satellite the ascending (day) local equatorial crossing time is 10:30 and descending (night) is 22:30 while in aqua satellite is 13:30 and 01:30 respectively. the twice-daily temporal resolution is also very important as allows to monitor the evolution of surface temperatures throughout the year. at the pixel scale, variations in topography, surface materials, vegetation and snow cover can influence the temperatures measured from space-borne thermal sensors (hachem et al. 2012). so, there is the assumption that lst values correspond to the 1km2 pixel footprint, taking into account potential geo-location errors of up to 50m for the center of the footprint and some variations in footprint area (hachem et al., 2012). these small errors can contribute to overall lst. thus, modis pixels centered to on or located close to the selected meteorological stations. the presence of water bodies of different sizes in the proximity of some stations can result a mixed modis pixel over a given station. the steps that followed for the estimation of lst from satellite data includes layer selection, radiometric correction, geo-referencing, lst calculation, land mask and cloud contamination. these steps may overlap or interact among them without estimating false values of lst (georgiou & akçit, 2016). more specific, the first step is to build the digital geo-database and rename the volume of data in order to set up a sequence accordingly the derived satellite and the time that data gathered. the next step is to select the correct layer of data, apply the mask of the study area and exclude the false values of sea (pixels contained over 75% of land are chosen) and clouds from the analysis to eliminate calculation errors. afterwards, for better visualization results data re-projected in mercator projection (georgiou & akçit, 2016). the final step is to convert the digital number (dn) values of the image into lst values. the calculation of lst values from the dn values can be calculated using eq.1. lst = a ∗ dn − k (1) where a is the scale factor (0.02), dn the digital number values and k the kelvin temperature unit (273.15) (wan, 1999). 2.5. data analysis and statistics data from the observational stations used to characterize the general spatio-temporal patterns of the study locations. then a comparison of the in situ tair time series with remote-sensing derived lst data throughout the deployment period for each location performed. differences between modis and in situ data in reproducing overall variability, inter-seasonal and inter-annual variability were quantified by calculating pearson’s correlations, mean bias, mean absolute bias and the ratio of the standard deviations (sds) for each of 4 pairs of time series (table 2). each statistic formulation that used provides a different information about the ability of modis to reproduce the variability recorded to stations. mean bias reveals the eventual tendency of modis to systematically underor overestimate in situ data; mean absolute bias gives a concise measure of the distance between the 2 time series and; the ratio of sds gives an indication of how much more (α > 1) or less (α < 1) variable modis data are with respect to stations (bernardello et al., 2016). the combined information provided by these statistic formulations, allow to evaluate particular aspects that are not revealed when considered individually. for example, when the mean bias and mean absolute bias are of a similar magnitude in absolute value, the systematic underor overestimation of modis can be corrected by that amount, as long as it is >0.50oc which is the accuracy of the data loggers in stations (bernardello et al., 2016). http://doi.org/10.14710/geoplanning.6.1.1-12 http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 6 | table 2. statistic formulations that used and symbols that adopted in the figures. m: modis; dl: data logger; n: number of days in the time series metric symbol definition pearson’s correlation r between m and dl mean bias δ 1 𝑛 ∑(𝑀 − 𝐷𝐿) mean absolute bias δ 1 𝑛 ∑ |𝑀 − 𝐷𝐿| ratio of standard deviations (sd) α 𝑆𝐷 (𝑀) 𝑆𝐷 (𝐷𝐿) as mentioned, the time series defined according overall variability, inter-seasonal variability and interannual variability respectively. for overall variability, modis time series were compared the respective data logger time series tair. for intra-seasonal variability, as value pairs (modis-logger) for analysis are chosen summer and winter respectively. finally, for inter-annual variability and to obtain anomalies for each time series, a comparison of each year with 2011 measurements for both modis and logger data has been done. 3. results and discussion 3.1. overall performance and variability when examining the data on an annual basis, a marked seasonality characterized all observational locations. the annual thermal amplitude in daily mean surface temperature ranging from 12.0oc in 2014 at prodromos station to 20.8oc in 2013 at larnaca station. the mean minimum value observed in prodromos at 2013 (-0,5oc) and mean maximum at athalassa in 2012 (33.0oc) (table 3). the correlation coefficient (r) between the modis 1-km land surface temperature and tair from data loggers was higher than 0.91 for all locations indicating a high level of agreement. all biased values are positive which means that the modis lst products are overestimated compared with the ground-measured data. table 3. surface temperature recorded by data loggers and statistics for each examined year no name year mean sd se min max no name year mean sd se min max 1 pafos 2011 19.6 5.4 1.6 9.8 28.2 3 larnaca 2011 19.8 6.4 1.8 7.8 29.6 2012 20.5 5.8 1.7 9.2 29.7 2012 20.3 6.7 1.9 8.2 30.7 2013 20.0 5.7 1.6 6.0 28.8 2013 20.8 6.3 1.8 5.6 30.2 2014 20.5 5.2 1.5 12.3 28.3 2014 20.6 5.5 1.6 12.5 28.4 2015 19.9 5.5 1.6 5.8 28.4 2015 20.2 6.4 1.8 4.3 30.7 2 athalassa 2011 19.6 7.5 2.2 6.8 31.8 4 prodromos 2011 12.1 7.0 2.0 3.2 24.9 2012 20.0 7.6 2.2 6.2 33.0 2012 14.0 7.9 2.3 -1.7 28.2 2013 20.2 7.7 2.2 3.8 31.2 2013 14.9 7.4 2.1 -5.0 26.0 2014 20.1 6.9 2.0 10.3 32.1 2014 12.0 7.1 2.1 0.8 25.0 2015 19.7 7.7 2.2 7.5 31.8 2015 12.7 7.2 2.1 1.1 24.4 mean absolute bias values for all locations are between 1.26oc and 4.13oc, and the ratio between modis and data logger sds was close to 1 (between 1.03oc and 1.36oc). therefore, the combined information provided by the statistics, point to similar seasonal variability between the series with some although systematic towards overestimation of seasonal fluctuations of surface temperature by modis (figure 3). http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 | 7 figure 3. comparison of overall variability between data logger tair values (black lines) and modis lst (red lines). statistics for each location are noted above each panel for all stations: [a] = pafos; [b] = athalassa; [c] = larnaca; and [d] = prodromos 3.2. intra-seasonal variability figure 4 shows that correlations between the 2 series of 8-day lst and tair at worm season were always significant and ranged from 0.68 at prodromos station to 0.84 at paphos station. modis tended to systematically overestimate surface temperature with a mean bias between 2.85oc (station #225) and 6.71oc (station #666). the ratio of sds during summer season, pointed to a general tendency of modis to underestimate intra-seasonal variability with values ranging between 0.72 (station #225) and 0.92 (station #82). in the other hand, correlations of those 2 series over cold season were almost significant with the values of the coefficient to be lower to those of summer and ranging from 0.42 (station #82) to 0.65 (station #731). modis tended to underestimate surface temperature in 2 stations with mean bias between -0.71oc (station #82) and -0.26oc (station #731) and mean absolute bias to be lower than summer. for the rest 2 stations (station #225 & station #666) modis tended to overestimate surface temperature with mean bias ranging from 0.69 to 1.14oc and mean absolute bias to be also lower than summer. despite summer season, modis tended to underestimate intra-seasonal variability in 2 coastal area stations and overestimate it in other 2 with sds ratio ranging between 0.58 (station #225) and 0.83 (station #666). http://doi.org/10.14710/geoplanning.6.1.1-12 http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 8 | figure 4. comparison of intra-seasonal variability between data loggers tair and modis lst. data were selected left/right a summer/winter lst reference to logger time series. statistics for each comparison is noted above each panel for all stations: [a] = pafos; [b] = athalassa; [c] = larnaca; and [d] = prodromos 3.3. intra-annual variability the examination of lst and tair differences among the years and thus the description of any anomalies derived from the comparison of each year data source with 2011 respectively. anomaly lst and anomaly tair values are ranging from -8.8oc to 9.4oc. figure 5 shows that the correlation coefficients were lower than the overall variability but were always significant and ranged from 0.38 (station #82) to 0.47 (station #666). there was no indication of a tendency for modis to systematically underor overestimate the amplitude of the inter-annual variability with mean bias ranking from -1.10 to 0.05oc. mean absolute bias was lower than the accuracy of the data loggers in all points except station #225. the ratio of sds was between 0.60 at prodromos station and 0.76 at paphos station. http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 | 9 figure 5. comparison of inter-annual variability between data loggers tair (black line) and modis lst (red line). statistics for each comparison is noted above each panel for all stations: [a] = pafos; [b] = athalassa; [c] = larnaca; and [d] = prodromos furthermore, as indicated from the analysis, the station of prodromos presents both maximum positive values in february of 2012 (~ 9.40oc) and maximum negative values in april of 2014 (~ -8.80oc) from the measurements derived from data logger. in the other hand, from modis measurements, the station of athalassa presents both maximum positive values in feb of 2012 (~ 5.90oc) and maximum negative values in may of 2013 (~ -6.60oc). figure 6 presents anomaly maps as derived with the comparison of mean lst of each year with 2011 respectively. lst anomaly values are ranging from -2.0oc to 2.0oc with blue color indicating negative changes and red positive lst changes. as it shown, 2012 presents the minimum alst values indicating negative lst change which is located mainly among pentadaktylos and troodos range. overall, an increase trend of surface http://doi.org/10.14710/geoplanning.6.1.1-12 http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 10 | temperature is presented from 2013 to 2015 with 2014 to present the maximum alst values indicating the increase of surface temperature over the study area. figure 6. yearly mean lst anomaly values for: [a] = 2012 vs 2011; [b] = 2013 vs 2011; [c] = 2014 vs 2011; and [d] = 2015 vs 2011 4. conclusions in order to assess the potential of remotely sensed data to estimate air temperature, a spatio-temporal analysis was realized to compare modis 8-day lst (both day and night) on board aqua and terra sun synchronous satellites with tair observations from 4 stations locating in south part of cyprus. the major findings of this research can be summarized in some key points. first, lst does not exactly match with ground tair measurements, as expected, but a strong correlation (r > 0.96) between the two sets of measurements is presented. the weaker correlations obtained between tair and lst are: (a) from a station located near shoreline (station of paphos) due to the effect of the different materials on the ground and near shore pixels and (b), from station located in mountainous area probably due to the effect of snow in cold season. secondly, modis presents a systematic towards overestimation of seasonal fluctuations of surface temperature is some stations. this feature was likely caused by the instantaneous nature of satellite data that may have introduced short-term sub-daily variability resulting in a more variable time series relative to the daily average of hourly logger data (bernardello et al., 2016). in addition, for intra-seasonal temperature variability modis presents a tendency to overestimate the variability in summer despite in cold seasons where a tendency to underestimate the variability is presented. the differences are largely due to the nature and state of the surfaces being sensed within the modis pixels (e.g. vegetation, bare rock, snow, surface waters, and distance from coast line). in the other hand, examining temperature anomalies, modis proved to be able to capture inter-annual variability without any systematic tendency towards either underor overestimation and with mean absolute bias very similar (except prodromos station). moreover, the mean 8-day lst yields stronger correlation with tair when the data from the two satellites are combined than when values are taken separately. this high correlation suggests that lsts can be used and applied from regional to continental scale. the temporal and spatial coverage as well as the 1km2 footprint are great advantages for improving mapping precision and for monitoring the lst variation. another key point, is that lst and tair show a high degree of temporal coherence and on broad spatial and temporal scales they follow the same http://doi.org/10.14710/geoplanning.6.1.1-12 georgiou and varnava / geoplanning: journal of geomatics and planning, vol. 6, no. 1, 2019, 1-12 doi: 10.14710/geoplanning.6.1.1-12 | 11 trajectory. the only concern, is that ground measurements are discrete in space, while satellite-derived lsts are discrete in time. the scaled mismatch issue in both space and time must be consider. furthermore, the averaged logger data over 8-day period probably resulted in less variable logger time series even if the general distribution of temperature did not show considerable changes. nevertheless, further investigation is needed in order to retrieve more accurate values in terms of spatial and temporal scale. additional data must be gathered and extend to a decay span of a daily interval for both ground and satellite derived data. moreover, higher density of stations where it is possible is needed to exclude more pronounced differences between modis and in situ temperatures, to investigate thermal variability and explore the effect of other parameters on lsts. bernali et al. (2012) used environmental and climate data to characterize the stations and to define to which extent they were representative of their study region. they took also into consideration the latitude distribution of the stations and their elevation. they highlight that wind velocity and vegetation are very important factors that influence the energy balance in the land-atmosphere system, thus, influence the lsttair relation. wang, (2005) demonstrated that there are no significant differences in the coefficients of different types of materials, except for ice, water and snow, although different materials influence the coefficients. some others (wan et al., 2004; wang et al., 2008; zhu et al., 2013) use the factors of surface temperature, humidity, wind speed, normalize difference vegetation index (ndvi), corine land cover, and soil moisture, to analyze their contribution to errors in modis lst products. yu et al. (2014) performed spatial heterogeneity analysis using a semi-variance analysis method, to analyze effects on the validation results based on a higher resolution land surface temperature data retrieved from landsat thematic mapper (tm) images. final, ermida et al, (2014) tabulates the possible sources of lst differences highlighting the difficulty to ascertain the actual accuracy of each retrieval. discrepancies in lst products may be associated to differences (i) in the top-of-atmosphere measurements (sensor calibration, spatial resolution), (ii) in the algorithm and auxiliary data used for atmospheric and surface emissivity correction, (iii) in cloud mask, and (iv) in angular anisotropy. in summary, lst is a key variable for climatological and environmental studies. modis-derived lst is a viable data source for monitoring the surface thermal regime in local and continental scale and for modeling seasonal or annual variability of the surface temperature. it proved to be reliable source especially for studies requiring temperature reconstruction in areas with lack of observational stations. 5. acknowledgments the authors would like to thank the department of meteorology of the ministry of agriculture, rural development and environment of cyprus, for providing the data set from observational stations and offered their valuable help and remarks for this research. 6. references anderson, m., norman, j., kustas, w., houborg, r., starks, p., & agam, n. 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pola keruangan penyakit menular (dbd) kota semarang widjonarkoa, i.rudiartob, s.rahayuc a universitas diponegoro, indonesia, email: widjonarko39@gmail.com b universitas diponegoro, indonesia, email: irudiarto@yahoo.com c universitas diponegoro, indonesia, email: sri.yksmg@yahoo.com abstract: the persistently high incidence of infectious diseases in the city of semarang and spatial dynamics of its development shows an indication that the urban development in the city of semarang not offset efforts to increase environmental health. despite the high incidence of infectious diseases is still not matched by adequate research. most of the research related to the spread of infectious disease is highlighted by the number and spatial spreading. based on the results of the research it is clear that the incidence of infectious diseases, particularly dengue fever leads to a pattern of endemic disease, where a high incidence of recurrence located in the same village in the time range 2006-2012. these symptoms also started to affect the spatial spread of disease, where the villages with high incidence is likely to provide a positive influence on the spread of dengue disease in the surrounding villages. incidence of transmission is not independent of the physical quality of housing environment is not good, causing the vector easy to breed. abstrak: masih tingginya angka kejadian penyakit menular di kota semarang dan dinamika perkembangannya secara keruangan menunjukkan satu indikasi bahwa pembangunan perkotaan di kota semarang tidak diimbangi upaya untuk peningkatan kesehatan lingkungan. meskipun kejadian penyakit menular yang tinggi masih belum diimbangi dengan penelitian yang memadai. sebagian besar penelitian terkait dengan sebaran penyakit menular masih menyoroti jumlah dan sebarannya secara keruangan. berdasarkan pada hasil penelitian terlihat jelas bahwa kejadian penyakit menular, khususnya demam berdarah mengarah pada pola penyakit endemik, dimana perulangan kejadian yang tinggi berlokasi pada kelurahan yang sama dalam rentang waktu 2006-2012. gejala ini juga mulai mempengaruhi penyebaran penyakit secara keruangan, dimana kelurahan dengan kejadian tinggi cenderung memberikan pengaruh positif terhadap penyebaran penyakit dbd pada kelurahan di sekitarnya. kejadian penularan ini tidak terlepas dari kualitas fisik lingkungan permukiman yang kurang baik sehingga menyebabkan vektor mudah berkembang biak 1. pendahuluan pembangunan perkotaan yang sangat pesat dalam saat ini telah memberikan satu dampak yang signifikan terhadap perubahan kualitas ekosistem. perubahan kualitas ekosistem sendiri akan berpengaruh terhadap kehidupan manusia, salah satunya terhadap derajat kesehatan di perkotaan. kesehatan kota merupakan satu isu mutakhir yang sedang berkembang saat ini, khususnya berkaitan dengan penyakit menular. permasalahan terkait penyakit menular semakin menjadi kompleks manakala dikaitkan dengan pola hidup, pola mobilitas dan interaksi dan kepedulian masyarakat untuk mencegahnya serta kualitas lingkungan perkotaan itu sendiri. kejadian penyakit menular yang berkembang menjadi epidemi pada satu info artikel; diterima: 25 september 2014 hasil revisi : 27 september 2014 disetujui: 29 september 2014 publikasi on-line: 1 oktober 2014 kata kunci: pola keruangan, penyakit menular article info; received: 25 september 2014 in revised form: 25 september 2014 accepted: 25 september 2014 available online: 1 october 2014 keywords: spatial pattern infectious disesases mailto:widjonarko39@gmail.com mailto:irudiarto@yahoo.com mailto:sri.yksmg@yahoo.com geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 115 kawasan sangat dipengaruhi oleh pola hidup dan kualitas kesehatan lingkungan, sebagai contoh kasus penyakit flu, kasus penyakit demam berdarah dan penyakit-penyakit lain yang disebabkan vektor perantara (mao, et al, 2010; chaikaew,et al, 2010; dan maio, et al, 2004). kejadian penyakit menular yang berkembang menjadi endemi juga terjadi di kota semarang, salah satunya adalah penyakit demam berdarah dengue. berdasarkan data dinas kesehatan kota semarang, terjadi peningkatan yang signifikan kejadian penyakit dbd dalam kurun waktu 2009-2010, dengan korban jiwa sebesar 10 orang meninggal dunia dan pada 2011 meskipun jumlah penderita mengalami penurunan tetap jumlah korban jiwa mengalami peningkatan (dkk semarang, 2012 dalam faiz, et al, 2013). selain dbd kejadian penyakit menular yang mengalami pertambahan kejadian adalah jenis penyakit filariasis dan malaria (balitbang kesehatan, 2007). kondisi ini mengindikasikan bahwa perkembangan penyakit menular secara keruangan mengalami perkembangan. disatu sisi informasi mengenai pola keruangan terhadap kejadian penyakit dan pola penyebarannya masih minim tersedia. beberapa penelitian terkait pola keruangan penyakit di beberapa kota masih terbatas pada model matematis dengan pendekatan indeks moran dan geary serta cellular automata (faiz, et all, 2013; praja, 2013; tyas, et al, 2010) dan masih sedikit yang mengkaitkan antara pola penyakit tersebut dengan ketersediaan serta akses masyarakat terhadap infrastruktur, khususnya sistem sanitasi. penelitian serupa juga masih belum mengkaitkan antara pola keruangan dengan perilaku hidup masyarakat dalam mitigasi penularan penyakit, seperti kepedulian terhadap program 3m, kepedulian untuk pengolahan limbah rumah tangga dan pola kepedulian hidup sehat lainnya. penelitian ini akan mencoba melihat pola keruangan kejadian penyakit menular yang terkait dengan vektor perantara dan kesehatan lingkungan di kota semarang, sebagai upaya untuk memberikan informasi betapa berisikonya masyarakat kota semarang terhadap jenis-jenis penyakit tersebut (hansen, et al, 2010). pendekatan yang digunakan dalam penelitian ini adalah dengan pendekatan sistem informasi geografis (sig) khususnya analisis keruangan dan statistik keruangan. penggunaan sig dalam penelitian ini sangat relevan mengingat penyakit menular dan penyebarannya sangat terkait dengan pola geografis, dan pola penyakit akan dengan mudah direpresentasikan dengan menggunakan teknologi sig. selain itu teknologi sig telah terbukti mampu menggambarkan dengan jelas berbagai penyebaran penyakit yang disebabkan karena penularan melalui vektor, analisis secara keruangan resiko penyakit dan telah banyak digunakan dalam berbagai analisis dan perumusan kebijakan untuk penanganan penyakit (queensland health, 2005). 2. data dan metode demam berdarah adalah penyakit akut yang disebabkan oleh virus dengue, yang ditularkan oleh nyamuk. penyakit ini ditemukan di daerah tropis dan sub-tropis, dan menjangkit luas di banyak negara di asia tenggara. kota semarang menjadi salah satu kota di indonesia yang memiliki kasus penyakit demam berdarah dengue ini. berdasarkan data dinas kesehatan tahun 2006 hingga tahun 2012 tercatat bahwa kelurahan yang sering terjadi kasus penyakit demam berdarah dengan jumlah total kasus diatas 145 kejadian adalah sebagai berikut: 1. kec. banyumanik : kelurahan pedalangan, padangsari, gedawang, pudak payung 2. kec.candisari : kelurahan tegalsari, wonotingal 3. kec. gajah mungkur : kelurahan petempon,, bendungan, lempongsari, gajah mungkur 4. kec. gayamsari : pandean lamper 5. kec. genuk : trimulyo, terboyo wetan, kudu, penggaron lor 6. kec. gunungpati : ngijo, nongko sawit, cepoko, mangunsari, pakintelan 7. kec. mijen : wonoplumbon, pesantren, ngadirgo, wonolopo, mijen, bubakan 8. kec. ngaliyan : wonosari, beringin 9. kec. pedurungan : tlogosari kulon, pedurungan lor 10. kec. semarang barat : tambakharjo, krobokan, cabean, bojong salaman, bongsari 11. kec. semarang selatan : bulustalan, barusari, wonodri, peterongan, lamper lor, lamper kidul 12. kec. semarang tengah : purwodinatan, pandansari, kranggan, pindrikan kidul, miroto, karang kidul, brumbungan 13. kec. semarang timur : mlatiharjo, bunangan geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 116 14. kec. semarang utara : panggung kidul, dadapsari, purwosari, plombokan 15. kec. tembalang : tandang, bulusan 16. kec. tugu : mangunharjo, randugarut, tugurejo gambar 1. sebaran kejadian penyakit demam berdarah kota semarang tahun 2006 – 2012 (dinas kesehatan kota semarang, 2013) dalam perkembangannya dari tahun ke tahun jumlah kasus penyakit demam berdarah cenderung berfluktuasi. dari tahun 2006 hingga 2008 jumlah kejadian penyakit demam berdarah cenderung meningkat. namun setelah tahun 2010 hingga 2012 jumlah kejadian penyakit menurun dan naik lagi pada 2013. gambar 2. grafik perkembangan kejadian penyakit demam berdarah kota semarang tahun 2006 – 2013 (dinas kesehatan kota semarang) penurunan ini disebabkan karena upaya intensif dari pemerintah kota semarang untuk mencegah penyebaran vektor penyakit melalui kegiatan fogging dan sosialiasi serta pemantauan jentik nyamuk pada tiap-tiap rumah tangga di masing-masing lingkungan rukun tetangga. upaya ini belum sepenuhnya berhasil meningkatnya kesadaran masyarakat untuk memutus rantai penyebaran penyakit dbd, hal ini dapat dilihat dari kecenderungan naiknya kembali kejadian penyakit dbd pada 2013. keberhasilan atau peningkatan 1845 2924 5249 3883 5556 1303 1250 2364 0 1000 2000 3000 4000 5000 6000 2006 2007 2008 2009 2010 2011 2012 2013 kejadian dbd geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 117 kesadaran masyarakat untuk memutus rantai penyebaran tidak merata diseluruh wilayah kota semarang. masih terdapatnya kelurahan-kelurahan yang tidak mampu memutus rantai penyebaran penyakit dbd di kota semarang khususnya pada wilayah pinggiran yang selama ini merupakan daerah endemik dbd. secara keruangan wilayah yang tidak berhasil memutus rantai penyebaran adalah kelurahan yang memiliki angka kejadian dbd tertinggi yaitu di kelurahan wonoplumbon. kelurahan wonoplumbon dari tahun 2006-2012 merupakan wilayah endemik dbd. gambar 3. kejadian penyakit demam berdarah kota semarang tahun 2006 – 2012 (dinas kesehatan kota semarang) dari tren perkembangan tersebut, kelurahan wonoplumbon menjadi kelurahan yang sering mengalami kasus demam berdarah. dari tahun 2006 hingga 2012 kelurahan wonoplumbon menjadi kelurahan paling tinggi terjadi kasus demam berdarah. berdasarkan data dinas kesehatan tahun 2010 kelurahan wonoplumbon mengalami kasus demam berdarah tertinggi di kota semarang dengan 342 kasus. dalam perkembangannya, dari tahun 2006 ke tahun 2007 kasus demam berdarah meningkat di bagian tengah dan selatan kota semarang dimana kelurahan wonoplumbon, bubakan, magunsari, gedawang dan 2012 2011 2010 2009 2008 2007 2006 geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 118 wonotinggal menjadi kelurahan dengan kejadian kasus tertinggi (56-82 kasus). pada tahun 2008 meningkat di bagian tengah dan barat kota semarang dengan angka kejadian tertinggi mencapai 231 kasus. di tahun 2009 kejadian demam berdarah mengalami penurunan dengan kelurahan wonoplumbon masih menjadi kelurahan paling tinggi (178 kasus) di kota semarang. di tahun 2010 mengalami peningkatan kembali di bagian tengah, utara dan barat kota semarang. kelurahan wonoplumbon mendominasi kejadian demam berdarah dengan 342 kasus. tahun 2011 perkembangannya mengalami penurunan drastis namun bagian barat dan tengah kota semarang masih memiliki kasus kejadian demam berdarah yang cukup tinggi dengan kisaran kasus antara 20-57 kasus. di tahun 2012 kejadian demam berdarah mengalami penurunan dengan kelurahan wonoplumbon, bulusan, krobokan, petompon dan gajah mungkur menjadi kelurahan paling tinggi kejadian demam berdarahnya yakni mencapai kisaran 2739 kasus. secara keseluruhan kejadian demam berdarah di kota semarang tahun 2006 hingga 2012 cenderung timbul di bagian barat dan tengah kota semarang. pengolahan dan analisis data dalam kegiatan ini akan dilakukan dengan bantuan perangkat lunak sig, dengan menggunakan perangkat analisis keruangan yang tersedia dengan menggunakan format data raster. penggunaan data raster dipilih karena sifat dan struktur data yang sederhana sehingga mempermudah proses manipulasi terhadap data (untuk reklasifikasi, interpolasi dan sebagainya). adapun jenis analisis data yang akan dilakukan dalam penelitian ini meliputi: 1. analisis angka kejadian penyakit menular analisis ini dilakukan dengan cara membandingkan antara jumlah penderita pada masing-masing penyakit menular terhadap jumlah penduduk pada masing-masing kelurahan dan akan disajikan dalam bentuk peta kejadian penyakit menular dengan unit spasial kelurahan. analisis ini akan dilakukan dengan rentang waktu 5-10 tahun terakhir untuk melihat kecenderungannya secara keruangan. 2. analisis jarak terbobot penyakit menular dilakukan untuk mengetahui pola distribusi ruang dari penyakit menular terhadap pusat-pusat permukiman pada masing-masing kelurahan. output dari analisis ini akan disajikan dalam format peta raster dengan rentang waktu pengamatan 5-10 tahun. 3. analisis korelasi spasial analisis ini digunakan untuk melihat keterkaitan antara angka kejadian penyakit dan kualitas fisik lingkungan dalam kurun waktu 5-10 tahun terakhir. output dari analisis ini adalah nilai korelasi antara kejadian penyakit menular dan kualitas fisik lingkungan. 4. analisis pola keruangan penyakit dan peluang penyebarannya dilakukan dengan pendekatan analisis kepadatan penyakit dalam kurun waktu 10 tahun dan kemudian dilakukan prakiraan penyebarannya berdasarkan pada data trend penyakit secara keruangan. 3. hasil dan pembahasan 3.1 tingkat kejadian dan pola keruangan penyakit demam berdarah dengue kota semarang dengan karakter morfologi berupa dataran rendah dan perbukitan memiliki angka kejadian penyakit dbd yang tergolong tinggi. berdasarkan pada data dinas kesehatan kota semarang tahun 2013, angka kejadian penyakit dbd di kota semarang selalu diatas kejadian dbd tingkat provinsi jawa tengah dan nasional (dinas kesehatan kota semarang, 2013). gambar 4. angka kejadian akibat dbd di kota semarang, jawa tengah dan indonesia 2006-2013 (sumber: dinas kesehatan kota semarang, 2013) geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 119 gambar 5. tingkat kejadian bencana dbd kota semarang 2006-2012 (dinas kesehatan kota semarang, 2013, analisis penulis, 2014) berdasarkan pada sebaran lokasi angka kejadian penyakit dbd terlihat bahwa pola penyebaran cenderung random, dan tidak memiliki satu bentuk pengelompokan-pengelompokan yang tetap. jika dilihat tingkat korelasi keruangan antar kejadian penyakit dbd pada tahun 2006-2012 terdapat beberapa bentuk korelasi spasial yang mengindikasikan bahwa kelurahan dengan angka kejadian tinggi cenderung mempengaruhi kelurahan sekitarnya. pada tahun 2006, terdapat tiga pengelompokan kejadian penyakit dbd di kota semarang, yaitu: 1. tawangmas, krobokan, bulu lor dan panggung kidul 2. petompon dan bendungan 3. tegalsari, peterongan, lamper lor dan lamper kidul sedangkan pada 2012, pola pengelompokan kejadian penyakit secara keruangan hanya terjadi pada dua lokasi yaitu sekitar wonoplumbon dan ngadirgo serta pengelompokan kejadian di sekitar gajahmungkur, lempongsari, petompon dan wonotinggal. potensi perkembangan kejadian geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 120 diperkirakan akan merembet ke wilayah kelurahan bendungan yang memiliki indeks korelasi keruangan yang mendekati nilai indeks korelasi keruangan kelurahan gajahmungkur. gambar 6. korelasi keruangan kejadian penyakit dbd kota semarang 2006 vs 2012 (analisis penyusun, 2014) 3.2 pengaruh kualitas lingkungan permukiman terhadap kejadian dbd kualitas lingkungan permukiman secara umum memiliki kontribusi terhadap kejadian penyakit yang disebabkan oleh vektor pembawa. kualitas lingkungan yang buruk memudahkan perkembangbiakan vektor, termasuk perkembangbiakan vektor nyamuk aedes aigipty yang merupakan vektor pembawa penyakit demam berdarah. kualitas lingkungan permukiman di kota semarang, secara umum dapat dikategorikan dalam kondisi yang cukup baik. berdasarkan pada data, hanya beberapa wilayah kelurahan yang berada pada wilayah pesisir memiliki kondisi lingkungan permukiman yang buruk. meskipun demikian pada wilayah-wilayah tersebut tidak semuanya memiliki kejadian penyakit demam berdarah yang tinggi. justru pada wilayah kelurahan dengan tingkat kualitas lingkungan permukiman yang relatif lebih baik seperti pada wilayah perbukitan dan pusat, angka kejadian penyakit justru lebih tinggi. berkaca dari fenomena kejadian penyakit dan kondisi fisik lingkungan permukiman di kota semarang mengindikasikan bahwa tidak ada satu pengaruh signifikan antara kualitas lingkungan permukiman terhadap kejadian penyakit demam berdarah. fakta ini diperkuat dengan hasil analisis regresi keruangan yang menghasilkan temuan bahwa meskipun ada hubungan keruangan kejadian penyakit demam berdarah dengan kualitas lingkungan, tetapi hubungannya tidak signifikan. hal ini dapat dilihat dari pola distribusi data pengamatan yang memiliki pola linier (lihat gambar) dan residual data cenderung random dan tidak memiliki nilai autokorelasi secara keruangan. nilai indeks moran untuk residual data adalah sebesar 0.01 dan z score = -0.23. sedangkan jika dilakukan analisis dengan menggunakan pendekatan kuadrat terkecil juga terlihat bahwa koefisien determinasi fungsi regresi keruangan kualitas fisik lingkungan terhadap kejadian dbd hanya mampu menjelaskan 3.2% dari total pengaruh kualitas fisik terhadap kejadian dbd. running script ordinaryleastsquares... summary of ols results variable coefficient stderror t-statistic probability robust_se robust_t robust_pr vif [1] intercept 15.826410 5.561361 2.845780 0.004970* 5.634856 2.808663 0.005551* ------- padat 0.002621 0.035853 0.073108 0.941794 0.040067 0.065419 0.947906 1.034036 kumuh 0.061448 0.038362 1.601806 0.111044 0.035517 1.730113 0.085411 1.164728 banjir1 -0.066006 0.042970 -1.536080 0.126367 0.065051 -1.014684 0.311673 1.211071 sanitas -0.051087 0.057730 -0.884937 0.377416 0.065423 -0.780878 0.435939 1.288240 ols diagnostics number of observations: 177 number of variables: 5 degrees of freedom: 172 akaike's information criterion (aic) [2]: 1342.917191 multiple r-squared [2]: 0.032044 adjusted r-squared [2]: 0.009533 joint f-statistic [3]: 1.423488 prob(>f), (4,172) degrees of freedom: 0.228189 joint wald statistic [4]: 4.895396 prob(>chi-squared), (4) degrees of freedom: 0.298200 koenker (bp) statistic [5]: 12.297945 prob(>chi-squared), (4) degrees of freedom: 0.015268* geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 121 jarque-bera statistic [6]: 339.971828 prob(>chi-squared), (2) degrees of freedom: 0.000000* berdasarkan pada parameter tersebut diatas dapat dibuat sebuah simpulan bahwa kejadian dbd di kota semarang tidak begitu dipengaruhi pula oleh faktor kualitas lingkungan fisik kawasan permukiman. faktor lain yang dimungkinkan mempengaruhi kejadian bencana yang tidak teramati dalam penelitian ini adalah faktor iklim. pengaruh faktor iklim cukup signifikan mengingat hampir sebagian besar kejadian penyakit dbd terjadi pada musim hujan. hanya saja faktor keterbatasan data kejadian penyakit bulanan yang tidak tersedia pada tiap puskesmas dan kantor dinas kesehatan kota semarang, maka dalam penelitian ini hanya digunakan rekap tahunan. dan dari hasil analisis ternyata data tahunan ini tidak mampu memberikan gambaran menyeluruh tentang pengaruh kualitas fisik lingkungan permukiman terhadap kejadian penyakit demam berdarah. gambar 7. sebaran distribusi data, nilai autokorelasi keruangan dan normal qq plot kejadian penyakit dbd di kota semarang 2012 (analisis penyusun, 2014) 3.3 peluang penyebaran penyakit dbd secara keruangan berdasarkan pada hasil kajian sebelumnya berkaitan dengan pola keruangan penyakit dbd yang menunjukan gejala yang sifatnya endemik. dengan menggunakan hasil kajian pada bagian 5.2 dapat dilihat peluang-peluang penyebaran penyakit dbd dengan mendasarkan pada kejadian penyakit dan faktor jarak antar pusat-pusat kejadian memiliki pola yang acak. peluang penyebaran penyakit dbd antarkelurahan akan memiliki nilai yang tinggi pada lokasi dengan tingkat kejadian tinggi dengan luasan wilayah yang tidak terlalu besar. peluang ini secara scatter plot matrix observed x residual 6040200 60 40 20 0 observed r e s id u a l predicted p re d ic te d geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 122 empirik sangat bersesuaian mengingat kemampuan vektor untuk terbang juga terbatas juga pengaruh penularan yang disebabkan karena terbawanya penyakit oleh carrier yaitu anak-anak dan orang lanjut yang cenderung memiliki pola pergerakan yang terbatas. fenomena ini dapat digambarkan secara jelas dengan menggunakan pendekatan analisis cluster secara keruangan. peluang penyebaran penyakit secara keruangan ternyata pada wilayah-wilayah kelurahan di pusat kota dengan luas wilayah yang tidak besar. pada wilayah yang berpotensi terkena pengaruh kejadian penyakit dbd dari wilayah sekitarnya ternyata memiliki permukiman dengan radius yang relatif pendek yaitu berkisar antara 10m-350m (lihat gambar 5.8). pada radius ini ternyata pergerakan nyamuk dbd betina dewasa masih cukup efektif. sehingga dengan demikian terlihat bahwa model kluster keruangan cukup dapat menggambarkan fenomena dan peluang penyebaran penyakit dbd di kota semarang. gambar 8. peluang penyebaran penyakit dbd di kota semarang (analisis penulis, 2014) jika dihubungkan dengan hasil pembahasan, meskipun hasilnya tidak begitu signifikan, peluang penyebaran penyakit pada kelurahan-kelurahan di pusat kota semarang sedikit banyak mampu menjelaskan pula bahwa sebenarnya ada pengaruh kualitas lingkungan permukiman terhadap kejadian penyakit dbd. beberapa kelurahan seperti sekayu, miroto, jagalan, rejosari dan kaligawe adalah kelurahan-kelurahan dengan kualitas fisik lingkungan yang kurang baik. 4. kesimpulan berdasarkan pada hasil analisis, dapat disimpulkan bahwa kejadian penyakit menular dbd di kota semarang memiliki pola keruangan yang acak. pola keacakan penyakit dbd dapat dilihat dari nilai indeks moran sebesar 0.03 dan standar deviasi 0.63. indeks moran yang kecil ini menunjukan ada pola kerandoman dalam sebaran keruangan kejadian penyakit. pola ini pun dapat tergambar dengan jelas dengan melihat pada peta sebaran keruangan kejadian penyakit dbd di kota semarang dari tahun 20062012. geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 123 pengaruh kualitas fisik lingkungan permukiman terhadap kejadian penyakit dbd di kota semarang tidak cukup signifikan. berdasarkan pada hasil permodelan regresi keruangan, model hanya mampu menjelaskan 3,2% pengaruh kualitas lingkungan permukiman terhadap kejadian penyakit. rendahnya nilai pengaruh kualitas fisik lingkungan terhadap kejadian dbd dipengaruhi oleh faktor informasi kualitas lingkungan yang sifatnya kualitatif (baik-sedang-buruk) yang kemudian direpresentasikan secara ordinal sehingga kurang dapat menggambarkan kondisi yang sebenarnya. faktor kualitas fisik akan memberikan hasil yang berbeda apabila direpresentasikan dari nilai sebenarnya, misal luas genangan, jarak antara permukiman dengan lokasi genangan, jarak antara lokasi permukiman dengan saluran drainase dan sebagainya. peluang penyebaran penyakit menular dbd sangat dipengaruhi oleh faktor jarak antar permukiman. semakin dekat faktor jarak antar permukiman peluang penyebaran akan semakin besar. keadaan ini disebabkan karena kemampuan terbang efektif nyamuk dbd betina adalah pada radius 40-100 meter. sehingga permukiman dengan kejadian penyakit dbd tinggi dengan luas wilayah tidak terlalu besar dan tingkat kepadatan permukiman tinggi memiliki peluang untuk memberikan pengaruh penyebaran penyakit dbd pada permukiman di wilayah kelurahan sekitarnya. berkaca dari hasil analisis statistik keruangan khususnya model regresi yang kurang signifikan, maka perlu kiranya dilakukan pendalaman kajian dengan menggunakan variabel yang lebih rinci, khususnya berkaitan dengan data kejadian penyakit. akan lebih baik jika dalam penelitian lanjutan menggunakan data kejadian penyakit yang dirinci dalam dua mingguan. selain itu perlu juga menambahkan variabel iklim sebagai salah satu variabel dalam pengembangan model keruangan penyakit dbd di kota semarang. diharapkan dengan menggunakan data yang lebih rinci dan variabel tambahan maka akan dapat dijelaskan secara lebih jelas bagaimana pengaruh dari kualitas fisik lingkungan terhadap kejadian penyakit dbd di kota semarang. 5. daftar pustaka balitbang kesehatan, 2007, riset kesehatan dasar provinsi jawa tengah, departemen kesehatan republik indonesia. chaikaew, nakarin, and nitin k tripathi and marc souris, 2010, exploring spatial patterns and hotspots of diarrhea in chiang mai, thailand, international journal of public health volume 8. danudoro, projo, 2003, fenomena keruangan penyakit menular, http://kesehatanlingkungan.wordpress.com/penyakit-menular/fenomena-keruangan-penyakitmenular/ diakses 16 juni 2013. dinas kesehatan kota semarang, 2012, profil kesehatan kota semarang 2011, dinas kesehatan kota semarang. dinas kesehatan kota semarang, 2013, profil kesehatan kota semarang 2012, dinas kesehatan kota semarang esri, 2008, geostatistical analyst, http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?topicname=how_inverse_distance_weight ed_%28idw%29_interpolation_works diakses 16 juni 2013. faiz, nuril dan rita rahmawati dan dian safitri, 2013, analisis spasial penyebaran penyakit demam berdarah dengue dengan indeks moran dan geary’s c (studi kasus di kota semarang tahun 2011) jurnal gaussian, volume 2, nomor 1, halaman 69-78. fobil, juliah najah, 2010, spatial urban environmental change and malaria/diarrhoea mortality in accra, ghana, university of bielefeld. hansen, kate and nicole van osdel, 2010, gis application in health: an introduction to gis, college of public health, university of nebraska. liu, desheng, maggi kelly and peng gong, 2006, a spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery, journal remote sensing of environment, elsevier. maio, sara, et al, 2004, gis for epidemiological studies, cnr institute of clinical physiology, unit of environmental pulmonary epidemiology, pisa, italy. http://kesehatanlingkungan.wordpress.com/penyakit-menular/fenomena-keruangan-penyakit-menular/ http://kesehatanlingkungan.wordpress.com/penyakit-menular/fenomena-keruangan-penyakit-menular/ http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?topicname=how_inverse_distance_weighted_%28idw%29_interpolation_works http://webhelp.esri.com/arcgisdesktop/9.3/index.cfm?topicname=how_inverse_distance_weighted_%28idw%29_interpolation_works geoplanning 2014,vol: 1, no: 2, 114-124 widjonarko, rudiarto, dan rahayu | 124 mao, liang and ling bian, 2010, spatial–temporal transmission of influenza and its health risks in an urbanized area, journal of computers, environment and urban system, volume 34, issue 3, may 2010, pages 204–215. praja, wisnu pranata, 2013, analisis pola spasial penyebaran penyakit demam berdarah dengue (studi kasus penyebaran penyakit demam berdarah dengue di kota bogor tahun 2007-2011), ipb. queensland health, 2005, report on gis and public health spatial application, queenland government. tyas, ayu nawang retno ning, fariza, arna dan wahjoe tjatur sesulihatien, 2010, analisa penyebaran penyakit demam berdarah di surabaya dengan menggunakan cellular automata, politeknik elektronika negeri surabaya, institut teknologi sepuluh nopember. http://www.sciencedirect.com/science/journal/01989715/34/3 pola keruangan penyakit menular (dbd) kota semarang abstract: the persistently high incidence of infectious diseases in the city of semarang and spatial dynamics of its development shows an indication that the urban development in the city of semarang not offset efforts to increase environmental health... abstrak: masih tingginya angka kejadian penyakit menular di kota semarang dan dinamika perkembangannya secara keruangan menunjukkan satu indikasi bahwa pembangunan perkotaan di kota semarang tidak diimbangi upaya untuk peningkatan kesehatan lingkungan... 1. pendahuluan keywords: spatial pattern infectious disesases 2. data dan metode 3. hasil dan pembahasan 4. kesimpulan 5. daftar pustaka | 257 geoplanning vol 4, no 2, 2017, 257-262 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.257-262 assessing the usefulness of unmanned aerial vehicle (uav) for monitoring spatial plan: legal and user perspective of bogor regency, indonesia d. mariaa, f. hamdania, j. pratomoa , m.a. pratamaa, g. s. a bidaria, s. sheridaa a lokalaras indonesia institute, indonesia. abstract: monitoring is a critical process in managing the land use plan. however, the current approach to collecting data related to the land use has a shortcoming. first, field survey has limitation due to the high number of resources needed, i.e., people, funds, time. second, the participatory approach has limitation due to the lack of involvement of the citizens. unmanned aerial vehicle (uav) has developed in recent years and it has been used in the various field, i.e., urban dynamics, asset monitoring, and so on. the usage of uav to monitor urban changes has some advantages. first, it can cover a large area and used fewer resources compared with the field survey, in term of man hour, funds and time. second, it may provide data with a high spatial resolution, which gives a broad possibility for analyzing urban features. this research aimed to assess the usefulness of uav in monitoring the spatial plan of bogor regency, indonesia. we developed indicator according to the legal and user perspective. our research has shown that uav may reduce the time and resources needed to monitor the spatial plan. however, the uav has limitation since it is difficult to indicate the changes of the land use. therefore, we suggest incorporating with the field survey. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to site (apa 6th style): maria, d., hamdani, f., pratomo, j., pratama, m. a., bidari, g. s. a., sherida, s. (2017). assessing the usefulness of unmanned aerial vehicle (uav) for monitoring spat1ial plan: legal and user perspective of bogor regency, indonesia: journal of geomatics and planning, 4(2), 257-262 doi:10.14710/geoplanning.4.2.257-262. 1. introduction monitoring is a critical process in managing the spatial plan to ensure the compliance of the implementation process (government of the republic of indonesia, 2007). in indonesia, the monitoring process is divided into two categories, i.e., technical and specific monitoring (government of the republic of indonesia, 2010). technical monitoring consists of procedures, output, functions and benefits, and monitoring the achievement of standards of minimum service. meanwhile, the specific monitoring includes data and information and technical study on specific problems. although monitoring process is crucial, current approach for data collection regarding spatial planning has limitations. first, field survey has limitation due to the high number of resources needed, i.e., people, funds, time. second, the participatory approach has limitation due to the lack of involvement of the citizens. another method, which is satellite based imagery also has limitation due to the impact of atmospheric conditions. unmanned aerial vehicle (uav) has developed in recent years and it has been used in the various field, i.e., urban dynamics and asset monitoring. according to kršák et al. (2016) uav can be used to create new opportunities for documentation since it can measure the surface in detail, create orthophoto maps of the entire area and documents the difficult areas. furthermore, the usage of uav may cover a large area and used fewer resources compared with the field survey, in term of man hour, funds and time. uav also may provide data with a high spatial resolution, which gives a broad possibility for various applications. several studies have demonstrated the usage of uav in an urban area. for instance, research from salvo et al. (2014) has shown the usage of uav for urban traffic analysis. another research from chen et al. (2016) developed a robust method for detecting building change from uav image. article info: received: 13 january 2017 in revised form: 10 july 2017 accepted: 1 august 2017 available online: 30 oct 2017 keywords: spatial plan, uav, monitoring, legal perspective, user perspective corresponding author: jati pratomo lokalaras indonesia institute, indonesia email: jati@lokalaras.org open access https://doi.org/10.14710/geoplanning.4.2.257-262 mailto:jati@lokalaras.org maria et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 257-262 doi: 10.14710/geoplanning.4.2.257-262 258 | as the buffer city of jakarta, bogor regency experienced a rapid urbanisation, which led to an increased demand for land. three factors have contributed to the land demands, i.e., population growth, structural change of the society and the economic developments (fajarini, 2014). the impact of the rapid urbanization results in various forms of environmental degradation and the probability of violation in the spatial plan. therefore, the local government is required to monitor the spatial plan regularly to prevent violations. however, to monitor the implementation of spatial planning also have an issue. according to the indonesian national law number 26/2007, the general spatial plan (rtrw) cannot be used for monitoring due to the lack detailed information (purba, 2015). however, local governments rarely have the detailed spatial plan (rdtr), which may be used for monitoring the spatial plan. often, they develop their procedure for this purpose. therefore, it is necessary to indicate the requirement for monitoring from the legal and the user perspective. currently, the government of bogor regency used field survey for data acquisition. although the size of bogor regency is large, which is 2.664 square kilometer, the number of people-in-charge for monitoring process is limited. the government of bogor regency itself has set the target for monitoring land use for more than 1,000 land parcels per year. however, they can accommodate only 200 cases per year. this research aimed to assess the usefulness of uav in monitoring the spatial plan of bogor regency, indonesia according to the legal and user perspective. 2. data and methods to evaluate the usefulness of uav in monitoring the spatial plan, we developed a set of indicators from the two sources. first, we conducted a literature review related to the legal requirement for monitoring the spatial plan. second, we conducted an interview, to understand the needs and requirement of the user. according to these two sources, we developed a set of indicators. the qualitative evaluation was done by comparing the requirement of the monitoring process by the uav’s feature. to elaborate the capability of uav to fulfil the requirement, we conducted a literature review. by the end, we came out with a matrix that indicates the usefulness of the uav for monitoring the spatial plan, particularly in bogor regency. the methods used in this research can be seen in figure 1. legal requirement user requirement literature review user interview evaluations indicators uav features usefulness of uav figure 1. research methods 3. result and discussion 3.1. legal requirement two components are needed to be considered in monitoring the spatial plan, which is structure and pattern (ministry of public works and public housing, 2015). regarding monitoring the structure, some https://doi.org/10.14710/geoplanning.4.2.257-262 maria et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 257-262 doi: 10.14710/geoplanning.4.2.257-262 | 259 information needs to be collected. first, the change of urban center. second, the change of the main infrastructure. last, the change of utility. meanwhile, monitoring pattern consists of three measurements. first, changes in the environmental protected area. second, changes of open green space. third, changes in the built-up area. acquisition of the data mentioned above is needed to indicate and prevent the violation of spatial plan. several patterns can be classified as a violation of the spatial plan. first, conversion of the land use. second, permit does not comply with the spatial plan. third, a spatial plan does not matches with the actual conditions. fourth, development without a licence. fifth, inaccuracy of data. sixth, administrative violations during the licensing process. the last is construction of particular parcels affecting the accessibility of public facilities. 3.2. user requirement the monitoring process of the spatial plan in bogor regency is carried out by the department of spatial planning and land management (dtrp). dtrp developed a standard operating procedure (sop) regarding the monitoring of the spatial plan. to conduct the monitoring process, dtrp also needs to cooperate with other working units, e.g. board of investment and integrated permit (bptsp) and civil service police (satpol pp). dtrp bogor serves as the first substation in monitoring activities as the issuance of the construction permit. further intensive of monitoring activities are escorted by a unit of the department of building management and housing (dtbp) to minimise the infringement. regarding the procedure, dtrp started by conducting a field survey for every permitted that submitted to bptsp. if violations are found, dtrp will issue a warning letter. if the offender does not settle the issue after the third warning, then the government has any right to dismantle or revoke the landowner permits. however, due to the limited resources, dtrp needs to create a priority, which developed according to the preliminary information obtained from district authorities, also information from the citizens. regarding the time that consumed is depends on the area and the parcels that surveyed. for the parcels sized one hectare in the industrial area, it can be done by one day. meanwhile, for the parcels in a densely populated residential, it required up to four days. 3.3. development of indicators as discussed in introductions, we developed a set of indicators according to the legal and user perspective. from the legal point of view, we noticed that three activities need to monitors, which are monitoring urban structure, urban patterns and avoiding violations. meanwhile, from the user perspective, we noticed that the user requires methods that may reduce the resources needed and increasing the collaboration among stakeholders. therefore, we can summarise the indicators that can be used for evaluations, which can be seen in table 1. table 1. indicators of evaluations perspective category indicators legal managing urban structure 1 development of the urban centre 2 development of the infrastructure 3 development of the utility managing urban patterns 4 changes in environmental protection area 5 changes of the green-open-space 6 changes in the built-up area avoiding violations 7 land use conversion 8 difference between permit and spatial plan 9 difference between spatial plan and actual conditions 10 development without permit 11 inaccurate data 12 administrative violations 13 construction that blocked access to public space user reducing resources 14 reducing the time for data collection 15 reducing the needs of manpower collaboration 16 increasing collaborations among stakeholders https://doi.org/10.14710/geoplanning.4.2.257-262 maria et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 257-262 doi: 10.14710/geoplanning.4.2.257-262 260 | 3.4. evaluations regarding change monitoring of urban center, it requires data that periodically collected. the change of urban center might be observed by the change of the built-up area. for this requirement, the usage of uav has an advantage due to its ability to be deployed in the particular area and various time. several studies have shown the usefulness of uav to acquire spatiotemporal data. for instance, kim et al. (2016) employed multi temporal sar data obtained from uav for detecting durable and permanent changes in urban areas. another study from rosnell et al. (2011) tested the performance of image acquired from uav in different seasons (winter, spring, summer, autumn) and conditions (sunny, cloudy, various solar elevations). for the second and third indicator, which are infrastructure and utilities, the usage of uav has advantages due to the high spatial resolutions. current uav camera may produce an image with 6 centimetres on the spatial resolution. the usage of very high resolution (vhr) may assist the detection of the small object. regarding the application of uav for monitoring infrastructure and utility has demonstrated in some research. for instance, salvo et al. (2014) used the uav for monitoring the traffic. another research from sankarasrinivasan et al. (2015) used the uav to monitor the condition of the building structure. similar with the advantage of the uav in monitoring urban center, infrastructure and utility, monitoring urban pattern also require methods that may acquire multi-temporal data with appropriate spatial resolutions. however, apart from nthese two requirements, monitoring urban pattern also needs a platform that can cover a large area. current development of the uav technology has created a platform with outstanding coverage and endurance, e.g., orion medium-altitude long endurance uav (figure 2) have a flight radius of 4,000 miles and can fly for 120 hours. figure 2. orion uav although the usage of uav has demonstrated its usefulness in the first to the sixth indicator, apparently it has a limitation in monitoring land use conversion. similar to different image acquisition methods, i.e., satellite imagery, not every land use class can easily detect from the image. for instance, office and the commercial area often have similar characteristics. also, it is hard to distinguish between the public park and the vacant land. hence, observing the land use from the ground is the most obvious approach. although, some land use may be observed from the image, e.g., rice field and water body. since it is hard to monitor the land use from the uav, it is also difficult to monitor the difference between a permit and spatial plan (indicator number eight) as well as the difference between spatial plan and actual conditions (indicator number nine). uav also have limited use to solve the problems related to the accuracy of the land use data (indicator number 11). however, the uav might be used to monitor development without a permit (indicator number ten), by comparing acquired image with the gis data of building permit. we will indicate the violation if we find any changes in the particular area that do not have a building permit. uav have a limited use for detecting construction site that blocked access to public space. in this case, we need to define the relationship between construction sites, public facilities and the access to the public https://doi.org/10.14710/geoplanning.4.2.257-262 maria et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 257-262 doi: 10.14710/geoplanning.4.2.257-262 | 261 facilities. furthermore, often the access itself is hard to define from the image. for instance, a vacant land has used as access to a public park. hence, this case only can be detected by the local knowledge. regarding the administrative process, uav is of course not possible to detect this violations. regarding the user perception, the uav is useful in term of reducing the time and manpower needed for data collections. therefore, it can impact on the availability of up-to-date data that also can be used to increase the collaboration among stakeholders. according to the above discussion, we summaries the usefulness of the uav in table 2. table 2. summary of the evaluations indicators usefulness useful limited not useful 1 development of the urban centre √ 2 development of the infrastructure √ 3 development of the utility √ 4 changes in environmental protection area √ 5 changes of the green-open-space √ 6 changes in the built-up area √ 7 land use conversion √ 8 difference between permit and spatial plan √ 9 difference between spatial plan and actual conditions √ 10 development without permit √ 11 inaccurate data √ 12 administrative violations √ 13 construction that blocked access to public space √ 14 reducing the time for data collection √ 15 reducing the needs of manpower √ 16 increasing collaborations among stakeholders √ 4. conclusion our research has demonstrated the usage of uav for monitoring the spatial plan. the combination of the legal and user perspective also gives a better understanding for assessing the usage of the uav. however, uav also has a limited usage in monitoring land use, which also similar with different image acquisition methods, i.e., satellite imagery. since only particular land use that can be detected, incorporating with the field data is needed. in term of user perspective, uav gives a better opportunity compared with the field survey in term of reducing the time and manpower needed. 5. acknowledgments funding for this research is obtained from lokalaras indonesia institute. we also grateful by the support from the government of bogor regency. 6. references chen, b., chen, z., deng, l., duan, y., & zhou, j. (2016). building change detection with rgb-d map generated from uav images. neurocomputing, 1–15. [crossref] fajarini, r. (2014). the dynamics of land use change and the spatial plan of bogor regency (in bahasa). bogor agriculture institute. retrieved from http://repository.ipb.ac.id/handle/123456789/73120?show=full). https://doi.org/10.14710/geoplanning.4.2.257-262 http://doi.org/10.1016/j.neucom.2015.11.118 maria et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 257-262 doi: 10.14710/geoplanning.4.2.257-262 262 | government of the republic of indonesia. national law of spatial planning (in bahasa), pub. l. no. 26/2007 (2007). indonesia. government of the republic of indonesia. implementation of spatial planning (in bahasa), pub. l. no. 15/2010 (2010). indonesia. kim, d. j., hensley, s., yun, s. h., & neumann, m. (2016). detection of durable and permanent changes in urban areas using multitemporal polarimetric uavsar data. ieee geoscience and remote sensing letters, 13(2), 267–271. [crossref] kršák, b., blišťan, p., pauliková, a., puškárová, p., kovanič, ľ., palková, j., & zelizňaková, v. (2016). use of low-cost uav photogrammetry to analyse the accuracy of a digital elevation model in a case study. measurement. [crossref] ministry of public works and public housing. (2015). draft of technical documents on monitoring and evaluation of spatial planning (in bahasa). purba, t. p. (2015). audit of spatial plan in indonesia: expectation and implementation (in bahasa). retrieved from https://www.academia.edu/12785507/audit_tata_ruang_di_indonesia_harapan_dan_tindak_lanju t rosnell, t., honkavaara, e., & nurminen, k. (2011). on geometric processing of multi-temporal image data collected by light uav systems. isprs international archives of the photogrammetry, remote sensing and spatial information sciences, xxxviii-1/, 63–68. [crossref] salvo, g., caruso, l., & scordo, a. (2014). urban traffic analysis through an uav. procedia social and behavioral sciences, 111, 1083–1091. [crossref] sankarasrinivasan, s., balasubramanian, e., karthik, k., chandrasekar, u., & gupta, r. (2015). health monitoring of civil structures with integrated uav and image processing system. procedia computer science, 54, 508–515. [crossref] https://doi.org/10.14710/geoplanning.4.2.257-262 http://doi.org/10.1109/lgrs.2015.2509080 http://doi.org/10.1016/j.measurement.2016.05.028 http://doi.org/10.5194/isprsarchives-xxxviii-1-c22-63-2011 http://doi.org/10.1016/j.sbspro.2014.01.143 http://doi.org/10.1016/j.procs.2015.06.058 | 225 geoplanning vol 4, no. 2, 2017, 225-232 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.225-232 subak land information system based on remote sensing and geographic information system in denpasar city i. lanyaa, n. n. subadiyasaa, j. hutauruka a study program agroecotechnology, faculty of agicultural, udayana university, indonesia abstract: subak in this paper is a unit of agricultural area, with names, have palemahan (paddy field resource), pawongan (human resources/farmers), and paddy field farming systems. subak as local genious bali, constructed since the 7th century, until now subak system still exist in denpasar. unesco, in 2011, rewarded subak as a world cultural heritage. ironically, not one district/city, and the province of bali has maps spatially subak, they only have statistical data. the development era of technology and communications requires the ease and speed of getting data and the latest information with a high degree of spatial accuracy. the answer requires data base information based on information and communication technology (ict). worldview satellite imagery coverage of denpasar in 2015, and arcgis 10.3 software used for mapping land and extensive rice fields of subak (spatial data). secondary data consists of land resources (lr), the primary data includes the name pekaseh delineation and area subak, human resources (hr) and agricultural activities were used as attribute data. denpasar city has 41 subak in 2015, subak area on the analysis of satellite imagery (2008.6 ha) was smaller (520.4 ha) than the central statistics agency (csa, denpasar 2529 ha), with r2 = 0.8967. soil fertility moderate, land suitability agro-ecosystem very suitable (s1) for rice field and suitable (s2) for second crops and horticulture lowlands, required land cultivation and fertilization, suitable to crop needs. hr status of farmers as cultivators 72% and landowners 28%. subak paddy crop rotation pattern denpasar city is paddy-paddy/palawija–palawija/paddy. the data base is composed of a map subak subak (spatial data), the data lr, hr and agricultural activities (attributes data). copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to site (apa 6th style): lanya, i., subadiyasa, n. n., hutauruk, j. (2017). subak land information system based on remote sensing and geographic information system in denpasar city. geoplanning: journal of geomatics and planning, 4(2), 225-232. doi: 10.14710/geoplanning.4.2.225-232. 1. introduction planning, implementing, and evaluating of regional development in all fields require data and the most recent update, correct, and accurately, to suit the purpose and on target. the available data is generally a correlation table between multiple names with the component area of land resources (lr), human resources (hr) and development activities are ongoing (fang et al., 2015). era of technology and communications would require the unit area based geospatial development, supplemented by data and basic information resource of the potential area. similarly, for the development of agriculture requires a data base, in the form of spatial data (units of land development) and attribute data that consists of data of lr, farmers hr and farming activities are carried out in every area of development (andersen et al., 1993). results of research conducted by lanya et al.(2014), produced maps of land zoning subak (protected, buffer and can be converted), using high resolution satellite imagery (ikonos, quick bread, worldview, landsat 8 and software gis (arc gis), high accuracy ( 98.5%), and can be obtained exact geographical location resulting from the analysis of satellite imagery (goodchild, 1987). further commented that the submission of information resource potential of agriculture based on remote sensing and gis facilitate in establishing zoning subak of rice field protection area of agricultural land in sustainable food. embryo of permanent agriculture in bali was done by the subak system; bench terraces on sloping land on the volcanic landform, water resources derived from springs and orographic precipitation. limited sources of water for agriculture of rice field, carried out through the equitable distribution of water, use open access article info: received: 04 oct 2016 in revised form: 10 oct 2016 accepted: 26 juny 2017 available online: 30 april 2018 keywords: the subak, spatial data, land resources, human resources corresponding author: indayati lanya study program of agroecotechnology, faculty of agicultural, udayana university, indonesia email: indahnet@yahoo.co.id https://doi.org/10.14710/geoplanning.4.2.225-232 https://doi.org/10.14710/geoplanning.4.2.225-232 mailto:indahnet@yahoo.co.id lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 226 | efficiently and planned water supply. traditional water management systems are known to farming subak systems. according to various sources, water control system built at the initiation of settled agriculture, is no longer moving, there is also mention the subak was built in the 7th century. in the regional regulation (perda bali province no.02/pd/dprd/1972) subak defined as traditional law community that have the characteristics of socio-agrarian-religious, a farmer association that manages the irrigation water in paddy fields. ironically, prior to 2015 there has been no study on spatial limits subak region. subak institutions in bali receive incentives from the provincial and regency / municipality, with goal to preserve the natural and agrarian culture. subak is perfect to implement the philosophy of tri hita karana, there pawongan (subak members/farmers), there palemahan (paddy field), and there parahyangan (pura). subak in this paper is a unit of agricultural area, with names, have palemahan (paddy field resource), pawongan (human resources/farmers), and paddy field farming systems. for it does not discuss of subak as water traditional setting organizations (subak irrigation system). tri hita karana (human, natural/environmental and parahyangan) is the philosophy of the balinese people, who always keep the balance of the relationship between society and nature, society and god. symbolized by the parahyangan (pura subak) a place to pray, palemahan (paddy field), and members of subak (pawongan). religious values and limited water resources are intelligently done by managing irrigation systems and irrigation agencies which are designated as socio-religious character. the main function is the management of irrigation water for the production of food crops, particularly rice and pulses (windia, 2006). the existence of subak as traditional institutions/organizations of indigenous bali in agriculture is still exis. subak as bali local genious established by the united nations organization (unesco) as world cultural heritage. subak system still exist in denpasar, so that denpasar entry into the world cultural heritage city. subak in denpasar has an important role, both as a counterbalance the urban ecosystem, as well as primary agricultural resources in the success of development food security programs. along with the information disclosure and communication, as well as the preservation of the subak in bali, gis software can then be used as a medium to represent data and information on subak into a computerized system. mapping rice area by means of satellite imagery and gis have been conducted throughout indonesia (pertanian, 2012). the result was a map of paddy fields in bali province 985 ha is smaller than the csa, bali in the same year. while the results of the mapping paddy field by using technology, region, and in same year conducted by the national land agency, data obtained by the difference paddy fields area with the data of the csa only 113 ha. similar research applications of remote sensing and gis (geographic information system) for mapping rice fields in bali has a 98.5% accuracy rate (i lanya et al., 2014). the third that research used to calculate the area of paddy fields in bali province associated with the balance of food. map (spatial data) are not yet equipped with data and information on land resources (lr) and human resources (hr) as well as the agricultural activities of each unit map (polygon paddy fields management unit) as a data base (attributes) that are needed for agricultural development. for the land resource information system, gis-based needs to be built to integrate spatial data and attribute data of agricultural resources (lr, hr), in order to facilitate the search for data and information easily and quickly (rodo et al., 2017) in this era of globalization, the development of agriculture requires a data base management unit of land, such as subak in bali region. data base of spatial (map of subak) need to be integrated with attribute data subak resources through information and communication technology (ict). subak as agricultural land resources should be preserved and recorded by computer-based potential to provide added value to the data base of subak. data base of paddy fields of subak is data and land information that controls plant growth. in outline, the data include: soil, water, farmers, and agricultural activity. these data are used to facilitate in implementation of smart people, smart environment, and smart agrarian economy. subak region needs to be mapped spatially and supplemented by data paddy fields resources (lr, hr, as well as farming systems conducted by farmers) in the region by means of remote sensing and gis, to get high accuracy and easily accessible data. land resources information system (lris) is one form of geographic information systems, where the data of agricultural land resources is a kind of sub-component data and geographic information. lris components and works the same way with com-component and how gis is public unless the objects to be http://doi.org/10.14710/geoplanning.4.2.225-232 http://doi.org/10.14710/geoplanning.4.2.225-232 lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 | 227 examined are data and information on agricultural land resources (sulaeman et al., 2015). similarly, lris is as agricultural land resources-based computers, used for data provision, manage, process, storage and security, product manufacturing information relating to the lr and hr subak paddy fields, as well as generate geographic referenced information. 2. data and methods 2.1. geographical area of denpasar city denpasar city is at 08035'31 "08044'49" south latitude and between 115010'23 "115016'27" east longitude. bordering badung regency in north and west, gianyar and badung strait to the east, the total area is 127.78 km2. denpasar city is composed of four sub-districts (north, east, south, and west), 43 villages/urban villages with 404 banjar/hamlets (figure 1). figure 1. map of village administrative denpasar, (bappeda, 2011) data from the bps (2015), denpasar city had 42 subak, scattered in four districts, west denpasar eight subak, east denpasar 14 subak, south denpasar 10 subak, and north denpasar 10 subak. 2.2. materials and research tool the research material consists of: satellite imagery worldview (wv) denpasar 2015, maps support as secondary data (maps appearance of the earth and soil types), land suitability agro-ecosystem, soil fertility, statistical data cbs and data from the dinas pertanian kota denpasar (2014). software quantum gis (qgis) is used for: 1) mapping of boundary and rice area of subak (spatial data) from the analysis of satellite imagery and field survey, 2) integrating the spatial data with the data of land resources (lr) consists of soil, water, vegetation, human resources (hr) and agricultural activities, and 3) presenting the spatial and attribute data in subak land resources information system based on gis. 2.3. research methods the research method used: (1) literature through secondary data collection, (2) the analysis of satellite imagery wv 2015, through a digitization polygon paddy field on the screen to get a subak tentative map, (3) the field survey to obtain information boundaries of subak, hr primary data, and agricultural activities, (4) http://doi.org/10.14710/geoplanning.4.2.225-232 lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 228 | create a land use map of subak paddy field and non-agricultural land, (5) develop a data base of spatial and attribute data, (6) establish land information system of subak. point 2.4, 5 and 6 were done by using qgis software. beginning with the equation of the reference coordinate system used wgs 84 s/utm grid, created a polygon vector data (shp) with features add layers, satellite imagery wordview 2015 visual interpretation and digitized on screen polygon layer of area subak. this research used calculating fitur in quantumgis to calculate the area of the subak area, join attribute and spatial data of subak in quantumgis, and make a layout map to add features composer, minimap menu was used to create edge of information maps. 3. results and discussion delimitation and delineation of boundaries of subak through a digitization screen on satellite imagery wv (tentative map), equipped with field measurements and observations, as well as interviews with the chairman/kelian subak a produce of subak map (figure 2). figure 2 shows that the denpasar city still has 41 subak area of 2,008.6 ha, it is smaller (520.4 ha) than data from denpasar cbs (2,529 ha), with the regression equation y = 0.7677 x +1.5887 with r2 = 0.8967. the value of r2 = 0.8967 means that there is a very real correlation between the area of subak from the analysis of satellite imagery with data from cbs with 89.67% confidence level. geographic locations spread across four districts. the smallest area was (1. 25 ha) srogsogan subak in west denpasar and the largest was (152 ha) in subak temaga, east denpasar district. figure 2. the map of subak in denpasar city (analysis, 2015) differences of subak paddy field from different sources are common, because it used different method. as the csa in the study area (denpasar) found a number of subak mapping results by means of satellite imagery and gis, which have been checking field, acquiring data 41 of subak. subak difference peraupan east with an area of 15 ha is still listed in the statistical data. subak existing condition in the field does not have paddy field since 1998 (the interview with pekaseh). the word subak paddy field mapping by using a remote sensing and gis technology will obtain data and information more quickly, accurately, current/recent and its existence could be traced geographically/spatially, the data and information were more quickly and accurately obtained, when compared with the conventional data from this terrestrial survey. research done by hutauruk et al. (2016) on land resources information system (lris) of subak paddy fields based remote sensing and gis in denpasar was able to provide information on potential lr, hr, agricultural activity in the 41 polygon of area subak, quickly and easily identified and accessed. the same study in badung produce 119 polygons of area subak (lanya & subadiyasa, 2016). these results assist local http://doi.org/10.14710/geoplanning.4.2.225-232 http://doi.org/10.14710/geoplanning.4.2.225-232 lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 | 229 governments in planning and implementing the agricultural development in the city of denpasar city and badung regency. csa data and the department of agriculture data were generally larger than the data results of mapping of by means of remote sensing and gis technologies (lanya et al., 2014). the subak paddy field area data as a baseline in agricultural development planning focuses on food security. the subak paddy field area affects the calculation of food availability in the region of bali. standard information acreage of agricultural land which was greater than the real conditions will impact negatively on the self-sufficiency in rice. to get standard extensive data of high-quality paddy fields, the subak paddy field mapping required based remote sensing and gis as spatial data and the need to put in regulations related to protect agricultural land and the national and regional food security. the interpretation of satellite imagery and field observations were found in many buildings in the the subak paddy field, and in the area of green open space (gos), such as building houses, roads and housing conducted by the developer (figure 3). the tendency of such violations the subak extends throughout the region, especially in south denpasar bordering the tourism center of kuta and sanur. paddy fields in the south denpasar are very vulnerable to sense of urgency space for urban development and tourism support facilities. this is caused by the allocation of space in the spatial plan (sp) denpasar city years 2011-2031, i.e. paddy field was allocated to non-agricultural land. figure 3. satellite image quickbird denpasar city 2013 (bappeda , kota denpasar) information potential of nr, hr, and farming systems in the subak paddy fields can be presented in table form (table 1) and a map base on gis (figure 4). the subak map (figure 2) from the digitized satellite imagery as a spatial database integrated with table 1 (examples subak kerdung) as a data attribute by using gis technology to produce figure 4. subak resource consists of land resource (subak area, munduk, plant type, water source, land suitability, soil type, soil drainage, soil texture, fer-abillity, fertilizer status ), human resource (pekaseh name, farmer total, farmer status, member total, land owner) and agricultural activities (crop index, fertilizer types, fertilizer dos, production, irrigation type, crop type, seed source, pest and marketing). the era of globalization, it will select information from figure 4, because it is easily accessible, more communicative and based on information and communication technology (ict). to find the desired information the subak potential, with the help of program gis, fairly choose/click subak spatial data locations are on the system, will perform a number of information nr, hr and agricultural activities that have been have developed as subak selected attribute data. http://doi.org/10.14710/geoplanning.4.2.225-232 lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 230 | table 1. data base of sdl , sdm and agricultural activities in subak kerdung in south denpasar subak name village subak area from pekaseh subak area from csa munduk plant type water source land suitability soil type 2 3 4 5 6 7 8 9 10 kerdung pedungan 215 215 m.siran, m.kerdung, m.buyuk, m.nyalin, m.timbul, m.abasan, m.babakan, m.pitik paddy, soy, cucumber, spinach, kale 1. tukad badung, bendung mertagangga, irigasi mertagangga 2. tukad badung, bendung batannyuh paddy, soy, corn, chille, all vegetable except onion and spice, ginger, turmeric, fruit as melon, sunflower, typic tropaquepts, isohipertermik table 1 ( advanced) soil drinase soil texture fert.ability fertilizer status pekaseh name farmer total farmer status member total land owner 11 12 13 14 15 16 17 18 19 hampered, rather hamperedterhambatagak terhambat smoothrather smooth ccgh-llgh medium i wayan tama 200 owner = 20, cultivator = 180 200 personal = 215 ha table 1 ( advanced) crop index fertilizer type fertilizer dos production irrigation type crop type seed source pest marketing 20 21 22 23 24 25 26 27 28 245 urea, npk phonska urea 200 kg/ ha, npk phonska 200 kg/ ha paddyin dry season= 10 ton/ ha. paddy in wet season = 8,5 ton. soy 2 ton/ ha, sekun der, tersier paddypaddypalawija paddy and vegetable seed from pt.pertanibenih wereng,snail, bird, rat paddy sale to pt.pertani, pt upb munggu, and middleman figure 4. display aplication of subak paddy field information system. example subak kerdung http://doi.org/10.14710/geoplanning.4.2.225-232 http://doi.org/10.14710/geoplanning.4.2.225-232 lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 | 231 generally nr denpasar city is classified as good: moderate soil fertility, are suitable (s1) for paddy rice irrigated and suitable for horticultural crops and lowland. needed input is the cultivation hr in general (72%) as peasants, landowners and only 28%. crop rotation pattern in north denpasar is paddy-grains, east denpasar is paddy-paddy-grains, south denpasar is paddy-paddy-crops, and west denpasar is paddy-paddy crops/horticulture of land, irrigation, and fertilization in accordance with the needs of the plant to increase production of food crops. the results provide information to the farming community that denpasar already has paddy field information system subak-based remote sensing and gis technology. this system can aid in the development of technology-based, with the aim to provide solutions to various problems to enhance community welfare. as planning (e-planning), implementation (e-governance), monitoring and evaluation (e-monev) agricultural development, as well as easy to give information on potential land resources, human resources, and agricultural activities in each subak to denpasar smart cityin managing the natural, cultural, and environmental. resources subak displayed using gis was more easily and accurately, compared to the provision and performance statistics. gis can integrate data attribute/of subak resource database with spatial data/geographical position of the region of subak, also makes it easy to calculate the area of raw paddy fields in the region of subak. the data of land resources, human resources and agricultural activities in the region of subak can be displayed and updated easily. during this time the data separate agricultural land resources and the only form of statistical data, so it is very difficult to obtain information on the resource potential of subak easily, quickly and accurately. research and development of land resources (rdlr) develop land resources information system, which is abbreviated sisultan in 2013, and develop in 2014 and it can be accessed via www.sisultan. litbang.pertanian.go.id. geospatial data presented in this system only (i) the rainy region, which presents the class of rainfall and its distribution. this data is sourced from climate resource map scale of 1: 1,000,000 published by the research institute for agro-climate and hydrology in 2000; and (i) spatial structure of agriculture (sulaeman et al., 2015). 4. conclusion delineation polygon of subak paddy fields on the image of the wordview 2015 and field surveys in denpasar amounted to 41 subak of subak 42 csa data. remote sensing technology can provide spatial data more accurate than the csa data. data base of subak paddy fields cover land resources (subak area, munduk, plant type, water source, land suitability, soil type, soil drainase, soil texture, fert-abillity, fertilizer status ), human resource (pekaseh name, farmer total, farmer status, member total, land owner) and agricultural activities (crop index, fertilizer types, fertilizer dos, production, irrigation type, crop type, seed source, pest and marketing), it can be integrated with a map of subak, and can be called up easily and quickly to acquire a data base of agriculture in each name of subak computer-based gis software. land resources information system (lris) of subak paddy fields can be used as a tool for local governments in formulating policies, take decisions or carry out activities related to agricultural development based on local wisdom. 5. acknowledgments we extend our thanks to mr. minister ristekdikti, especially mr. director of research and community services directorate general for strengthening research and development of the ministry of research technology and higher education, who has given the fund for this study on the national priorities research skim, masterplan for the acceleration and expansion of indonesia's economic development (mp3ei pemprinas) 2011-2015. research title: land resource information system for regional development planning destination sarbagita regional agro in bali province. thanks to the rector of udayana university, who has provided the opportunity to conduct research and support in this publication. similarly, to mr. chairman institute for research and community services and to mr. dean of the faculty of agriculture, university of udayana we say thank you, who provide the opportunity to conduct the research. we do thank to the research executive team and those who influence helped this research. http://doi.org/10.14710/geoplanning.4.2.225-232 lanya et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 225-232 doi: 10.14710/geoplanning.4.2.225-232 232 | 6. references andersen, r. a., snyder, l. h., li, c.-s., & stricanne, b. 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(2006). the subak irrigation system transformation based on concept of tri hita. retrieved from www.balipost.com. http://doi.org/10.14710/geoplanning.4.2.225-232 http://doi.org/10.14710/geoplanning.4.2.225-232 https://doi.org/10.1016/0959-4388(93)90206-e https://doi.org/10.1109/tgrs.2015.2445767 https://doi.org/10.1080/02693798708927820 https://doi.org/10.1088/1755-1315/47/1/012037 https://doi.org/10.1016/j.bbr.2016.12.009 http://www.balipost.com/ | 53 geoplanning vol 4, no. 1, 2017, 53-62 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.1.53-62 land price mapping of jabodetabek, indonesia a. m. elmanisa a, a. a. kartiva a, a. fernando a, r. arianto a, h. winarso a, d. zulkaidi a a research group in planning and policy development, bandung institute of technology, indonesia abstract: land provision is one of the biggest challenges for development in urban area. most of the available urban land will be the object of speculation to be resold at a higher price when the time is right. in jabodetabek, where the pace of urban development is faster than other parts of indonesia, the prices of land show an abnormal increase; they seem to rise too fast. this paper discusses the increasing land prices in jabodetabek area and argues that the increasing land price has encourages the private developer to bank the land in the area. based on land price survey in jabodetabek, urban activity is moving to south jakarta. the highest land prices were found at east kuningan, setiabudi, and south jakarta. by constrast, the lowest prices were observed in sumur batu and cimuning (bantar gebang, bekasi).it can be concluded that the land price increase also triggered land banking practice in jabodetabek reaching in total approximately 60% of total area of jakarta. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): elmanisa, a. m., et al. (2017). land price mapping of jabodetabek, indonesia. geoplanning: journal of geomatics and planning, 4(1), 53-62. doi:10.14710/geoplanning.4.1.53-62 1. introduction until 2015, within 35 years, the private sectors in jabodetabek (jakarta, bogor, depok, tangerang, and bekasi) have transformed large amount of land in jakarta and its surrounding areas. by 1997, a countryside land of 16,600 hectares of located outside the built up area, were converted into residential use, and sold about 25,000 units per year (winarso, 1999; winarso & firman, 2002). this development went further into rural areas and created urban sprawl. ten years since the 1990s, twenty new cities have emerged. during the year of 1990 to 1994, changes in land use significantly increased. land conversions were apparent. in this situation, land ownership concentration clearly was possessed by only few big developers. in1996, more than 1,000 hectares land in botabek (bogor, tangerang, bekasi) were owned by only 15 companies (winarso & firman, 2002). furthermore, winarso (1999) mentioned that land developers companies were working together to determine the land price. in the end, land speculations correspondingly occurred. land speculation is an attempt to gain the value of the land that arises from land use change (hermawan & syahbana, 2015). supply of land is inelastic; therefore, land prices will rise as demand increases. frequently, the rise of land price does not followed by increase of land availability (land supply). some owners even put off selling the land, expecting higher price in the future that will result in land supply reduction (darin-drabkin, 2013). the financial industry plays an important role in jabodetabek land development since this industry provides funding for developers. its role becomes more important along with enactment series of deregulation policies began in the 1980s. all domestic banks were given the liberty to open new branches and to allow establishment of new private banks. furthermore, soes were allowed to deposit their funds in these banks up to 50%. until now, this phenomenon consistently takes place in jakarta and its surrounding areas. developers allegedly set the price of land as happened in the 1990s. this might be due to land ownership concentration article info: received: 4 october 2016 in revised form: 17 november 2016 accepted: 23 february 2017 available online: 27 march 2017 keywords: land price, isoline-map, jakarta, jabodetabek, gis corresponding author: adisti madella elmanisa research group in planning and policy development, bandung institute of technology indonesia email: adis.madel@gmail.com open access http://dx.doi.org/10.14710/geoplanning.4.1.53-62 mailto:adis.madel@gmail.com elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 54 | that is yet being acquired by only few large developers. they own nearly all available strategic lands to be built into medium scale housing and finally sell it with high price. developers still determine the selling price of the property. for example, kompas (october, 2014) quoted that "despite the increasing price of fuel oil has not been officially announced, some developers already prepare to raise the price of housing in january 2015" (alexander, 2014). the increasing price has direct impact to land supply for low-price housing development, as reported by cnn finance in november, 2014 in quotation “this condition, land price that continuously increases has caused difficulty for a lowprice housing developers to develop their business” (supriyadi, 2014). urban land study cannot be separated from its role as a major input in city development, where there is transformation from land into built environment. urban development is a complex process that requires orchestration of financial, material, labor and expertise by various actors in an environment harboring social, economic, political and wider elements. in order to explain the complexity, at least three theoretical approaches could be used as an analysis tools such as neoclassical, neo-marxist and institutional approaches (healey, 1992; winarso, 1999). according to neoclassical approach, urban land use is determined by decisions made by firms and households based on their preferences. therefore, demand for land will be associated with location, distance proximity to a variety of urban services, availability of facilities, quality of surrounding environment, social factors, transportation, and various other attributes. finally, neoclassical approach leads to hedonic land price concept. meanwhile, based on healey & barrett (1990), institutional approach saw land price as an implication of interaction between the financial system, types and strategies of the actors, and the role of policies and interventions influencing the development process and strategies adopted by the actors in it. moreover, beckert (2011) saw the price as the implications of the macro-social structure, a network of actors in a market, where the network can determine the price through their effects on its internal competition and create a variety of phenomena such as cartels, monopolies, and other forms of collusion to manipulate the situation market. land price determined only by developers may indicate an oligopolistic market/monopoly and unhealthy competition which is feared will cause bubbling, disturbing the property market and infrastructure development (please see hu et al., 2016; liu, miao, & zha, 2016; liu, wang, & zha, 2013; tsutsumi & seya, 2008; zhang et al., 2017). it could also lead to a crash. during the second half of 1980s, land price in japan was on the highest level in two decades and created bubble then crash. until now, housing price remains a major problem in japan due to extremely high land price. based on these descriptions, identification and analysis land price in indonesia, especially in jabodetabek becomes indispensable. for that purpose, the gathering of land price information was carried out in jabodetabek using sampling method. land price data was then processed and displayed on isoline map using gis. moreover, interview processes were conducted and secondary information were collected in order to support the alleged practice of land banking by the developer. 2. data and methods 2.1. sampling selected informants to be interviewed were purposively conducted, as important actors involved in land development process for housing in jakarta and its surrounding areas. in addition, the snowball method was used to gain access through a network, owned by key informants that have been previously interviewed. the type of sampling used was multistage random sampling: geographical and probability samplings. geographical sampling was applied to divide the number of units of the study area (36 x 18). while probabilistic sampling approach was applied for land price mapping with the unit of analysis, as performed by the dowall & leaf (1991) in jakarta. to give an exact picture without having to conduct a survey in all villages, we used 300 samples out of 539 villages located in the study area (figure 1). each unit which data was taken have the following criteria location (formulated based on research dowall & leaf, 1991): (a) close to the highway, (b) local roads (a residential street), and (c) footpath / alleyway. the process of collecting land price data used sample data that represents the area of jakarta, bekasi, depok city, kota tangerang and south tangerang area where the land is used as residential http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 | 55 development, with administrative village as the smallest unit. informants were village officers from government and private agencies / brokers. composition of collected land price data consisted of two data from a broker and one data from village officers. a total of 9 data at every unit was acquired. figure 1. map of field study some used definitions in this paper are: (1) land price which is the land transaction price (in rupiah); (2) large developers which is a company engaged in the development of formal land with an area of at least 50 hectares project (winarso, 1999); (3) land banking meaning land owned by the developer either undeveloped or already developed but not yet vended (in hectares); (4) administrative village is an administrative staff who understands about land price; and (5) broker/realtor is intermediaries in of selling /buying/renting the property with experience of at least 5 years. 2.2. data source land price data sources were acquired from stakeholders or actors involved and played a role in land trading and land price. stakeholders include the broker/realtor, the large developers and the government. moreover, data from website, finance magazines and companies history were equally collected. 2.3. analysis method based on dowall & leaf (1991), land price will were analyzed based on information obtained from three informants, if differences were observed, the middle price was taken (median). based on the availability of infrastructure in each village, land price data were collected from three different locations. brokers were also asked about land price changes over the last 10 years (since 2005). land prices were furthermore analyzed based on the characteristics of the plot to observe the difference between some characteristics before shown on map. the map was created with geographical information system (gis) as database and surfer software as analysis tool. information related to land banking were obtained from the interviews. to obtain a deeper and wider grounding, secondary data from various sources were also collected such as books, journals, documents of regulations and policies, the central statistics agency (bps), bank indonesia (bi), property analysis reports, articles and reports from media of properties and various other sources. 3. results and discussion 3.1. land price in jabodetabek the increase of land prices in urban areas has constantly arisen in countries with strong economic growth and strong urbanization, including in indonesia (dowall & leaf, 1991; ferguson & hoffman, 1993). the following figure 2 compared the increasing of land property price between jabodetabek and 14 other major cities in indonesia. the data collection was routinely conducted by bank indonesia. property price index describing the development of property prices in 14 major cities had a tendency to increase, but with http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 56 | a relatively gentle slope. in contrast, the index of property prices in jabodetabek experienced a significant acceleration with a steep slope since mid-2011. figure 2. the increase in property prices in greater jakarta compared with bank indonesia index (bloomberg, bi, bank for international settlements, in east spring investments indonesia, 2015) ferguson & hoffman (1993) analyzed land price increases in jakarta from 1985 to 1990 by comparing it to variety of economic indicators, including consumer price index, various forms of investment, as well as the comparison of price increases occurred in other countries. while ward, jimenez, and jones (1993) conducted a study of land price increases in mexico in the context of affordability for the poor residents by comparing the increase in land prices with some factors such as: (a) the minimum wage; (b) actual wages based on surveys; (c) the price of basic commodities; and (d) the increase in the price of building materials. one of data source related to land price increases can be observed via land value data at several major locations in jakarta as reported by property indonesia (november, 2015). based on the displayed data, the land price increases was projected from two types of prices, which are market prices and tax object sales value (njop). value between njop and market price was often unbalanced, where njop was much lower than the transaction price. in practice, the actual transaction price was replaced in accordance with njop so that the paid tax became much lower than it should have been. such practices leading to the potential loss of local taxes, hence, anticipation were conducted by increasing njop as much as 20-140 percent in 2014, via jakarta governor regulation no. 175/2013. recapitulation of land price growth in jakarta and its surrounding areas from 2011 to 2014 was shown in table 1. this data were based on the survey conducted in 284 urban villages of jakarta and its surrounding areas. it included jakarta (132 villages), kota tangerang (45 villages), south tangerang city (41 villages), kota depok (39 villages), and bekasi city (27 villages) with administrative village officer and also brokers as informants. both informants were well-informed about the land price growth at each location. table 1. the land prices increase in jakarta and its surrounding areas (bank indonesia, 2015) area percentage of increasing land price average (2010-2011) (2011-2012) (2012-2013) (2013-2014) jakarta 18.21% 21.01% 14.65% 10.85% 16.18% tangerang 28.57% 22.22% 63.64% 27.78% 35.55% south tangerang 16.38% 18.52% 25.00% 22.00% 20.47% bekasi 28.57% 23.33% 49.10% 19.34% 30.08% depok 32.00% 51.52% 25.00% 40.00% 37.13% based on average calculation during 2013-2014 released by indonesia property (november, 2015), market prices growth in prime locations in jakarta has reached 18.72%. meanwhile, based on the analysis carried out by bank indonesia from 2010 to 2014, there was an increase in land prices on average of 24.54% in jakarta and its surrounding areas. based on warta ekonomi (1990), those figures were lower than the average growth in 1985-1988 which reached 36%, as quoted by ferguson and hoffman (1993). while based on dowall and leaf (1991), in the period 1987-1989, the average annual land price growth in http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 | 57 formal sector of jakarta was 20.77%. by contrast, in locations with low infrastructure, price growth was still relatively low reaching 29.17%. what about land price growth in other countries? based on the report from knight frank (2014) regarding the increase of premium residential land prices in asian countries from june 2013 to june 2014, jakarta was at second rank (23.60%) after phnom penh (25%) and above bangkok (20.30%) (table 2). table 2. premium residential land price increase in asia (knight frank, 2014) kota h1 2014 q1 2014 q2 2014 juni 13 – juni 14 bangkok 18.20% 9.10% 8.30% 20.30% phnom penh 13.70% 6.10% 7.20% 25.00% jakarta 11.60% 5.80% 5.50% 23.60% bengaluru 6.80% 6.80% 0.00% 9.70% mumbai 6.30% 2.80% 3.40% 9.10% shanghai 6.20% 6.80% -0.60% 18.80% tokyo 1.30% 1.30% 0.10% 7.70% guangzhou 0.40% 1.60% -1.20% 4.80% kuala lumpur 0.00% 0.00% 0.00% 9.00% singapore 0.00% 0.00% 0.00% 0.90% beijing -2.50% -1.80% -0.70% -0.10% ncr -4.90% -4.30% -0.60% -1.50% hong kong -4.90% -3.00% -2.00% -8.00% compared to other countries in asia, both on the premium residential or premium office (at least in the period 2013-2014) land price in jakarta was generally included in the group of cities experiencing land price rapid growth. these data was hard evidence showing that the price of land in jakarta in particular, and jabodetabek in general, was already extremely high and even still going higher. based on the above description, review of current land price was conducted in jabodetabek by measuring market price, which is the net price of land trading transaction (table 3). table 3. highest and lowest land price in jadetabek (primary survey data, 2015) city highest price (rupiah/square meter) highest price location (administrative village) lowest price (rupiah/square meter) lowest price location (administrative village) north jakarta 45,000,000 west pademangan 480,000 kamal muara west jakarta 50,000,000 duri kepa 1,000,000 east cengkareng central jakarta 72,000,000 gelora 1,500,000 tanah tinggi east jakarta 12,195,000 pulo gadung (pulo gadung) and cipinang cimpedak (kec. jatinegara) 900,000 pinang ranti south jakarta 100,000,000 kuningan timur 1,416,000 ciganjur tangerang 50,000,000 tanah tinggi 285,000 karang anyar depok 15,000,000 cinere 285,000 tirta jaya south tangerang 30,000,000 ciater 416,000 west pamulang barat bekasi 15,000,000 bekasi jaya 200,000 sumur batu and cimuning xx = highest price xx = lowest price the analysis generated conclusion that the highest price calculated at idr. 100,000,000/sqm was located at east kuningan, setiabudi, and south jakarta. the lowest price calculated at idr. 200,000/sqm was located at sumur batu and cimuning, bantar gebang, bekasi. this result suggested that urban development concentration heads to south jakarta. meanwhile in bekasi, land prices were relatively lower. it might be due to existence of industrial areas causing pollutions. further investigation showed several reasons that might affect the land price in every administrative village, i.e.; (a) pademangan has the highest land price in north jakarta allegedly due to tourism destination named taman jaya ancol and nearby shopping centers. by contrast, penjaringan has the lowest land price due to its frequent flooding. (b) kebon jeruk in west jakarta has highest land price due to its nearness to port of merak and has already planned as residential area. meanwhile, cengkareng has low land price due to its proximity to soekarno-hatta airport that produce high noise pollution. (c) tanah abang in central jakarta has the highest land price because it is located near to scbd and center of urban economic activity. while, johar baru has lower price due to the presence of slums and betawi villages having negative image. http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 58 | (d) the highest land price at east jakarta is located in pulo gadung due to its civic center with full facilities and jatinegara due to further development as luxury housing. while, the lowest land price is found at makassar since the density of this area is already too high and further development is difficult to be conducted. (e) in south jakarta, the highest price is at setiabudi due to the proximity to scbd civic center as well as its function allocation for ambassador housings, while the lowest land price is located at jagakarsa due to its poor accessibility and location on the outskirts of jakarta. (f) tangerang has the highest land price due to its function as government area as well as its development for luxury housings. by contrast, the lowest land price area was located in batu, ceper, due to its proximity to international airport and factories. in addition, frequent flooding could aggravate the condition. (g) cinere holds the highest land price in depok city because it has closest access to jakarta, and it has many urban amenities. meanwhile, the lowest land price is in sukmajaya due to its proximity to industrial area that produces pollution. (h)the district with the highest price in south tangerang is serpong due to its accessibility to the motorway and luxury housings (bsd and gading serpong). as opposed, the lowest price is located in pamulang due to its limited access. (i) east bekasi has the highest land prices in bekasi as it is being developed as residential center, while bantar gebang has the lowest price due to its nearness to bantar gebang landfill. based on market prices, information about land prices was then reanalyzed to determine its growth trend. the market price included not only land but also property prices. according to ali tranghada (executive director of indonesia property watch / ipw), during the european crisis in 2007-2008, property market in indonesia began to accelerate in 2009. it can be observed using two indicators: interest rates and purchasing power (dbs group research, 2014a). it is estimated that within 3-4 years after the acceleration, booming will usually occur. in fact, indonesian property market grows of about 50-60% in one year. the highest growth occurred in three locations: serpong, pantai indah kapuk and kelapa gading. property market in indonesia is unique because developer determines the price. this results in property prices that continue to rise until it becomes overvalued in which it will create speculation. however, this increasing price will arrive at a saturation point. in 2014-2015, the property was at its lowest stage. in sense, the prices are still rising but at a slower pace and are waiting for the moment to go back up. it is estimated that the price increase will be re-started in 2016. the property price and land prices rise in jabodetabek in the last 5 years (2010-2014) ranging from 5 to 50% each year. the increase varies in each city. in this study, land price data for the last 5 years (2010-2014) was utilized. information about property prices and land prices were obtained by directly asking to an administrative staff and two brokers in every administrative village. however, for some reason, the collected information was incomplete. therefore, data interpolation was conducted so that all data in the last 5 years were encountered. furthermore, the interpolated land price data was then summarized into one number for every year, taken from the median price of all the data in each city. it should be emphasized, that the figure 3 and figure 4 only describes the growth of price uniquely, but not portrays the real price per year. figure 3. growth of property price in jabodetabek, 2010-2014 http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 | 59 figure 4. growth of land price in jabodetabek, 2010-2014 based on this analysis, north jakarta has the highest property price in the last 5 years, while the information of land price growth is not available. tangerang and bekasi have the lowest property prices throughout the five years in a row. however, in this period, the land price in bekasi is continuously the highest, far than other areas in jabodetabek. these land prices is so high, it become barrier for housing supply, so that the property in bekasi has the lowest price. another outcome land price data is isolines map. this map was formed out using land price data that has been normalized using natural logarithm method, and combined with contour lines of land price drawn using interpolation method. this interpolation method was used to determine the similarity of values between the adjacent survey points, then, land price data that has same range of values were grouped. isolines map shown below has not currently counted the diversity of the existing value. isolines used to show the distribution of land price values and showed which areas having similar characteristics (figure 5). isolines map showed that there is a vast difference between the price of land in jakarta, tangerang, south tangerang, bekasi and depok. figure 5 showed the distribution of the highest land prices spreading in south jakarta, central jakarta, north jakarta and west jakarta. furthermore, dominance altitude land prices were concentrated in south jakarta. on the other hand, land price in north jakarta also begins to increase especially in the border area such as east jakarta, south tangerang, and depok. in addition, land price in tangerang has not shown a significant growth compared to other regions in jadetabek. figure 5. land price isoline map (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 60 | 3.2 land bank ownership in jabodetabek as described earlier, the high growth of land prices has allegedly caused the practice of land ownership by the developer in the form of land bank. land ownership has at least explained two things: (1) the ability of developer to master a greater market share both today and in the future (2) the indicator of the developer's financial strength, and (3) the ability to influence the price to be offered to the market. land ownership through land banking practice or land reserves for future development intends to accumulate land for some time before the development process begins. it is logical, because the land market is not perfect, and the process of land transactions often cannot be quickly resolved. in some cases, the land reserves of developers are used as insurance to obtain assistance from financial institutions, to fund ongoing project developments. besides, the land reserves can be media speculations for expecting greater price increases in the future. based on these considerations, the developer will increase the number of their land reserves, because it is extremely important for their businesses sustainability. based on the analysis of annual and quarterly reports as well as the company's data, an illustration of the land reserves in jabodetabek which are owned by large developers can be obtained as follow (table 4). table 4. land bank ownership by large developers in jabodetabek no developers land bank area in jabodetabek (ha) 1 pt bumi serpong serpong damai tbk 2,931.76 2 pt alam sutera realty tbk 2,135.00 3 pt jaya real property tbk 1,706.11 4 pt suryamas dutamakmur tbk 1,599.12 5 pt kawasan industri jababeka tbk 1,249.11 6 pt. modernland realty tbk 1,237.78 7 pt hanson international tbk 1,234.00 8 pt sentul city tbk 1,193.77 9 pt mnc land tbk 1,037.19 10 pt summarecon agung tbk 943.84 11 pt lippo cikarang tbk 930.00 12 pt duta pertiwi tbk 894.70 13 pt. bakrie land tbk 720.05 14 pt ciputra development tbk 603.80 15 pt intiland development 470.31 16 pt. metropolitan land tbk 416.40 17 pt. lippo karawaci tbk 165.00 18 pt megapolitan development tbk 81.85 19 pt metropolitan kentjana tbk 59.85 20 pt pembangunan jaya ancol tbk 20.39 21 pt. gading development 20.00 22 pt bekasi asri pemula tbk 19.24 23 pt pakuwon jati tbk 15.60 24 pt agung podomoro land tbk 11.85 25 pt duta anggada realty tbk 7.01 26 pt perdana gapuraprima tbk 3.93 total area of land bank (ha) 19,707.66 http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 | 61 according to data collection, reserve land owned by a private developer who has become a public company is 19,707.66 hectares of surface. assuming that one hectare could be built into 50 houses, then, these land banks owned by developers can accommodate as many as 985,400 housing units. assuming one housing unit is habituated by 4 people (winarso, 1999), land bank as describe above, could accommodate 4.24 million inhabitants. it is estimated that the needs/demands for a new house in jabodetabek, are 170 thousand units per year, but the absorption is still expected to remain below 70% of that amount (dbs group research, 2014b). thus, assuming absorption is amounted to 119 thousand houses per year, land banking could meet the housing needs in jabodetabek for the next 8 years. figure 6. land ownership in jabodetabek (analysis, 2015) compared with the total jabodetabek area reaching 602,818 ha, the land bank owned by the big developers only touch 3% of it. but when compared with jakarta which covers 66,233 ha, the percentage rises to 29.76%. the following illustration provides an overview of the extent of land banks owned by large developers, in comparison to the residential projects in jabodetabek. overall, residential projects in jakarta surfacing 42,269 hectares are compared to only 7% of the jabodetabek area, but reaches 60% of the area of jakarta (figure 6). in the previous discussed general problems of land prices in jabodetabek, the increase was fairly high. similarly, when compared to other cities in asia, jakarta belongs to cities with high price rises. from the outcome analysis, there are several important findings in this study: (a) the increased of land price in jakarta and its surrounding area is relatively faster than many variables. similar cases were found when compared to some other cities in asia. (b) in general, the residential market in jabodetabek, as many as 19 projects or 4.79% of the total 397 numbers of residential projects, contributing to 75.65% or 31,976 hectares of the total area of residential projects jabodetabek, amount to 42,270 hectares. from these 19 projects, there are developers who control more than one large-scale residential project. (c) ownership of land bank by developers in jabodetabek is quite high. at least 19,707 hectares of land reserves are controlled by a small number of developers. (d) developers regularly raise the land price in the developed project, as an attempt to attract the purchase of the property, equally as an investment instrument by offering property price that increases faster than other investment instruments. http://dx.doi.org/10.14710/geoplanning.4.1.53-62 elmanisa et al. / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 53-62 doi: 10.14710/geoplanning.4.1.53-62 62 | 4. conclusion the growth of land price in jabodetabek has been proved to be extremely high when compared to other cities in indonesia and some major cities in asia. the highest land prices were found at east kuningan, setiabudi, and south jakarta. by constrast, the lowest prices were observed in sumur batu and cimuning (bantar gebang, bekasi). it showed that urban activity is moving to south jakarta. land price increase also triggered land banking practice in jabodetabek reaching in total approximately 60% of total area of jakarta. 5. acknowledgments this research was funded partially by penelitian unggulan perguruan tinggi dikti (pupt dikti, 2016) and partially by bank indonesia (bi, 2015). 6. references alexander, h. b. 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[crossref] http://dx.doi.org/10.14710/geoplanning.4.1.53-62 http://properti.kompas.com/read/2014/10/28/122852321/siap-siap.harga.rumah.bakal.naik.lagi https://doi.org/10.1093/ser/mwr012 https://books.google.co.id/books?id=po1sbqaaqbaj https://books.google.co.id/books?id=po1sbqaaqbaj http://www.dbs.com.sg/treasures/aics/genericarticle.page?dcrpath=templatedata/article/generic/data/en/gr/042015/150428_insights_tread_carefully_with_indonesia_property.xml https://www.dbs.com.sg/treasures/aics/pdfcontroller.page?pdfpath=/content/article/pdf/aio/141203_insights_indonesian_property_when_the_going_gets_tough.pdf https://www.dbs.com.sg/treasures/aics/pdfcontroller.page?pdfpath=/content/article/pdf/aio/141203_insights_indonesian_property_when_the_going_gets_tough.pdf https://doi.org/10.1080/00420989120080881 https://doi.org/10.14710/geoplanning.2.1.38-50 https://doi.org/10.1016/j.apgeog.2016.01.006 https://doi.org/10.1016/j.jmoneco.2016.05.001 https://doi.org/10.3982/ecta8994 http://www.cnnindonesia.com/ekonomi/20141120130255-92-12731/pengembang-properti-tuntut-subsidi-uang-muka/ http://www.cnnindonesia.com/ekonomi/20141120130255-92-12731/pengembang-properti-tuntut-subsidi-uang-muka/ https://doi.org/10.1111/j.1435-5957.2008.00192.x https://doi.org/10.1016/j.landusepol.2016.12.011 | 67 geoplanning vol 3, no 1, 2016, 67-76 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.1.67-76 relationship between urbanization and dengue haemorrhagic fever incidence in semarang city i. p. pratamaa, s. rahayua a diponegoro university, indonesia abstract: unplanned urbanization can cause unhealthy urban environment, which in turn increases the population of mosquitoes carrying the dengue vector. consequently, this would reduce the urban life quality because public health is an important aspect of it. the increasing incidence of dengue haemorrhagic fever (dhf) in semarang city has been alarming. in 2013, the incidence was 2,364 cases, which increased up to 89.11% from the 1,250 cases of 2012. so, it is necessary to study about what relationship is there between the level of urbanization and the incidence of dhf in semarang. this study used quantitative and spatial approach. the unit of analysis is sub-district with time series data from 2006 to 2013. the analysis technique is spatial analysis through image interpretation, regression, and descriptive analysis. the level of urbanization has been measured through the variables of population growth, population density, land use change, and building density. the results have shown that there is no significant correlation between the level of urbanization and the incidence of dengue fever. the urbanization is acknowledged as influencing only about 28% of the dhf incidence in the city, while the other 72% has been influenced by other factors. copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): pratama, i. p. and rahayu, s. r. (2016). relationship between urbanization and dengue haemorrhagic fever incidence in semarang city. geoplanning: journal of geomatics and planning, 3(1), 67-76. doi:10.14710/geoplanning.3.1.67-76. 1. introduction the phenomenon of urbanization has been common in big cities in indonesia. it is undeniable that the growth of the urban physical space is strongly influenced by urbanization (arta & pigawati, 2015). the development of cities is accompanied by changes in positive and negative aspects. positive changes can be seen, for example, in rapid economic growth. the phenomenon creates urban dynamics, changes in land use, as well as the emergence of legal and illegal settlements and other issues. in addition, urban areas are growing and developing to lead to the development of social heterogeneity as shown in the different characteristics of the population. the high rate of urbanization occurred in metropolitan regions, such as jakarta (including bekasi, bogor, and tangerang), semarang, surabaya, bandung, medan, palembang, and makassar has made them to be the main magnets. statistical records show that since 1970, the proportion of indonesia's urban population increased from 17.4% in 1970 to 22.3% in 1980, 30.9% in 1990, 43.99% in 2002 and, finally, 52.03% in 2010. it means that within 40 years, urbanization has tripled the urban population. unplanned urbanization can result in a quality urban environment that is not healthy, so it has the potential to enhance the development of mosquitoes carrying the dengue vector (rigau-pérez et al., 1998; wu et al., 2009). this statement is collaborated by the results of several study (conroy et al., 2015; devaleenal et al., 2015; murugananthan et al., 2014; phung et al., 2015; vasquez-velasquez et al., 2015), which stated that the adverse effects of the phenomenon of urbanization form the ideal environment for the aedes aegypti mosquito as a carrier vector for breeding in the urban settlement area . article info: received: 15 january 2016 in revised form: 30 march 2016 accepted: 17 april available online: 30 april 2016 keywords: urbanization, dengue haemorrhagic fever (dhf), semarang city, gis corresponding author: isnu putra pratama diponegoro university, semarang, indonesia email: putraisnu@gmail.com open access http://dx.doi.org/10.14710/geoplanning.3.1.67-76 mailto:putraisnu@gmail.com pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 68 | in indonesia, dengue fever has become a public health problem during the past four decades. there has been an increase in the spread of the endemic disease to a number of provinces and districts or cities, from only two cities in two provinces in 1968 to 382 districts or cities (77%) in 32 provinces (97%) in 2009. in addition, there has also been an increase in the number of dengue cases, from only 58 cases in 1968 to 158,912 cases in 2009. the increase and spread of dengue fever are likely caused by the high mobility of the population, urban growth, climate change, changes in population density and distribution as well as other epidemiological factors that still require further research (health department of indonesia, 2013). as one of the major cities in java, semarang city also experienced population growth caused by the phenomenon of urbanization. historical facts indicate that the process of urbanization has been going on since the arrival of the dutch. the growth has been more rapid after the construction of the air and sea ports, as well as the establishment of various industrial facilities and services. the increase of population in the city of semarang is caused by many factors, one of them is urbanization. it is characterized by land use change from agriculture to settlement and commercial zones. semarang total population reached 1.54 million in 2011. this figure continues to rise and by 2013 had reached 1.57 million. the annual rates of population growth in the last three years are fluctuated, with 1.11% in 2011 and began to slow down in the following year to 0.83% (semarang central bureau of statistics, 2014). the incidence of dengue in semarang also has also been fluctuated over the last 5 years, but the data shows an increasing trend. in 2013, the incidence was 2,364 cases, which was an increase of 89.11% from 1,250 cases in 2012 semarang city government continues to deal with the dengue haemorrhagic fever (dhf) disease. if seen from the impact of urbanization factors, the disease is indicated to have a relationship with the phenomenon of urbanization in big cities. data from the central statistics agency of semarang (2014) shows the trend of population increase each year that is accompanied by an increase in population density. on the other hand, the change of land use for settlement development and more dense built-up areas in semarang can also be an indication of the high rate of urbanization that can create ideal conditions for mosquitoes carrying the dengue vector breeding. moreover, the tendency of the incidence of dengue is also increasing so we need a study that aims to examine the relationship between the level of urbanization and the incidence of dengue fever in semarang city (see figure 1). figure 1. semarang as the study area (semarang city development planning agency, 2015). pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 | 69 2. data and methods based on the research aim, this study used quantitative and spatial approach. this study investigated the relationship between the level of urbanization and the incidence of dengue in semarang city using districts (kecamatan) as the analysis units. the data used was time series over a period of 8 years from 2006 to 2013. the data collection was done by observation, survey to the relevant agencies to obtain secondary data, literature review, and light interviews to get recommendations and advice from the related stakeholders. analysis was carried out in various stages analyzing the variables, such as variables of the level of urbanization, dhf variables, and analysis of the correlation between urbanization and the dhf. 2.1 building density the variable of building density can be known through the help of quickbird imagery in 2006, 2010 ikonos imagery and imagery of google earth in a period of rest for 8 years (2006-2013). in analyzing the image, it used satellite image interpretation technique and gis in order to test the validity of the accuracy of data interpretation results can be used as the basis of analysis and evaluation (halder et. al, 2011). results of interpretation which tested its accuracy be used to locate data building density and rate of change of land use. calculation of the building density is approached through a number of building basic coefficient (kdb) per building coverage. assessment of the building density coefficient is based on a comparison between the area of the ground floor of the building with an area of land use of an area or region. 2.2 identification of landuse change the calculation of land use change was by using the help of the spatial conversion rate of satellite imagery. the type of land use change analyzed in this study is the change from non-settlement to settlement area, indicated by changes in the area of non-built-up into the built-up area. analysis using this method were done through the following equation; where : v = the rate of change of land use lt= the land area is currently / year-t (ha) lt-1= land area the previous year (ha) 2.3 population growth the population growth was calculated using the geometric equation as follows: where, for example using the period of 2006-2013: r = the average rate of population growth pt = the total population in 2013 p0 = population in 2006 n = interval years 2006 2013 (n=7) r = ( (pt/p0)1/n -1 ) x 100% pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 70 | 2.4 population density calculation of the population density used the following equation: population density = population of region a: area of region a (km²) 2.5 analysis of incidence of dengue haemorrhagic fever (dhf) the data of the incidence of dengue fever was obtained from department of health, semarang city . the time series data of 2006-2013 was then analyzed to understand the trend or tendency. 2.6 analysis of the relationship between urbanization rate and incidence of dengue the analysis methods used in the next stage after identifying each variable were descriptive and regression analyses. the descriptive analysis aims to provide an overview of data distribution in the form of each variable. meanwhile, the regression analysis was used to examine the relationship between the dependent variable (incidence of dhf) with independent variables (population, a number of population density, building density, and changes in land use). in general, the multiple regression equation can be written as follows: where: y = dependent variable (dhf) a = constant b = independent variable x1 = building density x2 = landuse change x3 = population growth x4 = population density 3. results and discussion 3.1 analysis of the incidence of dengue haemorrhagic fever (dhf) tembalang district is the highest in the incidence of dhf with 37 cases on average annually while mijen district is the lowest with only four cases. there are eight districts that have an average rate of incidence of 20 cases or more, where most of them are densely populated. the highest incidence rates occurred in 2010 in the district of tembalang with an average of up to 77 cases of dengue fever incidence, especially in sendangguwo sub-district that reached 342 cases in the same year while the lowest occurred only 1 case in the tugu district in 2012. the pattern of incidence of dengue in the city of semarang shows numbers fluctuating up and down over a period of 8 years. from 2006 to 2008 there was an increase of up to an average of 32 cases in the city, then fell in 2009 and again reached its peak in 2010 with the same number of cases, ie 32 events. in the following years the incidence of dengue fever down with 8 cases, but rebound in 2013 to an average of 13 cases of dengue incidence (see figure 2). the high incidence of dengue in tembalang district is caused by various factors, one of which is as expected, i.e. the high population and building density of the settlement areas. in addition, it is because of the unstable climate and high rainfall. y = a + b1x1 +b2x2 + b3x3 +b4 x4 pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 | 71 figure 2. distribution of dhf incidence in semarang (semarang city health office and analysis, 2015). 3.2 analysis of the urbanization a) building density the growth of built-up area occurs almost in every district in semarang city shown by the land conversion. this will greatly affect the density of the building as one variable of the level of urbanization. the building density map can be seen in figure 3. the district of central semarang has the highest density, while the district of mijen is the lowest, around 3%. district of central semarang building density unchanged from 2006 while the highest change occurred in the district of west semarang with value accretion was 6%. the district of west semarang consists of settlement areas, settlement, and commercial. in general, changes in the built-up land in this region occur in an open space into settlement development. on the other hand, district of mijen as the region with the lowest density is one of the plateau regions on the edge of semarang dominated by agricultural land, forests, and plantations. pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 72 | figure 3. building density in semarang city, 2006 and 2013 (analysis, 2015). b) the rate of land use change the areas with high rate of conversion to settlement use are generally located on the outskirts of the city of semarang. these areas were dominated by agricultural land and open space, which were then converted to settlements. areas that are not indicated for the conversion of settlement land are generally located in the center of a densely populated and buildings, as well as settlement areas. it already can not expand the surrounding areas because there is areas barrier like the area of trade and services, government agencies, industry, and others. figure 4 shows the growth of settlement use in semarang between 2006 and 2013. figure 4. settlement growth at semarang in 2006 and 2013 (analysis, 2015). pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 | 73 there are seven districts which on average did not change land use, in addition to the district of mijen be the region with the highest rate of change of land use by 3% during 8 years. in general, differences in the rate of change of land use in the city of semarang was affected by the conditions of land use and housing needs in each of the districts. the increasing need for settlement land in the city of semarang tends to increase the rate of land conversion, especially in regions such as ngaliyan, gunungpati, and mijen. land use change into settlements in the district mijen is the fastest because there are still a lot of potential land for settlement areas or housing. bukit semarang baru (bsb) city became one of the major projects to the concept of settlement has changed the scale of the city as a suburb mijen district of semarang to become an alternative place to settle in the future. c) population growth regions with the highest average population growth in semarang contained in 7 districts with a value of 3%. on the other hand, there are 2 sub-district which is worth an average of -1% which semarang district of central and east semarang. population growth in the city of semarang is always changing every year, it is influenced by the dynamics of birth, death and migration in each of the districts. conditions housing environment has a great influence as a container to accommodate the growing population. areas of districts with high growth were 3%, generally do settlement development or new settlements during the last 8 years, as the district gunungpati, mijen, and tembalang. demographic dynamics such as fertility, mortality, and migration can also be the cause of the rapid rate of population growth, some areas with good housing conditions have the potential to be an option migrants to reside. d) population density north semarang district is a region with highest average population density at 17, 242 inhabitants per km², and the lowest is tugu district with 1,032 people per km². high population density in the city of semarang is generally found in settlement areas around the city center such as east semarang, candisari, and gayamsari districts. the population density in each district shows the same distribution pattern in the period of 8 years, which is dominated by the north semarang district while the mijen and gunungpati district including the lowest population density over a period of 8 years. the population density figure is the quotient between the number of population and area so that the district mijen and gunungpati who have a large area but few areas tend to be the dense settlement. 3.3 regression analysis of the relationship between urbanization and dengue haemorrhagic fever the analysis that resulted in the sig. of 0.863 showed no significant relationship between the independent variable (x), which indicates urbanization, with the dependent variable (y) that is the incidence of dengue because the value of sig. is greater than 0.05. a large value of r is 0.534, according to the assessment criteria correlation concluded that the independent variables (x1, x2, x3, and x4) have a moderate correlation with the dependent variable (y). while the coefficient of determination r ² of 0.28 or 28%, in other words, the influence of independent variables (x1, x2, x3, and x4) jointly to y is at 28% while the remaining 72% is determined by other factors, outside variables x1, x2, x3, and x4 (see table 1). table 1. relationship between urbanization levels and dhf (analysis, 2015). model unstandardized coefficients b sig. (constant) -349.95 .716 population_density 0.05 .735 population_growth 1,081.76 .385 building_density -293.29 .843 landuse_change 230.14 .813 pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 74 | unstandardized value coefficient shows the results of regression analysis that states the equation y = 349.95 + 0.05x1 + 1081.76 x2 +293.29 x3 + 230.14 x4. the equation shows the relationship between each independent variable (y) with the dependent variable (x), it can be concluded that the first variable (x3) is the density of the building shows negative sign is -293.29, so that the relationship between these variables with independent variables that dengue has negative relationship (opposite direction). the second variable (x2) population growth showed positive signs, namely 1081.76 thus inferred to have a positive relationship (unidirectional) with dengue incidence rates because the greater the value of population growth, the higher the incidence of dengue, a third variable (x1) population density, shows 0.05 positive sign that concluded there is a positive correlation with dengue, as well as a fourth variable (x4) ie settlement land use changes that indicate a positive value 230.14 to infer the existence of a positive relationship between these variables with independent variables that the incidence of dhf. a positive relationship occurs when the independent variable increases, the dependent variable also increases accordingly. on the other hand, negative relationship means that the decline in the value of the independent variable correlates the increase of the dependent variable and vice versa whereas if the value is zero then there is no relationship between variables. the value of the relationship may explain the nature of the division of the classification of the nature of the relationship of each of the districts in accordance with the value of the variable in the column unstandardized coefficients b. classification of the nature of the relationship is divided into 5 groups, namely negative with components 0, negative without the component of 0, positive and negative, positive with components 0 and positive, negative and 0. classification division is expected to explain the different characteristics of each of the districts concerned with closeness relation to dengue, which in the future, this condition can be changed in accordance with the dynamics that occur in districts (see figure 5). in general, each variable degree of urbanization does not have a significant relationship to the variable of dengue; however, positive association in some districts is found in each sub-district analysis. the most substantial positive relationship between land use change and dhf is in the district of tembalang. the overall value of the average incidence rate of dengue district tembalang increased in 2010, a total of 32 cases occurred in the same year changes in land use amounted to 4%. although not directly related, activity on district tembalang land use change could potentially slow down, speed up or even form a new habitat of mosquitoes carrying the vector which then affect the incidence of dengue fever in the region. the most substantial positive relationship between the variables and variable density of buildings is the district of tembalang dhf. the average number density of buildings that represented kdb was always increased, growing by 9% in 2006, then rose to 12% in 2013, it also occurs at the level of the average urban neighborhoods is always increasing. if it is compared with a history of dengue incidence rates tend to fluctuate, only an increase simultaneously in 2006 and 2013. indirectly density settlements might also have implications on the temperature and humidity of this region. the more and the lack of green open spaces, the temperature will increase the effect on the rapid growth of dengue mosquito in the settlement. the most substantial positive relationship between the variables and the population density is the district of candisari dbd variables. subdistrict candisari is quite solid with a density figure of 12,070 inhabitants/km². the village of the most densely populated village jomblang which has a population of 18,426 inhabitants. while kaliwiru village with a population of at least has a population of 3,943 inhabitants. candisari sub-districts with high population density conditions has the potential to affect the incidence of dengue, because humans are the main carriers of the dengue virus, with the density of population in an area then people around the neighborhood will be vulnerable to disease. the most substantial positive relationship between population growth and dhf was in tembalang district. population growth in this region showed a drastic increase in 2008 by 5%, which corresponded with an increase of 40 cases of dengue incidence. the average percentage of population growth was highest in meteseh sub-district, i.e. 6% for 8 years, correlated with a fairly high incidence of dengue fever, i.e. 38 cases. the pattern started in 2007, in which there is an increase in the incidence of dengue fever from 6 cases to 27 cases, and in the same year there was an increase of 2% of population growth. the population growth was followed by a change in the social dynamics in the district of tembalang. community activities that occur in this region certainly have an impact on changes in the environmental pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 | 75 quality of the settlements. in addition, community behavior also plays a role in creating an environment that is susceptible to the occurrence of dhf. figure 5. map of relationship between urbanization rate and dhf (analysis, 2015). 4. conclusion the analysis of relationship between urbanization and dhf with the data in every district in the city of semarang during a period of 8 years (2006-2013) shows that there is no significant relationship between the levels of urbanization in the incidence of dengue fever. variable rate of urbanization only affects 28% of the incidence of dengue fever that occurred in the city, while the remaining 72% comes from the influence of other factors such as climate, weather, air temperature, and humidity. 5. references arta, f. s., & pigawati, b. (2015). the patterns and characteristics of peri-urban settlement in east ungaran district, semarang regency. geoplanning: journal of geomatics and planning, 2(2), 103–115. http://dx.doi.org/10.14710/geoplanning.2.2.103-115. conroy, a. l., et. al. (2015). host biomarkers are associated with progression to dengue haemorrhagic fever: a nested case-control study. int j infect dis, 40, 45–53. http://doi.org/10.1016/j.ijid.2015.07.027. devaleenal, b., et. al. (2015). dengue fever in saidapet health unit district in tamil nadu: trends from 2004 to 2011. clinical epidemiology and global health, 3(2), 94–98. http://doi.org/10.1016/j.cegh.2014.07.002. halder, a., et. al. (2011). supervised and unsupervised landuse map generation from remotely sensed images using ant based systems. applied soft computing, 11(8), 5770–5781. http://doi.org/10.1016/j.asoc.2011.02.030. health department of indonesia. (2013). health profile of indonesia. jakarta. murugananthan, k., et. al. (2014). demographic and clinical features of suspected dengue and dengue haemorrhagic fever in the northern province of sri lanka, a region afflicted by an internal conflict for more than 30 years-a retrospective analysis. international journal of infectious diseases, 27, 32–36. http://doi.org/10.1016/j.ijid.2014.04.014. phung, d., et. al. (2015). identification of the prediction model for dengue incidence in can tho city, a mekong delta area in vietnam. acta tropica, 141(part a), 88–96. http://doi.org/10.1016/j.actatropica.2014.10.005. pratama and rahayu / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 67-76 doi: 10.14710/geoplanning.3.1.67-76 76 | rigau-pérez, j. g., et. al. (1998). dengue and dengue haemorrhagic fever. the lancet, 352, 971–977. semarang central bureau of statistics. (2014). kota semarang dalam angka 2014. semarang: badan pusat statistik kota semarang. vasquez velasquez, c., et. al. (2015). alpha tryptase allele of tryptase 1 (tpsab1) gene associated with dengue haemorrhagic fever (dhf) and dengue shock syndrome (dss) in vietnam and philippines. human immunology, 76(5), 318–323. http://doi.org/10.1016/j.humimm.2015.03.009. wu, p. c., et. al. (2009). higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical taiwan. science of the total environment, 407(7), 2224–2233. http://doi.org/10.1016/j.scitotenv.2008.11.034. volume 1, no 1, 2014, 33-43 http://ejournal.undip.ac.id/index.php/geoplanning | 33 open access geoplanning e-issn: 2355-6544 pemetaan pengaruh perkembangan pasar wage kota purwokerto terhadap lingkungan permukiman sekitar t. k. pamuliha, widjonarkob a universitas diponegoro, indonesia, email: tantio90@yahoo.co.id b universitas diponegoro, indonesia, email: widjonarko93@yahoo.com abstract: fire accident that occurred in the pasar wage in 2002 forced the government of banyumas district doing renovations on the building as well as expand the area of pasar wage. now, the design of the building in pasar wage has 2nd floors, which raises new issues, where traders don't want to open the stall on the 2nd floor due to “no merchandise sold as goods as in 1st floor”, so the traders were forced to open the stall on the pedestrian ways which can interfere the activity in the neighborhood market. this research uses a spatial approach. data collecting has been done through mapping gis analysis, visual observation, questionnaire distribution, and interview. the result shows several impacts of pasar wage to the housing area at its surroundings. firstly, the impact of the land use with the emergence of commercial and mixed used buildings replacing the housing area. the second impact is the increase of business and job opportunity for the local residents such as on street parking, street vendors, and retail. the activity in pasar wage also induces the traffic congestion at a particular hour, especially near the east entrance of the building. last, it affects the condition of the physical environment such as the inundation and bad scent due to the clogged drainage or sewerage system. therefore, need a government and community effort to overcome and minimize the effects of market activity for solving those problems. abstract: penelitian ini menggunakan pendekatan positivistik, dengan cara menyelidiki dan mengkaji berbagai gejala yang terjadi beserta hubungan di antara gejala-gejala tersebut untuk dapat meramalkan apa yang akan terjadi. pengumpulan data dilakukan melalui observasi visual, distribusi kuesioner, dan wawancara. hasil dari penelitian ini menunjukkan beberapa dampak dari perkembangan pasar wage terhadap lingkungan permukiman di sekitarnya. di antaranya adalah dampak terhadap perubahan guna lahan dengan munculnya beberapa bangunan perdangan dan campuran yang sebelumnya adalah permukiman. daya tarik pasar wage juga meningkatkan peluang usaha masyarakat sekitar, seperti munculnya parkir on-street, pkl di pinggir jalan, hingga peluang perdagangan retail. dari sisi sistem pergerakan, dampaknya terlihat dari munculnya titik –titik rawan macet pada jam tertentu, khsusnya di pintu timur pasar wage. lingkungan fisik binaan juga terkena dampak, seperti penyumbatan drainase dan saluran limbah, genangan dan bau tidak sedap yang merambah ke lingkungan permukiman. pasar wage memiliki depo sampah khusus, fasilitas ini dimanfaatkan oleh masyarakat sekitar sebagai lokasi pembuangan sampah. temuan di atas perlu mendapat perhatian oleh pemerintah dan masyarakat, seperti adanya upaya pembatasan pertumbuhan perdagangan dan jasa khususnya pkl, penertiban potensi parkir di bahu jalan, pengaturan dan pengawasan untuk fasilitas drainase, limbah dan sampah pasar. 1. pendahuluan seiring waktu dengan bertambahnya tuntutan (demand) terhadap pemenuhan kebutuhan hidup, maka pasar wage juga mengalami perkembangan secara perlahan. jumlah pedagang dan pembeli semakin banyak, tempat berdagang semakin luas serta waktu transaksi semakin lama. sementara jika ditarik kembali ke teori penentuan lokasi sebuah pasar, dibutuhkan beberapa faktor yang harus dipenuhi agar dapat tercipta lingkungan yang baik dan tertata rapih. menurut miles (1999), terdapat 9 faktor yang perlu diperhatikan, yaitu peruntukan lahan (zoning), penampakan fisik (physical features), utilitas, transportasi, info artikel; diterima: 7 maret 2014 hasil revisi : 17 maret 2014 disetujui: 29 maret 2014 publikasi on-line: 1 april 2014 kata kunci: dampak, pasar tradisional, lingkungan permukiman article info; received: 7 march 2014 received in revised form: 17 march 2014 accepted: 29 march 2014 available online: 1 april 2014 keywords: impact, traditional market, housing environment mailto:bitta.pigawati@gmail.com geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 34 parkir, dampak lingkungan (sosial dan alam), pelayanan publik, penerimaan/respon masyarakat (termasuk perubahan perilaku) serta permintaan dan penawaran (pertumbuhan penduduk, penyerapan tenaga kerja dan distribusi pendapatan). pasar wage purwokerto dibangun kira-kira pada abad ke-19 dijaman penjajahan belanda. pasar wage lama terletak di perempatan tengah-tengah kota di jalan jendral soedirman. pasar itu sendiri dibangun oleh belanda bertujuan untuk memperlancar aktifitas perekonomian dan pemasaran belanda yang saat itu masih menjajah indonesia. pada saat masih di jalan jendral soedirman, pasar wage lama dengan kedemangannya yang saat ini dibangun sebuah klenteng di utara pasar. pasar wage yang saat ini berdiri, dulunya hanyalah sebuah lapangan yang digunakan untuk kegiatan olagraga ataupun yang lain. pasar wage lama terdapat sekitar 1600 pedagang yang berdagang dapat menampung kurang lebih 1200 los dan 61 kios. permasalahan di dalam pasar wage terjadi itu karena perubahan bentuk pasar yang direnovasi ulang dan diperluas kawasannya oleh pihak pemerintah pasca bencana kebakaran pada tahun 2008 dengan luas kawasan 10.305,44 m². meskipun sudah direnovasi namun kenyataannya tetap saja masih banyak pedagang yang berjualan di jalan-jalan. hal ini juga diperumitkan dengan sikap pedagang yang tidak mau menaati peraturan yang telah ditetapkan oleh pemda. misalnya dengan mengurangi luas tempat untuk pembeli satu meter agar agar pedagang dapat tertampung, memisahkan jenis dagangannya yang kering di lantai dua dan yang basah di lantai satu. tetapi pedagang beralih bahwa peraturan itu dapat merugikan pedagang sehingga barang dagangannya tidak laku, hal ini dibuktikan dengan banyaknya kios-kios di lantai dua yang kosong serta tatanannya yang tidak terlalu tertib. melihat permasalahan diatas, pasar wage yang sekarang ini tidak sesuai dengan apa yang direncanakan oleh pemerintah dengan merenovasi pasar tersebut pasca kebakaran. penjual yang di lantai dua hampir sebagian turun ke badan jalan dan trotoar untuk membuka lapak kondisi seperti ini membuat kondisi pasar semrawut dan tidak tertata. begitu juga dengan sampah yang dihasilkan oleh para pedagang yang berjualan di pasar ini tidak terkelola dengan baik . dengan adanya pasar wage yang lambat tahun semakin pesat perkembangannya di tengah kota purwokerto dan perubahan kondisi fisik pasar wage yang berubah menjadi pasar modern dengan maksud dan tujuan untuk menyelesaikan masalah-masalah yang sebelumnya terjadi. hal ini menjadi menarik untuk diteliti, bagaimanakah dampak yang terjadi atau sebab-akibat yang akan timbul pada kondisi fisik permukiman yang berada disekitar pasar wage sekarang ini? 2. kajian literature permukiman versi doxiadis (1971) mengatakan bahwa permukiman terdiri dari 2 (dua) unsur utama, yaitu isi atau content dan wadah atau container, yang kemudian diturunkan menjadi 5 (lima) unsur, yaitu: alam atau nature, manusia atau man, masyarakat atau society. rumah atau shell dan jaringan atau network. kemudian doxiadis membagi kembali permukiman kedalam 4 (empat) kategori, antara lain yaitu: 1) homogeneous atau seragam, terdiri dari ladang-ladang atau fields; 2) central atau pusat, terdiri dari gedung dan rumah-rumah; 3) circulatory atau peredaran, terdiri atas jaringan jalan pada suatu daerah; 4) special atau khusus, terdiri bangunan khusus yang ada pada bagian yang homogen. pola penggunaan lahan sangat dipengaruhi oleh pola aktivitas ekonomi dan kondisi geografis kotanya, arah pola penggunaan lahan akan mengikuti pola aktivitas yang terjadi. menurut catanesse (1988), tidak pernah ada rencana tataguna lahan yang dilaksanakan dengan satu gebrakan. memerlukan waktu yang panjang oleh pembuat keputusan dan dijabarkan dalam bagian-bagian kecil dengan perencanaan yang baik. sedangkan menurut (gallion, athur,b and simon eisner, 1986:27) mengemukakan bahwa pemanfaatan lahan perkotaan terbagi menjadi 5 kategori, yaitu: (a) lahan pertanian, (b) perdagangan, (c) industri, (d) perumahan,dan (e) ruang terbuka. winarso (1995:11) mengklasifikasikan pemanfaatan lahan menjadi; (a) lahan permukiman; (b) lahan perdagangan; (c) lahan pertanian; (d) lahan industri; (e) lahan jasa; (f) lahan rekreasi; (g) lahan ibadah dan (i) lahan lainnya. geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 35 dampak adalah suatu perubahan yang terjadi sebagai akibat suatu aktivitas (otto, 1998). aktivitas tersebut dapat bersifat alamiah, baik kimia, fisik, ,maupun bioogi. dalam konteks amdal, penelitian dampak dilakukan karena adanya rencana aktivitas manusia dalam pembangunan. dampak pembangunan menjadi masalah karena perubahan yang disebabkan oleh pembangunan selalu lebih luas daripada yang menjadi sasaran pembangunan yang direncanakan. misalnya, jika petani menyemprot sawahnya dengan pestisida untuk memberantas hama wereng, yang mati oleh semprotan pestisida bukan hanya wereng saja melainkan juga lebah madu yang terbang di udara. secara umum dampak pembangunan diartikan sebagai perubahan yang tidak direncanakan yang diakibatkan oleh aktivitas pembangunan. ginanjar (1980) menyatakan bahwa pasar adalah tempat untuk menjual dan memasarkan barang atau sebagai bentuk penampungan aktivitas perdagangan. pasar pada mulanya merupakan perputaran dan pertemuan antara persediaan dan penawaran barang dan jasa. sedangkan bagi campbell (1990) mendefinisikan pasar sebagai institusi atau mekanisme di mana pembeli (yang membutuhkan) dan penjual (yang memproduksi) secara bersama-sama melakukan pertukaran barang dan jasa. tak berbeda seperti yang dipaparkan oleh stanton (1996) dimana pasar merupakan tempat pembeli bertemu dengan penjual, di mana terdapat barang-barang atau jasa-jasa yang ditawarkan untuk dijual dan kemudian terjadi pemindahan hak milik. selain itu dinyatakan pula bahwa pasar adalah sebagai tempat orang-orang yang mempunyai kebutuhan untuk dipuaskan, mempunyai uang untuk dibelanjakan dan kemauan untuk membelanjakan uang. berbeda dengan pendapat para ahli diatas, phillip kotler (1998) melihat arti pasar dalam beberapa sisi, yaitu: 1. pasar dalam pengertian aslinya adalah suatu tempat fisik di mana pembeli dan penjual berkumpul untuk mempertukarkan barang dan jasa. 2. pengertian pasar bagi seorang ekonom adalah semua pembeli dan penjual yang menjual dan melakukan transaksi atas barang/jasa tertentu. para ekonom dalam hal ini lebih tertarik akan struktur, tingkah laku dan kinerja dari masing-masing pasar ini. 3. pengertian pasar bagi seorang pemasar pasar adalah himpunan dari semua pembeli nyata dan pembeli potensial dari suatu produk. 3. metodologi penelitian pendekatan yang digunakan dalam penelitian ini menggunakan pendekatan positivistik, dimana merupakan pendekatan dengan cara menyelidiki dan mengkaji berbagai gejala yang terjadi beserta hubungan-hubungannya diantara gejala-gejala tersebut agar dapat meramalkan apa yang akan terjadi. metode yang dipakai studi ini adalah metode kuantitatif yang digunakan untuk mengetahui faktor–faktor yang mempengaruhi munculnya permasalahan permukiman akbiat perkembangan pasar wage. metode ini menggunakan data numerik sehingga dapat ditarik suatu kesimpulan analisis. data yang digunakan dalam penelitian ini yaitu data kualitatif dan kuantitatif. pengumpulan data di lapangan dilakukan dengan cara kajian dokumen, observasi lapangan, kuesioner, dan wawancara. wawancara dilakukan kepada pihak-pihak terkait yang ada di wilayah studi, serta kuesioner yang ditujukan kepada penduduk setempat secara sampling. 4. temuan studi 4.1 kondisi dan peran pasar wage berdasarkan pendapat para pedagang, desain bangunan berlantai dua tersebut merupakan permasalahan utama bagi mereka karena mempersulit persaingan dengan pedagang di lantai dasar. hal tersebut dinyatakan oleh 80% responden dalam kuesioner mengenai kondisi pasar wage yang diberikan kepada 70 orang pedagang. lebih lanjut diungkapkan bahwa dengan desain bangunan pasar berlantai dua ini mengakibatkan rasa malas pengunjung untuk naik ke lantai dua karena kebutuhan mereka sudah dapat terpenuhi di lantai dasar. geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 36 gambar 1. bangunan dua lantai pasar wage (survey lapangan, 2013) dampak selanjutnya adalah banyak pedagang yang sudah menyewa kios di lantai dua kemudian turun dan ikut membuka lapak di bawah. pada akhirnya, semakin banyak pedagang yang menempati lahan di ruang-ruang sirkulasi dan mengakibatkan terbatasnya pergerakan dan timbul kemacetan di beberapa titik. selain permasalahan mengenai keterbatasan ruang karena penggunaan lahan yang tidak semestinya, ketersediaan prasarana pendukung aktivitas pasar khususnya sistem pengelolaan sampah dan jaringan drainase juga menimbulkan permasalahan. kondisi tersebut teramati dari proses observasi primer dan juga hasil distribusi kuesioner. sebanyak 57.14% responden menyatakan bahwa kondisi sistem persampahan adalah salah satu masalah yang menonjol dan perlu diperhatikan. kurangnya penyediaan keranjang sampah di lokasi pasar dan tempat penampungan sampah sementara yang jauh dari lokasi pasar mengakibatkan sering terjadi timbunan sampah pada beberapa sudut pasar. gambar 2. kosongnya lantai dua pasar wage (survey lapangan, 2013) selain itu, 38.57% responden menyatakan bahwa sistem drainase perlu ditingkatkan lagi karena keadaan saat ini dengan drainase tersier dengan lebar kurang dari 30 cm kerap tersumbat dan mengakibatkan genangan kecil. walau genangan tersebut tidak mengakibatkan banjir namun sangat mengurangi kenyamanan berbelanja yang akibatnya mengurangi jumlah pengunjung yang ingin berbelanja terutama pada musim hujan. ditinjau dari skala yang lebih luas, pasar wage mampu memenuhi kebutuhan penduduk hingga pada jarak ± 60 menit waktu tempuh. untuk mendukung hal tersebut, lokasi pasar wage dilalui oleh sedikitnya tiga (3) rute angkutan umum yang dapat menjadi pilihan masyarakat untuk mengakses pasar selain dengan menggunakan kendaraan pribadi. keberadaan rute angkutan umum tersebut mendukung keberadaan pasar wage untuk melayani kebutuhan masyarakat dari berbagai tempat di lingkup kabupaten banyumas. namun meskipun demikian, jumlah pengunjung pasar yang menggunakan kendaraan pribadi relatif tinggi terlihat dari padatnya arus lalu lintas dan lahan-lahan parkir yang tersedia di sekitar area pasar. geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 37 4.2 fungsi bangunan di sekitar pasar wage sebaran permukiman di sekitar pasar wage umumnya berada di laporan kedua jalan utama. lapisan pertama jaringan jalan utama umumnya dialokasi untuk perdagangan dan lokasi fasilitas umum (terlihat pada peta fungsi bangunan). masyarakat yang tinggal di permukiman tersebut pada umumnya adalah orang-orang yang telah tinggal cukup lama, sehingga telah terbiasa dengan kondisi yang ada. hal tersebut tercermin pada hasil responden yang mayoritas menyatakan kondisi yang biasa saja pada infrastruktur yang telah tersedia. namun beberapa permasalah umum yang selalu terjadi di permukiman yang berdekatan dengan pasar adalah pencemaran udara dan limbah yang dihasilkan dari pasar. gambar 3. sebaran dan fungsi bangunan di kelurahan purwokerto wetan (analisis penyusun, 2013) aspek penting bahwa keberadaan pasar di sebuah lokasi cenderung memicu perkembangan lokasi tersebut. seringkali ditemui bahwa wilayah di sekitar pasar berkembang menjadi wilayah dengan beragam fungsi, baik yang berkaitan dengan fungsi perdagangan sebagai perpanjangan dari pasar ataupun fungsi lain. kondisi ini ditemukan di permukiman sekitar pasar wage. permukiman di sekitar pasar wage cenderung untuk berkembang, baik untuk mewadahi sumber daya manusia yang menjadi pedagang pasar, ataupun membuka peluang usaha baru sebagai imbas dari perkembangan pasar khususnya dan jenis aktivitas jasa lainnya. permukiman yang berada di sekeliling pasar wage merupakan pihak yang mengalami secara langsung perkembangan lokasi pasar tersebut, baik sebagai pelaku maupun sebagai penerima manfaat maupun dampak yang ditimbulkan oleh aktivitas perdagangan yang berlangsung. untuk itu perlu diketahui secara lebih detail mengenai kondisi permukiman terhadap keberadaan dan perkembangan pasar wage. 4.3 kondisi fisik dan kualitas lingkungan permukiman kondisi fisik dan lingkungan permukiman di sekitar pasar wage menggunakan beberapa indikator umum, yaitu aspek ruang tinggal, kualitas ruang, ruang terbuka hijau, ketersedian air bersih, serta saluran drainanse dan limbah. geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 38 tabel 1. kualitas lingkungan permukiman di sekitar pasar wage (hasil analisis 2013) aspek lingkungan indikator kondisi lapangan ruang bertinggal luas rumah sebagian besar rumah sudah memenuhi standar luas rumah, 55% memiliki luas lebih dari 30m 2 untuk keluarga ideal. hanya sebesar 25% rumah tidak yang memliki luas kurang dari 30 m 2 . jumlah ruang jumlah ruang yang ada di rumah sebanyak 1-3 ruang (30%) atau sebanyak 4-6 ruang (53%). khusus untuk rumah dengan jumlah ruang 1-3, umumnya menggunakan ruangan secara campuran, seperti ruang keluarga yang tergabung dengan ruang makan atau ruang tamu kualitas ruang pengudaraan alami rata-rata persentase ruang di dalam rumah yang memiliki akses terhadap pengudaraan alami secara langsung adalah sebesar 68%. sebanyak rata-rata 10% memperoleh pengudaraan alami secara tidak langsung. masih terdapat rata-rata 22% dari ruang yang ada dalam rumah yang tidak mendapat pengudaraan alami. pencahayaan alami rata-rata persentase ruang di dalam rumah yang memiliki akses terhadap cahaya alami adalah sebesar 73%. masih terdapat rata-rata 25% dari ruang yang ada dalam rumah yang tidak mendapat cahaya alami. di antara ruang yang tidak memperoleh pencahayaan alami sama sekali, 62% adalah kamar tidur, 12% adalah kamar mandi dan 7% adalah dapur. ruang hijau ketersediaan halaman rumah sebanyak 53% rumah tidak memiliki halaman sama sekali, 27% hanya memiliki teras. hanya 10% yang memiliki halaman yang dapat menyerap air, dan 10% memiliki halaman yang terdiri dari tanah dan teras. ketersediaan tanaman sebanyak 50% rumah tidak memiliki tanaman sama sekali. 40% rumah hanya memiliki sedikit tanaman (umumnya berupa pot tanaman) dan hanya 10% rumah yang memiliki tanaman yang cukup banyak. air bersih sumber air bersih untuk keperluan sehari-hari sumber air utama untuk minum adalah air tanah (55%) dan pdam (15%). untuk memasak, mandi dan mencuci, hampir semua rumah menggunakan air tanah. tidak ada yang menggunakan sumber air bersih dari pam. fasilitas kamar mandi pada sebagian rumah tidak terdapat kamar mandi sendiri sehingga harus menggunakan kamar mandi umum. drainanse dan pembuangan limbah drainase sebagian besar drainase di kawasan permukiman tidak berfungsi, tersumbat ataupun tidak terdapat saluran drainase yang baik. sistem limbah sistem pengolahan limbah belum ada, sehingga umumnya masyarakat langsung mengalirkan limbah ke sungai. 4.4 kondisi fisik dan kualitas lingkungan permukiman penilaian dampak pasar wage terhadap permukiman sekitar dalam penelitian ini ditinjau dari 5 aspek, perubahan guna lahan, sistem pergerakan, drainase dan limbah dan persampahan di permukiman sekitar pasar wage. a. perubahan guna lahan bagi permukiman yang berada di sekitar pasar wage terdapat beberapa permasalahan yang terlihat menonjol, khususnya pada perubahan guna lahan. pasar wage merupakan salah satu spot/titik pusat perdagangan di purwokerto. sejak kejadian kebakaran pasar wage tahn 2008, perubahan fisik bangunan pasar wage sangat signifikan. daya tarik pasar wage sebagai pusat ekonomi tradisional menjadi potensi khusus wilayah ini. sebagai pasar tradisional terbesar di kabupaten banyumas, pasar wage menjadi potensi geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 39 perputaran uang dan potensi daya tarik pertukaran barang. potensi ini juga ditangkap oleh jenis jenis aktivitas perdagangan dasa lain, seperti perbankan, pertokoan retail dan mini market. sejauh ini pertumbuhan bangunan dan aktivitas di kawasan ini sangat cepat, terlihat dari perkembangan guna lahan terbangun yang cenderung lebih kepada pertumbuhan sifat perkotaan. selain itu pertumbuhan bangunan fungsi perdagangan dan jasa juga sangat signifikan. pertumbuhan aktivitas perdagangan ini umumnya berawal dari alih fungsi lahan atau bangunan fungsi permukiman dan ruang terbuka. selain itu, pertumbuhan fungsi bangunan tempat tinggal dengan sistem sewa juga sangat tinggi. muncul beberapa rumah kos/rumah sewa di sekitar pasar wage. hal ini dikarenakan tingkat kebutuhan rumah tinggal di sekitar pasar wage cukup tinggi, umunya di huni oleh pendatang atau warga di luar kawasan yang bekerja di pasar wage. berdasarkan penilaian dari masyarakat 75% masyarakat yang bertempat tinggal di rumah kos/rumah sewa memiliki alasan karena dekat dengan pasar (tempat bekerja), sisanya menjawab karena lokasi yang dekat dengan fasilitas lain. b. kondisi sistem pergerakan pertumbuhan dan perkembangan pasar wage hingga saat ini berimplikasi pada pertumbuhan arus pergerakan di sekitar pasar, khususnya jalan jalan utama dari atau menuju pasar wage. kondisi ini memunculkan beberapa spot/titik kemacetan. beberapa penyebab kemacetan di sekitar pasar wage dipengaruhi uleh beberapa hal, antara lain adalah:  pkl sebagian besar jalan utama di sekitar pasar wage menjadi bagian dari lapak pedagang. hal ini disebabkan tidak semua pedagangan menggunakan fasilitas pasar, terlihat dari kondisi lantai 2 pasar wage cenderung kosong. keberadaan pkl ini berada di bahu jalan, selain menganggu keindahan, sebagian besar berdampak pada hambatan samping untuk arus kendaraan bermotor.  arus keluar masuk pengunjung sebagian besar pengunjung melakukan perhentian/pergantian moda di pinggir jalan. kondisi ini menimbulkan antrian kendaraan yang melaju dengan kendaraan yang sedang menurunkan/ menaikan penumpang. tidak hanya pengguna angkutan umum, tetapi juga angkutan pribadi.  parkir on-street salah satu aktivitas yang timbul karena tarikan pasar wage adalah parkir. kebanyakan pengunjung parkir menggunakan fasilitas parkir on-street. keberadaan parkir ini sebagian besar adalah parkir liar yang dikelola oleh penduduk sekitar. mayoritas responden memberi konfirmasi bahwa keberadaan pasar wage kerap menimbulkan kemacetan terutama pada jam-jam sibuk di pagi, siang dan sore hari. dengan rincian 5.25% menjawab sangat setuju, 62.5% menjawab setuju dan 32.25% menjawab biasa saja. kemacetan yang terjadi disebabkan karena bahu jalan kerap digunakan sebagai tempat beraktivitas untuk berdagang, padahal jalan tersebut adalah jalan utama bagi permukiman yang berada disekitarnya. gambar 4. sebaran spot kemacetan di sekitar pasar wage (analisis penyusun, 2013) geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 40 gambar 5. peta sebaran spot kemacetan di sekitar pasar wage (analisis penyusun, 2013) c. drainase dan limbah drainase dan pengolahan limbah merupakan salah satu prasarana dasar lingkungan permukiman dan lingkungan perdagangan dan jasa. senada dengan kebutuhan tersebut, kondisi drainase dan limbah di sekitar pasar wage akan berdampak pada kualitas lingkungan permukiman, khususnya drainase dan limbah, yang merupakan saluran/ jaringan perkotaan. kondisi saluran drainase dan pengolahan limbah di pasar wage secara langsung tidak tersedia dengan baik. umumnya terjadi penyempitan atau macet karena pengaruh timbunan sampah di lingkungan pasar. implikasi dari sampah tersebut adalah sering terganggunya saluran drainase yang berada di permukiman sekitarnya, seperti yang diungkapkan 60% responden. genangan kerap terjadi akibat tersumbatnya saluran drainase karena sampah, dan diperparah dengan saluran drainase yang kecil dan kerap ditutup untuk menambah ruang aktivitas. selain itu, bau yang menyengat yang datang dari sampah dan saluran drainase, juga kerap mengganggu aktivitas 32.5% responden sehingga kualitas lingkungan dan tingkat kenyamanan untuk tinggal menjadi menurun. khususnya di sebagian besar saluran drainase di sekitar pasar wage dilakukan “penutupan” sepihak oleh pedagang kaki lima, sebagai tapak dagangan. hal ini menjadi bahaya untuk potensi penyumbatan saluran drainase. seperti yang dikeluhkan oleh sebagian besar masarakat di sekitar pasar, muncul beberapa lokasi genangan pada musim hujan. potensi limbah hasil aktivitas pasar sangat tinggi, baik limbah padat ataupun limbah cair. masyarakat juga mengelukan kondisi ini, karena kerap tercium aroma bau busuk hingga ke permukiman, khususnya pada radius 25-50 meter dari pasar wage. d. persampahan sebagai pusat aktivitas perdagangan dengan beragam komoditas yang ramai pengunjung, pengelolaan sampah penting untuk diperhatikan. berdasarkan pendapat sebagian responden yang ditemui menyatakan bahwa mereka merasa kesulitan untuk membuang sampah karena kurangnya penyediaan tempat sampah di area pasar. kondisi tersebut juga dirasakan oleh pedagang khususnya untuk komoditas makanan basah seperti sayuran dan daging yang sangat membutuhkan ketersediaan kerangjang sampah. kurangnya penempatan keranjang sampah pada akhirnya a c b a. kemacetan diakitbakan oleh parkir on-street dan naik turun pengunjung b. kemacaten yang di akibatkan pedagang kaki lima c. jalan yang menerima efek domino dari kemacetan di jalan sekitar pasar. geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 41 mengakibatkan para pengunjung dan juga pedagang membuang atau bahkan menumpuk sampah secara sembarangan yang menimbulkan bau dan mengganggu kenyamanan pengunjung. gambar 6. salah satu timbunan sampah ilegal di sekitar pasar wage (survey lapangan, 2013) selain itu, ketersediaan depo sampah dari upt pasar wage yang terletak relatif jauh dari pasar tampak kurang dimanfaatkan dengan optimal sebagai tempat penampungan. sejauh ini belum ada sistem yang jelas dan terintegrasi untuk pengelolaan sampah dari kios-kios di area pasar hingga ke depo. kondisi depo pembuangan sampah pun perlu ditingkatkan karena seringkali tampak tumpukan sampah karena kurangnya frekuensi pengambilan sampah dari depo ke tempat pengelolaan sampah selanjutnya. namun di sisi, keberadaan depo sampah tersebut sejauh ini dapat dimanfaatkan oleh penduduk yang tinggal di permukiman sekitar sebagai tempat pembuangan sampah sementara. hal tersebut mengindikasikan bahwa ketersediaan fasilitas pendukung pasar dapat memberikan manfaat positif bagi kondisi lingkungan permukiman. namun perlu digarisbawahi bahwa jika hal tersebut dibiarkan tanpa adanya upaya pengintegrasian sistem pengelolaan sampah permukiman dan pasar dengan jaringan sampah perkotaan secara keseluruhan dapat menimbulkan permasalahan di kemudian hari. gambar 7. upaya buang sampah masyarakat setelah perbaikan pasar wage (survey lapangan, 2013) 5. temuan studi secara langsung keberadaan pasar wage dinilai mempengaruhi keberadaan permukiman di sekitarnya. baik dari kondisi lingkungan, perkembangan permukiman dan kondisi lingkungan binaan. dampak keberadaan pasar wage terhadap permukiman sekitarnya di kelurahan purwokerto wetan antara lain adalah: geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 42 a. dampak perubahan guna lahan terdapat beberapa perubahan guna lahan dan alih fungsi bangunan dari lahan kosong atau fungsi rumah menjadi bangunan perdagangan dan jasa terjadi khusunya di pinggir jalan utama timur dan utara pasar wage. bentuk perubahan ini, pada bangunan rumah dilakukan alih fungsi menjadi pertokoan. selain itu pertumbuhan bangunan fungsi tempat tinggal juga mulai berkembang, seperti bangunan rumah tinggal sewa yang umumnya dihuni oleh masyarakat yang bekerja di pasar wage. b. dampak pada sistem pergerakan perkembangan pasar wage meningkatkan aktivitas pergerakan masyarakat. sebagai tempat pemenuhan kebutuhan, pasar wage manjadi salah satu pusat perdaganagan. kondisi ini memunculkan kemacetan pada jalan jalan di sekitar pasar wage. beberapa faktor penyebab kemacetan adalah penyedian lahan parkir on-street, pkl di bahu jalan, dan aktivitas naik-turun penumpang (pengunjung). c. dampak pada kualitas lingkungan drainase dan limbah sistem drainase dan pengolahan limbah tidak berfungsi dengan baik, diperparah dengan beberapa saluran drainase dijadikan tapak pedagangan kaki lima. sehingga sebagian besar terjadi penyempitan saluran. untuk beberapa kondisi sering terjadi genangan dan bau busuk di sekitar pasar wage, hal ini menggagu kualitas lingkungan permukiman di sekitar pasar wage. d. dampak pada persampahan sistem pengelolaan sampah sebagai infrastruktur pendukung kegiatan perdagangan belum disediakan secara optimal. kurangnya penyediaan keranjang sampah dan belum terinteg rasinya pengelolaan buangan dari aktivitas pasar menjadi permasalahan yang menimbulkan bau karena tumpukan sampah yang dibuang secara sembarangan. depo sampah sebagai fasilitas pasar saat ini juga digunakan oleh penduduk sekitar. namun sejauh ini, permasalahan berkaitan dengan pengelolaan sampah hanya dirasakan di area di sekitar pasar dan tidak berdampak secara langsung pada aktivitas permukiman. 6. rekomendasi dari hasil penelitian ini dapat disampaikan beberapa rekomendasi terkait dampak keberadaan pasar wage terhadap permukiman di sekitarnya. rekomendasi ditujukan pada semua stakeholder, pemerintah, masyarakat, swasta serta rekomendasi untuk penelitian lanjutan sebagai bentuk penyempurnaan penelitian ini. a. pemerintah pemerintah sebagai pemangku kebijakan dan pengelola pasar wage melalui upt pasar wage harus mempertimbangkan beberapa hal di antaranya adalah:  pengelolaan alokasi pedagang di sekitar pasar, khususnya pkl yang berada di pinggir jalan agara di arahkan kembali kedalam pasar. pkl ini dapet menempati lantai 2 pasar yang saat ini cenderung kosong.  pengelolaan sistem jaringan perkotaan, seperti persampahan, drainase dan limbah. 3 jaringan ini dinilai masih berdampak negatif untuk permukiman sekitar.  sebagai pendegah kemacetan, pemerintah harus mampu meyediakan lahan parkir off-street serta pengawasan untuk parkir liar di bahu jalan.  pengendalian pertumbuhan dan alih fungsi lahan, khususnya di dalam kawasan permukiman, untuk menghindari kepadatan bangunan yang berlebihan. b. masyarakat dan swasta masyarakat dan swasta sebagai pengguna pasar dan penerima dampak dan manfaat dari pasar harus mampu menggunakan fasilitas perdagnagan ini dengan baik. potensi pasar sebagai arus pertukaran kebutuhan dapat menjadi potensi ekonomi yang memadai untuk peningkatan kualitas hidup. geoplanning 2014,vol: 1, no: 1, 33-43 pamulih dan widjonarko | 43 7. daftar pustaka campbell, r mc conned and stanley l brue. 1990. economic, problem and policie. mc graw publishing company. catanese, antoni j, 1988, pengantar perencanaan kota, erlangga surabaya. doxiadis c.a. 1971. ekistics: an introduction to the science of human settlement. london: hutchinson. gallion, a.b dan simon eisner, 1996, pengantar perancangan kota, jilid i, terjemahan susongko dan januar hakim. jakarta : galia indonesia. ginanjar, nugraha jiwapraja. 1980. masalah ekonomi mikro. jakarta: acro. soemarwoto, otto. (2007). analisis mengenai dampak lingkungan. yogyakarta: gadjah mada university press. stanton,william j. 1996. prinsip pemasaran. jakarta: penerbit erlangga. | 113 geoplanning vol 6, no 2, 2019, 113-121 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.2.113-121 land provision for decent and affordable housing for low-income community in salatiga city s. sunartia*, n. yuliastutia , i. indriastjariob a department of urban and regional planning, faculty of engineering, diponegoro university, indonesia b department of architecture, faculty of engineering, diponegoro university, indonesia abstract: the need for land for urban housing construction was increasingly difficult and more pricey, so low-income communities for owning a house were not easy. limited land in an urban area, especially in small cities such as salatiga, is not all used in housing construction. this condition needed an intervention from the local government to facilitate their needs for housing could be fulfilled. based on the problems, this research's goals studied a providing of land for decent and affordable housing for low-income communities in salatiga. the method used was a mixed method with a sequential explanation strategy by overlaying secondary data on the land potential map from various sources that can be used for decent housing with primary data, such as interviews and document reviews with the local government reduce of housing cost. the study results figure out that housing used land owned by the village government can be affordable for low-income communities with price less expensive below the standard set by the government. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): sunarti, s., yuliastuti, n., & indriastjario, i. (2020). land provision for decent and affordable housing for low-income community in salatiga city. geoplanning: journal of geomatics and planning, 6(2), 113-121. doi: 10.14710/geoplanning.6.2.113-121 1. introduction the declaration of the universal declaration of human rights (1948) states that everyone has the right to a standard of living in inadequate housing conditions. in accordance with the declaration, housing is one of the basic necessities of every human being. everyone needs a home, but not everyone can fulfill housing needs. the increasing rate of population growth and the phenomenon of urbanization in big cities that affect the surrounding small towns has resulted in an increase in the need for urban space, especially the fulfillment of housing and settlement needs. when the scale of urbanization has increased, the provision of decent housing for low income people in cities survive as one of the problems is quite difficult to deal with in developing countries (teke, 2011). other researchers stated that urban areas in developing countries are experiencing difficulties in providing adequate and affordable land to meet the housing needs of urban communities. this impacts low-income communities who have difficulties accessing decent and affordable housing (gbadegesin, heijden, & boelhouwer, 2016). generally, housing affordability is a relationship between household income and the housing price level (li et al., 2017). affordable housing for low-income communities is housing that has a price of less than 30% of household income. the united nations defines when families spend more than 30% of housing considered as expenses cannot even meet other needs such as food, clothing, education, transportation, and medical (alaghbari et al., 2011). the study of housing has long been an important target of public policy in society, especially in urban areas (cai & lu, 2015). the turkish government carries out one implementation of the public government in the provision of housing and settlements for lowand middle-income people in the provision of land for article info: received: 10 august 2018 in revised form: 10 october 2018 accepted: 10 december 2018 available online: 30 dec 2019 keywords: affordable housing, land for housing, low-income communities *corresponding author: s. sunarti department of urban and regional planning, diponegoro university, semarang, indonesia email: sunarti@pwk.undip.ac.id open access https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 114 | settlement areas and payment schemes for lowand middle-income communities. thus, meeting the needs of providing housing for low-income people in need of government intervention. salatiga is one of the small cities in central java province, which is developing quite rapidly and heterogeneous areas around the big cities of central java (prawatya, 2013). along with the natural population growth and rapid urbanization, movement affects the availability of land. the administrative availability of land is a fixed value, but the demand for housing land is increasing. this will have an impact on low-income communities, not having the ability to own a house. ironically, the tendency of the high number of backlogs that occur due to the gap between the demand and supply of houses to low-income communities is very worried. the existence of these conditions, salatiga's government has managed to help low-income communities, especially for government civil servants (pns) in the provision of houses by releasing land assets belonging to the local government to the public, so as to reduce the price determined in accordance with the policy of the central government. land release owned by the local government is based on government regulation no. 27 of 2014 on the management of state/regional property. in previous research related to the provision of housing and settlements for low and middle-income communities (palancioglu & cete, 2014). the research is a housing development scheme for low and middle-income people who are adequate in terms of quantity with social and physical conditions in a short time and affordable. based on this gap, this study focuses on public government efforts in providing affordable housing for low-income communities where using assets from local governments. this study is related to the efforts of salatiga's government in managing local government assets for the provision of decent housing for lowincome communities. 2. data and methods the method used in this study is a mixed-method, which is a combination of qualitative and quantitative approaches (creswell, 2010; bryman, 2006). the research method was a mixed-method with a sequential explanatory strategy. the strategy of this method applied data collection and quantitative analysis followed with the collection data and qualitative analysis. data collected by primary and secondary. they are primary data collected by interviewing informants and secondary data collected by literature, planning studies, policies, articles, and reference books. informants are the governments involved in hand over the land assets from the government to the public, such as departments of housing and settlement, department of public works and spatial planning, and departments of local financial revenue. this section will describe the research design and analytical model for the provision of land for affordable housing for low-income communities in salatiga and supported by primary and secondary data as data sources. this study combines qualitative and quantitative approaches in different phases of the research process (terrell, 2012). analysis of the land provision for housing low-income communities seen of the value of housing needs in salatiga. housing needs are based on the value of the backlog in salatiga and low-income demographic data. this study focuses on government civil servant, especially classes of ii and iii, as objects of lowincome communities in housing land provision. the registered names filed to be able to own a house are 1920 persons. the number of housing needs in salatiga as the basic data to analyze the potential of the region as a settlement. potential for the provision of residential land for mbr based on an overlay of land mapping using a settlement area based on the urban land use plan document (rtrw) of salatiga relate to the settlement area salatiga. the overlay of both data figures out the locations of potential areas as new settlements. location determination is not only based on potentially residential locations. local government intervention as an effort of the provision of affordable housing using local assets, which are located in the plan of a settlement area. the availability of land assets and new settlement potential areas were utilized to provide land for affordable housing for low-income communities. https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 | 115 3. result and discussion 3.1. general description of salatiga city salatiga is one of the small cities in central java, which is located in the middle of the semarang regency. administratively, salatiga consists of 4 sub-districts and 23 urban villages with an area of 5.678 hectares (central bureau of statistics in salatiga, 2017). they were argomulyo subdistrict has 6 urban villages, tingkir sub-district has 7 urban villages, sidomukti sub-district has 4 urban villages, and sidorejo sub-district has 6 urban villages. in 2016 the land area used for housing and settlement was 1.618,85 hectares, which is spread in every sub-district. the condition of the existing location of housing and settlement in salatiga could see in the figure 1. figure 1. existing location of housing and settlement in salatiga of 2016 in 2015, houses in salatiga as many as 41,889 housing units. however, this number has not accommodated the housing demand in salatiga. the number of backlog in the salatiga reached 4.068 housing units. reducing the number of backlogs, the salatiga government is trying to build housing for lowincome communities with affordable prices. actions are undertaken by local governments, especially coordinate with the governing board of korpri (corps of government civil servants of the republic of indonesia) in the form of list their names in accordance with the requirement as many as 1920 employees class ii and iii who do not have a house yet. a government civil servants (pns) class ii was civil servants who have maximum educated was the senior high school with maximum salary as much as regional minimum wage (umr) and class iii was civil servants with minimum educated was undergraduate with a minimum salary above a regional minimum wage (umr). land plans for housing and settlements in salatiga in the next 20 years until 2036 years as much as 639.22 hectares. the land acquisition location plan was used to fulfill the demand for houses in salatiga city, both for reducing a backlog of house ownership or for all populations' growth. 3.2 analysis of housing land potential this analysis explains the potential of land in salatiga from the existing location of settlements and settlement plans. this study helps in viewing the locations of land that can be used as a new settlement in accordance with the urban land use plan document of salatiga. the study of potential as settlement land https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 116 | is supported by demographic data based livelihoods in productive age, a percentage of the backlog in salatiga, with existing location and land use plan as settlement. planning for housing area refers to national law number 1 of 2011 concerning housing and settlement area planning. there are several criteria in the planning of housing area, they are : 1. the use of space appropriate land capacity and providing a healthy environment, safe, and can provide a living environment in accordance with community development. 2. available for road infrastructure and public transportation services are available/served by public transportation. 3. management of area supported by public facilities and social facilities. determination of housing area based on location characteristics and land suitability adjusted with characteristic and carrying capacity. landed use for the new housing area is 40% until 60% of the existing land (hilmasyah & rudiarto, 2015). besides that, a new housing area equipped with public utilities and a maximum of housing building density did not structure as much above 50 houses per hectare (hilmasyah & rudiarto, 2015). in addition, it refers to the ministry of regulation number 41/prt/m/2007 mention related criteria to develop housing area, they are : 1. the land's topography is the flat slope and moderate slope (slope is 0% until 25%). 2. available of the water source, both groundwater or water, managed by an organizer. 3. the location was not in a disaster-prone area. 4. the drainage condition is good to a medium. 5. location was not in river barriers, beaches, reservoirs, lakes, wellspring, irrigation, railways, and safe flight areas. 6. the location was not in an area needing protection and was not in fishery farming, agriculture, or buffer area. according to the urban land use plan document and rp3kp of salatiga mention, some land locations were potential as new settlements (city government of salatiga, 2016). the area of potential land as new housing and settlements in each sub-district (table 1). table 1. the land area as potential new housing and settlements of 2016 – 2036 in salatiga based on rp3kp document sub-district land area (hectares) argomulyo 121,76 tingkir 116,08 sidomukti 170,80 sidorejo 230,58 total 639,22 the land area can be used to a new location for housing and settlement all society and especially for providing a settlement of low-income communities. argomulyo sub-district and sidomukti sub-district have a wider area to used as a settlement. thus, both sub-district were more potentially used for a new settlement because they have a wider area than other sub-district. the potential of settlement land also saw a number of the backlog in salatiga. salatiga had experienced a deficit in providing the housing. the difference between the demand for house dan existing housing (backlog) was 4.089 house units in 2015 (figure 2). the highest number of a gap in sidorejo subdistrict with number reached 2.915 house units. then, sidomukti sub-district, with a number of gaps was 799 house units, tingkir sub-district gap number reached was 317, and the lowest gap number reached 37 house units in argomulyo. the table above was the planned land potential for the provision of housing and settlements for all community groups, including for low-income and poor communities. https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 | 117 figure 2. proportion demand for housing in salatiga of 2016 in the picture above that the gap between the demands of the housing and the availability of land in each sub-district for the developing of houses for all community groups. the provision of housing land for low-income communities was included in the plan. the cost of urban land in every sub-districts is more expensive, making it difficult for low-income people to reach. thus, it requires government intervention in the provision of adequate and affordable housing for low-income communities. the provision of service housing for low-income communities was not effective by the formal market mechanism because production and housing construction payments were more expensive. in contrast, informal mechanisms tended to produce a solution that was less expensive than the standard price (rojas & greene, 1995). thus, to fulfill the needs of formal housing and settlement in salatiga was not easy because the limited land caused housing costs more expensive. for low-income communities, it was hard enough to have formal market mechanisms. with limited land, the potential land that can be constructed for housing must be effective and can be used for public welfare for all civil society (figure 3). figure 3. location of housing potential in salatiga of 2016 2036 https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 118 | provision of affordable housing for low-income communities saw from several aspects to supported affordability for the communities. affordable housing design by a case study the government queensland give the initiative in promoting the design and construction of the settlement. affordable for housing summarized in several aspects, they are : 1. the social settlement was designed safe and comfortable for residents. 2. environment sustainability with the availability of waste management, water, and energy. 3. economic, affordable and efficient cost from time to time. potential land for housing and settlement in salatiga has in accordance with the criteria, but it could not be affordable by low-income communities with their limited economics. this condition can cause the government policy of salatiga to helped low-income communities by handed over half of the land assets owned by the government (ex-a land by the village administration) to low-income communities for reducing cost standards. 3.3 analysis of land local government assets the government of salatiga's effort in the provision of land for housing saw the potential of the new area as settlement based on the urban land use plan of salatiga. the availability of land as a settlement has a lot of potentials, which is distributed on some sub-district. but, not all communities could access dan reach of that land. this is the financial constraint of low-income communities to have land for good and decent fulfillment. the government of salatiga has several land assets scattered in every sub-district of salatiga. land assets were (tanah bengkok), which is private land owned by village administration and handed over to the local government of salatiga. those assets landed use, such as agriculture, plantation, moor, field, public facilities, and social facilities. the area of land from landed use of government assets in every sub-district could be seen on the table 2-5. table 2. landed use of ex-a land owned by the village government (etb) in argomulyo sub-district in 2017 table 3. landed use of ex-a land owned by the village government (etb) in sidomukti subdistrict in 2017 landed use area (m2) percentage plantation 402.629 45% settlement 1904 0% office 7407 1% social facilities 20.838 2% building water storage facilities 3.765 0% field 30.360 3% agriculture 327.358 37% rice field 52.783 6% moor 39.420 4% total 886.464 100 % landed use area(m2) percentage moor 2.674 1% rice field 2.521 1% agriculture 338.821 89% park 3.570 1% field 5.739 1% social and public facilities 29.687 8% settlement 5.021 1% office 14.155 4% total 382.838 100 % table 4. landed use of ex-a land owned by the village government (etb) in sidorejo sub-district in 2017 table 5. landed use ex-a land owned by the village government (etb) tingkir sub-district in 2017 landed use area (m2) percentage agriculture ( rice field) 42.372 5% agriculture (moor) 45.305 5% agriculture 724.990 86% office building 7.039 1% landed use area(m2) percentage agriculture 175.853 27% rice field 415.604 64% moor 18.548 3% settlement 7.205 1% https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 | 119 field 11.480 1% social facilities 9.865 1% bathing place 255 0% total 841.306 100 % office 8.238 1% social facilities 4.152 1% field 20.148 3% total 649.748 100 % based on ex land owned by the village government of land use in 2017, the majority percentage of landed use of etb as local government assets used for plantations and agriculture (rice field and moor). the largest area of land in argomulyo sub-district. that land was land owned by the village administration and handed over to the local government of salatiga. the availability land of local government and then fulfillment demand of housing for low-income communities by made use of a land asset, which is located in accordance with urban planning of salatiga. the above data represented land assets in 2017, but a land asset for providing housing for government civil servants (pns) took asset land in 2007. that land asset as dry-soil agriculture and was converted as a settlement area. in 2007 for providing a demand for housing for low-income communities related to housing because of the inability of low-income communities to have a house, the local government has removed half of their assets (land) for society as their housing settlement. the location of local government land assets has been removed for housing in the land that was used before as dry-soil agriculture. the chosen location was kecandran urban village, sidomukti sub-district, and randuacir urban village, argomulyo sub-district in salatiga. the selection of both location because it was the largest land asset than other land assets of local government that could use to housing. land acquisition of local government for providing housing of low-income communities, especially for government civil servants distributed in some locations. provision of facilities and infrastructure for the housing of government civil servants (pns) were in some location, they are : 1. ex-land owned by the village government (eks tanah bengkok) in kecandran urban-village with area of 59.207 m2. 2. right over land number 27 on behalf of salatiga government with an area of 48.115 m2. but the land acquisition of the salatiga government with compensation to prospective landowners or pns divide in two ways, they are : 1. half of the land bengkok in kecandran urban village with an area of 31.420 m2 for built 400 house units in prajamukti. 2. half of right over land number 27 in salatiga with an area of 31.420 m2 which is builed 345 house units in prajamulya. disposal of right assets from government to public accordance regulation in lieu of law number 27 of 2014 about management regional/national owned goods stated that owner of regional or national owned goods could propose the utilization and transfers regional owned goods form of land or building that was not required the approval municipal regional houses of people's representatives (dprd) and regional owned goods besides from land and building. and the last could be owned for the public. still, to avoid any problems in the future, the indonesian civil servant's corps submitted an application to the municipal regional houses of people's representatives (dprd) of salatiga to approve the disposal of land assets owned by the government to the public. in relation to the local government's land assets, dprd approved the transfers and removed land from the inventory list for housing development to government civil servants in salatiga. disposal of assets refers to some regulation provision which is supported for transfers regional owned assets to society, such as : 1. government regulation of indonesia number 38 of 2008 about management of national/regional owned goods. 2. government regional number 50 of 2006 about managements of goods. 3. minister of home affairs regulation number 17 of 2007 about technical guidelines for regional property management. 4. decision of municipal regional houses of people's representatives (dprd) of salatiga about committee for the removed of regional owned goods for provision housing korpri in 2012. https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 120 | based on regulation before, government land assets have to release can be utilized for the settlement of low-income communities. then the price of government civil servants, especially class ii and iii, can be cheaper. they only replaced with the land at a price in accordance with land and building tax imposition base (njop). land used for housing comes from these government assets, it can depress home prices that are offered to the public at a price below the standards set by the government. 3.4 analysis of affordable housing for low-income communities generally of affordable housing could see from economic aspects in the ability to buy a housing unit. based on spending money on housing, this condition was not more than 30% of household income. this agrees with the research of providing affordable housing for low-income communities in yamen, which is an economic aspect of affordable housing. the provision of affordable housing for low-income communities from the land relinquishment of government assets could reduce the number of the backlog in salatiga. reduction of the backlog in salatiga was providing 745 of house units to 745 families from 1920 government civil servants who had not had a house. government interventions in providing land for low-income communities in accordance with formal market mechanisms could help reduce the price of housing, which has to be paid by the community. affordability in the provision of housing and settlement for low-income communities provided the land and the effort of pressing cost. the affordability of housing payments was seen at the price of one house unit of idr 85,000,000. this price was below the standard of price housing and could access a mortgage (kpr flpp) by the ministry of public works and human settlement (pupr). based on the standard was given by pupr, the housing price of the mortgage was idr 130.0000.0000. the local government of salatiga has pressed as many as 34% from standard pricing. in addition, pressed of cost a housing unit of mortgage, pressing of cost also got price support by the ministry of public works and human settlement (pupr) as much as idr 4.000.000 as an advance payment for the land. and then pressing of the cost was also gotten help from bank btn (bank tabungan negara) to make commercial loans. so, the owner's candidate could make a payment of credit in one month was idr 835.000 within a period of 15 years until 20 years. provision of housing dan settlement for low-income communities, especially for government civil servants classes of ii and iii, has been done since 2007. since 2007 has done list names of owner's candidate with several criteria, such as : 1. government civil servants (pns) in salatiga. 2. priority for pns class ii and iii. 3. priority for pns who has worked in salatiga more than 5 years. 4. couples husband and wife of pns can only have 1 house unit. 5. prioritized government civil servants who had not have a house. 6. government civil servants who have a job tenure before december 2012. requirement of above, government civil servants who register as much as 1.920 employees, but a limited land asset of ex-land owned by village government and has owned by the local government only has built 735 house units. the next plan of salatiga's government will build housing for low-income communities in the sub-district, who have some land assets that can be used for housing and settlement. but over time, in the process of housing development, the owner's candidate has changes job titles. so the number of job status changes has influenced inappropriately in the purpose and goals of providing housing for low-income communities. so, some owner's candidate was not appropriate with criteria. 4. conclusion salatiga is one of the small cities in central java with a lot of development as an attraction to the increase in activities and demand for housing, especially for low-income communities. however, not all low-income communities have the opportunity to obtain affordable land for housing demand. provision of government land assets by the transfers to the public as an effort providing affordable housing. from the government, assets have some asset location that can be used as providing low-income communities https://doi.org/10.14710/geoplanning.6.2.113-121 sunarti, yuliastuti, indriastjario / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 113-121 doi: 10.14710/geoplanning.6.2.113-121 | 121 housing. intervention from the local government for fulfillment providing affordable housing not only focuses on the land provision. payment scheme as an effort to pressing of cost in the payment process for low-income communities. the standard of a unit house with a mortgage (kpr) was idr 130.000.000, but the local government has pressed of cost until 34% or idr 45.000.000. low-income communities can claim affordable ownership of a unit house with a price under the standard. this salatiga city government policy can be a lesson learned for other local governments to help low-income communities in affordable homeownership. the government policy of salatiga in providing housing and settlement in the future should do not reduce land assets owned by local government. a land status did not remove the public-right but providing a housing and settlement approach was rent. 5. acknowledgments researchers would like to thank the donor of funds, namely the university of diponegoro for research on the development and application of funds in addition to the state budget of diponegoro university in the 2018 fiscal year with contract number 474-36/un7.p4.3/pp/2018. so with these funds, the researcher can complete the research. 6. references alaghbari, w., salim, a., dola, k., & ali, a. a. a. (2011). developing affordable housing design for low income in sana’a, yemen. international journal of housing markets and analysis, 4(1), 84–98. [crossref] bryman, a. (2006). integrating quantitative and qualitative research: how is it done? qualitative research, 6(1), 97–113. [crossref] cai, w., & lu, x. (2015). housing affordability: beyond the income and price terms, using china as a case study. habitat international, 47, 169–175. [crossref] central bureau of statistics in salatiga. (2017). salatiga in fiures 2017. creswell, j. w. (2010). research design: pendekatan kualitatif, kuantitatif dan mixed. yogyakarta: pustaka pelajar yogyakarta. gbadegesin, j. t., heijden, h. van der, & boelhouwer, p. (2016). land accessibility factors in urban housing provision in nigeria cities : case of lagos ., (2015). hilmasyah, h., & rudiarto, i. (2015). kajian perkembangan dan kesesuaian lahan permukiman eksisting di kecamatan indramayu, 4(1), 54–65. li, l. h., wu, f., dai, m., gao, y., & pan, j. (2017). housing affordability of university graduates in guangzhou. habitat international, 67, 137–147. [crossref] palancioglu, h. m., & cete, m. (2014). the turkish way of housing supply and finance for lowand middleincome people. land use policy, 39, 127–134. [crossref] city government of salatiga. (2016). salatiga spatial planning. rp3kp program: salatiga. prawatya, n. a. (2013). perkembangan spasial kota-kota kecil di jawa tengah. jurnal wilayah dan lingkungan, 1(april), 17–32. rojas, e., & greene, m. (1995). reaching the poor: lessons from the chilean housing experience. environment & urbanization, 7(2), 31–50. [crossref] teke, n. (2011). challenges of urban housing provision in lagos and johannesburg. net publication. terrell, s. r. (2012). mixed-methods research methodologies. the qualitative report, 17(171), 254–280. [crossref] https://doi.org/10.14710/geoplanning.6.2.113-121 https://doi.org/10.1108/17538271111111857 https://doi.org/10.1177/1468794106058877 https://doi.org/10.1016/j.habitatint.2015.01.021 https://doi.org/10.1016/j.habitatint.2017.07.007 https://doi.org/10.1016/j.landusepol.2014.04.001 https://doi.org/10.1177/095624789500700217 https://doi.org/10.1177/1744987106064635 geoplanning vol 2, no 1, 2015, 10-21 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning | 10 open access identifikasi potensi multi-bencana di kabupaten landak kalimantan barat a.wahyuningtyasa, r.a.pratomob a mahasiswa magister manajemen bencana ugm yogyakarta, indonesia, a.wahyuningtyas@gmail.com b master student of applied earth science faculty of geo-information science & earth observation, university of twente, the netherlands, arispratomo05@gmail.com abstract: one of the important aspects in studies on regional potentials and problems is the sectoral issues in a disaster. it is because disaster is an event or series of events, caused whether by natural or unnatural factors that inflict damages and losses to people and the environment. landak regency (kabupaten) is categorized highly disaster-prone region by the national disaster management agency (bnpb). the aim of this research is to identify the disaster-prone area of kabupaten landak. the identification is based on assessment variables of multihazards that may occur and threat kabupaten landak. the analysis has used geographic information systems, scoring and weighting techniques. disaster vulnerability was measured based on the existing physical aspects, such as land use, land slope, soil type, and others. it is an explorative and evaluative research with a mix of qualitative and quantitative approaches. the results show that there are disaster potential hazards in landak regency, such as flood, landslide, cyclone, bushfire, and forest-fire. each potential hazard has different vulnerability level and different distribution area based on the risk assessment. © 2015 gjgp undip. all rights reserved. abstrak: salah satu pengkajian potensi dan permasalahan daerah yang penting adalah permasalahan sektoral dalam kebencanaan. hal ini dikarenakan bencana adalah peristiwa atau rangkaian peristiwa yang disebabkan oleh faktor alam maupun non alam dan dapat menimbulkan kerusakan dan kerugian. kabupaten landak termasuk dalam kategori tinggi dalam indeks daerah rawan bencana yang dikeluarkan oleh badan penanggulangan bencana nasional.tujuan penelitian ini adalah untuk mengidentifikasi potensi daerah rawan bencana. identifikasi tersebut didasarkan pada variabel-ariabel penilai pada beberapa multi-bahaya yang mungkin timbul dan mengancam di kabupaten landak. teknik analisis menggunakan sistem informasi geografis, skoring, dan pembobotan terhadap variabel-variabel penilaian potensi bencana yang ada. kerawanan bencana ini diukur berdasarkan aspek-aspek fisik yang ada, seperti penggunaan lahan, kelerengan, jenis tanah, dan lain-lain. jenis penelitian ini tergolong penelitian eksploratif dan evaluatif dengan pendekatan secara kualitatif dan kuantitatif. hasil penelitian ini menunjukkan bahwa terdapat beberapa bahaya yang dianggap berpotensi menjadi bencana di kabupaten landak, diantaranya banjir, longsor, angin puting beliung, dan kebakaran hutan seta kebakaran pada permukiman. masing-masing dari bahaya tersebut memiliki tingkat kerawanan yang berbeda-beda dengan sebaran lokasi yang berbeda sesuai tingkat kerawanannya. © 2015 gjgp undip. all rights reserved. 1. pendahuluan indonesia merupakan negara yang sangat rawan bencana. hal ini dibuktikan dengan terjadinya berbagai bencana yang melanda berbagai wilayah secara terus menerus, baik yang disebabkan oleh faktor alam (gempa bumi, tsunami, banjir, letusan gunung api, tanah longsor, angin ribut, dll), maupun oleh faktor info artikel; diterima: 27 maret 2015 hasil revisi : 30 maret 2015 disetujui: 25 april 2015 publikasi on-line: 30 april 2015 kata kunci: multi-bahaya, kerawanan, kabupaten landak article info; received: 27 march 2015 in revised form: 30 march 2015 accepted: 25 april 2015 available online: 30 april 2015 keywords: multi-hazards, disaster prone, landak regency mailto:a.wahyuningtyas@gmail.com mailto:arispratomo05@gmail.com geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 11 non alam seperti berbagai akibat kegagalan teknologi dan ulah manusia. umumnya bencana yang terjadi tersebut mengakibatkan penderitaan bagi masyarakat, baik berupa korban jiwa manusia, kerugian harta benda, maupun kerusakan lingkungan serta musnahnya hasil-hasil pembangunan yang telah dicapai (pelling, 2003). kabupaten landak terletak di provinsi kalimantan barat. perkembangan perekonomian di kabupaten landak berjalan cukup cepat sehingga pembangunan juga berjalan dengan cepat. perkembangan pembangunan yang pesat di kabupaten landak akan berpengaruh terhadap perubahan kondisi lahan secara spasial, yang secara langsung memberikan kontribusi terhadap peningkatan kerentanan bencana. dalam buku rencana aksi nasional pengurangan risiko bencana (ran-prb) 2006-2009 disebutkan bahwa kejadian bencana di indonesia terus meningkat dari tahun ke tahun. begitu juga halnya dengan hasil indeks pada data badan nasional penanggulangan bencana 2011 yang menyatakan bahwa kabupaten landak memiliki tingkat kerawanan tinggi sebagai kabupaten yang berpotensi terhadap kerawanan bencana. menyadari pentingnya mengetahui adanya ancaman bencana di kabupaten landak, maka dilakukan penelitian untuk mengidentifikasi mengenai potensi daerah rawan bencana di kabupaten landak. identifikasi ini akan memuat informasi tentang lokasi-lokasi bencana yang mungkin timbul di wilayah kabupaten landak. dengan mengetahui wilayah-wilayah yang berpotensi terjadi bencana diharapkan risiko yang timbul ataupun kerugian akibat dampak yang terjadi dapat diminimalkan. 2. data dan metode penelitian ini merupakan penelitian eksploratif dan evaluatif dengan pendekatan secara kualitatif dan kuantitatif. jenis penelitian eksploratif adalah jenis penelitian yang bertujuan untuk menemukan sesuatu yang baru yang dapat saja berupa pengelompokan suatu gejala, fakta, dan penyakit tertentu (surakhmad, 1980:131). sementara itu, untuk penelitian evaluatif memiliki dua kegiatan utama, yaitu pengukuran atau pengambilan data dan membandingkan hasil pengukuran dan pengumpulan data dengan standar yang digunakan. data yang dikumpulkan didapat secara langsung melalui survei di lapangan, dokumentasi, dan wawancara serta data sekunder yang diperoleh dari data-data referensi pada instansi terkait dan data statistik yang terkait dengan kebencanaan di kabupaten landak. sedangkan analisis risiko bencana menggunakan pedoman yang tertera pada undang-undang nomor 2 tahun 2012 tentang pengkajian risiko bencana. adapun variabel-variabel yang digunakan untuk analisis sesuai pedoman tersebut kemudian dispasialkan menggunakan software arc gis 10.2 untuk mempermudah proses pembagian tingkat kelas rendah, sedang, dan tinggi untuk masing-masing kerawanan bencana, kerentanan, dan risiko bencananya (wisner, blaikie, & canon, 2005). 3. hasil dan pembahasan 3.1 indeks kerawanan bencana berdasarkan histori kejadian di kabupaten landak secara umum penilaian ancaman bencana menggunakan beberapa informasi dasar yang bisa didapat dari legenda dan mitos, rekaman sejarah serta data penelitian yang pernah dilakukan di wilayah tersebut. penilaian indeks kerawanan di kabupaten landak menggunakan dua metode, yaitu faktor keterkaitan (cf) dan bobot proporsi peluang (bp). faktor keterkaitan menggunakan potensi keterkaitan antar bencana sesuai dengan karakteristik bahaya alam yang ada di kabupaten landak. untuk mendapatkan keterkaitan antar bencana, digunakan tabulasi keterkaitan antar bencana di tiap kabupaten landak. berdasarkan data tabulasi yang telah dibuat selanjutnya faktor keterkaitan ditentukan sebagai rerata antara bahaya paling berpengaruh dan bahaya paling sensitif. untuk perhitungan risiko perkecamatan karena jumlah bahaya yang ada lebih dari satu, maka untuk mendapatkan nilai keterkaitan dilakukan perhitungan sebagai berikut : x cfw cfw = merupakan jumlah komulatif masing-masing cfw untuk setiap kecamatan geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 12 proporsi peluang merupakan asumsi peluang terjadinya bencana. nilai probabilitas yang didapat selanjutnya dibandingkan satu sama lain untuk selanjutnya dikonversi menjadi bobot proporsi probabilitas (bp) (north, 2006). untuk menentukan bobot proporsi probabilitas digunakan acuan dokumen rehabilitasi rekonstruksi pasca bencana kabupaten landak yang mengaitkan antara kemungkinan kejadian (likelihood) dengan dampak (consequences) (wisner et al., 2005). kabupaten landak memiliki empat (4) potensi bencana. keempat bencana tersebut adalah tanah longsor, banjir, angin puting beliung, dan kebakaran termasuk kebakaran hutan dan kebakaran permukiman. adapun penilaian indeks kerawanan berdasarkan metode di atas dapat dilihat pada tabel 1. tabel 1. potensi multi-bahaya berdasarkan histori kejadian di kabupaten landak (hasil analisis, 2013) no kecamatan kejadian bencana jumlah faktor keterkaitan bobot proporsi peluang banjir tanah longsor angin puting beliung kebakaran hutan kebakaran permukiman 1 sebangki 0 0 0 0 0 0 0 0 2 ngabang 2 0 1 0 0 3 2 12,50 3 jelimpo 1 0 0 0 0 1 1 6,25 4 sengah temila 2 0 0 0 0 2 1 6,25 5 mandor 0 0 1 0 0 1 1 6,25 6 menjalin 0 0 1 0 0 1 1 6,25 7 mempawah hulu 0 0 1 0 0 1 1 6,25 8 sompak 1 1 0 0 0 2 2 12,50 9 menyuke 3 2 2 0 0 7 3 18,75 10 banyuke hulu 0 0 0 0 0 0 0 0 11 meranti 0 0 0 0 0 0 0 0 12 kuala behe 3 0 1 0 0 4 2 12,50 13 air besar 2 0 1 0 0 3 2 12,50 jumlah 14 3 8 0 0 25 16 100 berdasarkan perhitungan faktor keterkaitan dan bobot proporsi peluang di atas, maka untuk mendapatkan indeks kerawanan terhadap bencana pada masing-masing kecamatan di kabupaten landak dilakukan perankingan guna memperoleh klasifikasi kelas kerawanan. kelas kerawanan tersebut dibagi menjadi tiga, yaitu: a. kelas kerawanan rendah untuk wilayah yang memiliki nilai rentang antara 0 6,75 dengan nilai skor 1; b. kelas kerawanan sedang untuk wilayah yang memiliki nilai rentang antara 6,75 12,50 dengan nilai skor 2, dan; c. kelas kerawanan tinggi untuk wilayah yang memiliki nilai rentang antara 12,50 – 18,75 dengan nilai skor 3. hasil dari klasifikasi tingkat kerawanan terhadap bencana di kabupaten landak dapat dilihat pada tabel 2. tabel 2. indeks kerawanan bencana di kabupaten landak (hasil analisis, 2013) no. kecamatan bobot proporsi peluang skor indeks kerawanan 1 sebangki 0 1 rendah 2 ngabang 12,50 2 sedang 3 jelimpo 6,25 1 rendah 4 sengah temila 6,25 1 rendah 5 mandor 6,25 1 rendah 6 menjalin 6,25 1 rendah 7 mempawah hulu 6,25 1 rendah 8 sompak 12,50 2 sedang 9 menyuke 18,75 3 tinggi 10 banyuke hulu 0 1 rendah 11 meranti 0 1 rendah 12 kuala behe 12,50 2 sedang 13 air besar 12,50 2 sedang geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 13 berdasarkan klasifikasi di atas, didapatkan bahwa wilayah dengan tingkat kerawanan bencana tinggi berada pada kecamatan menyuke. sementara wilayah dengan tingkat kerawanan sedang berada di empat kecamatan, yaitu kecamatan ngabang, kecamatan sompak, kecamatan kuala behe, dan kecamatan air besar. sedangkan sisanya sejumlah 8 kecamatan merupakan wilayah dengan tingkat kerawanan rendah, yaitu kecamatan sebangki, kecamatan jelimpo, kecamatan sengah temila, kecamatan mandor, kecamatan menjalin, kecamatan mempawah hulu, kecamatan banyuke hulu, dan kecamatan air besar. untuk melihat indeks kerawanan bencana berdasarkan histori kejadian di kabupaten landak secara detail dapat dilihat pada gambar 1. gambar 1. indeks kerawanan bencana berdasarkan histori kejadian di kabupaten landak (hasil analisis, 2013) 3.2 potensi kerawanan bencana kabupaten landak setelah dilakukan identifikasi kerawanan bencana di kabupaten landak dengan sistem informasi geografis, selanjutnya dilakukan skoring dan pembobotan terhadap variabel yang dibutuhkan oleh masing-masing potensi bencana. kerawanan bencana ini diukur berdasarkan aspek-aspek fisik yang ada, seperti penggunaan lahan, kelerengan, jenis tanah, dan lain-lain. berdasarkan pengamatan kejadian bencana, yang dianggap berpotensi menjadi bencana di kabupaten landak diantaranya, banjir, longsor, angin puting beliung, dan kebarakan hutan serta kebakaran pada permukiman. a. banjir banjir dapat terjadi akibat faktor alam seperti curah hujan yang tinggi dan lama, lokasi banjir berada pada topografi yang relatif datar dengan pola sungai yang berbelok-belok (meandering) dan dataran banjir yang luas, keadaan struktur tanah atau batuan yang lambat meresapkan air, dan kapasitas sungai yang tidak dapat menampung dan mengalirkan air ke laut . di samping itu, faktor manusia juga berperan menyebabkan banjir, diantaranya bertambahnya penduduk sehingga menempati daerah bantaran sungai dan dataran banjir alamiah sehingga mengurangi kantongkantong air dan daerah parkir banjir, dan hilang atau berkurangnya daerah resapan akibat perubahan fungsi lahan untuk berbagai keperluan. identifikasi sebaran kawasan rawan banjir dan daerah pengaruh, ditetapkan berdasarkan tingkat atau daya rusak air yang membahayakan jiwa maupun harta benda (material). identifikasi kawasan rawan banjir tersebut menggunakan teknik overlay dari data-data kondisi fisik wilayah kabupaten landak serta kejadian-kejadian banjir yang terjadi sebelumnya secara periodik. adapun overlay tersebut dilakukan dengan data berupa: 1) peta topografi 2) peta geologi geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 14 3) hidrologi (hidrogeologi dan hidrometeorologi) 4) wilayah rawan bencana banjir yang pernah terjadi tabel 3. variabel skoring potensi bencana banjir (modifikasi pedoman aspek final peraturan menteri pekerjaan umum no.20/prt /m/2007) variabel bobot indikator (%) sensivitas tingkat kerawanan verifer bobot penilaian nilai bobot tertimang nilai kerawanan banjir intensitas curah hujan 35 tinggi >3.000 mm/tahun 3 1,05 sedang 2.000-3000 mm/tahun 2 0,70 rendah <2.000 mm/tahun 1 0,35 kelerengan 25 tinggi 0-25% 3 0,75 sedang 25-40% 2 0,50 rendah >40% 1 0,25 penutup lahan 30 tinggi permukiman, lokasi kejadian banjir terdahulu 3 0,90 sedang sawah 2 0,60 rendah semak, kebun, hutan 1 0,30 bentuk lahan 10 tinggi daratan aluvial pantai, dataran banjir, sempadan sungai 3 0,30 sedang daratan aluvial 2 0,20 rendah bukit sisa, lembah perbukitan karst, perbukitan 1 0,10 tingkat kerawanan banjir 100 tinggi 210-300 3 3,00 sedang 101-200 2 2,00 rendah <100 1 1,00 berdasarkan hasil skoring di atas, kabupaten landak memiliki tingkat kerawanan banjir yang terbagi menjadi tiga, yaitu kerawanan tinggi, sedang, dan rendah. sebanyak 40,11% wilayah di kabupaten landak memiliki tingkat kerawanan banjir yang tinggi. kerawanan tinggi terhadap banjir ini tersebar di semua kecamatan, kecuali kecamatan menjalin yang tidak memiliki potensi kerawanan tinggi. sedangkan sebanyak 57,85% merupakan wilayah terluas yang memiliki tingkat kerawanan sedang terhadap bencana banjir. sisanya, sebanyak 2,02% merupakan wilayah dengan sebaran kerawanan rendah yang berada di kecamatan banyuke hulu, mempawah hulu, menjalin, mandor, dan kecamatan sompak. untuk lebih jelasnya mengenai lokasi wilayah dengan masingmasing tingkat kerawanan terhadap banjir pada gambar 2. gambar 2. peta potensi bencana banjir kabupaten landak (hasil analisis, 2013) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 15 b. longsor penilaian potensi longsor ditinjau dari kondisi kemiringan lereng, kondisi tanah, curah hujan, dan penggunaan lahan (dragicevic, terence, & shivanand, 2015). keempat variabel tersebut di overlay sebagaimana yang dilakukan untuk menentukan potensi banjir. untuk lebih jelasnya dapat dilihat pada tabel 4. tabel 4. variabel skoring potensi bencana longsor (modifikasi pedoman aspek final peraturan menteri pekerjaan umum no.20/prt /m/2007) no. indikator bobot indikator (%) sensivitas tingkat kerawanan verifer bobot penilaian nilai bobot tertimbang nilai kerawanan longsor 1. kemiringan lereng 35 % tinggi lereng relatif cembung dengan kemiringan lebih curam dari (di atas) 40% 3 1,05 sedang lereng relatif landai dengan kemiringan antara 25% s/d 40% 2 0,70 rendah lereng dengan kemiringan <25 1 0,35 2. kondisi tanah 25 % tinggi lahan kritis, lereng tersusun dari tanah penutup tebal (>2m), bersifat gembur dan mudah lolos air, misalnya tanah-tanah residual, yang umumnya menumpang di atas batuan dasarnya (misal andesit, breksi andesit, tuf, napal, dan batu lempung) yang lebih kompak (padat) dan kedap lereng tersusun oleh tanah penutup tebal (>2m), bersifat gembur dan mudah lolos air, misalnya tanahtanah residual atau tanah koluvial, yang di dalamnya terdapat bidang kontras antara tanah dengan kepadatan lebih rendah dan permeabilitas lebih tinggi yang menumpang di atas tanah dengan kepadatan lebih tinggi dan permeabilitas lebih rendah 3 0,75 sedang lereng tersusun oleh tanah penutup tebal (<2m), bersifat gembur dan mudah lolos air, serta terdapat bidang kontras di lapisan bawahnya 2 0,50 rendah lereng tersusun dari tanah penutup tebal (2m), bersifat padat dan tidak mudah lolos air, tetapi terdapat bidang kontras di lapisan bawahnya 1 0,25 3. curah hujan 25% tinggi curah hujan yang tinggi (dapat mencapai 100 mm/hari atau 70 mm/jam) dengan curah hujan tahunan lebih dari 2500 mm. curah hujan kurang dari 70 mm/jam, tetapi berlangsung terus-menerus selama lebih dari dua jam hingga beberapa hari 3 0,75 sedang curah hujan sedang (berkisar 30 – 70 mm/jam), berlangsung tidak lebih dari 2 jam dan hujan tidak setiap hari (100-2500 mm) 2 0,50 rendah curah hujan rendah (kurang dari 30 mm/jam), berlangsung tidak lebih dari 1 jam dan hujan tidak setiap hari (kurang dari 1000 mm) 1 0,25 4. penggunaan lahan 15% tinggi alang-alang, rumput-rumputan, tumbuhan semak, tumbuhan perdu 3 0,45 sedang tumbuhan berdaun jarum seperti cemara, pinus 2 0,30 rendah tumbuhan berakar tunjang yang perakarannya menyebar seperti jati, kemiri, kosambi, laban, dlingsem, mindi, renghas, sonokeling, trengguli, tayuman, asam jawa dan pilang 1 0,15 tingkat kerawanan longsor 100 tinggi 210-300 3 3,00 sedang 101-200 2 2,00 rendah <100 1 1,00 berdasarkan hasil skoring di atas, kabupaten landak memiliki tingkat kerawanan longsor yang terbagi menjadi tiga, yaitu kerawanan tinggi, sedang, dan rendah. sebanyak 56,54% wilayah di geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 16 kabupaten landak memiliki tingkat kerawanan longsor yang tinggi. kerawanan tinggi terhadap longsor ini tersebar di semua kecamatan, kecuali kecamatan menjalin yang tidak memiliki potensi kerawanan tinggi. sedangkan sebanyak 43,03% merupakan wilayah terluas yang memiliki tingkat kerawanan sedang terhadap bahaya longsor. sisanya, sebanyak 0,41% merupakan wilayah dengan sebaran kerawanan rendah yang berada di kecamatan banyuke hulu, mempawah hulu, sebangki, air besar, meranti, kuala behe, dan kecamatan sompak. untuk melihat lebih jelas mengenai sebaran lokasi wilayah dengan masing-masing tingkat kerawanan terhadap longsor pada gambar 3. gambar 3. peta potensi bencana longsor kabupaten landak (hasil analisis, 2013) c. angin puting beliung penilaian potensi terhadap angin puting beliung ditinjau dari kondisi curah hujan, penggunaan lahan, dan topografi. ketiga variabel tersebut di overlay sebagaimana yang dilakukan untuk menentukan potensi banjir dan longsor. adapun variabel yang digunakan sebagai indikator faktor penentu penilaian rawan angin puting beliung dapat dilihat pada tabel 5. tabel 5. variabel skoring potensi bencana angin puting beliung (modifikasi pedoman aspek final peraturan menteri pekerjaan umum no.20/prt /m/2007) no. indikator bobot indikator (%) sensivitas tingkat kerawanan verifer bobot penilaian nilai bobot tertimbang nilai kerawanan angin puting beliung 1. curah hujan 40 % tinggi curah hujan yang tinggi (dapat mencapai 100 mm/hari atau 70 mm/jam) dengan curah hujan tahunan lebih dari 2500 mm. 3 1,20 sedang curah hujan sedang (berkisar 30 – 70 mm/jam), berlangsung tidak lebih dari 2 jam dan hujan tidak setiap hari (1000-2500 mm) 2 0,80 rendah curah hujan rendah (kurang dari 30 mm/jam), berlangsung tidak lebih dari 1 jam dan hujan tidak setiap hari (kurang dari 1000 mm) 1 0,40 2. penggunaan lahan 25% tinggi lahan resapan air, lahan terbuka, 3 0,75 sedang perkebunan, pertambangan, permukiman, wisata 2 0,50 rendah hutan 1 0,25 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 17 3. topografi 35% tinggi >3000 m dpl 3 1,05 sedang 1500-3000 m dpl 2 0,70 rendah <1500 m dpl 1 0,35 tingkat kerawanan angin puting beliung 100 tinggi 210-300 3 3,00 sedang 101-200 2 2,00 rendah <100 1 1,00 berdasarkan hasil skoring di atas, kabupaten landak hanya memiliki dua tingkat kerawanan terhadap angin puting beliung, yaitu kerawanan sedang dan kerawanan rendah. sebanyak 93,57% wilayah di kabupaten landak memiliki tingkat kerawanan angin puting beliung yang rendah. kerawanan sedang terhadap angin puting beliung ini tersebar di semua kecamatan. sisanya, sebanyak 6,42% merupakan wilayah memiliki kerawanan rendah terhadap bahaya angin puting beliung. untuk lebih jelasnya dapat dilihat pada gambar 4. gambar 4. peta potensi bencana angin puting beliung kabupaten landak (hasil analisis, 2013) d. kebakaran hutan identifikasi sebaran kawasan rawan terhadap kebakaran hutan dan daerah pengaruh, ditetapkan berdasarkan tingkat atau daya rusak yang membahayakan jiwa maupun harta benda (material). identifikasi kawasan rawan kebakaran hutan tersebut menggunakan teknik overlay dari data-data kondisi fisik wilayah kabupaten landak serta kejadian-kejadian kebakaran hutan yang terjadi sebelumnya secara periodik. adapun overlay tersebut dilakukan dengan data berupa kondisi vegetasi, penutup lahan, bentuk lahan, dan curah hujan. untuk lebih jelasnya dapat dilihat pada tabel 6. tabel 6. variabel skoring potensi bencana kebakaran hutan (modifikasi pedoman aspek final peraturan menteri pekerjaan umum no.20/prt /m/2007) no. indikator bobot indikator (%) sensivitas tingkat kerawanan verifer bobot penilaian nilai bobot tertimbang nilai kerawanan kebakaran hutan 1. vegetasi 30 tinggi alang-alang, rumput-rumputan, tumbuhan semak, tumbuhan perdu 3 0,90 sedang tumbuhan berdaun jarum seperti cemara, pinus 2 0,70 rendah tumbuhan berakar tunjang yang perakarannya menyebar seperti jati, kemiri, kosambi, laban, dlingsem, mindi, renghas, 1 0,35 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 18 sonokeling, trengguli, tayuman, asam jawa dan pilang 2. penutup lahan 15 tinggi semak, kebun, hutan 3 0,75 sedang sawah 2 0,50 rendah permukiman 1 0,25 3 bentuk lahan 35 tinggi tanah yang mengandung gambut (organosol) 3 1,20 sedang latosol dan podsolid 2 0,80 rendah batuan 1 0,40 4. curah hujan 20 tinggi <1000 mm per tahun 3 0,60 sedang 1000-2500 mm per tahun 2 0,40 rendah >2500 mm per tahun 1 0,20 tingkat kerawanan kebakaran hutan 100 tinggi 210-300 3 3,00 sedang 101-200 2 2,00 rendah <100 1 1,00 berdasarkan hasil skoring di atas, kabupaten landak memiliki tiga tingkat kerawanan terhadap kebakaran hutan, yaitu kerawanan rendah, sedang, dan kerawanan tinggi. sebanyak 18,98% wilayah di kabupaten landak memiliki tingkat kerawanan terhadap bahaya kebakaran hutan yang rendah. sementara sebanyak 76,67% merupakan wilayah dengan tingkat kerawanan sedang. daerah yang memiliki tingkat kerawanan rendah dan sedang terhadap kebakaran hutan ini tersebar di semua kecamatan. sisanya, sebanyak 4,34% merupakan wilayah yang memiliki kerawanan tinggi terhadap bahaya kebakaran hutan. wilayah dengan tingkat kerawanan tinggi ini hanya tersebar di kecamatan sebangki, sengah temila, banyuke hulu, mempawah hulu, mandor, dan kecamatan sompak. untuk lebih jelasnya dapat dilihat pada gambar 5. gambar 5. peta potensi bencana kebakaran hutan kabupaten landak (hasil analisis, 2013) e. kebakaran permukiman penilaian potensi terhadap kebakaran pada permukiman ditinjau dari kondisi curah hujan, penggunaan lahan, dan topografi. ketiga variabel tersebut di overlay sebagaimana yang dilakukan untuk menentukan potensi banjir, longsor, angin puting beliung, dan kebakaran hutan. adapun variabel yang digunakan sebagai indikator faktor penentu penilaian rawan kebakaran pada permukiman dapat dilihat pada tabel 7. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 19 tabel 7. variabel skoring potensi bencana kebakaran permukiman (modifikasi pedoman aspek final peraturan menteri pekerjaan umum no.20/prt /m/2007) no. indikator bobot indikator (%) sensivitas tingkat kerawanan verifer bobot penilaian nilai bobot tertimbang nilai kerawanan kebakaran pada permukiman 1. bentuk lahan 15 tinggi tanah yang mengandung gambut (organosol) 3 1,20 sedang latosol dan podsolid 2 0,80 rendah batuan 1 0,40 2. penutup lahan 50 tinggi permukiman kepadatan tinggi 3 0,75 sedang permukiman kepadatan rendah 2 0,50 rendah non permukiman (hutan, semak, 1 0,25 3. curah hujan 35 tinggi <1000 mm per tahun 3 1,05 sedang 1.000-2.500 mm per tahun 2 0,70 rendah >2.500 mm per tahun 1 0,35 tingkat kerawanan kebakaran pada permukiman 100 tinggi 210-300 3 3,00 sedang 101-200 2 2,00 rendah <100 1 1,00 berdasarkan hasil skoring di atas, kabupaten landak memiliki tiga tingkat kerawanan terhadap kebakaran pada permukiman, yaitu kerawanan rendah, sedang, dan kerawanan tinggi. sebanyak 95,92% wilayah di kabupaten landak memiliki tingkat kerawanan terhadap bahaya kebakaran pada permukiman yang rendah. sementara 3,84% merupakan wilayah dengan tingkat kerawanan sedang. daerah yang memiliki tingkat kerawanan rendah dan sedang terhadap kebakaran pada permukiman ini tersebar di semua kecamatan. sisanya, sebanyak 0,22% merupakan wilayah yang memiliki kerawanan tinggi terhadap bahaya kebakaran pada permukiman. wilayah dengan tingkat kerawanan tinggi ini hanya tersebar di kecamatan banyuke hulu, mempawah hulu, dan kecamatan. untuk lebih jelasnya dapat dilihat pada gambar 6. gambar 6 peta potensi bencana kebakaran permukiman kabupaten landak (hasil analisis, 2013) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 20 berdasarkan overlay pada keseluruhan ancaman bencana yang mungkin terjadi di kabupaten landak, bahwa kabupaten landak memiliki tiga kelas kerawanan terhadap semua bencana, yaitu daerah dengan tingkat kerawanan rendah, sedang, dan tinggi. secara statistik, dominasi kerawanan multi-bencana adalah tingkat kerawanan sedang, yang tersebar di seluruh kecamatan dan memiliki jumlah persentase luasan keseluruhan sebesar 82,96%. sedangkan sisanya secara berurutan adalah daerah dengan tingkat kerawanan rendah sebanyak 10,15% dan tingkat kerawanan tinggi sebanyak 6,88%. hanya saja, berdasarkan karakteristik wilayahnya, daerah untuk tingkat kerawanan tinggi tidak termasuk untuk kecamatan mempawah hulu dan menjalin. untuk melihat jelas mengenai sebaran lokasi wilayah dengan masing-masing tingkat kerawanan terhadap multibahaya di kabupaten landak dapat dilihat pada gambar 7. gambar 7. peta potensi multi-bencana kabupaten landak (hasil analisis, 2013) 4. kesimpulan berdasarkan kajian analisis potensi bencana yang didasarkan pada kejadian dan analisis kerawanan fisik diketahui bahwa kabupaten landak memiliki beberapa ancaman bencana (multi-bencana), diantaranya adalah ancaman terhadap banjir, longsor, angin puting beliung, kebakaran hutan, dan kebakaran pada permukiman. setiap wilayah tidak akan memiliki jenis maupun tingkat bencana yang sama antara yang satu dengan yang lainnya. ada beberapa faktor yang menjadi penentu hal ini, yaitu perbedaan karakteristik kondisi fisik, geografis, dan alam atau lingkungan yang ada di setiap wilayah. berdasarkan overlay pada keseluruhan ancaman bahaya yang mungkin terjadi, kabupaten landak memiliki tiga kelas kerawanan terhadap semua bencana, yaitu daerah dengan tingkat kerawanan rendah, sedang, dan tinggi. secara statistik, dominasi kerawanan multi-bencana adalah tingkat kerawanan sedang, yang tersebar di seluruh kecamatan dengan persentase luas keseluruhan sebesar 82,96%. sedangkan sisanya secara berurutan adalah daerah dengan tingkat kerawanan rendah sebanyak 10,15% dan tingkat kerawanan tinggi sebanyak 6,88%. 5. daftar pustaka dokumen rehabilitasi dan rekonstruksi pasca bencana kabupaten landak tahun 2012. dragicevic, s., terence, l., & shivanand, b. (2015). gis-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. habitat international, 45(2), 114–125. doi:10.1016/j.habitatint.2014.06.031 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 10-21 wahyuningtyas dan pratomo | 21 bps landak. (2012). kabupaten landak dalam angka tahun 2012. north, p. (2006). the use of gis and remote sensing to identify areas at risk from erosion in indonesian forests : a case study in central java. pelling, m. (2003). the vulnerability of cities: natural disaster and social resilience. london: earthscan ltd. peraturan bupati landak nomor tahun 2012 tentang prosedur tetap penanganan tanggap darurat bencana kabupaten landak. permen kementerian pekerjaan umum. (2012). modifikasi pedoman aspek final peraturan menteri pekerjaan umum no.20/prt /m/2007. kementerian pekerjaan umum. rencana tata ruang wilayah kabupaten landak tahun 2012-2032. sugiyono. (2009). metode penelitian kuantitatif kualitatif dan r&d. alfabeta : bandung. surakhmad, winarno. (1980). pengantar penelitian ilmiah: dasar, metode, teknik. bandung: tarsito. uu bnpb. (2007). undang-undang penanggulangan bencana no.24 tahun 2007. badan nasional penanggulangan bencana. wisner, b., blaikie, p., & canon, t. (2005). at risk, natural hazard, people’s vulnerability, and disaster. london: routledge. | 163 geoplanning vol 5, no. 1, 2018, 163-174 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undipd.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.163-174 spatial statistics for mapping solid waste generation mapping in tembalang, semarang city s. y. ardiansyaha, m. maryonob a master of urban and regional development in diponegoro university, indonesia b department of urban and regional planning in diponegoro university, indonesia abstract: the annually increasing number of urban populations will have impacts on waste generation. tembalang sub-district as a sub-district located on the outskirts of semarang city has significant developments in the term of population growth in correlation with waste generation. within four years, waste generation in the tembalang sub-district increased from the fifth rank to the third rank. it is possible that this sub-district will become the first rank in semarang city in waste generation. to be able to identify influential factors and spatial distribution pattern of waste generation in tembalang sub-district, it is necessary to apply statistical and spatial approach. this study uses quantitative methods with a statistical spatial analysis approach by using gis. in addition, this research also intends to model the relationships of solid waste generation by applying socio-economic variables. based on the results of ordinary least square analysis, social economy variables that affect the amount of waste generation in tembalang subdistrict are the number of population and trading activities. the model of formed socio-economic variables has the effect of 25% towards the amount of waste generation. spatial patterns identified from waste generation shows that what needs to be considered is the waste management in tps (temporary waste disposal) in tembalang and sendangmulyo. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): ardiansyah s. y & maryono, m. (2018). spatial statistics for mapping solid waste generation mapping in tembalang, semarang city. geoplanning: journal of geomatics and planning, 5(1), 163-174. doi: 10.14710/geoplanning.5.1.163-174 1. introduction solid waste generation problem is a common problem faced by developing countries. the increasing number of urban waste is caused by increasing population and urbanization (jica, 2005). waste is the remaining part of human activities that needs to be managed properly so as not to cause problems to human life or disturbance to the environment such as environmental pollution, the spread of disease, reduced aesthetics and as a carrier of the disease. the problem of waste management is very serious in urban areas due to the complexity of the problems and high population density, so waste management is often a prioritized handling in urban areas (moersid, 2004). waste management in indonesian cities has yet to achieve optimum results. population number has been one of the reasons why wastes in cities are piling up so high (damanhuri et al., 2010). previous studies only focused on the flow of solid waste management processes, but they did not specify link between waste generation and the spatial aspects (olukanni et al., 2014). mapping provides the exact location, amount, and type of garbage. waste mapping had also been done although only limited to location mapping and the paths used to dump waste (lee et al,. 2015). to find out how big the influence of urban waste generation, the identification requires a spatial approach to make it more easily understandable (yousif & scott, 2007). in the context of spatial data, it can be seen how the patterns are formed by the spread of the existence of waste generation. in this study, the author intends to identify spatially the distribution of waste generation in tembalang sub-district. this research's objective is to map waste dumping spots and to derive cross-factor relationships from related socioeconomic factors. the exact pattern of waste generation will show which open access article info: received: 7 july 2017 in revised form: 10 dec 2017 accepted: 30 january 2018 available online: 25 april 2018 keywords: spatial statistics, waste generation, ordinary least square corresponding author: septa yudha ardiansyah urban and regional planning in diponegoro university, indonesia email: septayudha573@gmail.com https://doi.org/10.14710/geoplanning.5.1.163-174 https://doi.org/10.14710/geoplanning.5.1.163-174 mailto:septayudha573@gmail.com ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 164 | areas have the highest concentrations from which then a waste handling method can be formulated. the analytical method used is quantitative analysis with the spatial approach by using spatial statistics analysis. ordinary least square (ols) models are used to determine the relationship that is formed from the spread of waste generation. the author uses the software arc map 10.1. 2. data and methods 2.1. study area tembalang sub-district is one of semarang city suburbs that has been developed very rapidly. this suburb has its own pull factors for the people to settle and live there. the development activities of this sub-district are also characterized by the presence of diponegoro university that turns this area to experience function changes of the former rice fields, gardens, and fields into trading activities, housing and boarding houses for students. people who live in tembalang are not only from semarang city but also from out of town or province. with an area of 41.74 km2, tembalang sub-district consists of 12 villages which are rowosari, meteseh, kramas, tembalang, bulusan, tandang, mangunharjo, sendangmulyo, jangli, sambiroto, kedungmundu, and sendangguwo (figure 1). the population of tembalang sub-district in 2015 amounted to 154,697 inhabitants and is the second highest number of inhabitants in semarang city after pedurungan sub-district. total average production of waste / day in tembalang sub-district is the second largest in semarang which amounted to 325.29 m3 / day (cbs, 2015). this means that the average waste generated by tembalang society is a sizeable 2.35 kg / day. each year, the waste generation in tembalang sub-district has increased in an average of about 1.73 tons per year (figure 2). generally, tembalang sub-district has 5 units of arm roll trucks, 28 units of truck containers, and 30 plots of landfills. based on the data from dkp (cleaning and landscaping agency) of semarang city, the current situation of waste generation in tembalang sub-district is already handled as figure 1. administrative map of tembalang sub-district https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 | 165 much as 83%. this means that the remaining 17% of waste is still piled up and cannot be addressed by the government. figure 2. total number of waste generation in tembalang sub-district (dkp, 2015) table 1. tps name, container amount and estimation of daily waste generation tembalang sub-district (cbs, 2015) num village tps name cont. volume/ day (m3) num village tps name cont. volume/ day (m3) 1 rowosari 0 ±1000 16 sendangmulyo tps ketileng atas 1 ±5000 2 meteseh tps dinar mas 1 ±1500 17 tps ketileng bawah 1 3 tps bukit kencana 1 18 tps aspol sd.mulyo 1 4 tps pasar meteseh 1 19 tps psis 1 5 kramas 0 ±1000 20 tps rsud 1 6 tembalang tps politeknik 1 ±4000 21 tps klipang 1 7 tps tembalang 4 22 tps cempaka 1 8 tps bukit diponegoro 1 23 tps menur 1 9 bulusan ±1000 24 tps tulus harapan 1 10 mangunharjo tps jl. elang raya 1 ±1000 25 tps keliling 1 11 tps rumpun 1 26 sambiroto tps salak utama/intan 1 ±1500 12 tandang tps tandang 1 ±1000 27 tps sambiroto rw vi & vii 1 13 tps rogojembangan 1 28 tps wana mukti 1 14 kedungmundu tps kini jaya 1 ±500 29 jangli tps tps jangli 1 ±500 15 sendangguwo tps sendangguwo 1 ±1000 *note: tps (tempat pembuangan sampah sementara) : temporary waste disposal place cont. : number of container volume (ton) https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 166 | *note: number 0 indicates open dumping tps figure 3. tps location map (observation, 2016) figure 4. temporary waste disposal place & open dumping waste (observation, 2016) 2.1.1. socio-economic data in this study, the data to be used as an independent variable influence on waste generation in tembalang sub-district are socio-economic variables (table 2). those variables consist of the total population, household size, and the number of trading activities. here is the socio-economic data of tembalang sub-district in 2015. https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 | 167 table 2. socio-economic data in tembalang sub-district (cbs, 2015) num. village wide area (km2) population (person) household size number of trading activities density (people/km2) 1 rowosari 8.7 11019 3214 84 1316 2 meteseh 4.99 15621 4888 383 2669 3 kramas 0.93 3384 1024 15 1311 4 tembalang 2.68 5519 1294 75 1480 5 bulusan 3.04 5125 1588 147 1772 6 mangunharjo 3.03 8468 2532 68 3552 7 sendangmulyo 4.61 33697 10440 622 5855 8 sambiroto 3.18 12357 3584 84 6548 9 jangli 2.07 6402 1676 13 2208 10 tandang 3.75 20382 6115 104 10744 11 kedungmundu 1.49 11127 2883 156 5680 12 sendangguwo 3.27 21596 5997 87 17396 total 41.74 154697 45235 1838 2.2. analytical procedure the research sample location is the whole temporary waste disposal (tps) in each village which then the results of observations and obtained data are to be used as input for the analysis of total community waste production. from the distribution of point locations in the study area, model in the term of spatial aspect spreading can be observed. the author also finds the relationship between the community waste generation by factors that affect the amount of waste. in this case, the author only uses socio-economic variables as independent variable. the relationship is modeled into an equation by using ordinary least square (ols). the software used for ordinary least square analysis is arcmap 10.1. 2.3. spatial statistics using gis for mapping spatial statistics is a tool of analysis in a geographic information system that serves to analyze the spatial distribution, spatial patterns, processes, and spatial relationships. while there may be similarities between the spatial and non-spatial (traditional) statistics in terms of concepts and objectives, spatial statistical uniquely developed specifically used for data concerning spatial geographic. spatial statistics combines the space of distance, area, connectivity and spatial relationships directly by using math. statistical analysis is also used to identify and confirm the form of spatial patterns, such as the centralization of the group, finding out the trend direction, or whether to form a cluster. statistical functions analyze the underlying data and provide some measures that can be used to determine the existence and strength of the pattern. spatial statistics contains cluster analysis which takes formed cluster pattern into account. 2.3.1. average nearest neighbor & hot-spot analysis average nearest neighbor is one of analysis used to explain the distribution pattern of location points by using a calculation that considers distance, the number of location points, and total area. this analysis requires data regarding the distance between the settlements with the closest settlement which is settlement nearest neighbor. average neighbor analysis calculates the distance between each feature and the nearest neighbor, then computes the data for the entire distance of the nearby neighborhood. the output of the average nearest neighbor analysis is in a form of a z-score and p-value index that indicate whether the case is clustered, random or dispersed. before analyzing, the requirement to obtain a z-score is to know the extent of the territory within the limits of the study, in this case is tembalang sub-district. based on calculations by using arc map application, the total area of tembalang sub-district is 41,452,310 https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 168 | m2. the input data to be processed is the data of point results of waste disposal sites scattered around tembalang sub-district. nearest neighbor index describes the ratio of the observed distance median value to the expected distance median value. the expected distance is the mean distance between neighbors in random distribution hypothesis. if the index is less than 1, the pattern shows a grouping (cluster); if the index is greater than 1, the trend is spreading. in average nearest neighbor analysis, there is also significance level (p-value) and the critical value (z-score). both these values are worth 0.01 -> 2.58. p-value and z-score indicate whether h0 is rejected or not. in average nearest neighbor, h0 indicates that features are randomly distributed. if the z-score has a value <-1.65, then the cluster will more likely to shape. whereas if it is> 1.65, the pattern will be more random. hot spot is the concentration of the incident with the geographic area boundaries that appear from time to time. hot spot can also be used to assess concentrations of specific land use, or between activity and land use (block & block, 1995). hot spot may not exist in real life, but it represents where there is a concentration of activities or specific cases so that the area can be labeled as areas of high concentration. there are dozens of statistical analysis techniques to identify the hot spot (everiit, 1974). most of the statistical analysis technique used is commonly called cluster analysis. this is a technique of grouping together cases in a relatively coherent group. because the hot spot is a perception construction, the techniques must use an approach on how one understands the study area. here are a few types of methods hot spot / cluster analysis (everiit & megbolugbe, 1996) : 1. point location 2. hierarchical techniques 3. partitioning techniques 4. density techniques 5. clumping techniques 6. risk-based techniques 7. miscellaneous techniques 2.3.2 spatial pattern according to tobler in his book "the first law of geography", he revealed that all things are always related to everything else, but something closer is to have more influence than something far (tobler, 1970) a spatial autocorrelation. spatial cluster is a positive spatial autocorrelation when there are similar values forming cluster, while the opposite is if there are separate values called as negative spatial autocorrelation (boots & getis, 1988). the spatial clusters can help the understanding of geographic processes underlying the relationship with the phenomenon under study. based on existing spatial clusters, there will be formed different spatial patterns. spatial pattern is something that shows the placement or arrangement of objects on the earth's surface (lee & wong, 2001) . the spatial pattern will explain how geographic phenomena is distributed and how it compares with other phenomena. the spatial pattern can be either a point or area (polygon), and they can form a pattern of clustered, dispersed, and random. 2.3.3 modeling using ordinary least square regression as with the simple linear equation regression analysis, the basic idea behind ols is to explore the relationship between a dependent variable (y) and one or more independent variables (the x’s). the simple model of ols equation can be expressed as (ryan, 1996), which is defined as follows:  ++= k iikki xy 0 where • { ikx } are observations for i = 1,..,n cases and k = 1,..,m explanatory variables, • {yi} are the dependent variables, •  ’s are the estimates of the coefficients, • and ’s are normally distributed error terms. https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 | 169 3. results and discussion 3.1. average nearest neighbor & hot spot of waste generation under the table shows that the expected mean distance between one point to another point is 578 meters, however, the real condition is that the observed mean distance between one point to another point is 560 meters (table 3). the value of z-score value is -0.326302 because the value is > 1.65, so the case of observation points forms a random pattern. more details are shown in the following figure. table 3. calculation results of average nearest neighbor analysis (analysis, 2016) explanation result observed mean distance 560.468465 meters expected mean distance 578.180614 meters nearest neighbor ratio 0.969366 z-score -0.326302 p-value 0.744196 figure 5. result diagram of average nearest neighbor analysis (analysis, 2016) hot spot analysis on waste generation uses attributes of the average amount of waste disposal per capita / day. hot spot analysis is performed by using analysis tools in arc map named "hot spot analysis (getis-ord-gi’)" (figure 5). in addition to the above attributes, the maximum distance of each household in one sub-district is required based on the previous analysis result. so, the author uses observed mean https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 170 | distance as a threshold which is 560 meters. based on the input data in the arc map application, the hot spot result is obtained as follows. figure 6. result map of hot spot analysis (analysis, 2016) based on the picture (figure 6), the hotspots show the occurring concentration of solid waste generation. there are two main colors namely red and blue. the red color indicates the higher concentration of waste generation while blue color conversely indicates the lower concentration. the yellow color indicates the medium concentration. tps tembalang has the hot spot, so it means in this place amount of solid waste generation agglomerated here. 3.2. spatial pattern of solid waste generation spatial pattern analysis of solid waste generation is carried out by using the input feature from the hot spot result of waste generation. hot-spot points are interpolated according to the attributes similarity so it will make up the classification class to be determined. here is a picture interpolated hot spot. https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 | 171 figure 7. spatial patterns map of interpolation (analysis, 2016) based on the following image (figure 7) , the color gradation of blue-yellow-red indicates the concentration of waste in landfills. the bluer the color indicates the less and more insufficient amount of waste generation. while the redder the color indicates that the amount of waste has exceeded its capacity. from the map, it appears that a pretty solid waste generation are around the area of sendangmulyo and meteseh. while the area that has a solid waste, generation is around the area of tembalang village. 3.3. modelling solid waste generation in making the equation model of waste generation in tembalang sub-district, ols analysis is used as supporting equipment. the dependent variable in the model is the data of waste generation, while the independent variable is a variable of socio-economic characteristics including the number of populations, number of households, population density, and the amount of trading activity. here are the results of the processed data by using spatial statistics analysis in arcmap 10.1: figure 8. number of household & number of population map (bappeda1, 2011) 1 bappeda (badan perencanaan pembangunan daerah): regional development planning agency https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 172 | figure 9. total number of trading activities & number of density map (bappeda, 2011) y = 398.776650 + 0.720890 x1 1.972677x2 + 2.317599x3 0.203706 x4 where: y : solid waste generation x3 : number of trading activities x1 : number of population x4 : number of density x2 : number of household the results of adjusted r-squared (r2) analysis indicates the value of 0.253366. this shows that the percentage of independent variable influence contribution towards the dependent variable is 25%. this means that there are other variables that affect the amount of society waste generation. based on the above linear equation, the coefficients which have positive value towards waste generation are population and trading activities, while the coefficients which have negative value are the number of household and density. this equation explains that the increase of population and trading activities will intensify the production of waste generation. while the number of household and density don’t necessarily affect the amount of waste generation in tembalang sub-district. 3.4. discussion according to the spatial pattern map identified by the amount of solid waste generation, it can be seen that the dominant red color is in tps tembalang (tps number 7). this is because tps tembalang accommodates waste from three villages namely rowosari, kramas, and bulusan. although tps tembalang has a considerable number of containers, but it is still not able to accommodate all waste from those three villages. another factor that led to the amount of waste in tps tembalang is due to the presence of diponegoro university. the existence of diponegoro university causes the growth of residential areas, trading activities, and the increasing number of students who live in the area around tembalang. the second largest waste generation is in tps sendangmulyo mainly caused by the fact that sendangmulyo has the largest population in tembalang sub-district. the presence of market and trading activities also cause this sub-district is very noteworthy. this is in line with yousif & scott (2007) argument on how the amount of waste's growth in cities is driven by rapid population increase and economic expansion. recommendations that can be applied in the tembalang sub-district is the need to create new tps in the kramas and rowosari. ideally, every village must have at least one tps. because the socio-economic https://doi.org/10.14710/geoplanning.5.1.163-174 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 | 173 variable that contains the model of waste generation models influence only 25%, this means that there are other variables that affect the increase in the amount of waste generation (lee et al,. 2015). therefore, the author recommends further research to add variables such as culture (lifestyle), the income of people (more detailed), and land use (spatial aspects) to be able to make a more complete model. 4. conclusion based on the performed ols result in the study area of tembalang sub-district, socio-economic variables that influence waste generation are variables of population and the number of trading activities. the growing number of residents and trading activities will affect the amount of waste generation. the results of the relationship model between the dependent variable (waste generation) with independent variables (population, the number of households, trading activities, and density) have an effect of 25%. it means there needs to be a search for other variables that can affect the amount of waste generation in tembalang sub-district. spatial patterns identified from the waste generation in tembalang sub-district shows that tps tembalang in bulusan village is red. it indicates the need for special handling in that location. tps tembalang accommodates garbage from three villages namely rowosari, bulusan, and kramas. however, the number of containers to accommodate the amount of community waste is still lacking. therefore, it is necessary to establish new tps in the village of rowosari, kramas, and bulusan. by using spatial statistics, we can map, search for relationships between variables and model a case around us. observing previous studies of a case in terms of the spatial view would make it more easily understood and solved. spatial statistics by using gis is not only to be used for waste disposal or waste generation but can also be used in the case of health, crime, transport, etc. the author also suggests forming models using other variables related to waste piles such as economic, demographic, physical and other factors. 5. acknowledgments the author would like to give an appreciation to the anonymous reviewer who has provided a lot of input during the writing of this paper. this study is supported by indonesia endowment fund for education (lpdp) ministry of economic in indonesia. the author thanks the lecturers in master program of urban and regional planning development for providing supports to this research. 6. references bappeda. (2011). spatial planning development document. semarang. block, r. l., & block, c. r. (1995). space, place and crime: hot spot areas and hot places of liquor-related crime. crime and place, 4(2), 145–184. boots, b. n., & getis, a. (1988). point pattern analysis (vol. 8). sage publications, incorporated. cbs. (2015). tembalang sub-district in figures. semarang: central bureau of statistics. damanhuri, e., wahyu, i. m., & padmi, t. (2010). evaluation of waste recycling potential in bandung municipal solid waste. world review of science, technology and sustainable development, 7(3), 282. [crossref] dkp. (2015). number of waste generation in tembalang sub-district. semarang. everiit, b. (1974). cluster analysis, london: heinemann education books. everiit, b., & megbolugbe, i. (1996). the geography of underserved mortgage markets. american real estate and urban economics association meeting. jica. (2005). supporting capacity development in solid waste management in developing countries; towards improving solid waste management capacity of entire society. tokyo. lee, d., kung, k., & ratti, c. (2015). mapping the waste handling dynamics in mombasa using mobile phone gps. in proceedings of the 14th international conference on computers in urban planning and urban management. lee, j., & wong, d. w. . (2001). statistical analysis with arcview gis. new york: john wiley & sons inc. https://doi.org/10.14710/geoplanning.5.1.163-174 https://doi.org/10.1504/wrstsd.2010.032530 ardiansyah & maryono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 163-174 doi: 10.14710/geoplanning.5.1.163-174 174 | moersid, m. m. (2004). konsep national action plan pengelolaan sampah dalam rangka millenium development goals. semarang: dalam acara kajian pengelolaan sampah secara terintegrasi. olukanni, d., and oladipupo akinyinka, ede, a., akinwumi, i., ajanaku, k., & and. (2014). appraisal of municipal solid waste management, its effect and resource potential in a semi-urban city: a case study. journal of south african business research, 1–13. [crossref] ryan, t. p. (1996). modern regression methods. chichester: john wiley and sons. tobler, w. r. (1970). a computer movie simulating urban growth in the detroit region. economic geography, 46, 234. [crossref] yousif, d. f., & scott, s. (2007). governing solid waste management in mazatenango, guatemala: problems and prospects. international development planning review, 29(4), 433–450 [crossref] https://doi.org/10.14710/geoplanning.5.1.163-174 https://doi.org/10.5171/2014.705695 https://doi.org/10.2307/143141 https://doi.org/10.3828/idpr.29.4.2 | 131 geoplanning vol 4, no. 2, 2017, 131-142 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.131-142 remote sensing and gis approaches to a qualitative assessment of soil erosion risk in serang watershed, kulonprogo, indonesia n. arif a,b, p. danoedoro b, h. hartono b a faculty of science and technology, universitas muhammadiyah gorontalo, indonesia b faculty of geography, universitas gadjah mada, yogyakarta, indonesia abstract: this research aims to determine the risk of soil erosion qualitatively by integrating remote sensing with the geographic information system. factors that contributed to the occurrence of erosion in the area of study were analyzed using the method of the variation of combined input data of the factors controlling erosion (soil, climate, topography, vegetation, and humans). the input data were quantitative data changed into qualitative data that were obtained from field data and extracted from remote sensing imagery, i.e. spot 5. a number of parameters were calculated using the rusle model equation. the model was validated by observing the qualitative erosion indicators in the field (pedestal, tree root exposure, armor layers, rill erosion, and gully erosion) by observing slope steepness in each sample area. the area of study was serang watershed located in kulon progo regency, yogyakarta. it is one of the critically potential watersheds viewed from the landform and land use. the results of various combinations generated the highest of accuracy by 90.57 % with extremely erosion dominating the area of study. the factors with the highest contribution to erosion in serang watershed were slope length and steepness (ls) and erodibility (k). copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): arif, n., danoedoro, p., & hartono, h. (2017). remote sensing and gis approaches to a qualitative assessment of soil erosion risk in serang watershed, kulon progo, indonesia. geoplanning: journal of geomatics and planning, 4(2), 131-142. doi:10.14710/geoplanning.4.2.131-142 1. introduction soil erosion is one of the indicators of land quality due to the destructive effect it has on land and the effect of reduced productivity of land (morgan, 2009; parveen & kumar, 2012). erosion affects the sustainability of agricultural production on a global scale (bouaziz, leidig, & gloaguen, 2011). an assessment of erosion in an area is vital in order to evaluate land management and to provide a basis for land users and decision makers with regard to land conservation efforts and environmental monitoring. numerous researches have been conducted, especially in the field of applied environment, including the research in erosion and landslides. these researches integrated remote sensing with the geographic information system which managed to generate more accurate and effective predictions (asis & omasa, 2007; liao et al., 2012; pradhan, lee, & buchroithner, 2010; pradhan & saro, 2007). remote sensing and gis can be used to generate information about the variables associated with the erosion calculation formula. there are many factors that contribute to erosion, namely rainfall, vegetation, topography, soil, and land use, all of which were used as the basis for assessing the erosion risk. this research relied on remote sensing data to obtain landscape information such as vegetation and land use while gis was implemented to process, simulate scenarios, and visualize modeling results. the spot 5 imagery was used in this study because it offers a higher resolution of 2.5 to 5 meters in panchromatic mode and 10 meters in multispectral mode providing potential solutions in the study of natural resources. this is due to its capacity in covering vast areas such as area of study, as well as having channels that can decrease vegetation information through index c as one of the model inputs. article info: received: 17 february 2017 in revised form: 06 may 2017 accepted: 7 july 2017 available online: 30 october 2017 keywords: remote sensing, serang watershed, soil erosion corresponding author: nursida arif faculty of science and technology, universitas muhammadiyah gorontalo, indonesia email: nursida.arif@gmail.com open access https://doi.org/10.14710/geoplanning.4.2.131-142 https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 132 | most researches on soil erosion in the area of study were conducted using quantitative approaches to determine the amount of soil eroded in tons per hectare (dibyosaputro et al., 2012; santoso & senawi, 2012; widarsih & senawi, 2012) and it is not common to assess erosion qualitatively. basically, the qualitative approach employed in these researches was a combination of the quantitative approach. factors controlling erosion were calculated using the rusle model equation and qualitatively divided into several classes. the output of the model was in the form of a qualitative map of the erosion risk without information about the amount of soil loss. a model is considered quantitative when the values are mathematically combined to provide an index at a certain scale (la rosa & van diepen, 2002). the numerical value of a variable may change at a certain period, unlike a qualitative assessment which tends to be more constant and unchanged (bredeweg et al., 2009). in this research, the field validation was qualitatively conducted by developing the formula for the assessment of the qualitative indicators of erosion. a high rate of erosion can be seen from the erosion indicators such as pedestal, armor layers, tree root exposure, rill erosion, and gully erosion (stocking & murnaghan, 2000). to develop a quantitative model that can accurately represent the real condition in the field, it is necessary to conduct validation through detailed measurements for a long period of time requiring higher costs. in fact, the use of the erosion plot was rarely calibrated with the local condition and many used less realistic assumptions resulting in less reliable measurement results (bergsma, 2008). this short coming makes qualitative methods reliable as a quick solution to predict erosion (bouaziz, leidig, & gloaguen, 2011; desmet & govers, 1995). ypsilantis (2011) stated that qualitative models of area method are effective and more affordable. they can be implemented in a larger area within a relatively short period of time, unlike quantitative methods that require intensive and more detailed monitoring of particular land conditions. this is in accordance with the conditions in indonesia where the technical facilities and history of actual erosion measurement are minimal as shown in the study area. so that method is needed which can be the solution of the limitation with low cost and efficient but more accurate that is through qualitative based modeling by utilizing remote sensing image and geographic information system. it is expected that the method of fast assessment will be able to quickly locate which erosion-prone areas whose conservation should get priority. 2. data and methods 2.1. study area the research was undertaken in serang watershed which is situated between progo watershed and bogowonto watershed in kulon progo regency, the province of yogyakarta special region. geographically, it is located at 7°43’40” s 7°55’30” s and 110°03’49” e 110°13’50” e. administratively, it is located in kulon progo regency, which includes several subdistricts, namely wates, sentolo, temon pengasih, kokap, girimulyo, and some area of panjatan and nanggulan subdistricts. based on the monthly rainfall data from 2004 to 2014 in 11 rainfall stations around serang watershed, the wet season occurred from november to april while the dry season occurred from may to october. most stations in serang watershed fell into category d, i.e. in a temperate climate. most land in the area of study is utilized as mixed farms. in regard to the landform, the area of study is considered as an erosionprone area comprised of denudation-generated hills, which were formerly a volcano, and structural hills (figure 1). 2.2. methods the variables employed to construct the model were the extraction of factors affecting erosion, namely climate, soil, vegetation, and humans. field observation of the qualitative indicators of erosion was undertaken instead of quantitative calculations of the actual erosion for validation of the model. this research employed the same approach of qualitative methods conducted by bouaziz, leidig, & gloaguen (2011), namely trials on several combinations of factors controlling erosion to determine the most influential factor in the area of study. the input data set used in this modeling were factors influencing erosion, i.e. erosivity (r), erodibility (k), slope length and steepness (ls), vegetation coverage and management (c), land management (p). https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 | 133 figure 1. appearance of landforms (spot, 2014 and analysis, 2016) (a) structural rocky hills of andesite breccia (401494 mt, 9140214 mu), (b) and (c) structural rocky hills of andesite (399202mt, 913234 mu), (d) alluvial plain (401908 mt, 9,129279 mu), (e) sermo reservoir, (f) hills of the remains of a volcano (400833 mt, 9139855 mu) (1). rainfall erosivity factor (r) erosivity index was calculated using 10 years of daily rainfall data from 13 rain stations around the research location, utomo (1994) was calculated erosivity index using the equation (1) by bols: 𝑅 = 6,12(𝑅𝐴𝐼𝑁)1,21𝐷𝐴𝑌𝑆−0,47𝑀𝐴𝑋𝑃0,53 ……………………………………………………………………………… (1) where, rain = average annual rainfall (cm), days = total day of average rain per year (day), maxp= maximum average rainfall in 24 hours per month within one year (cm), rm = monthly erosivity index, ry = annual erosivity index (2). soil erodibility factor (k) k value was determined by the equation (2) used in rusle model developed by renard et al. (1991) as follows: k = 7.594 {0.0017 + 0.049 exp [− 1 2 ( log(𝐷𝑔)+1.675 0.6986 ) 2 ]} …………………………………………………….. (2) where, k = soil erodibility, dg=diameter of soil geometric particle (mm) (3). slope length and steepness factor (ls) length of slope was calculated using the equation (3) developed by wischmeier et al. (1978) while steepness (s) was calculated using the ls equation (4-5) for usle model developed by mccool et al., (1989) l = ( 𝜆 22.13 )𝛽 ……………………………………………………………………………….. (3) https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 134 | β = ( 𝑠𝑖𝑛𝜃 0.0896 )/[3∗(𝑠𝑖𝑛𝜃)0.8+0.56] 1+( 𝑠𝑖𝑛𝜃 0.0896 )/[3∗(𝑠𝑖𝑛𝜃)0.8+0.56] ……………………………………………………………………………….. (4) s = { 10.8 𝑠𝑖𝑛𝜃 + 0.03 𝜃 𝜃 < 5° 16.8 𝑠𝑖𝑛𝜃 − 0.5 5° ≤ 𝜃 < 10° 21.9𝑠𝑖𝑛𝜃 − 0.96 𝜃 ≥ 10° ……………………………………………………………………………….. (5) where, l = slope length; s = slope steepness; ɵ = slope value of dem; λ = slope horizontal length; β = slope index, cell size = size of grid cell (4). vegetation coverage and management factor (c), xu, xu, & meng (2012) explained that c is defined as the ratio of soil loss from land cropped under spesific conditions to the corresponding loss from cleantilled, continous fallow. c value was calculated using gutman & ignatov (1998) equation (6): c = 1𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼𝑚𝑖𝑛 𝑁𝐷𝑉𝐼𝑚𝑎𝑥−𝑁𝐷𝑉𝐼𝑚𝑖𝑛 ………………………………………………………………………………. (6) (5). support practices factor (p) the support practice factor (p-factor) is the soil-loss ratio with a specific support practice to corresponding soil loss with up and down slope tillage. the erosion level due to land management and conservation activities (p) varied, especially depending on slope steepness. classification of p values based on classification of slope developed by shin (1999), show in table 1. table 1.classification of p-values (modified from shin (1999)) slope (%) p-values 0 8 0.55 8 15 0.60 15 25 0.80 25 40 0.90 40 > 1.00 calculation of erosion factors was performed on arcgis 10 platform and converted into raster format. the result of quantitative calculation was validated using qualitative approach by observing erosion indicators in the field. the erosion factors as the input data of the model were put in four combinations to examine the influential factors (table 2). additional data of the input layer were added to combination 1 (c1), namely the map of solum depth and the map of organic matter. both factors were considered affecting the ability of eroded soil. organic matter do not only greatly affect the health of the soil but also soil properties, both the chemical properties and the physical properties, including the soil structure (bot & benites, 2005). while the depth of the soil affects the soil-water-plant ecosystem so as to affect the quality and yield of plants (jabro et al., 2010). combination 2 (c2) was a combination of five erosion factors used in the rusle model equations, combination 3 (c3) was comprised of only four factors without the factor of land management (p). as for combination 4 (c4), it only used three erosion factors, namely slope length and steepness factor (ls), erodibility (k), and the vegetation factor (c). overall, the conceptual framework is illustrated in the form of a diagram shown in figure 2. table 2. input parameters of three different combinations for the erosion risk assessment (analysis, 2016) factors controlling erosion combinations c1 c2 c3 c4 topographic factor(ls)     soil properties erodibility (k)     solum depth  organic matter (om)  rainfall erosivity ( r)    cover management factor (c)     practice factor (p)  https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 | 135 figure 2. the conceptual framework of the research 3. result and discussion 3.1. evaluation of influential erosion factors r-factor value was made using spline interpolation method because the sample points were not spread evenly. this method has sufficient accuracy despite using a small amount of data. the spatial distribution of r-factor in serang watershed ranged from the highest erosivity index value of 2,078.52 to the lowest 1,156.58 (figure 3a).the equation used to calculate k-factor relied on soil texture data from the laboratory test results of soil samples. soil texture is the most influential soil attributing to erodibility. low erosion happened in soil with dominant element of sand (coarse texture) and soil with dominant fraction of loamy, while soil with main elements of dust and fine sand was easily eroded. the erodibility index in serang watershed ranged from 0.46 to 0.09 (figure 3b). the spatial distribution of ls-factor ranged from the lowest value of 0.03 to the highest 427.50 (figure 4a). the low slope steepness will have small contribution to ls value. if ls value is small, the erosion potential is equally small. the spatial distribution of c-factor show values between 0.01 and 1.43 (figure 4b). c value approaches 0 for areas with denser vegetation (forest and mix plantation). factor c value gives contribution to interpretation and land use. remote sensing through the spot 5 satellite could give solution to the extraction of factor c value without performing measurement in the field. however, there was difference of factor c value with previous researchers in the same area (arsyad, 2010). month of recording the images in use and climate difference, including rainfall, influence c index value. the spatial distribution of p-factor with minimum index 0.55 in the flat slope and maximum index 1 in the steep slope (figure 5a). soil organic matter and soil depth are additional data in c1. soil organic matter in research area was obtained from laboratory test result on several samples while the soil depth of measurement result was obtained from the field. the sample value of soil organic matter and soil depth were then interpolated using kriging method to represent the maximum and minimum value of sample data. the spatial distribution of soil organic matter in serang watershed showed values between 0.06 and 6.55 (figure 5b). the spatial distribution of soil depth ranged from the lowest value of 19 to the highest 135 (figure 6). https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 136 | after a comprehensive analysis of the entire combinations, the area of study is an erosion-prone area as indicated by the wide spread distribution of extremely to moderate erosion, whereas slight and very slight erosions occurred in a smaller percentage (table 3, figure 7). the model for c1 involved two soil attributes other than erodibility, namely organic matter and soil depth while the other combinations solitary used the factor of erodibility. however, c1 had a merely similar percentage of spatial distribution with the c3 and c4 for the slight erosion class (figure 8b) and the severe erosion class for c4 (figure 8d). this means that soil attributes other than erodibility did not significantly affect the erosion risk in the area of study. figure 3. spatial distribution: a.) rainfall erosivity factor (r) and b.) k factor figure 4. spatial distribution: a.) slope length and steepness factor (ls) and b.) crop management factor (c) a b a b https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 | 137 figure 5. spatial distribution: a.) support practice factor (p) and b.) organic matter factor (om) figure 6. spatial distribution of solum depth factor the distribution of c2 was almost the same as that of c4 for the entire erosion classes (figure 8). c2 added the factors of erosivity (r) and land management (p) in addition to the factors used in c4. it means that these two factors, namely factors r and p, did not have a significant influence on the control of erosion in the area of study. the p factor map (figure 5a) has the same distribution pattern as the ls factor map (figure 4a) since both factors are derived from the same contour data, so the ls factor can replace the representation of factor p. c3 and c4 had an almost equal distribution percentage for the very slight erosion class (figure 8a). both combinations used different factors, in which c4 did not use the factor of erosivity. in this case, it can be concluded that the factor of erosivity did not have a significant influence on the erosion in the area of study. a b https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 138 | table 3. distribution of the affected area by erosion classes (analysis, 2016) model combination (%) c1 c2 c3 c4 very slight 8.37 4.60 2.75 3.26 slight 10.08 8.07 11.63 10.18 moderate 20.07 30.54 26.12 32.16 severe 18.77 22.04 13.29 18.96 extremely 42.71 34.75 46.22 35.44 figure 7. distribution of erosion risk classes (analysis, 2016) figure 8. distribution of the erosion risk model based on erosion risk classes (analysis, 2016) (a). very slight, (b) slight, (c) moderate, (d) severe, (e) extremely https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 | 139 based on analysis results of various combinations (figure 8) showed that the factors affecting erosion in the area of study were the slope length and steepness factor (ls) and erodibility (k). kamaludin et al. (2013) showed the same thing that factors which potentially trigger erosion are ls and k. the factors of erodibility (r) and vegetation cover (c) affect erosion if they take place simultaneously with the two influential factors (ls and k) as illustrated in combination 2 (c2). farhan, zregat, & farhan (2013) drew the same conclusion that a combination of the factors of soil, slopes, and vegetation can describe the risk of erosion. the rainfall factor in significantly affected erosion in the area of study, except if high erosivity takes place steep slopes, the erosion risk will change into moderate up to extremely as in some areas of girimulyo and kokap subdistricts in the north (figure 9). the classification results based on the map of erosion risk distribution (figure 9) reveal that the distribution of erosion in the research site was dominated by the following erosion classes, namely extremely erosion spreading across most of the area of kokap sub-district, girimulyo sub-district, and some of the area of pengasih sub-district; severe erosion spreading all over panjatan sub-district, pengasih subdistrict, nanggulan sub-district and some of the area of kokap sub-district; moderate erosion spreading across pengasih sub-district, some of the area in wates sub-district, panjatan sub-district and some of the area of kokap sub-district; as well as slight erosion and very slight erosion spreading all over temon subdistrict and wates sub-district. figure 9. spatial distribution of erosion risk (c2) 3.2. validation accuracy was ensured by testing 53 plots in the sample location in the erosion map generated using the four combinations using qualitative indicators in the field and the highest accuracy was generated by combination 2 (table 4) where the factors used were consisted of the five factors of erosion used in the model of erosion (r, k, ls, c, p). the lowest of accuracy, i.e. by 83.02%, still can be used as a reference even though only three erosion factors were used, namely the slope length and steepness factor (ls), erodibility (k), and vegetation (c). https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 140 | erosion classes were also determined by the slope steepness factor. despite the indicators of erosion in the field, if they exist in a flat and sloping slope, the erosion will belong to the slight erosion class. vrieling, sterk, & vigiak (2006) argued that the slope factor and the occurrence of erosion significantly correlate; a very steep slope belongs to the severe erosion class. the more steepness slope is, the higher the number of particles that spreads to the lower slope so as to result in splash and rill erosion (assouline & ben-hur, 2006). the erosion indicators showed the vulnerability of soil to erosion. tree root exposure occurred in a place where plants or trees grow in an eroded area and, likewise, pedestals indicated a high erosion rate as they take place in soil that is easily eroded (high erodibility) by rainfall of high intensity (stocking & murnaghan, 2000). sheet erosion belonged to the slight and moderate categories because the runoff flow rate was not faster than that taking place in the rill and the gully, the resulting erosion did not lead to the formation of a rill and gully. like the gully erosion, the rill erosion is one of the indicators of severe erosion, but the gully erosion cannot be removed through normal soil cultivation, like in the rill erosion. therefore, the occurrence of gully erosion in an area indicates extremely erosion despite the absence of observation of other indicators such as pedestals (figure 10) and tree root exposure (figure 11). table 4. comparison of the accuracy (analysis, 2016) overall accuracy (%) index kappa c1 86.79 0.80 c2 90.57 0.86 c3 86.79 0.80 c4 83.02 0.73 figure 10. appearance of pedestals and armour layers slight erosion risk (405113 mu, 9131329 mt) figure 11. tree root exposure, location (410800 mu, 9140780 mt), severe erosion risk (analysis, 2016) armour pedestal https://doi.org/10.14710/geoplanning.4.2.131-142 arif, danoedoro, hartono / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 131-142 doi: 10.14710/geoplanning.4.2.131-142 | 141 4. conclusion results of testing of the four combinations revealed that the area of serang watershed was dominated by the extremely erosion class with the most influential factors consisting of the slope length and steepness factor (ls) and erodibility (k). results of the trial showed that the factor of soil management and cultivation (p) did not have a significant influence on the occurrence of erosion in the area of study because the p value is derived from the same data to obtain the ls value of the contour data, so that the ls factor can replace the representation of factor p as input data. likewise, the addition of soil attributes in c1, namely organic matter and soil depth, in this research did not improve the accuracy value. 5. acknowledgments the authors would like to thank national institute of aeronautics and space, indonesia and meteorological climate and geophysics agency for providing the data. fieldwork assistance was provided by alfiatun nur khasana, bagus pamungkas, lesan purnomojati, natassa soeroso and iwuk lestari. the authors thank the financial support from department of higher education of indonesia, which has provided postgraduate scholarship in universitas gadjah mada, yogyakarta. 6. references arsyad, s. 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(2011). upland soil erosion monitoring and assessment. bureau of land management, national operations center. https://doi.org/10.14710/geoplanning.4.2.131-142 https://doi.org/10.13031/2013.31192 https://doi.org/10.4236/jgis.2012.46061 https://doi.org/10.1016/j.compenvurbsys.2009.12.004 https://doi.org/10.1016/s1872-5791(08)60008-1 https://doi.org/10.1002/ldr.711 https://doi.org/10.1016/j.catena.2012.08.012 doi: 10.14710/geoplanning.4.2.131-142 copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): arif, n., danoedoro, p., & hartono, h. (2017). remote sensing and gis approaches to a qualitative assessment of soil erosion risk in serang watershed, kulon progo, indonesia. geoplanning: journal of geomatics and planning, 4(2), 131-142. doi:10.14710/... 1. introduction keywords: remote sensing, serang watershed, soil erosion corresponding author: nursida arif faculty of science and technology, universitas muhammadiyah gorontalo, indonesia email: nursida.arif@gmail.com 2. data and methods 3. result and discussion 4. conclusion 5. acknowledgments 6. references | 31 geoplanning vol 6, no. 1, 2019, 31-42 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.1.31-42 assessing landscape pattern relationship with dengue incidence in peninsular malaysia n. mohamad a, w. y. w. ibrahim b, a. n. m. ludin a a department of urban and regional planning, faculty of built environment and surveying, universiti teknologi malaysia. b department of landscape architecture, faculty of built environment and surveying, universiti teknologi malaysia abstract: dengue is the most common urban disease that is most prevalent in tropical areas. who 2009 stated that these diseases has grown a public health concern due to the risk of dengue infection that has increased dramatically between 50 and 100 million cases every year. this issue was very corresponded with landscape and environment changes. the objective of this paper is to discuss how landscape patterns in relation to dengue incidence. open website; idengue were highly contributed in this study to locate the most risky area for dengue fever incidence at the township level. geographic information system (gis) was used to demonstrate the spatial patterns of all dengue cases in johor bahru and geoprocessing was used to measure the boundary of risk according to the distribution of dengue outbreak. after that, to analyze the spatial landscape pattern, satellite images were used. spatial descriptive analysis shows nonstrata housing, open space, road, planned commercial, strata housing and drainage system network is the most prevalence land use activity for dengue incidence in iskandar region. the finding shows the common landscape composition that relates to dengue cases. in conclusion, the future development of land use should be considered on landscape pattern towards rapid urbanization. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6 th style): nuramalina, m., ibrahim, w.y.w., & ludin, a. n. m. (2019). assessing landscape pattern relationship with dengue incidence in peninsular malaysia. geoplanning: journal of geomatics and planning, 6(1), 31-42. doi:10.14710/geoplanning.6.1.31-42. 1. introduction dengue fever is the most important vector-borne disease (aedes mosquitoes) in tropical areas. the disease has grown a public health concern due to the risk of dengue infection that has increased dramatically between 50 and 100 million cases every year (nathan et al., 2009). it is predictable that dengue cases can be one of the serious vectors borne disease in asian region especially in malaysia if there is no vector effective control and action are taken. these bad scenarios are getting worse usually during monsoon seasons where there was in the spell of wet weather (pang & loh, 2016). it has a complex relationship with environmental factors that influence the transmission of dengue infections such as the environment, climate and weather, human behaviour and immunity among the human population (cheong et al., 2014; xiang et al., 2017). united nation development programme 2018 goals are to achieve the target whereby 2030, end the epidemics of aids, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases. according to pang et al. (2017) the risk of dengue is now higher due to the urbanization and globalization process. instead increasing the dengue immunity, decreasing the mosquito population is very important. mosquito population and habitat rely on the urban landscape pattern. the relationship between urban landscape and dengue incidences is apparent where the landscape structure composition in urban areas prominently influenced the dengue incidences (cheong et al., 2014). colonization of new habitat can be demolished by the process of land use changes. land use changes can extend or reduce of the new habitat. in fact, land use and landscape changes can modify the composition of the mosquitoes because vector species rely on their new habitat preferences (patz & norris, 2004). land use and land cover change now recognized as an important driver of disease. for emerging or re-emerging article info: received: 27 aug 2018 in revised form: 15 march 2019 accepted: 30 april 2019 available online: 30 august 2019 keywords: landscape pattern, dengue incidence, geographic information system corresponding author: nuramalina binti mohamad universiti teknologi malaysia email: nuramalina.mohamad1211 @gmail.com open access https://doi.org/10.14710/geoplanning.6.1.31-42 https://doi.org/10.14710/geoplanning.6.1.31-42 https://www.researchgate.net/institution/universidade_federal_de_uberlandia_ufu mailto:nuramalina.mohamad1211@gmail.com mailto:nuramalina.mohamad1211@gmail.com mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 32 | infectious diseases, landscape change offers context and serves as a likely proximate driver of risk particularly when considering vector-borne or zoonotic diseases (messina et al., 2015). the transformation process of landscape significantly related to the dengue incidences where the composition of land use types and land covers are the factors associated with mosquito ecology. in relation with scenario, the aim of this study is to shows landscape pattern that affected dengue incidence with the four important factors which is socio-economic, environment, human behaviour and land use and land cover. 2. data and methods 2.1. study area located strategically in the heart of asia at southernmost tip in peninsular malaysia. world busiest shipping routes and possesses an abundance of natural and human resources. iskandar region development (figure 1) were expected 3 million people in 2025 and now it is home to 1.6 million people. it was selected because undergoing rapid urbanization. iskandar malaysia region is a comprehensive development region that covers 221,634.1 hectares (2,216.3 square km) of land area at the most southern part of johor consisting the whole of johor bahru district and some areas in pontian district. however, this study only focuses on 6 district which is tebrau, bandar johor bahru, jelutong, pulai, tanjong kupang and sg. tiram. by taking johor bahru as a study area undergoing rapid urbanization process, that will significantly change the future landscape scenario. the final result will determine the most risk land use and factors for dengue cases based on the condition of landscape composition and land use type configuration in the urbanization process of johor bahru. figure 1. key plan of iskandar malaysia 2.2. methods several stages were involved in the study such as database development, database analysis and synthesis. figure 2 shows the framework of the study that describes sequence of the process in this research. the first stage of this research is clarifying the relationship of dengue incidence with landscape pattern through literature review. after that, dengue incidence data was extract from the website idengue. dengue cases from march, april and may 2018. after that longitude & latitude of the location were geocode by using arcgis. a pair of coordinates represents a points of dengue incidence. the location of the cases was plot to the map as well as the number of cases by using the method of kernel density through arcgis application. after that, land use data 2016 were layered to figure out the cluster area. https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 | 33 figure 2. research framework dengue cases were classify by zone and analysed by the cluster area within 500m as dengue cluster area is the area where two or more dengue cases occur within the 200 meter radius area of the index case within 14 days from the date of notification of the index case (figure 3). there are 6 zone were classify within this area whereby this zone will analyse more detail. meanwhile, there are several categories of land uses activity classified such as non-strata housing, open space, planned commercial, schools, mosque, police station, drainage, pump house and etc (figure 4). after that the cluster area were overlay with land use map and intersect within 500 m to more focus on spatial landscape area. figure 3. dengue incidence in the study area https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 34 | figure 4. type of land use within the study area 3. results and discussion 3.1. the prevalence of land use activity a. zone a zone a is located at pulai district where there have 9 cases from march until may. table 1 shows the exact location and the number of cases. graph shows non strata housing is the highest percentage with 35.04 % follow by planned commercial with 12.62 % and bushes 9.49 % (figure 5). this result consequence with the theory by whitehorn & farrar (2010) aedes aegypti prefer in urban context and high population density area. there several type of housing pattern in this zone and most dominated with terrace house; single storey. besides, another type that also popular at this zone is apartment type of housing which is apartment meranti and apartment melawis at taman universiti. this zone a is the centre for local people at pulai district to buy their groceries, and for outsider for sightseeing since this area are located with commercial area which is u mall, skudai, aeon taman u, taman rekreasi majlis pebandaran johor bahru, gunung pulai and other commercial shop houses where correlated with the result whereby the second most highest percentage is planned commercial area. moreover, bushes can be categorized as the highest prevalence of land use activity for dengue incidence. there are many reasons on how the certain location can be a bushes spot area it is either the owner of the land did not maintain well or the land is the abundance area that belongs to government or any other developer or agencies. these bushes can cause the water flow stop and the water stagnant will develop into breeding site for aedes mosquitoes. recently, studies show that dengue vectors have been captured in vegetated areas and it can be found in bushes area (hayden et al., 2010; vezzani et al., 2005). however, drainage and open space got the highest percentage of land use activity in this cluster area. this type of land use activity was related with this research because aedes mosquitoes will survive. drainage system mostly depends on the water stream line which is natural source. this is another favourable dengue breeding site (uribe et al., 2008). open space such as field, recreation park and riverside can be categorized as public space. https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 | 35 figure 5. the percentage of land use activity in zone a table 1. land use map of zone a zone a taman universiti satellite image land use map zone a zone a (pulai districs) cases taman pulai utama, jalan pulai 7-18 1 taman pulai indah, jalan pulai indah 1 taman pulai utama, flat taman pulai utama 3 taman universiti, apartment meranti 1 taman universiti, apartment melawis 1 taman universiti, kebangsaan jalan 1-29 1 taman universiti, jalan pendidikan 1 b. zone b location of zone 2 were at taman mutiara rini and taman damai jaya (table 2). taman mutiara rini is the most crucial area for cluster area dengue cases. graph shows that approximately 26.01% of land use activity in cluster dengue area is pump house (water supply), 24.45% is non-strata housing and followed by bushes 16.37% (figure 6). pump house water supply is the main highest percentage in this zone. the result from this zone is very closely related to the recent studies by cheong et al. (2014) that water element got the second highest percentage at the study area, subang selangor. non strata housing is the second highest percentage because this zone is mainly used for residential area. housing type for this cluster zone mostly were terrace house with double and single storey. however, there are certain location of housing area by terrace low cost house and apartment. https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 36 | bushes and open space quite high in this zone because some land was belonging to other parties and they did not maintain well and become abundance land. bushes land use activity is causes by excessive expansion of bush at the expense of other plant species, especially grasses. there are major causes of bushes encroachment (de klerk, 2004), this is because the grass layer is over utilised, and loses the competitive advantage and can no longer use water and nutrients effectively. this results in a higher water and nutrient infiltration rate into the subsoil. such a scenario will benefit trees and bushes and allow them to dominate. bush will impact negatively or positively on biodiversity at certain context. a large number of mammals, bird species, reptiles and anthropoids are associated with the bush thickening in that area. however, if there are right densities of bush with mix of trees and shrubs there will produce more favourable sub habitat that resulting greater variety of herbaceous species which is good for ecological cycle. local should aware on the routine of the removal of bushes because it will affect another plant to grow healthy (de klerk, 2004). in south africa land cover data 2010, there were identified bushes as untransformed areas whereby the percentage woody thickening which is less than 20% (stafford et al., 2017). besides, the attitude of the local people does not really like to maintain the surround landscape. they let the bushes grow and did not realized it can give a negative impact. figure 6. the percentage of land use activity in zone b table 2. the land use map of zone b zone b taman mutiara rini sattelite image land use map zone b zone 2 cases jalan bakti 23, mutiara rini 4 taman mutiara rini, jalan utama 8-17 3 taman mutiara rini, pangsapuri jasa 4 taman damai jaya, jalan makmur 4 https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 | 37 c. zone c the first influenced land use activity for dengue cases in zone c are open space 5.8% followed by non-strata housing 5.0% and bare earth 3.7% (figure 7). there are located near to sungai sekudai with total 3 dengue cases from march to may (refer table 3). sungai sekudai is one of the potential natural resource for johor bahru that need to develop to become as one of recreation area. however, now this area become bare land and not develop that can cause negative impact for health and environment to the context area. \ figure 7. the percentage of land use activity in zone c table 3. the land use map of zone c zone c (bandar jb district) satellite image land use map zone c zone c cases taman tampoi indah 2, apartment sri kenari 1 taman tampoi indah 2, apartment park avenue 1 lily and jasmine apartment, jalan titiwangsa utama 1 d. zone d the highest percentage of this zone are planned commercial with 15.21% followed by non-strata housing 11.67% and open space 8.14% (table 4). this zone located at the centre of johor bahru town. johor bahru is the crowded place and act as focal point for outsider and visitors to stay at nearest hotel. nowadays, travel played an important factor to the distribution of dengue virus and it is associated with the pucallpa outbreak during 2012. non strata housing is the second highest percentage in this zone. mostly time of housing in this area was flat and apartment at flat stulang laut, jalan stulang. this type of housing which is flat and apartment are with high population density. besides, people prefer to live and stay at this area because this zone are very accessible to others places and facilities. apart from that, open space also is the https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 38 | highest percentage in this zone. since this is busiest area, there a lot of transportation network and traffic. this is related with the theory by wen et al. (2012) that human and movement of individuals could potentially increase the distribution of dengue infections as it can go beyond the dispersal range of mosquito population and can causes large scale outbreaks. recent studies show that human settlements also one of the spots of land use activity where there found aedes mosquitoes breed in water filled containers (nyamah et al., 2010). table 4. the land use map of zone d zone d satellite image land use map zone d cases bangunan sultan iskandar 2 jalan segget jb 1 flat stulang laut, taman stulang laut 3 e. zone e the highest percentage of land use activity in zone e is road network 29.33%, non-strata housing 27.53% and open space 6.67% (figure 8). this zone e are located at taman puteri wangsa (table 5), jalan lading with 4 cases from march until may. road network is the highest percentage because this housing area locate near to the major road network; jalan puteri. housing pattern in this area mostly are low cost house; terrace with single storey and terrace with double storey. apart, there are open space of an abundance and did no maintained well and filled with bushes. figure 8. the percentage of land use activity in zone e https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 | 39 table 5. the land use map of zone e zone e satellite image land use map zone e cases taman puteri wangsa, jalan lading 4 f. zone f the highest percentage of land use activity in this area are open space with 42.71% follow by road 39.58% and non-strata housing 13.13% (see figure 9). this zone located at jalan sejambak (plentong district). mostly type of housing in this zone are terrace-double storey houses (table 6). the composition of house is closer from another house and it is surround with open space where mostly filled with bushes. figure 9. the percentage of land use activity in zone f table 6. the land use map of zone e zone f satellite image land use map zone f cases taman bukit dahlia, jalan sejambak 3 https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 40 | 3.2. spatial descriptive analysis after calculate and considering of all type of land use activity, there is 6 type of land use activity which is the most risk for dengue incidence in iskandar region, johor bahru; (1) non-strata housing, (2) open space, (3) road, (4) planned commercial, (5) strata housing and (6) drainage system network. from all analysis zone a, b, c, d, e and f the most commonness land use activity is non-strata housing, open space, road, planned commercial, strata housing and drainage system network. there are several similarities of characteristic from all zone whereby most of land use for all zone are in urban context with high population density. according to wu et al. (2009) the rapid growth of environment could lead to the increasing of dengue incidence. this is much related with this area where most of the dengue cases are happen in urban area. housing is crucial area in this study whereby the pattern of the housing and the outdoor environment increased the possibilities of dengue transmission to other people. however, non-built up area such as open space, road and drainage network is part of the land use activity also acts as determinant of dengue mosquito’s presence. all of this land use needs more attention for future planning since this is the most risk land use activity in iskandar region. unplanned urbanization causes the development of breeding site and increasing the potential of aedes mosquitoes to disperse. different context of housing has different type of landscape composition (table 7). most of the houses tend to have their own green space in front of their houses especially for terrace house. these cultures are related with recent studies by rohani et al. (2011) whereby malays prefers to have green element at their living spaces. apart, well defined of landscape structure can boost the quality of neighbourhood context and provide a conducive living space in residential area (shahli et al., 2014). however, this culture could lead to negative impact to the neighbourhood if they did not maintain well because most of the research has been prove that aedes mosquitoes were trap at vegetated areas. entomological studied prove that dengue vectors mostly found in vegetated areas, such as orchards, rubber plantation also in brackish water (hayden et al., 2010; vanwambeke et al., 2007; vezzani et al., 2005). besides, open spaces at this context mostly are abundance and filled with lot of bushes. this can support the aedes mosquitoes breeding activities because some of the leaves can collect clean water and becomes a breeding site for them. this result was closely related with recent studies in putrajaya where the residents aware that the risk of the potential breeding site at green space (dickinson & hobbs, 2017). these land use activity; open space did not correspond with the theories by (tyrväinen et al., 2007) where green space can improve the interaction and healthy lifestyle to the surrounds. table 7. spatial descriptive in all zone no. total percentage all zone % total hectare all zone m2 no of existency zone 1 non-strata housing 117% non-strata housing 310.25 non-strata housing 6 zone 2 open space 84% pump houses 178.79 open space 6 zone 3 road 80% bushes 176.56 drainage system network 5 4 planned commercial 33% open space 175.04 road 5 5 bushes 30% planned commercial 80.61 planned commercial 5 6 pump house (water supply) 26% road 69.41 strata housing 5 7 strata housing 20% strata housing 65.92 electrical substation 5 8 drainage system network 14% drainage system network 49.65 mosque 5 9 secondary school 10% river 23.98 water tank 4 other than that, road is very significant in urban planning design. according to khormi and kumar (2011). high qualities of neighbourhood which is have a wider street have a low risk of dengue infection while low qualities of neighbourhood which is have narrower street more potential for dengue infection. major roadways are part of determinants to studies the existence of dengue (mahabir et al., 2012). in contrast, the major motorways may form major barriers to the flying mosquitoes because the volume of traffic is greater on these roads and especially during blood feeding periods for mosquitoes (early morning https://doi.org/10.14710/geoplanning.6.1.31-42 mohamad, et al. / geoplanning: journal of geomatics and planning, vol 6, no. 1, 2019, 31-42 doi: 10.14710/geoplanning.6.1.31-42 | 41 and later evening) since these usually coincide with journeys to and from work (mahabir et al., 2012). by 2020, the forecast number of households increasing at a faster rate than the growth of population. this statement related with this site studies where most of the cluster zone with dengue cases are at low cost housing area (terrace and flat). un habitat 2003 forecast where the number of people living in this settlement is expected to increase to 2 billion by 2030. this fact should synchronize to strategies on how to improve living condition for poor and average people. un habitat, 2003 revealed the attributes and the condition of low-cost housing that relate with this research; lack of basic services, substandard housing or illegal and inadequate building structures, overcrowding and high density, unhealthy living condition and hazardous locations and poverty. however there are strong reason why flat or apartment type of house become one of risk type land use activity for dengue cases, this is because multi-layer type of house lead increasing the shaded area of the context thus this could be a potential temperature for aedes mosquitoes breeding where the ideal temperature for aedes mosquitoes breeding is between 20-30 celcius (tun-lin et al., 2000). according to nazri et al. (2009) temperature and humidity of the environment are the core factors to influence the aedes mosquito’s performance; maturation, replication and their survival time. however, temperature can be one of the reasons for terrace house pattern. this is because from analyse of satellite image the house sizes are smaller and the houses are closer together. the closer of the house, the higher of shaded area. landscape composition of the context area will affect the rate of dengue incidence in certain area. in short, the main character of dengue cases in landscape composition is housing (low cost house), green and open space, drainage system network, commercial area and road. all of this land use needs more attention for future planning since this is the most risk land use activity in iskandar region. 4. conclusions the future development of land use especially for residential and open space should be considered on landscape pattern towards rapid urbanization. this study shows that, the transmission of dengue cases is likely more on housing area; low cost house which is terrace and flat, commercial area, open space, road and drainage system network. all of this land use activity is the highest prevalence at dengue cluster area in iskandar region, johor bahru. however, others factor such as households waste, indoor environments of the housing must be considered. 5. references cheong, y. l., leitão, p. j., & lakes, t. 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[crossref] https://doi.org/10.14710/geoplanning.6.1.31-42 https://doi.org/10.1016/j.apjtm.2016.03.004 https://doi.org/10.1016/j.ecoser.2016.11.021 https://doi.org/10.1046/j.1365-2915.2000.00207.x https://doi.org/10.1016/j.landurbplan.2006.03.003 https://doi.org/10.1093/jmedent/41.5.133 https://doi.org/10.1080/00045608.2012.671130 https://doi.org/10.1016/j.scitotenv.2008.11.034 https://doi.org/10.1016/j.envres.2016.11.009 31 geoplanning journal of geomatics and planning vol. 10, no. 1, 2023 original research the effects of green open spaces on microclimate and thermal comfort in three integrated campus in yogyakarta, indonesia nurwidya ambarwati1,2*, lies rahayu wijayanti faida2, hero marhaento2 1. pusat pengendalian pembangunan ekoregion jawa, siliwangi st. no.100, nusupan, nogotirto, sleman, indonesia 2. faculty of forestry, universitas gadjah mada, agro 1st, bulaksumur, sleman, indonesia doi: 10.14710/geoplanning.10.1.37-44 abstract this study aims to assess the effect of green open space (gos) on the microclimate and thermal comfort in three integrated campuses namely universitas gadjah mada (ugm), universitas muhammadiyah yogyakarta (umy), and universitas pembangunan nasional (upn) veteran. in order to achieve the research objective, three main steps were conducted. first, we mapped the gos area and density of the three integrated campuses using a high-resolution satellite imagery. second, three microclimate parameters such as air temperature, relative humidity, and wind speed were measured to each detected green spaces in the morning (08:00 am), at noon (01:00 pm), and afternoon (5:00 pm). subsequently, the results of microclimate measurements were used to calculate the level of thermal comfort using thermal humidity index (thi) method. third, we carried out statistical analysis to investigate the correlation between the distribution and the density of gos and the microclimate conditions. the results showed that a negative (-) correlation occurred between the pattern and density of gos with temperature and wind speed indicating that clustered gos significantly reduces the air temperature as well as the wind speed. on the contrary, the relative humidity has been increased. upn campus has the highest temperature and wind speed and the lowest humidity among other campuses. according to the results of thi, a 100% of the upn areas are uncomfortable, while at ugm and umy 42,08% and 11,28% of their area are uncomfortable, respectively. this study found that the existence of gos has an effect on microclimate depending on pattern and density. copyright © 2023 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction special region of yogyakarta (diy) has several leading universities that attract prospective students from within and outside the city to study. based on data from bappeda di yogyakarta (2021) the number of students in both public and private universities in 2021 will reach 387,310 people. zubaidah et al. (2015) explained that the high migration of students entering diy and students from diy would have an effect on increasing the volume of vehicles in diy. based on data from the community mobility report (google mobility reports, 2022) during the covid 19 pandemic, mobility in the workplace increased by 6% from baseline and in parks in the form of city parks, national parks, public beaches, pet parks, and open fields. an increase of 45% from the baseline. this increase in the volume of vehicles will have an impact on increasing air pollution, because according to puspitawati (2014) exhaust gases from motorized vehicles are a source of air pollution in diy, reaching 60-70%. these exhaust emissions can then cause the greenhouse effect (kurnia, 2021; primary, 2019). e-issn: 2355-6544 received: 25 october 2022; accepted: 31 october 2023; published: 31 october 2023. keywords: green open space, microclimates, thermal comfort, university campus, yogyakarta *corresponding author(s) email: nurwidyaaw@gmail.com https://doi.org/10.14710/geoplanning.10.1.37-44 mailto:chetanrpatel@rediffmail.com ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 32 greenhouse effect is a condition where the temperature of the earth's surface increases drastically due to the trapping of long-wave sunlight by greenhouse gases (co2, ch¬4, cfc, o¬¬3, and n¬¬¬¬20) and causes climate change (pratama, 2019). facing the problem of vehicle increasing volume due to the migration of students who enter diy every year so that it can cause changes in the micro-climate in the campus environment, the strategic role of universities in reducing global warming or controlling micro-climate change is to carry out the concept of a sustainable campus or green campus (mukaromah, 2020). one of the indicators of achieving a green campus is to control the microclimate through environmental management by providing green open space (rth). microclimate is the climate of the lowest air layer but can also be interpreted as climate in narrow areas such as forests, cities, villages, and swamps (daldjoeni, 1986).the elements of microclimate consist of temperature, relative humidity, light intensity, and wind speed (susanto, 2013). green open space plays a role in regulating climatic conditions by delivering air masses and reducing air movement (perini et al., 2018). green open space is the allocation of space in an area for the growth of vegetation that has many benefits, including producing fresh air, absorption of co¬2, and a place for biodiversity such as flora and fauna (irvine et al., 2013) vegetation or plants help in reducing carbon dioxide so that it can improve the microclimate (lobaccaro & acero, 2015). areas with a tropical climate require vegetation as a natural cooler to provide comfort during the summer or dry season (priya & senthil, 2021; zhang et al., 2019). several studies have discussed the benefits of green open space in the campus environment. hami & abdi (2021) found that most of college students spent much time in front of computer inside their classroom, so they need a fresh air and some green open spaces for outdoor activities to maintain their health and stress level. however, in order to maintain a healthy campus environment, lau et al. (2014) found that for a compact campus, it recommended to limit the size of an open space that may handicap circulation and accessibility. on the other side, a small open space can provide more intimate contact and also a more controllable microclimate for physical comfort. universitas gadjah mada (ugm), universitas muhammadiyah yogyakarta (umy), and universitas pembangunan nasional (upn) veteran (campus i) are three large integrated campuses in yogyakarta that have adopted the green campus concept. until now, there has been no attempt to evaluate the implementation of green campuses and their micro-climatic impacts in these three campuses. thus, we aim to assess the effect of green open space conditions on the microclimate and comfort level in these three integrated campuses in diy. the results of our study are expected to encourage the campus management and policies in environmental management, especially related to the provision of green open space as a microclimate controller and to increase thermal comfort. thermal comfort is a state of mind that expresses satisfaction with the atmospheric conditions of the environment (iso 7730, 2005 in bouzidi et al. (2023)). this study will provide information on how the green open spaces in three different campuses affects the microclimate and the comfort level. 2. data and methods 2.1. research material materials used in this study consisted of primary and secondary data. the primary data were obtained from direct measurement, while the secondary data were obtained from the satellite image and documents of relevant institutions. table 1 describes the materials needed in this study. table 1. research material name scale/ resolution source canopy density and pattern, land use map 1:5.000 high resolution satellite image (csrt), geospatial information agency (big) air temperature and relative humidity march-april thermohydrometer wind speed data march-april velocity meter field coordinates gps receivers documentation camera https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 33 the research locations are universitas gadjah mada (ugm), universitas muhammadiyah yogyakarta (umy), and universitas pembangunan nasional (upn) veteran (campus i). all the campuses are located in locations with relatively similar characteristics, including in areas with the same average rainfall conditions of 1500-2000 mm/year (bappeda di yogyakarta, 2021) flat topography, the same slope of 2%, and is located at an altitude of 600 masl, and relatively the same climatic conditions, namely the oldeman climate type d3. umy represents microclimate conditions and thermal comfort levels in the western part of diy campus, central part of ugm, and upn veteran yogyakarta (kampus i) eastern part of diy. figure 1 shows the research locations of the study. a. b. c. figure 1. a) ugm land cover map; b) umy land cover map; c) land cover map of upn veteran yogyakarta (campus 1) 2.2. effect of green open space on the microclimate in order to measure the effects of green open space on the microclimate, three main steps were conducted as stated in figure 2. first, we mapped the green open space area and density using high resolution imageries with the classification of buildings, road pavements, tennis courts, ponds, fields, and vegetation/green open space. from the results of land cover classification, we then calculated the density of green open space with the following formula. vegetation density = 𝑉𝑒𝑔𝑒𝑡𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 𝐴𝑟𝑒𝑎 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑆𝑡𝑢𝑑𝑦 x 100%..................................... equation (1) https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 34 furthermore, we classified the density of green open space into four categories following the classification in the table 2 as follows. table 2. classification green space density no percentage coverage (%) density vegetation 1 >39 high 2 25-39 moderate 3 <25 low 4 0 not vegetated source: budiyanto (2006) figure 2. research methodology flowchart next, we identified the pattern of green open space based on government regulation number 63 of 2002 namely clustered, dispersed, and longways green spaces. after interpretation the pattern of green open scape, in order to measure the patterns in the fields, we created grid to each campus and deployed the stratified random sampling point as visualized in figure 3. the results of the field data and interpretation data were then tested for accuracy using a confusion matrix table. a. b. c. figure 3. point sample climate data collection micro a) ugm 34 samples; b) umy 18 samples; c) upn veteran yogyakarta (campus 1) 19 samples https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 35 the data collection of microclimate parameters namely air temperature, relative humidity, and wind speed was carried out simultaneously in each point sample inside the grid when there were no consecutive rain days. measurements were taken at 07.00-08.00 (morning), 12.00-13.00 (noon), and 16.30-17.30 (afternoon). the research was conducted when the number of the covid 19 pandemic began to decline in february-march 2022 and community mobility activities began to run normally again. the results of point measurements in each grid are then calculated for the average temperature and humidity. the total number of samples for temperature and humidity data collection at ugm is 25 sample points, umy is 13 points, and upn veteran yogyakarta (campus i) is 10 sample points. after processing the data, a statistical analysis was carried out using spearman rank correlation techniques to determine the direction of the relationship between the characteristics of green open space and the microclimate. 2.3. thermal comfort of the academic community comfort level can be determined quantitatively based on the comfort index with parameters of air temperature and relative humidity (nieuwolt, 1977). in a previous study suarma et al. (2019) found that factors of climate that influence comfort level namely temperature, humidity, wind velocity, and solar radiation. relative humidity is the ratio between the measured (actual) water vapor pressure and the saturated water vapor pressure. the comfort index according to nieuwolt (1977) can be obtained by the following formula. thermal humidity index = 0.8 t + (rh x t): 500………………………… equation (2) description: t = air temperature (°c) rh = relative humidity (%) from those calculation formula, we classified the comfort levels according to nieuwolt (1977). for low latitude tropical climates (as can be seen in table 3). table 3. thi comfort classification table thi (°c) comfort level classification < 27°c comfortable 27-29°c partly uncomfortable > 29°c uncomfortable source: nieuwolt, 1975 the thi value was then assigned for each point sample grid at ugm, umy, and upn veteran yogyakarta campus 1. the results from the thi were then interpolated using the inverse distance weighted (idw) method. thi data analysis was carried out using quantitative descriptive methods through the description of the area objectively using numbers with comfort level conditions based on the results of thi classification in each campus. 3. result and discussion 3.1 the effect of green open space on microclimate 3.1.1 micro climate on integrated campus the measurement results show that range of temperature at ugm is 29,50°c -32.97°c (as can be seen in figure 4a), relative humidity is 50.67%-73.13% (as can be seen in figure 5a), wind speed is 0.26 m/s 1.08 m/s (as can be seen in figure 6a). the highest temperature at ugm was around the grha saba pramana (gsp) building. green open space in this area has a dominant pattern of an elongated shape with low density. meanwhile, the lowest temperature in the ugm area is 29.50°c on the area dominated by green open space in the arboretum city forest, faculty of forestry. the arboretum of the faculty of forestry has a grouped pattern of green open space with the density of green open space is high density. https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 36 range of temperature at umy is 29.60°c-32.32°c (as can be seen in figure 4b), relative humidity is 61.60%-69.87% (as can be seen in figure 5b), and wind speed is 0.45-0.87 m/s (as can be seen in figure 6b). the highest temperature located in an open area, close to buildings and fields. the pattern of green open space that dominates in this area is dispersed and longways path. the lowest temperature in umy have high density canopy and clustered green open space pattern. around the location there are also artificial ponds or lakes within the campus area. next, range of temperature at upn veteran yogyakarta campus is 31.83-34.40°c (as can be seen in figure 4c), relative humidity is 50.12%-52,07% (as can be seen in figure 5c), and wind speed is 1.09-2.74 m/s (as can be seen in figure 6c). location with highest temperature has an elongated pattern and low-density vegetation. the dominant land cover at this location is high density building and open land or access roads within the campus. meanwhile, the lowest temperature at upn area has a clustered pattern and high-density vegetation. a. b. c. figure 4. map of regional air temperature a) ugm. b) umy, c) upn veteran yogyakarta (campus 1) a. b. c. figure 5. map of relative humidity a) ugm, b) umy,c) upn veteran yogyakarta (campus 1) https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 37 a. b. c. figure 6. map of wind speed area a) ugm. b) umy, c) upn veteran yogyakarta (campus 1) 3.1.2 characteristics of green space green open space with longways pattern and low density at ugm are dominated by ketapang (terminalia catappa), sawo kecik (manilkara kauki), krei payung (fillicium decipiens), cemara bundel (cupressus papuana), glodokan tiang (polyalthia longifolia), meranti (shorea selanica), ketapang kencana (terminalia mantaly), angsana (pterocarpus indicus), trembesi (samanea saman), teak (tectona grandis), and bungur (lagerstroemia speciose). the arrangement of green open space along the side of the road and around the field at ugm has an ecological function and creates an aesthetic impression. ketapang (terminalia catappa), trembesi (samanea saman) (figure 7) and bungur (lagerstroemia speciose) have a wide canopy and can reduce no, co, and pb pollution with the ability of the leaves to absorb toxins. the wide canopy will affect the formation of the microclimate by lowering the temperature around the location. glodokan tiang (polyalthia longifolia) is a tree that has a high trunk, branches, and a flexible crown so it is not easily broken. this tree is suitable to be placed along the side of the road because it can reduce wind speed. characteristics of trees that are not easily broken will minimize the occurrence of fallen trees due to strong winds. the dispersed pattern with low density at ugm is dominated by angsana (pterocarpus indicus) (figure 8), green sapodilla (chrysophyllum caimito), biola cantik (ficus lyrate), guava (anacardium occidentale), jackfruit (artocarpus heterophyllus), and mango (mangifera indica). biola cantik (ficus lyrate) has a low tree height, broad leaves, and a lush canopy. green open space with a dispersed pattern at ugm is dominated by fruits such as mango, jackfruit, and guava with leaves that are not wide but have thick crowns. these plants have strong roots so that in case of strong winds it minimizes the occurrence of fallen trees. however, due to the irregular distribution of plants, when compared to green open space with high density, the temperature in moderate density will be higher and relative humidity will be lower. the high-density clustering pattern at ugm are dominated by walnut (canarium littorale) (figure 9), longan (euphoria longana), cape (mimusops elengi), mahogany saga (swietenia macrophylla), sapodilla (chrysophyllum caimito), randu (ceiba pentandra), merbau (intsia bijuga), sawo kecik (manilkara kauki), and pinus merkusi (pinus merkusii). based on observations in the field, trees with clustered patterns and high density have several different types of trees. the tree canopy in this green open space is relatively thick or dense, each tree was planted at closer spacing, and there are tree strata in this area. urban vegetation could be designed by combining multiple layers of shrubs and small trees beneath larger tree canopies (richards et al., 2020). this typical area will be a significant decrease in temperature such as in urban forest arboretum forestry, wisdom park, and biological forest. green open space with an elongated pattern and low density in umy are dominated by dadap merah (erythrina crista-galli), angsana (pterocarpus indicus), trembesi (samanea saman), mango (mangifera indica), https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 38 biola cantik (ficus lyrate), and ketapang (terminalia catappa). ketapang at umy is planted in an elongated pattern is used as a shade plant in the parking lot. it is intended that the air temperature under the canopy decreases because ketapang tree has a wide canopy. the dispersed pattern with low density were dominated by trembesi (samanea saman) and ketapang (terminalia catappa). trees planted with a dispersed pattern have relatively thick canopy, not too wide canopy, not stratified, and high enough. the clusterred pattern with high density is dominated by trembesi (samanea saman), mahogany (swietenia macrophyla), ketapang (terminalia catappa), banyan (ficus benjamina), and pole glodokan (polyalthia longifolia). several types of trees in high density with clustered patterns at umy have plant types that can absorb lead (pb) and are suitable to be placed in city parks. the results of the research by hindratmo et al. (2019) stated that the mahogany (swietenia macrophyla) had a fairly high pb absorption capability with a value of 30.76 ppm. this is influenced by the type of leaves on mahogany which are wide and rougher (not slippery). in addition, based on this research, another plant that has the ability to absorb high pb in the green open space of the umy campus is banyan (ficus benjamina). in addition to its ability to absorb pb, trees planted at high density at umy were also planted in strata with various types of trees with different heights so that they had a dense canopy and thick canopy. this affects the decrease air temperature in this area. green open space with a longways pattern and low density in upn are dominated by krei payung (filicium decipiens), angsana (pterocarpus indicus), tabebuya (tabebuia aurantica) (figure 10), palm (wodyetia bifurcate), ketapang (terminalia catappa), kepel (stelechocarpus burahol), and mindi (melia azedarach). several trees that planted in an elongated pattern at upn veteran campus 1 around the field have a type of canopy, such as krei payung and palm trees. some tabebuya trees at upn veteran yogyakarta campus i are not big enough and have small leaves so the tree canopy is not wide enough. this causes the temperature around the field at upn veteran yogyakarta campus 1 is the highest temperature among the three campuses. the dispersed pattern with low density is dominated by the palm putri (veitchia merilli), jackfruit (artocarpus heterophyllus), and mango (mangifera indica). the clustered pattern with high density is dominated by mango (mangifera indica), sawo kecik (manilkara kauki), jackfruit (artocarpus heterophyllus), angsana (pterocarpus indicus), and glodokan tiang (polyalthia longifolia). angsana trees that planted in a group at upn veteran yogyakarta already have a thicker canopy compared to the angsana trees on the edge of the field so that the canopy cover in the green open space area is denser. in accordance with the results in the field, the denser the vegetation, the temperature around the green open space will decrease. figure 7. elongated medium density green open space pattern of trembesi (samanea saman) figure 8. spreads sparse density green open space pattern of angsana (pterocarpus indicus) figure 9. cluster density/high density green open space pattern of walnut (canarium littorale) figure 10. elongated sparse green open space pattern of tabebuya (tabebuia aurantica) https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 39 3.1.3 correlation between green open space and micro-climate table 4 shows the results of the correlation analysis between the density and pattern of green open space with the microclimate at ugm, umy, and upn. it was revealed that the correlation value between density and air temperature is negative (-) meaning an inverse relationship between density and air temperature that the closer the green open space is, the lower the air temperature. meanwhile, the relationship between the density of green open space and humidity is positive (+) which indicates there is a directly proportional relationship where the denser green open space is, the higher the relative humidity value will be. table 4. correlation of green open space characteristics with microclimate temperature humidity wind speed ugm density pearson correlation -.862** .797** -.859** pattern pearson correlation -.697** .739** -.665** umy density pearson correlation -.687** .693** -.647* pattern pearson correlation -.705** .708** -.663* upn veteran yogyakarta (campus 1) density pearson correlation -.847** .930** -.717** pattern pearson correlation -.829** .744** -.576** source: data processing results, 2022 this condition is the same as the correlation between the green open space pattern and the air temperature which has a value of negative (-). the clustered green open space pattern will have an effect on lower temperatures. on the other hand, the correlation value (+) between the green open space pattern and humidity indicates that the rarer the green open space pattern is, the lower the relative humidity value will be. table 4 is the result of statistical data processing of the characteristics of green open space with a microclimate: 3.2 thermal comfort level on integrated campus 3.2.1 thi at ugm figure 11 shows that 57.92% of the area is in the partially uncomfortable category. the locations are around the engineering cluster, science cluster, administration cluster, agro-cluster, conservation forest cluster, and some health clusters. figure 11. thermal humidity index (thi) map at ugm https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 40 the temperature and humidity in the area with the category of being partly uncomfortable are controlled by the presence of green open space so that the temperature in this area is still within the threshold of 27-29°c or partly uncomfortable. the results of the thi at ugm with an uncomfortable classification in the ugm area is 42.08% of the total area. thi with an uncomfortable classification at ugm located in some health clusters, humanities clusters, some administrative clusters, mice clusters, and lecturer housing clusters. the uncomfortable classification indicates that the air temperature in this area is >29°c. the high air temperature is influenced by the presence of green open space with medium to rare density and elongated and spread patterns in areas classified as uncomfortable. 3.2.2 thi at umy the results of the thi calculation at umy show that an area of 88.53% of the area within the umy area is classified as partly uncomfortable with a class of 27-29°c and an area of 11.28% has an uncomfortable classification with an average temperature of >29°c. the temperature and humidity survey locations at points 3 and 1 (figure 12) are open areas with minimal green open space so that the temperature at these locations is high with low humidity. green open space in this location has an elongated characteristic with a rare to moderate density level. this results in areas 1 and 3 and their surroundings having an uncomfortable classification. figure 12. thermal humidity index (thi) map at umy 3.2.3 thi at upn veteran yogyakarta (campus 1) the results of the thi calculation at upn veteran yogyakarta (campus 1) show that 100% of the thermal comfort level in the campus environment is uncomfortable. this condition can occur because the temperature in the upn environment is > 29°c. in the upn 1 campus environment there are three open areas in the form of fields on grids 6, 8, and 10 so that in these locations the temperature is high due to the lack of vegetation cover in the open area. in addition, at upn there are several survey locations where tree rejuvenation is being carried out by felling the canopy like what happened on grid 9 so that the ambient temperature is increasing. figure 13 is a picture of thi at upn veteran yogyakarta (campus 1): https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 41 figure 13. map of thermal humidity index (thi) at upn veteran yogyakarta (campus 1) 3.3 discussion the results showed that there was a correlation between the presence of green open space with temperature and relative humidity which represented the level of thermal comfort. according to data from ugm public relations, ugm has 50% green open space and 70% will be developed, umy has 30% green open space from the campus area. these three integrated campuses have implemented the green campus concept by procuring green space. however, based on the results of the thi analysis (nieuwolt, 1977) ugm, umy, and upn veteran yogyakarta (campus 1) have a level of comfort with some classifications as partly uncomfortable and uncomfortable. this can be because the reference for the classification of the comfort level at the temperature and humidity at the time of measurement using classification from nieuwolt (1977) is different from the current existing conditions. therefore, it is necessary to adjust the comfort level classification according to the conditions in the field. in addition, the analysis based on thi is based on physical conditions, it is necessary to add a perception analysis as complementary data from the social aspect by paying attention to what conditions are felt by the academic community on the level of thermal comfort in the campus environment. other studies related to this research were conducted by sodoudi et al. (2018), they do research on the influence of spatial configuration of green areas on microclimate and thermal comfort. the aim of their study is to knowing the surface temperature at each vegetation density and shape or pattern of vegetation. the results show the level of fragmentation of patch density and edge density, shape, and type of vegetation on the cooling effect. the highest cooling effect occurs at 2 pm in the scenario of a green area in the form of methamphetamine parallel to the wind direction and in a tree with a large canopy. the difference with the research in this study is field sampling determination. sodoudi et al. (2018) determine the sample on each type of vegetation so that so that the thermal comfort known only at that location. meanwhile, this study determines the sample using a grid method which can be located in green open space (vegetated areas) or non-green open space area. grid method was used to make data collection easier throughout all of the area because cooling effect of vegetation not only not only felt under the shade of vegetation but also in the surrounding area. this is evidenced by when collecting temperature data around the ugm wisdom park, the temperature value is still lower than the temperature in an open area with sparse vegetation because it is affected by the existence of the wisdom park. in 2020, there was a study that specifically related to this topic written by mallen et al. (2020), study about thermal impacts of built and vegetated environments on local microclimates in an urban university campus. the https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 42 regression method was used to see the relationship between land cover parameters and air temperature in the summer of 2017. in a contrary, this study using spearman rank correlation to knowing the effect of green open space on microclimate. the use of this method is because one of the data used is ordinal data. the difference between this study and other studies about green open space is at the level of detail. this study specifically mentions about the type of plant in each pattern and density of green open space. this can be used as a consideration for other campus management or other areas in a tropical country to determine the types of plants that are suitable for planting as a green open space in their area. the existence of green open space is important, but it is necessary to add a description about the spatial layout of urban forest and the type of plant to suit the microclimatic conditions of each region. as the result of the research that has been done by ghaffarianhoseini et al. (2019) study about analyzing the thermal comfort conditions of outdoor spaces in a university campus in kuala lumpur malaysia concludes that effective redesign of outdoor spaces in the tropics has a significant impact of shading and vegetation. as a result, its improved comfort level. the selection of green open space aims to avoid falling trees due to strong winds, maximize the function of green open space as a lead (pb) absorber or ambient air controller, and as an aesthetic function in an area. the results of calculating the level of thermal comfort using the thi method by nieuwolt (1977) are less relevant for current climate conditions, so it is necessary to update and adjust the thermal comfort class based on temperature and humidity in each study area. this is proven that the results of thi calculations at upn are 100% uncomfortable, even though in the upn campus area there are several areas with high density and clustered vegetation. in a previous study, he et al. (2020) found that people either adopt behaviors, either to maintain comfort or they change locations to satisfy their demands. it shows that the level of thermal comfort needs to be validated with the perception of humans living in the area because the level of human adaptation is different in each region's characteristics. 3.4 limitation there are various factors that influence the formation of the microclimate, so it is necessary to conduct further research on this study to complement the factors that influence the microclimate. research on the effect of green open space on the microclimate and comfort level can have some uncertainty about the measurement results due to several things. first, the tools used, namely the thermohydrometer and velocity meter, require instrument calibration to obtain accurate results approaching the conditions in the field. however, the tools used in this study have been calibrated. second, it is necessary to learn and practice directly how to use the tool so that the measurement results are not biased. microclimate measurements are carried out with tools placed at a height of > 1 meter and < 2 meters because there are studies of typical climate characteristics in the lower atmosphere layer (< 2 meters below the ground surface) which is referred to as microclimate (haurwitz and austin, in utomo, 2009). furthermore, the third is related to the effect of using air conditioning which has the potential to release heat energy from the waste of the air conditioner which makes the outdoor temperature hotter. the results of research by hermawan (2014) stated that the heat discharged (released) from the ac condenser can reach a maximum temperature of 53.5°c with an average of 47.47°c. this parameter was not included in the study, while buildings in the campus area mostly use air conditioning so that there can be bias in the measurement of microclimate data. another factor that influences the formation of a microclimate at ugm, umy, and upn veteran yogyakarta (campus 1) is the form of the integrated campus area. ugm has a wider area, clustered or more compact, and wider edge effects so that the microclimate in the form of air temperature, relative humidity and wind speed is more stable than upn veteran yogyakarta (campus 1). this is related to the influence of the large number of emissions outside the area that cause global warming so that it can affect microclimate conditions in the campus area. upn veteran yogyakarta (campus 1) has a narrower area and has a smaller edge effect so that the possibility of microclimate influences from outside the area is still large, especially because this campus is associated with the northern ring road which is quite densely traversed by vehicles every day. this resulted in https://doi.org/10.14710/geoplanning.10.1.37-44 ambarwati et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 31-44 doi: 10.14710/geoplanning.10.1.37-44 43 the results of data acquisition showing that upn veteran yogyakarta (campus 1) had the highest air temperature (32.23-34.41°c) compared to other campuses. meanwhile, umy has an elongated area, but the microclimate conditions are almost the same as the conditions at ugm, this can be due to the influence of environmental associations at umy which are still in the form of rice fields so that it does not have a big effect on microclimate conditions in the umy area. this study has a limitation that the effect of emissions from outside the area that produces ambient and causes changes in the microclimate within the area is not included in the parameters of the research method. 4. conclusion three large integrated campuses in yogyakarta (ugm, umy, and upn) have adopted the green campus concept. but until now, there has been no attempt to evaluate the implementation of green campuses and their micro-climatic impacts. this study stresses the level of density and pattern of vegetation can affect the microclimate. future possible studies can arrange spatial layout of green open space and the type or characteristic of vegetation that needed to suit the microclimatic conditions of each campus. a negative (-) correlation occurs between the pattern and density of green open space with temperature and wind speed, which indicates that the denser green open space is with the clustered pattern, the air temperature will decrease, and the wind speed will decrease. on the other hand, there is a positive (+) correlation between the pattern and the density of green open space with humidity which indicates that the denser green open space is, the higher the relative humidity. upn veteran yogyakarta (campus 1) has the highest temperature and wind speed conditions and the lowest humidity among the three campuses because it has several open spaces (field area) with green open space conditions around the field has not big enough and wide canopy. in addition, the shape of the area at upn veteran yogyakarta (campus 1) is narrower and has a smaller edge effect so that the possibility of microclimate influences from outside the area is still large, especially because this campus is associated with the north ring road which is quite congested by vehicles every day. the results of the calculation of the level of comfort based on the thermal humidity index (thi) method, in the three integrated campuses show that at upn veteran yogyakarta (campus 1) 100% of the area is in the uncomfortable category, at gadjah mada university, 57.92% of the area is in the partial uncomfortable and 42.08% of the area is uncomfortable, in umy, 88.53% of the area is categorized as partly uncomfortable and 11.28% of the area is uncomfortable. uncomfortable conditions in almost all areas of upn veteran yogyakarta (campus 1) are affected by high temperatures >29°c and evenly low humidity. this study found the level of thermal comfort using the thi method by nieuwolt are less relevant for current climate conditions, so it is necessary to update and adjust the thermal comfort class based on temperature and humidity in each study area. this is proven that the results of thi calculations at upn are 100% uncomfortable, even though in the upn campus area there are several areas with high density and clustered vegetation. it shows that the level of thermal comfort needs to be validated with the perception of humans living in the area because the level of human adaptation is different in each region's characteristics. 5. acknowledgments the first author gratefully acknowledges faculty of forestry, university of gadjah mada (ugm) and ministry of environment and forestry republic indonesia for supporting this study. all authors thank to ugm, umy, and upn staffs who helped during the data collection. 6. references bappeda di yogyakarta. 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(2015). migration students and students immigrants to the city of education. https://doi.org/10.14710/geoplanning.10.1.37-44 https://doi.org/10.1016/j.scitotenv.2019.01.284 https://doi.org/10.1177/1420326x19887207 https://doi.org/10.1016/j.buildenv.2020.106739 https://doi.org/10.3390/ijerph10010417 https://doi.org/10.1016/j.foar.2014.06.006 https://doi.org/10.1016/j.uclim.2015.10.002 https://doi.org/10.1016/b978-0-12-812150-400011-2 https://doi.org/10.1016/j.buildenv.2021.108190 https://doi.org/10.1016/j.ufug.2020.126651 https://doi.org/10.1088/1755-1315/256/1/012040 https://doi.org/10.20885/jstl.vol5.iss1.art1 https://doi.org/10.3934/environsci.2019.6.417 | 113 geoplanning vol 7, no 2, 2020, 113-130 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.7.2.113-130 application of spatial multi-criteria analysis and least-cost path on the highway route planning (case study of bawen – yogyakarta highway) b. marjuki a*, i. rudiartob a center of data processing – ministry of public works and housing, indonesia b urban and regional planning & development department – diponegoro university, indonesia abstract: infrastructure planning ideally considers the geotechnical aspects and physical conditions of the infrastructure development location and must be able to support regional development. one kind of spatial analysis technique, which has the capabilities to integrate various regional characteristics associated with its suitability for a particular use, is spatial multi-criteria analysis. by using bawen yogyakarta toll road plan as a case study, this research is intended to apply route planning that takes into account regional characteristics, through the involvement of spatial multi-criteria analysis, analytic hierarchy process, and least cost path analysis. the analysis results then compared with the government preferred route to see its advantages and disadvantages. study results show that the generated route from the analysis has several advantages over the government preferred route while also having some shortcomings. the advantages of route analysis results compared to government preference routes include: better able to avoid earthquake and landslide-prone areas, better support to the preservation of protected areas, has more areas with flat to gentle topography, and have smaller additional construction cost as the consequences of the intersection with existing roads, rivers, and railways, in terms of affected land-use, generated route also has minimum negative impacts on the sustainability of agricultural land in the study area. the shortcomings of the analysis result are: not yet able to avoid flood and volcanic eruptions-prone areas as well as government’s preferences route, higher land acquisition cost estimation, and less support for industrial and tourism activities in the research area. improvement of analysis methods, data, and cost assessment strategy is needed to obtain better results and more appropriate modeling and analysis, in order to support regional infrastructure planning and development. copyright © 2020 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): marjuki, b., & rudiarto, i. (2020). spatial multi-criteria analysis and least-cost path on the highway route planning: a case study of bawen – yogyakarta highway, indonesia. geoplanning: journal of geomatics and planning, 7(2), 113-130. doi: 10.14710/geoplanning.7.2.113-130 1. introduction regional disparities between the western region and eastern region of indonesia become the primary focus in the infrastructure development planning of the indonesian government. in this regard, the challenge of contemporary indonesian infrastructure development is on how to reduce disparities and balance growth and development. having to achieve this expectation, the strategic plan of the ministry of public works and people's housing 2015-2019 mandates that infrastructure development must integrate on the inter-regional, inter-sectoral, and inter-governmental levels. this mandate is realized in the form of the strategic development area concept (sda), which becomes the basis of infrastructure development in indonesia. ministry of public works divides the indonesian territories into 35 sda's, where every sda has a specific planning policy according to the potential and problems that exist in each region. one of the planned sda in java island is the integrated growth center yogyakarta-solo-semarang sda (strategic plan ministry of public works and housing 2015-2019). various infrastructure is planned to build in this sda, where is one of them is bawen – yogyakarta toll road. given that sda-based infrastructure article info: received: 7 april 2018 in revised form: janury 2019 accepted: january 2020 available online: 1 november 2020 keywords: spatial multi-criteria analysis, route planning, ahp, least cost path, toll road. *corresponding author: b. marjuki center of data processing – ministry of public works and housing, jakarta, indonesia email: b_marjuki@pu.go.id open access http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.7.2.113-130 marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 114 | development must be followed, the determination of the bawen yogyakarta toll road route should take into account those integration aspects. road construction should be integrated with regional development plans undertaken by provinces and districts traversed by the planned toll road. also, toll road existence must also be able to support the existence of economic, social, and tourism activities in the region. the realization of integrated road planning requires formulation, analysis, and evaluation techniques that can involve these considerations. multi-criteria evaluation is one of the analysis and evaluation techniques that can be used for that purpose. the role of multi-criteria evaluation in decision making has a long history, ranging from the traditional form (e.g., conventional mediation) to the form of modern automated programming through the help of computers and information technology (köksalan, wallenius, & zionts, 2013). this development also includes spatial and regional planning through the development of spatial multi-criteria analysis (malczewski, 2006). in spatial multi-criteria analysis (smca), regional indicators become the criteria used as the basis for determining the toll road route. through analysis of the involved criteria, toll road alternative scenarios can be made, followed by an evaluation to determine which are the best scenarios, in the context of integration of infrastructure development and regional development. however, the factors and criteria involved in smca need to be formulated in advance of hierarchical urgency, since the importance of each factor is different from one to another. the urgency of each involved factor can be formulated using experts judgment techniques. various techniques have been developed to determine the urgency of factors from the experts, but one of the most commonly used is the analytic hierarchy process (ahp), developed by wind & saaty (1980). ahp can produce weight values and scores of involved factors and criteria that can be inputted in smca to find alternative solutions to the problems faced, followed by an evaluation to determine which one is the best. smca and ahp integration for site selection application has been conducted by many authors either in transportation route planning (atkinson et al., 2005; beukes, vanderschuren, & zuidgeest, 2011; abdi et al., 2009; keshkamat, looijen, & zuidgeest, 2009; effat & hassan, 2013) or other fields of study (sánchez-lozano & bernal-conesa, 2017; mishra, deep, & choudhary, 2015; bunruamkaew & murayam, 2011). thus, through a combination of smca and ahp, a toll road route plan that reflects not only the information integration of the involved criteria but also the experiences and preferences of the stakeholders involved in toll road infrastructure development can be obtained. by looking at the gap, this study combines smca with ahp as a tool to see an effective and efficient evacuation route based on least cost path analysis. 2. data and methods 2.1. study area the study is conducted at several subdistricts in central java province and yogyakarta special region, indonesia, which likely will be traversed by bawen yogyakarta toll road. these sub-districts are within the administrative area of semarang regency, temanggung regency, magelang regency, and magelang city, which are part of the central java province. as for special region of yogyakarta, the study area covers some sub-districts of sleman regency and kulonprogo regency (table and figure 1). marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 115 table and figure 1. study area regency/district sub-district semarang bergas bawen bandungan jambu banyubiru tuntang sumowono ambarawa pringapus temanggung kaloran temanggung kranggan pringsurat tembarak selopampang magelang secang grabag windusari kaliangkrik bandongan ngablak ngluwar tegalrejo candimulyo sawangan tempuran mertoyudan mungkid muntilan pakis borobudur salam sawangan srumbung dukun magelang city north magelang south magelang central magelang sleman tempel turi sleman pakem ngaglik mlati minggir seyegan kulon progo kalibawang 2.2. route planning criteria criteria in smca is the basis for determining the evaluated object is to meet the requirements or not. criteria, sub-criteria, and alternatives criteria for toll road route planning in this study are derived from a literature review of similar research (atkinson et al., 2005; effat & hassan, 2013; kushari, mulyono, & hendratno, 2015; and government regulations. the results of the criteria formulation are presented in table 2. table 2. proposed toll road route planning criteria (analysis, 2018) factors criteria sub criteria geotechnics soil • soil texture topography • slope geology • rock type environmental natural protected area • natural protected areas in existing spatial plan cultural protected area • distance from archeological cultural heritage social land-use • existing land-use land value • estimation of current land value regional activity system regional activity system • distance from industrial areas • distance from urban growth centers • distance from tourism destinations safety and additional construction cost disaster hazards • landslide-prone areas • flood-prone areas • earthquake-prone areas • volcanic eruption prone areas road safety • slope direction (aspect) additional construction cost • intersection with the road network • intersection with the railway network • intersection with the river network 2.3 data sources various geospatial data has been collected and processed (i.e., vector data digitization, map projection conversion, vector to raster conversion) into one spatial database (geodatabase) to meet the minimum requirements for analysis. data used in this study came from primary sources through field surveys and secondary data obtained from various agencies and institutions (table 3). marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 116 | table 3. data sources (analysis, 2018) data type data sources type of data land-use visual at interpretation of 1.5 meters orthorectified spot-6/7 satellite imagery acquired 2016 to 2017 (national geospatial agency – national institute of aeronautics and space) secondary data topography and slope 8 meters ifsar dem generated from terrasar-x sar imagery (national geospatial agency – national institute of aeronautics and space) secondary data natural protected areas existing regency spatial plan (regency regional planning board) secondary data cultural protected areas temples and archeological site mapping resulted from degroot & j klokke (2010) study. secondary data volcanic eruption hazard volcanic eruption hazard map from national geological agency secondary data landslide hazard landslide hazard map from national geological agency secondary data flood hazard flood prone map from existing regency spatial plan (regency regional planning board) secondary data earthquake hazard national earthquake hazard map 2017 (ministry of public works and housing) secondary data soil texture 250 meters of global soil texture data from soilgrids (isric – world soil information) secondary data land value land value map published in 2015 by the national land agency secondary data rock type remote sensing based geologic map at 1:50.000 scale (national geological agency) secondary data industrial, tourism and growth center location field survey, existing regency spatial plan primary and secondary data road network national, provincial, and regency road network map published in 2015 by the ministry of public works and local government. secondary data railway network national topographic map at scale 1: 50.000 published in 1915 by nederland indie topographic survey agency, verified by field survey because the railway network in the study area has been closed at 1976, though there is a government plan to reactivate it in the future. primary and secondary data river network national topographic map at scale 1:25.000, spot-6/7 satellite imagery visual interpretation secondary data government’s preferred route plan of bawen – yogyakarta toll road latest official report of bawen – yogyakarta toll road development (ministry of public works and housing) secondary data 2.4 data standardization and normalization determination of the optimal toll road route based on smca involves a series of criteria, sub-criteria, and alternatives that have a different scale and value measurement. these scale and value differences will affect the accuracy of the analysis results if it is not standardized into standard value (drobne & lisec, 2009). therefore, before the analysis, any alternatives of sub-criteria were assigned and reclassified into a relative scoring scheme (from 1 to 9). the assumption used is, the higher the score of the alternative criteria, the more it is not suitable as a toll road route. standardization parameters and references are presented in table 4. marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 117 table 4. cost value standardization factor criteria sub-criteria alternative cost value cost classification references geotechnics soil soil texture 0 -30% sandy soil 9 natural breaks classification 30 -80% sandy soil 5 80 -100% sandy soil 1 geology rock type quartenary deposits (alluvium, kolluvium), breccia 1 expert judgment extrusive igneous and sediment rock (sandstone, tuff) 5 intrusive igneous rock (andesite, granite, dyorite) 9 environment natural protected area natural protected area type non-conservation area 1 modified from kushari et al. (2015) and expert judgment conservation forest, national park 9 cultural protected area distance from temples 0 500 meters 9 expert judgment > 500 meters 1 social land-use land-use type shrubs, bare land, grassland 1 modified from kushari et al. (2015)and expert judgment mixed garden, 3 rice fields, poultry, plantation 5 sparse settlements, cemetery, 7 industrial area, economic services, historical places, dense settlements, lake 9 land value land value 7,000 180,000 rupiahs 1 natural breaks classification 180,000 2,100,000 rupiahs 5 > 2,100,000 rupiahs 9 regional activity system regional activity system distance from industrial area 0 5 kilometers 1 equal interval classification 5 10 kilometers 5 > 10 kilometers 9 distance from urban growth centers 0 5 kilometers 1 5 10 kilometers 5 > 10 kilometers 9 distance from tourism locations 0 5 kilometers 1 5 10 kilometers 5 > 10 kilometers 9 road safety and additional construction cost additional construction cost intersection with road network non road area 1 expert judgment local road 3 regency road 5 provincial road 7 national road 9 intersection with railway network non-railway area 1 railway area 9 intersection with river network first river order 1 expert judgment second to third river order 3 fourth river order 5 fifth river order 7 sixth to seventh river order 9 natural hazards landslide hazards non hazard area 1 construction standards number. 007/bm/2009, and expert judgment low hazard area 3 moderate hazard area 5 high hazard area 9 flood hazard non hazard area 1 low hazard area 3 moderate hazard area 5 high hazard area 9 earthquake hazard pga 0 0.25 g 1 pga 0.25 0.3 g 3 pga 0.3 0.4 g 5 volcanic eruption hazard non hazard area 1 hazard zone i 3 hazard zone ii 5 hazard zone iii 9 marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 118 | 2.5 ahp based weighting criteria and sub-criteria weighting in this study were conducted using the analytic hierarchy process (ahp) method developed by wind & saaty (1980). in this study, the ahp analysis was conducted on two levels: first, ahp at the sub-criteria level (applied to sub-criteria incorporated in disaster risk criterion, regional activity system criterion, and additional construction cost criterion), and ahp at the criteria level (figure 2). topographic factors are not involved in ahp and smca because topographic factors will be used as a horizontal and vertical factor according to the modeling approach used, which is an anisotropic approach (to be described in subsequent chapters). figure 2. hierarchy of criteria (analysis, 2018) as input from ahp is the result of a questionnaire survey and interview with three experts from the ministry of public works and housing and one transportation practitioners. the result of the questionnaire was then arranged in the pairwise comparison matrix. from the obtained pairwise comparison matrix, the priority vector and its consistency index can be calculated. the value of the obtained priority vector is then used as the weight value of the criteria if the consistency index is less than 0.1. the final weight value from the obtained weight of each respondent then is calculated using the geometric mean formula. 2.6 spatial multi-criteria analysis (smca) smca is implemented in geographic information system (gis) through map algebra operation based on the raster data structure. smca requires the weight value of each criterion and sub-criteria to determine which criteria are more important to determine the most optimal toll road route. this weight value is derived from the ahp process described in the previous sub-chapter. the analytic method used to determine the best route of the toll road plan in this study is using the least-cost path (lcp) method. lcp is a technique to determine the shortest cost distance from one location to another based on a raster surface data called accumulated cost surface (douglas, 1994). douglas (1994) defines accumulated cost surface (acs) as a dasymmetric representation in the form of a grid model of the earth's surface, which refers to how much resources to spend or how much of the frictions must be passed over the model of the earth's surface. in this study, acs is derived from the cumulative cost raster (called cumulative cost surface/ccs) obtained from smca. ccs was obtained from the map overlay result of various baseline data (see table 3) that have been marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 119 given standardized score values and weight values in accordance with the ahp results. several smca algorithms have been developed to obtain ccs. one of them is the weighted linear combination (wlc) technique, which has been implemented in most of geographic information system software available on the market and used for this study. acs calculation within the gis can be based on two approaches, namely the isotropic approach and anisotropic approach (yu, lee, & munro-stasiuk, 2003). the isotropic approach is an approach in which the movement of calculation of cost accumulation in all directions is considered equal, while the anisotropic approach considering the movement of cost accumulation in all directions (both horizontal and vertical) has different implications to cost accumulation. in the context of road planning, some authors such as collischonn & pilar (2000) and yu et al. (2003) argue that the anisotropic approach is more appropriate than the isotropic one. this opinion departs from the fact that less step and sinuous slopes are preferred than slopes that are short but steep. either in isotropic or anisotropic approach, acs calculations require ccs data obtained from wlc operation result. in the anisotropic approach, the difference is that in addition to requiring ccs information, anisotropic operations also require two other input data, which are a horizontal and vertical factor. the horizontal factor determines the horizontal friction, which will increase the cost of the horizontal movement direction (0 to 360 degrees), while the vertical factor determines the vertical friction, which will increase the cost in upward vertical movement (0 to 90 degrees), or downward vertical movement (0 to -90 degrees). implementation of the horizontal and vertical factor to extracting the acs using anisotropic approach can be done by determining the horizontal factor and vertical factor tables first. this horizontal and vertical factor table will be used as the basis to determine the cost value of the friction in horizontal movement (slope direction) and vertical movement (slope gradient). for this study, horizontal friction is considered in the analysis to facilitate the selection of road routes that avoid sunrise and sunset direction, so when the toll road is in the operational stage, the road users will not experience visibility problems due to glare disruption. whereas, vertical friction is a representation of the increased cost due to terrain slope gradient changes, either during the road construction stage or road operational stage. the horizontal and vertical factor tables used in this study developed from the result of yu et al. (2003) study and presented in table 5. the inf value in the vertical factor table indicates that the cost to pass the area with a given slope is too large, so the area that has that kind of slope will be impassable. an overview of the performed analysis process is presented in figure 3. table 5. vertical and horizontal factor used for anisotropic route extraction (analysis, 2018) vertical factor horizontal factor slope (degrees) cost value aspect (degrees) cost value (-45) (-90) inf 0 22.5 1 (-25) (-45) 32 22.5 67.5 7 (-15) (-25) 9 67.5 112.5 9 (-12) (-15) 7 112.5 157.5 3 (-9) (-12) 5 157.5 202.5 1 (-3) (-9) 3 202.5 247.5 7 0 (-3) 1 247.5 292.5 9 0 3 1 292.5 337.5 3 3 9 3 337.5 360 1 9 12 5 12 15 7 15 25 9 25 45 32 16 90 inf marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 120 | figure 3. optimal toll road route analysis workflow (analysis, 2018) 2.7 comparison and evaluation of the analysis results route comparison and evaluation in this study is done to find out to which extent the routes generated from smca and lcp is better than the government's preferred route. comparison and evaluation are performed by looking to some indicators that affect: (1) ease of construction; (2) risk in the operational phase of the toll road; and (3) integration and support to regional development. details of the comparison and evaluation indicators are presented in table 6. table 6. evaluation indicators (analysis, 2018) factors criteria comparison and evaluation indicators ease of construction and level of investment cost land-use the extent of land-use type passed by the route topography the extent of the slope passed by the route protected area the extent of the protected area passed by the route land value estimation of land acquisition cost along the route existing transportation network number of the intersection with the road number of the intersection with railway sungai number of the intersection with river road geometry route length risk in operational phase of toll road road safety the extent of terrain aspect passed by the route natural hazards the extent of earthquake prone area passed by the route the extent of volcanic eruption prone area passed by the route the extent of landslide prone area passed by the route the extent of the flood-prone area passed by the route integration with regional development integration with regional development proximity to industrial areas proximity to urban growth centers proximity to tourism destinations proximity to archeological protected sites marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 121 3. results and discussion 3.1. criteria standardization and ahp results standardization of alternative criteria value in this study was conducted by reference to three sources of information, namely: (1) existing similar research and literature, (2) government rules and laws about road management, and (3) discussions with experts which be done simultaneously with ahp survey. for criteria mapped in discrete geographic objects (vector data), standardized cost value assignment is performed by converting object information (which is nominal data) to value (ratio data), in accordance with literature and expert recommendations, whereas cost value assignment for criteria mapped in continuous geographic fields is performed by classifying those values into standardized values. complete visualization of the standardization result for each sub-criterion is presented in figure 4. figure 4. standardization results (analysis, 2018) marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 122 | the ahp processing in this study was conducted after the compilation of the questionnaire by experts has been completed. the ahp is performed to obtain the value of the eigenvector used as the weighting value of the criterion. the confidence level of the eigenvector value in ahp itself is measured from the consistency index (ci) obtained from the analysis. based on the obtained result, the ci obtained for each respondent indicates a value below 0.1, which can be interpreted that the ahp analysis for each respondent is consistent. thus the obtained eigenvector can be used as the criteria weight value. eigenvalues (weights) calculation result of the criteria along with its consistency index value is presented in table 7. table 7. ahp results (analysis, 2018) criteria ministry of public works transportation expert geometric mean weights normalized geometric mean expert 1 expert 2 expert 3 expert 4 geology 0.1949 0.1231 0.0481 0.0817 0.10 0.11 soil 0.1839 0.1122 0.0350 0.0376 0.07 0.08 natural protected area 0.1675 0.1496 0.1529 0.1596 0.16 0.18 cultural protected area 0.1653 0.1415 0.1529 0.1596 0.15 0.17 land-use 0.1341 0.1065 0.1529 0.1298 0.13 0.14 land value 0.0191 0.0445 0.0171 0.0210 0.02 0.03 regional activity system 0.0228 0.0201 0.1529 0.1386 0.06 0.06 natural hazards 0.0691 0.2787 0.1529 0.2523 0.17 0.18 additional construction cost 0.0434 0.0239 0.1354 0.0199 0.04 0.05 total weights 1 1 1 1 0.90 1.00 consistency index 0.07 0.08 0.09 0.07 from the results of the ahp analysis described above, the final criteria and sub-criteria weights can be determined. in this study, the final weights computed from the normalized weights resulted from the geometric mean operation. before inclusion as input for ccs generation, the decimal format's final weight values are converted to a percentage by multiplying it with 100 value. conversion to percentage is applied to avoid the reduction effect of the final cost value (due to the multiplication operation of cost value with weight value). as for the final weight value of sub-criteria is determined from the division of the concerned criteria, in accordance with the proportion of the weight value of each sub-criterion. the final weight value of criteria and sub-criteria that has been converted to the percentage scale is presented in table 8. table 8. criteria and sub-criteria final weights (analysis, 2018) criteria sub criteria criteria weights sub-criteria weights geology 11 soil 8 natural protected area 18 cultural protected area 17 land-use 14 land value 3 natural hazards 18 volcanic eruption 5.61 landslide 6.80 earthquake 4.53 flood 1.45 regional activity system 6 distance from urban growth center 2.21 distance from industrial area 3.49 distance from tourism destination 0.52 additional construction cost 5 intersection with railway network 1.23 intersection with road network 1.85 intersection with river network 1.47 marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 123 the result of consensus drawing from several experts using ahp and geometric mean in this study favored criteria of natural hazards, protected area existence, land use, and geological condition. thus, the lcp analysis will be more sensitive to the above criteria than other criteria. this sensitivity is apparent after comparison with the government’s preferred route, where the lcp generated route cannot identify feasible and effective routes when viewed from regional activity system criterion and the land value criterion. the route generated from the lcp analysis ideally should be sensitive to the regional activity system criterion because the bawen yogyakarta toll road is expected to support regional development and tourism activities in the study area. lcp generated route ideally should also be sensitive to the variation of land value over the study area because it will be related to the role of minimizing the complexity of land acquisition as one of the leading problems of toll road development in indonesia. the relative value assignment strategy of cost (cost score) also plays a vital role in addition to the weighting strategy of the criteria itself. this statement can be understood from the results of the route comparison on the additional construction cost criterion. the weight value of the additional construction cost criterion is relatively smaller than the other criteria, but the route obtained from the lcp analysis generated intersection number with rivers and roads less than the government's preferred route. thus, the cost value assignment strategy for the additional construction cost criterion is sufficient. the level details of the data used are one of the aspects that also need to be considered further. for example, this study uses the land use map at scale 1: 10,000, where is in this data, the built-up land-use is still visualized as area (e.g., residential area or industrial area). this data may not be detail and useful enough as the input in smca and lcp application for route planning. as can be seen from the analysis results, the route generated from lcp analysis has not shown adequate sensitivity compared to the government’s preferred route for the residential area, although the criterion weight value is quite high. this insensitivity happens because the score assignment of the residential area has been done based on its relative density, in accordance with the level of the details of those data. if detailed information that can be used as a basis for determining cost score in a more precise way is available (such as the number of buildings per block), cost models and lcp analyzes may be more sensitive to the variation in the cost value of each criterion, and then can have significant implication to the improvement of the analysis and modeling. the use of anisotropic approach (arcgis path distance algorithm) in this research is capable of generating a toll road route that has the cost as low as possible. as shown by the comparison results of topographic aspects in table 8, the route of the lcp analysis can find areas with the topography as flat as possible, to minimize the cost when compared with the government's preferred route. nevertheless, the results obtained are still can be debated further, especially in the analysis results around bawen sub district, ambarawa city, and rawa pening lake area. the anisotropic approach, which considers slope gradient as a vertical cost factor, tends to favor flat topography with the slope of a small slope as the area with the best suitability for toll road routes. however, in the case of bawen yogyakarta toll road, the government study route has chosen to avoid rawa pening lake area, although topographically, the rawa pening lake area is entirely appropriate. the government may want to avoid the complexity of construction costs associated with the soil engineering treatment in the rawa pening lake area, which tends to be soft and has low engineering capacity to support road infrastructure. this fact has not been well anticipated by the model generated from this study since the soil criterion in this study is based only on the percentage of the sandy texture of the soil and has not considered other soil characteristics (due to lack of data), such as effective soil depth. if we refer to similar studies, among others by ismail & jusoff (2009), beukes et al. (2011), and chandio et al. (2012), these studies found that multi-criteria spatial analysis and lcp can provide effective and efficient route analysis results in terms of distance and time spent to travel, as well as minimal risk in terms of construction and operational cost. however, the criteria involved in those studies were not as much as the criteria used in this study. consideration of more criteria will make the cost model have more complicated behavior, and there is a possibility to produce route analysis results that do not meet some of the criteria under consideration (as can be seen from the results of this study), although it is also there is the possibility that complex model will better represent the complexity of the cost conditions in the field. marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 124 | the results of this study indicate the complexity of those mentioned complex behavior of the cost model above, which results in the non-fulfillment of some of the criteria. this finding confirms similar studies using relatively numerous criteria, as did by keshkamat et al. (2009) and kushari et al. (2015). both studies also confirm the dynamic lcp analysis results as a result of involving more criteria in the preparation of the cost model, which resulted in some evaluation indicators are not fulfillment. apart from the disadvantages of the analysis results, the modeling methods applied in this study along with the results that have been obtained indicate the potential that the analytical methods and techniques that have been applied can be utilized and developed further to support the transportation route planning, that can be more sensitive to various criteria, not only physical criteria but also to the sustainability of environmental functions, besides supporting the regional development. 3.2. criteria standardization and ahp results ccs model generated based on input: (1) result of standardization and classification of criteria and subcriteria; and (2 weight value of each criterion and sub-criterion. model design and execution is performed within the arcgis desktop 10.6 software using weighted sum geoprocessing operations. the weighted sum operations are carried out at two levels. first, at the sub-criteria level for disaster risk criteria, regional activity system criteria and additional construction cost criteria, and second, at criteria level that integrates the cost values of the geology, soil, protected areas, land use, land values, regional activity system, natural hazards, and additional construction costs criteria. figure 5. ccs and acs results (analysis, 2018) marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 125 the acs model is constructed using input data consisting of: (1) initial location of road plan; (2) dem of research area; (3) ccs model that has been produced; (4) horizontal factor table; and (5) vertical factor table. processes performed within the arcgis path distance algorithm (presented in figure 5 along with ccs result), which can be described as follows: 1. based on the initial location of the route plan and specified dem, the algorithm calculates the surface distance from the initial location to all research areas. 2. aspect is derived from the specified dem, followed by a cost value assignment for each mapped direction angle in every pixel with reference to the horizontal factor table. this sub-dataset is called horizontal cost. 3. slope is derived from the specified dem, followed by a cost value assignment for each mapped slope angle in every pixel with reference to the vertical factor table. this sub-dataset is called vertical cost. 4. ccs cost model is then multiplied by the horizontal cost. 5. through the iteration process, the accumulated sum of the cost of each pixel from the origin pixel then is summed, and the summation result of every two pixels is multiplied by surface distance value and vertical cost value between the evaluated pixels. figure 6. lcp routing result (red line) compared to government’s preferred route (green line) marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 126 | least cost path (lcp) analysis is an optimal path determination analysis technique based on pixel-pixel searches across the study area, using criteria: (1) the shortest distance from the origin location to the destination location; along with (2) the lowest accumulation of cost. lcp requires input data consisting of (1) the result of the acs model, (2) backlink data obtained from acs generation, and (3) the destination point, which is the end location of the path to be extracted. the backlink data is a raster data indicating the direction of movement of the acs data (quantified by the pixel value of backlink data ranging from 1 to 8 to represent the eight directions of possible horizontal movement) from the origin point to the destination point. the result of lcp analysis that has been done is presented in figure 6. 3.3. comparison and evaluation results obtained toll road route from lcp analysis then compared and evaluated versus government's preferred route. to properly evaluate the affected areas, the generated route from lcp analysis and the government's preferred route has been buffered 80 meters either on the right side or to the left side of the route. buffering of 80 meters is assumed to be sufficient to cover the necessary road space (own road space, road benefit space, and road monitoring space). the results of the performed evaluation are presented in table 9. table 9. comparison and evaluation results (analysis, 2018) criteria sub criteria alternative criteria route generated from lcp government’s preferred route land use land-use traversed by route settlements 166. 59 ha 128.1 ha industry and services 3.6 ha 2.4 ha government facilities 0.2 ha 0 ha forests 0 ha 0 ha plantations 16 ha 35.4 ha agricultural areas 694.9 ha 751. 2 ha mixed gardens 216. 2 ha 298.1 ha bare lands 0.7 ha 3.2 ha slope route slope 0 3 % 114. 41 ha 95. 24 ha 3 9 % 514. 28 ha 386. 1 ha 9 25 % 449. 21 ha 620. 9 ha > 25 % 31. 76 ha 123. 3 ha protected area protected areas traversed by route natural protected areas 0 ha 0 ha cultural protected areas (300-meter radius from temples) 111.3 ha 122 ha land value land acquisition estimation land value 4,188,176,199,328 idr 3,581,766,068,528 idr intersection with existing transportation network intersection with national. provincial and regencies road national roads 6 intersections 5 intersections provincial roads 4 intersections 5 intersections regencies roads 27 intersections 30 intersections local roads 102 intersections 112 intersections intersection with railway railway 4 intersections 2 intersections first river order 45 intersections 46 intersections marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 127 criteria sub criteria alternative criteria route generated from lcp government’s preferred route intersection with river network intersection with river second to third river order 47 intersections 49 intersections fourth river order 12 intersections 9 intersections fifth river order 0 intersection 8 intersections sixth to seventh river order 0 intersection 0 intersection road geometry route length total route length 69 km + 408 m 76 km + 720 m risk on toll road operational phase route direction north. south 30.45 km 24. 36 km north west. south east 13.35 km 17. 52 km south west. north east 19.39 km 26. 37 km west. east 6.22 km 8.46 km volcanic eruption hazard hazard zone 1 0 ha 0 ha hazard zone ii 0 ha 0 ha hazard zone iii 14.99 ha 8.5 ha landslide hazard hazard zone 1 702.12 ha 835 ha hazard zone ii 89.27 ha 179.9 ha hazard zone iii 18.67 ha 20.8 ha flood hazard flood prone area 12.9 ha 0 ha earthquake hazard peak ground acceleration 0.25g -0.3g 63.9 ha 79.7 ha peak ground acceleration 0.3g 0.4g 945.1 ha 1034.6 ha peak ground acceleration > 0.4g 100.1 ha 111.2 ha integration with regional development accessibility from industrial area proximity to industrial areas 73 industry locations 113 industry locations accessibility from urban growth centers proximity to urban growth centers 10 growth centers (ambarawa. grabag. temanggung. kranggan. secang. muntilan. salam. tempel. sleman. pakem) 8 growth centers (temanggung. kranggan. secang. kota magelang. mertoyudan. mungkid. borobudur. dekso) accessibility from tourism destinations proximity to tourism destinations 129 tourism destinations 142 tourism destinations (includes borobudur temple) accessibility from cagar budaya proximity to temples and archeological sites 57 archeological sites 68 archeological sites note: cells marked with green shade indicate the superior result development and implementation of transportation infrastructure. either in central or local governments can consider the results obtained from this study to conduct further evaluation of the bawen marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 128 | toll road yogyakarta route plan. as well as adopting the method to similar infrastructure projects. route obtained from the study can also be integrated with government studies on road segments that are perceived as having not met the disaster threat criteria. environmental criteria. support for food security. and construction costs criteria. the method of determining the trace used in this study. which includes smca. ahp. and anisotropic lcp analysis. is recommended to be incorporated in government's transportation planning activities. especially at the initial planning stage of the feasibility study. in addition to being able to provide route analysis through an automated process along with tools and evaluation techniques. this method can be extended to scenariobased route modeling. so various alternative scenarios can be proposed and further evaluated. which are most appropriate and feasible. formalization of methods can be done in the form of legislation. technical guidance. or standard of procedures (sop). a comparison between isotropic and anisotropic approaches in determining the optimal route also needs to be studied and simulated further in future studies. this assessment is needed to ascertain which approach is the best in transportation infrastructure route planning. assessments can be conducted in different regions with different regional characteristics to see how each approach performs. the testing of different lcp algorithms also needs to be applied. lcp analysis used in this research is using lcp djikstra algorithm (dijkstra. 1959) with a queen pattern (eight directions of movement). on the other hand. various lcp algorithms have been developed. for example. a* (hart. nilsson. & raphael. 1968). best first search. a* manhattan heuristics. a* diagonal shortcut heuristics. or lcp algorithms dedicated explicitly to road-based route planning such as smartterrain (yu et al.. 2003) and baek's cut and fill (baek & choi. 2017). the above lcp approaches and algorithms can be an alternative to determine transportation routes that may generate better results. knight movement patterns (24 directions of movement) that have been implemented in the grass gis software can also be studied further along with those various algorithms. the development of a cost model based on actual cost is also recommended. incorporation of actual cost into the cost model will have a strategic value. among others is: (1) projecting the real cost-benefit condition of the implementation of infrastructure development; (2) can be valuable information in preparing infrastructure development budget plans; (3) represents the condition of the cost that is closer to the actual conditions in the field. real cost information for each criterion can be simulated using current cost standards. or based on experience/budget realization reports from toll road or other infrastructure projects that have been implemented. 4. conclusion based on the obtained results. we can conclude that the utilization of an anisotropic approach in the toll road route planning can give the result of route analysis. which can minimize the cost. the cost model is getting more complex along with the addition of more criteria and may negate one criterion with other criteria. resulting in the insensitivity of route analysis to specific criteria. particularly on criteria that have low weight and/or criteria that use generalized data. nevertheless. these findings are not fixed because there is a possibility that the cost model can be more sensitive to low weight criteria. as long as it is applied a proper cost assessment strategy and/or represents the real cost condition in the field. the existence of criteria that have real cost values such as land value (which used in this study) can be useful to estimate the project budgets and costs. which is essential in the planning stage of infrastructure development programs. in order to obtain appropriate planning products.the obtained route from this study is also shorter about 8 kilometers compared to government's preferred route. allowing road users to save a certain amount of resources when the toll road has been operational. these findings indicate that there are alternative routes to government's preferred route. which is environmentally friendly and helpful to support food security sustainability. this study still has some limitations related to the implementation of smca. lcp. and ahp analysis to support the route planning of the bawen yogyakarta toll road. there are several criteria for toll road planning that still use a relatively subjective value assessment and have not been involved in research. both marjuki & rudiarto / geoplanning: journal of geomatics and planning, vol 7, no 2, 2020, 113-130 doi: 10.14710/geoplanning.7.2.113-130 | 129 in terms of road planning 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(2003). extensions to least-cost path algorithms for roadway planning. international journal of geographical information science. 17(4). 361–376. https://doi.org/10.1016/j.ejrs.2015.06.005 https://doi.org/10.1016/j.landusepol.2017.01.021 https://doi.org/10.1287/mnsc.26.7.641 121 geoplanning: journal of geomatics and planning, vol. 11, no. 2, 2024, 121-138 original research informal settlement characterization and socio-economic vulnerability assessment in kolkata metropolitan city, india shravani banerjee1, diksha1, alisha prasad1, amit kumar1,2,3,4* 1. department of geoinformatics, central university of jharkhand, ranchi -834205. india 2. department of geography, sikkim university, gangtok-737102, india 3. department of forestry and natural resources, purdue university, west lafayette, in 47906, usa 4. iucn commission of ecosystem management (south asia), gland, switzerland doi: 10.14710/geoplanning.11.2.121-138 abstract the study investigates the physical, social, and economic environment of the kolkata metropolitan area (kma) to elucidate the living conditions of informal settlements and its influence on the local environment using geoinformatics and multicriteria decision making-analytical hierarchical process (mcdm-ahp). the informal settlements were delineated using high-resolution google earth imagery and generic ontology informal settlements. knowledge considering building characteristics, building density, locations of the dwelling units, and their characteristics. the study exhibits that most informal settlements were concentrated in the wards located in the eastern and central parts of the city. the neighborhood land-use functions of the major informal settlements indicated that the informal settlements were highly influenced by green space (r2=0.97), followed by water bodies (r2=0.74), unplanned settlement (r2=0.68) and planned settlement (r2=0.67) in kma. in addition, the informal settlements were closely associated with very low relief zones (3m to 13m) followed by moderate relief zones (13-23m). the municipal ward-level analysis of the physical-socio-economic health conditions exhibited that most of the areas located in the low vulnerable zones (53.71 km2; primarily in southern, and eastern periphery), followed by very highly vulnerable zones (43.09 km2; primarily in central and northern parts). the study provides an insight into urban areas with special reference to informal settlements and necessitates the implication of effective policy for poverty alleviation. this study encourages the availability of real-time data that can improve mitigation activities in the event of a health disaster, such as sars covid-19 through methods for qualitative investigation of disadvantaged locations in kolkata. copyright © 2024 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction the proliferation of informal settlements is a complex derivative of the organic development of growing cities (marques & saraiva, 2017). as per un habitat (2022), 20% of the global population resides in inadequate, crowded, and unsafe housing, out of which 1 billion live in slums and informal settlements and is expected to grow to 2 billion in the next 30 years, which represents roughly 183,000 people each day (hughes et al., 2021). the factors that contribute to the formation and growth of informal settlements in urban areas include but are not limited to unprecedented population growth, an influx of rural migrants, uneven industrial development lack of employment opportunities, poverty, and inequalities, lack of building space, and concentration of land in few hands (mahabir et al., 2016; ministry of housing & urban poverty alleviation government of india, 2013; ooi & phua, 2007; tripathi, 2015; un habitat, 2003). in india, informal settlements in urban areas are amalgams of different linguistics, religions, and caste groups due to the inclusion of populations from diverse socio-economic e-issn: 2355-6544 received: 15 january 2024; revised: 13 september 2024; accepted: 24 september 2024; available online: 30 november 2024; published: 04 december 2024. keywords: slums ontology, informal settlements, geoinformatics, ahp *corresponding author(s) email: amit.iirs@gmail.com https://doi.org/10.14710/geoplanning.11.2.121-138 mailto:amit.iirs@gmail.com banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 122 backgrounds and regions (schenk, 2010). these unplanned and unauthorized dwellings fail to provide adequate livable conditions due to various reasons such as dilapidation, overcrowding, the faulty arrangement of buildings and streets; lack of ventilation, light, sanitation facilities, safety and health, or a combination of these factors (martínez et al., 2008; ministry of housing & urban poverty alleviation government of india, 2013). the concept of informal settlements and its definition varies from country to region, its characteristics, the socio-economic conditions, dilapidated housing conditions; irrespective of location, be it the core or outskirts (richter et al., 2011). even within the same country slum definitions can vary among various degrees of administration (patel et al., 2014). un-habitat defines informal settlements as areas that lack at least one of the amenities viz., a durable housing structure, access to clean water, sanitation with ample living space, and secure tenure (un habitat, 2003). the temporary roof structureroof, walls, and floor, non-compliance with building codes, the dwelling near the toxic waste, flood plain, unstable slopes, and vulnerable sites are also other determining variables (un habitat, 2003). metropolitan areas almost everywhere and especially in the cities of the ‘third world’ are occupied by squatters near the city center (mahabir et al., 2016). slums have both positive and negative impacts on the physical, social as well as the economic health of urban environments. the dwellers are prone to health problems (awadall, 2013; riley et al., 2007) in many cities in developing countries, especially adolescents and young adults due to overcrowding (satterthwaite, 1993). the prevailing physical threat of dwellers from various disasters (united nations development programme, 2012) and improper housing (napier, 2007) is mainly due to the low enduring capacity of slum dwellers to revive and combat disasters, such as floods and earthquakes, compared with more formal communities (ajibade & mcbean, 2014; braun & aßheuer, 2011; ebert et al., 2009). they create an unhealthy milieu (dana, 2011; kjellstrom et al., 2007) due to a lack of basic services, which results in contaminated soil, air, and water bodies as well as the overall urban environment at regional and national levels (richter et al., 2011). this results in a perpetuated cycle of deterioration for both slum inhabitants as well as for the environment, with the plausibility of impacts extending to communities beyond the informal settlements (ali & sulaiman, 2006). consequently, the growth and expansion of informal settlements deteriorate the sustainability of urban development at the local, and global level (patel et al., 2012). the low literacy level of the slum people negatively affects the robustness of the urban environment socially as well as economically (mahabir et al., 2016; zaman et al., 2018). as they are deprived of proper education, there is a lack of awareness among the dwellers which later affects the economy of the urban area (mahabir et al., 2016). poor sanitation facilities act as the breeding grounds for pathogenic bacteria which causes serious health illness (zaman et al., 2018). the high population density and complex social interactions in slum areas, with overcrowding, may result in health cataclysm (patel & burke, 2009) as evident in the case of covid-19 in dharavi slum, mumbai in april-may 2020. consequently, this nature of housing and living conditions strongly affects all aspects of life in the squatter community. due to the aggregation of census data at administrative units, the characteristics of individual slums become opaque (kuffer et al., 2017). many scholars adopted various approaches and collected information on slums in order to characterize informal settlements such as participatory method (hasan, 2006; joshi et al., 2002; lemma et al., 2006), integrating census data with gis as well as analysis of very high-resolution satellite images for slum detection (duque et al., 2018; sliuzas et al., 2008), simulation models to understand the emergence and expansion of slums (roy et al., 2014). the informal settlements can be differentiated in terms of their surface characteristics and texture variations, in the satellite images (duque et al., 2015). the satellite images area then used to identify, classify, and monitor slums in both space and time, providing a deeper understanding of their physical manifestations in growing urban surfaces. very high-resolution images can also be very appropriate to study the different characteristics of slum units at different scales more precisely (hofmann, 2001; kohli et al., 2012). many methods such as cellular automata and agent-based models have been used to develop dynamic models to simulate and project the growth of urban areas (tripathy & kumar, 2019) and the evolution of slums (roy et al., 2014). the usage of sar images for urban mapping especially for slum area characterization has proliferated recently (gamba et al., 2011; kuffer et al., 2016). the nature of housing structure, other neighboring land surface features, neighborhood environment characteristics, and topography are vital components of ontology https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 123 (hofmann, 2001; kohli et al., 2012; shekhar, 2013). the ontology reduces the semantic gap created in image interpretation (durand et al., 2007) and contributes to the identification of features more precisely (durand et al., 2007; frank, 1997; tomai et al., 2009). generic slum ontology has been utilized using very high-resolution imagery such as quick bird, ikonos imagery, etc. to identify slum-dwelling units based on the structural compositions of the dwelling units (kohli et al., 2012; un habitat, 2003). the generic slum ontology (gso) consists of characteristics at three spatial levels including object level, settlement level, and slum environment. however, remote sensing images have enhanced capacity to identify the physical and structural heterogeneity in such environments, which is not captured in census information (weeks et al., 2007). however, satellite images cannot independently capture the socio-economic properties of dwellings and slums. therefore, the amalgamation of the satellite image-based physical properties with census-based socioeconomic properties is crucial in understanding the slum characteristics. kolkata is one of the largest metropolitan cities in india, located in the eastern part of the country, which is an inchoate metropolis, where 32.9% of families are devoid of basic amenities. there are 0.2% of households in the slum areas of kolkata, who suffer from extreme housing deprivation, i.e., lacking all of the five basic elements of housing including water, air, earth, light, and greenery (patel et al., 2014). in 2011, 44,96,694 people were living in the city of kolkata. of those, 14,57,273 lived in informal settlements, which were distributed throughout 144 wards of the kmc (kolkata municipal corporation), which accounts for 32.4% of the city's urban population (ray, 2017). the city's informal settlements are most concentrated in the east along the eastern metropolitan bypass, in the north in the cossipore area, and the west around the dock area. the rise in the informal population with a severe lack of basic services has an adverse impact on india's overall target to attain the water and sanitation sector (ali & islam, 2015). being a coastal city, together with its vast hinterland attracted multiple industries, which enhanced the scope of employment (bhattacharya & chatterjee, 1973; ghosh, 2013). the informal settlements grew in central parts of kolkata during the early urbanization of the british raj with jute and other cotton factories in the suburbs (kundu, 2003). the living condition in urban areas is profoundly influenced by the type of shanty towns and living conditions (das et al., 2012). the growth of informal settlements in the city cannot be prevented as a result of excessive urbanization and rural poverty (roy et al., 2014). therefore, the physical and socio-economic characteristics are needed to analyze the informal settlements more accurately to devise a suitable urban development policy pertaining to poverty alleviation and healthier living conditions (wekesa et al., 2011). thus, this study emphasizes the characterization of slums and analyzes the impact of urban functions on slum units in kolkata. further, the study focuses on the effect of relief on the slum locations and explains the impact of slum areas on the physio-socio-economic health of the urban environment. while much of the previous research focuses on the general causes of slum growth and lack of basic services (das et al., 2012; patel et al., 2014), this study offers a more nuanced approach by specifically characterizing slums in kolkata through a combined analysis of their spatial distribution, relief patterns, and socio-economic impacts. unlike other studies, which primarily concentrate on infrastructural deprivation, our research uniquely integrates an understanding of urban functions and how they intersect with slum distribution. by examining the role of relief (topography) in the formation and persistence of slums, this study fills a critical gap in the literature. additionally, we explore the bidirectional relationship between slum areas and the physio-socio-economic health of the urban environment, contributing to the development of targeted policies for urban sustainability and poverty alleviation. 2. data and methods 2.1. study area kolkata is the capital of the state of west bengal and one of the largest metropolitan cities in eastern india (figure 1). it comprises the kolkata municipal corporation (kmc) region and is located between 22⁰ 25’ n to 23⁰39’ n latitude and 88⁰15’e to 88⁰28’e longitude. it has a jurisdictional area of 187.33 km2 and comprises 141 electoral wards, as shown in figure 1. kolkata has multiple active business centers including the benoy-badalhttps://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 124 dinesh bagh area, burrabazar area, shobhabazar, shyambazar, chitpur, esplanade, park street, sudder street, etc. because the business centers are well spread across the city, residential areas including informal settlements are associated and spatially distributed in and around these economic centers. figure 1. location map of study area representing kolkata metropolitan area in west bengal, india (dark blue color represents ganga river) 2.2. data the study adopted the survey report of the bustee (habitation) department, kma for the year 2001 was used. the survey report includes the house structure, sanitary condition, and various socio-economic variables including income, number of workers, etc. remotely sensed data including google earth, sentinel-2a, and aster digital elevation model (dem) were used. in addition, the ward-level census variables were also used (see table 1). the data flow diagram and methods of this research are described in figure 2. figure 2. methodology flow chart 2.2.1. ontology-based identification of informal settlements sentinel-2 satellite data and 2017 google earth imagery were used to identify and delineate informal settlement clusters across the kma. a total of 127 slum pockets were delineated based on their image https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 125 characteristics on the satellite image and then they were verified using google earth. the specification of the built form in the satellite imagery was carefully interpreted visually at three different levels, as mentioned in table 2 as adopted from kohli et al. (2012), table 1. data used in the present study data resolution source significance survey (2001) ward level kolkata municipal corporation demographic, physio-socio-economic aspects of informal settlements in each ward sentinel-2a (2017) 10m european space agency1 delineating informal settlements and monitoring spatio-temporal changes in land cover features aster dem 30m united states geological survey2 topography and relief profile of the landscape google earth (2017) 5m google earth desktop application cross-checking delineated polygons from sentinel-2a and adding the left-out patches note: 1) https://scihub.copernicus.eu/dhus/ 2) http://earthexplorer.usgs.in/ table 2. observed elements in the satellite images related to informal settlement level observation environs surroundings of the settlement, i.e., its location with respect to neighboring land uses prominent land cover features settlement overall form/shape/density of the settlements object components of the settlement, such as characteristics of buildings and roads source: kohli et al., 2012 2.2.2. identifying major zones of informal settlements to identify major clusters of informal settlements and analyze different land use functions in the vicinity, we conducted proximity analysis using the buffer tool with a 500 m radius. the resulting polygons were dissolved to generate larger polygons and a total of eight new polygons were generated. these polygons were treated as zones of major informal settlements and were labeled as a, b, c, d, e, f, g, and h for analysis in the present study (see figure 5). 2.2.3. analytical hierarchical process (ahp) for urban physio-social-economic vulnerability analysis all the eight ward-level factors were assigned relative weights based on the respective number of classes such that a higher weight complements unfavorable health conditions with reference to physical, social, and economic characteristics of the informal settlements and vice-versa (table 3). the weights were assigned based on an informed assumption, local field experience, as well as expertise based on the relative contribution of each class to the suitability of informal settlements within each ward. the individual weights were normalized using satty’s ahp technique (saaty, 1980). the normalization process reduces the subjectivity associated with the assigned weights of the thematic maps and their features as recommended by saaty (1980) to maintain consistency. the consistency ratio (cr) for each theme and unit, initially, principal eigenvalue (λ) was computed by the eigenvector technique followed by the consistency index (ci) (equation 1) was calculated from the following multi-criteria equation (mce) (saaty, 1980): https://doi.org/10.14710/geoplanning.11.2.121-138 https://scihub.copernicus.eu/dhus/ http://earthexplorer.usgs.in/ banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 126 ci = (λ_max n) / (n – 1) ……………. (equation. 1) where n is the number of criteria. using the above equation, the cr (equation 2) was computed using (saaty, 1980): cr = ci / rci ………………………. (equation. 2) where rci is an acronym for random consistency index. cr value of less than 0.1 indicates good consistency (saaty, 1980). if cr=0.1, the comparison is inconsistent and requires reconsideration of weights. 2.2.4. ward level factors the following information was compiled at the ward level and examined in order to describe the wards according to the state of the informal settlements and to comprehend how the latter interact with the environment (see table 3). table 3. description for the ward level factors variables description number of informal settlements the number of delineated informal settlements in each ward number of dwellers in the informal settlements population of informal residents was based on the survey report, kma population density of the informal settlements this was computed by dividing the total population of the informal settlements’ clusters with the area covered by the informal settlement’s clusters in that ward percentage of the kutcha and pucca houses there are various types of housing structures in the informal settlements in kolkata metropolitan area, which were primarily broadly classified into kutcha houses and pucca houses based on the dominance of the housing material used for house construction. the kutcha structure comprises roof materials and wall materials that vary from tile, tin to khapra (mud tiles) and thatched roof and 'mud' etc., respectively. whereas, the pucca structure consisted of hutments with a brick wall, cemented wall and rcc roof. the percentage of kutcha/ pucca houses is based on the total number of kutcha/ pucca houses with respect to total houses in kma per capita sanitation facility this is the sanitation facility per person in the informal settlements with respect to the total population of informal settlements in kma percentage of literates this is the number of literate persons expressed in percentage with respect to the total number of people in the informal household workers aged below 18 the number of workers below the age of 18 years land use function this comprises various land use functions in the kma obtained from sentinel 2a. google earth and field-based information relief the average elevation (in meters) of informal settlement patches within each ward. the elevation is from the mean sea level as obtained from the aster data note: all the parameters were normalized between zero to one such that the most significant zones get the highest value and vice-versa https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 127 2.2.5. contributing factors to physio-socio-economic health vulnerability number of informal settlement patches: the number of informal settlements within a municipal ward represents the discreteness of the informal settlements and the influence of either geographic or socio-economic or both. the wards with a higher number of informal settlements will have a relatively higher population density and are less suitable with reference to the health of urban environments and vice-versa. therefore, the wards with informal settlements clustered in the range >49 and 37-48 were assigned higher weightage with reference to the proliferation of poor and unhealthy environments (table 3, figure 8a). percentage of kutcha houses in clusters of informal settlements: the presence of different types of kutcha houses in the urban areas poses obstruction with reference to the development of the urban areas, thus hampering the overall setting of the urban environment. therefore, the wards with more percentage of kutcha houses i.e., 72-96 and more than 96 in the slum clusters result in poor urban health (agarwal, 2011; awadall, 2013; gambo et al., 2012), thus are more vulnerable and have been given a higher weightage (table 3, figure 8g). no. of persons per household in informal settlements: overcrowding contributes to the growing psycho-social health problems of many urban dwellers in developing countries, especially adolescents and young adults (satterthwaite, 1993). as the number of persons in a dwelling increase, the availability of necessary resources is compromised which leads to an unhealthy environment. therefore, an informal settlement with a higher number of persons per household was given higher weightage and vice-versa (table 3, figure 8c).percentage of literate in informal settlements population: the literacy of the people living in the informal settlements clusters is important for determining the social health condition of the urban environment in many ways such as better accomplishment of health and nutritional status, economic growth, and empowerment of the community (lahon, 2017; mahabir et al., 2016; pawar & mane, 2013). thus, the wards with more than 50% of the literate in informal settlements’ population will tend to provide a healthier environment, thus are more suitable and given lower weightage (table 3, figure 8d). per capita sanitation facility in informal settlements: lack of access to sanitation leads to the presence of pathogenic microorganisms, which in turn affects health but also affects social and economic development (awadall, 2013; hanchett et al., 2003). thus, the wards with poor sanitation facilities (i.e., per capita sanitation 0.04-0.08 and less than 0.04) were considered more vulnerable and unsuitable in terms of urban health (agarwal, 2011) and have been given higher weightage (table 3, figure 8e). engagement of children of informal settlements for earning: the poverty, lack of good schools, and growth of the informal economy were considered as the vital causes for engaging the children for earning. thus, the wards with a higher number of children labor (workers below the age of 18 i.e., 70-100 and greater than 100) were more vulnerable and were given a higher weightage (table 3, figure 8f). relief of informal settlements: the informal settlements lying in low-lying areas are highly susceptible to waterlogging/ flood inundation (braun & aßheuer, 2011). thus, the low-elevation zones are more vulnerable and have been given higher weightage and vice-versa (table 3, figure 8h). lu functions in the proximity of informal settlements: based on previous knowledge and examples from existing literature (kundu, 2003; shekhar & others, 2013; uddin, 2018), places close to land-use functions including green space, waterbody, open-land, and institutions; are prone to the formation of informal settlements, were assigned a higher weightage. on the contrary, land use functions, which negatively influence the proliferation of informal settlements, were assigned lower weightage (table 3, figure 8i). these ward-level factors were converted to raster layers of 30m cell size for high spatial accuracy. each sub-class of each feature was assigned the respective eigenvectors in their attribute table. finally, the ahpbased normalized weighted maps were spatially accumulated in a gis environment. the resulting map was reclassified into five classes, namely, ‘very high’, ‘high’, ‘moderate’, ‘low’, and ‘very low’ vulnerable zones with reference to the health of urban environments https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 128 table 4. normalized weight based on ahp with reference to the health of urban environment assigned to various classes of input parameters theme rank normalized weight sub class rank normalized weight theme rank normalized weight sub class rank normalized weight number of slum units 1 0.17 <12 1 0.16 number of workers (under 18 years age) 6 1.04 <10 1 0.16 13-24 3 0.29 10-40 3 0.3 25-36 5 0.64 40 70 5 0.55 37-48 7 1.23 70 100 7 1.29 >49 9 2.68 >100 9 2.7 percentage of kutcha houses 7 1.5 <24 1 0.18 relief 8 2.13 >43 1 0.17 24-48 3 0.3 33-43 3 0.3 48-72 5 0.54 23-33 5 0.6 72-96 7 1.26 13-23 7 1.31 >96 9 2.72 <13 9 2.62 population density 3 0.34 <0.06 6 1 0.18 land use functions 9 2.98 others 1 0.16 0.060.13 3 0.32 green space 1 0.18 0.130.19 5 0.56 water bodies 2 0.19 0.190.24 7 1.2 institutional 3 0.26 >0.24 9 2.75 cantonment 4 0.36 percentage of literate in slum population 4 0.49 >80 1 0.16 industrial 5 0.42 60-80 3 0.29 administrative 5 0.61 40-60 5 0.53 historical 5 0.66 20-40 8 1.36 service sector 6 0.97 <20 9 2.65 mixed 7 1.21 per capita sanitation 5 0.71 >0.16 1 0.16 planned residential 8 1.48 0.120.16 3 0.3 commercial 8 1.86 0.080.12 5 0.56 unplanned residential 9 2.4 0.040.08 7 1.26 slum areas 9 2.84 <0.04 9 2.72 3. results the study describes the ward-level characterization of informal settlements, the distribution and interaction of different land-use functions with informal settlements, and the analysis of vulnerability zones. 3.1. characterization of wards based on informal settlements 3.1.1. number of informal settlements clusters the study indicated that the informal settlements were distributed unevenly in the different wards of kma (figure 3a and b). a very high number of informal settlement clusters (i.e., number of informal settlements >49) was observed in the ward located at the eastern part of kma (ward no. (wn) 66). followed by wards located in the western parts (wn 58 and 73), comprising a high number of informal settlement clusters (37-48). the moderate occurrence of informal settlement clusters was explicitly located in the central (wn 65) and southwestern parts (wn 82). the remaining wards of the city had low (13-24) and very low (<12) occurrence of informal settlement clusters. https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 129 3.1.2. population distribution the spatial distribution of population in informal settlements (figure 3b) exhibited a high population density (population density > 0.24) throughout the city and there are only three wards without any clusters of informal settlements (wn 42, 45, 87). the high (> 0.24) to very high population density (0.19-0.24) of informal dwellers was observed in the wards located in north-west directions. whereas the moderate population density (0.13-0.19) of informal dwellers was found in the wards located in the northern and central parts (wn 40, 28, 69, 90) of kma. the remaining wards consisted of the lowest population density (<0.06) of informal dwellers. 3.1.3. physical conditions of the informal dwellers the wards were categorized based on the percentage of kutcha houses into five major classes, viz., very high (>96), high (72-96), moderate (48-72), low (24-48), and very low (<24) (figure 3c). most wards were found to have a higher percentage of kutcha houses (72-96 and >96). the low percentage of kutcha houses (<24) were found in four wards (42,45,67,87), located at the western, eastern, and central parts of the kma. the moderate concentration of kutcha houses (48-72) was observed in a few wards located in the southern and southeastern periphery. it was observed that the wards comprising active business centers have a high concentration (72-96) of kutcha houses mainly in the central parts of kma. in a few remaining wards, the concentration of kutcha houses was observed very high. on the contrary, the availability of sanitation facilities for informal dwellers in all the wards is poor (less than 1.2) (figure 3d). there is only one ward, in which the condition is good (greater than 1.6 in ward number 101). the poor defecation facility leads to the poorer environment of the informal settlements’ areas and the nearby areas, which give rise to various health problems. 3.1.4. economic condition of dwellers of the informal settlements the study indicates the dominance of the wards in medium (42.89 to 64.33 usd i.e., rs. 3200 to rs.4800) and low-income (rs.1600-3200) groups. very low(rs. <1600) and low-income groups were mainly located in the southern, eastern, and northern parts, respectively (figure 3e). while the medium groups are mostly centered in the central, and western peripheral parts of kolkata. so, the informal settlement dwellers of the extreme peripheral wards of the city belong to a low-income group and are below the poverty line, whereas informal settlement dwellers located in the wards of the central part of the city were in a better condition (rs. >4800). 3.1.5. literacy rate in informal settlements in informal settlements, the literacy rate is much affected by poverty. (figure 3f). the study revealed that a very low and low (20%-40%) literacy rate was found in only 9.9% of the wards (14 out of 141 wards). in the remaining wards, a medium to very high literacy rate (>80%) was observed. it is evident that moderate (40%60%) to high (60%-80%) literacy rate was found in 112 wards out of 141. 3.1.6. social condition of informal settlements (based on the engagement of children in earning) there were only 7.09% (10 wards out of 141) wards in kma that have moderate to very high numbers of children (age <18 years) engaged in earning in the slum clusters. the very high (100) to high numbers (70-100) of child workers (aged below 18 years) were found in the wards located in upper central, central, and southern parts followed by moderate numbers of child workers in the central and western parts of kma. (figure 3g). it is revealed from the map of the percentage of total earning members in informal settlements (figure 3h) that the highest concentration of earning or employed informal settlement dwellers (>44%) are situated in the upper central part of the city. the concentration of earning members was observed primarily in low to very low in central, western, and northern parts. https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 130 figure 3. ward-wise distribution map representing (a) number of slum clusters, (b) population density, (c) percentage of kutcha houses, (d) per capita sanitation facility, (e) average monthly income, (f) total literates, (g) number of below 18 age workers, (h) percentage of total earning members, (i) percentage of pucca houses, in the slums of kolkata municipal corporation 3.2. land-use functions to study the characteristics of the informal settlement discreetly, the major informal settlement areas and urban land-use (lu) function were delineated. the study exhibits that a major part of the city is occupied with unplanned residential areas (52.5% of kma), followed by green spaces (27.3%) comprising vegetation and agricultural lands, with the intrusion of unplanned settlements in between (figure 4). the central part of the city consists of the commercial areas and the administrative units covering 4.06% of kma (6.91 km2) (table 4). the industrial areas (4.53%) are mainly located in the western part, along the periphery of the hugli river and the eastern part of the city. the service sector (0.70%), which comprises the dock areas and other transportation https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 131 services, is near the hugli river as well as yards of railways and trams (table 4). the water bodies and wetlands are primarily located in the eastern part of the city along with green spaces. kolkata is well distributed with small water bodies within the city except for the cbd patches. ponds and lakes occupy ~4.85% of kma (8.77 km2) (table 4). the mixed land used functions (2.25%) comprising both commercials along with residential areas, banks, institutes, hospitals, etc. cover mostly the central and western part of the city. the rest of the region is classified as others (1.89%) including the major open spaces, vacant and arable lands of the city which mainly occupy the areas along the canal and the wetlands of the eastern fringe. figure 4. major land use functions in kolkata table 5. area statistics of the land use functions in kmc lulc functions area (km2) administrative 0.42 commercial 6.92 planned residential 2.05 unplanned residential 94.98 institutional 0.24 mixed 4.06 cantonment 0.41 historical 0.66 green space 49.41 industrial 8.19 service sector 1.26 water bodies 8.77 others 3.42 total 180.78 https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 132 3.3. geospatial analysis of land use functions within the proximity of informal settlements the buffer zones of major clusters of informal settlements (a, b, c, d, e, f, g, and h) were examined with their associated land-use functions, both, spatially and statistically to deduce the level of control of various urban functions on formation and propagation of informal settlements (figure 5). pearson's correlation coefficient (r) was computed for different land-use functions and informal settlement dwellers based on the data tabulated in table 6. figure 5. major land use functions in the proximity of the slum areas of (a) zone a, (b) zone b, (c) zone c, (d) zone d, (e) zone e, (f) zone f, (g) zone g, and (h) zone h table 6. quantitative analysis of the control of different land use functions on geographical distribution of informal settlement (slum) lulc functions r value control level w.r.t slum formation planned settlement 0.67 moderate unplanned settlement 0.68 moderate water bodies 0.74 major green space 0.97 major industries 0.003 very less mixed 0.002 very less service 0.00 very less educational 0.00 very less historical 0.0004 very less cantonment 0.00 very less others 0.22 less the very high correlation of informal settlements was observed with green spaces (r=0.97), followed by water bodies (r = 0.74) exhibiting the major control level of these land-use functions on slum formation and proliferation. also, a high correlation of informal settlements was observed with unplanned settlements (r= 0.68) and planned settlements (r=0.67) (figure 6). the commercial zones and informal settlement dwellers are also related to a good correlation value of r =0.60. in contrast, the association of informal settlements with the industrial areas was very poor (r=0.003). https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 133 figure 6. graph showing correlation between slum population and (a) planned settlement, (b) unplanned residential, (c) water bodies, (d) green space, (e) commercial area, (f) other features, (g) mixed land use, (h) industrial area 3.4. topographical influence over informal settlements the relief of buffer zones of major informal settlement clusters was analyzed to study the influence of relief over the proliferation of informal settlements (figure 7). although the overall relief of the kolkata metropolitan area was low (3-51 meters above msl) due to its proximity to the bay of bengal, the informal settlement clusters are confined to the relief zones ranging between 3m to 23m. most of the informal settlement units are confined to the lowest relief (<13m). figure 7. relief variation in the proximity of the slum areas of (a) zone a, (b) zone b, (c) zone c, (d) zone d, (e) zone e, (f) zone f, (g) zone g, and (h) zone h. a) b) c) d) e) f) g) h) https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 134 3.5. urban physio-socio-economic vulnerability modelling various parameters related to informal settlement characteristics, lu function, and topographical variability were employed to deduce the physio-socio-economic vulnerable zones in kma using ahp and gis (figure 8). the resultant zones exhibit that the major parts have moderate (37.80 km2) to low vulnerability (53.71 km2), primarily comprising western, southern to eastern parts of kma (figure 9). contrary the high and very highly vulnerable (70.26 km2) zones were located in the central, northern, and northeastern parts of the city. while the southern and eastern peripheral areas were the least (20.35 km2) vulnerable in terms of the physical, social, and economic deprivation and health of the urban environment. figure 8. contribution of (a) no. of slum clusters, (b) percentage of kutcha houses, (c) population density, (d) percentage of literates, (e) per capita sanitation, (f) workers below 18 years of age, (g) relief, (h) land use functions to urban health condition within the municipal wards of kolkata municipal boundary figure 9. urban physical-socio-economic vulnerability in the kolkata metropolitan area https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 135 4. discussion the high concentration of informal settlements and high density of slum dwellers in eastern and central parts may be attributed to the availability of required vacant space in proximity to the workplace of dwellers. the nature of the distribution of the informal settlement population implies that the density of the informal settlements’ population varies inversely with the distance from the city’s geographical center, barring very few exceptions (mahabir et al., 2016). this can be attributed to a large number of cbds or economic centers (multiple nuclei) in and around the kolkata metropolitan area. the very high proportion of kutcha houses in most of the informal settlements (primarily in the central and the northern parts) of kma represents lower socio-economic conditions of the informal settlements. despite a higher literacy rate in all the informal settlement clusters, the lack of proper sanitation facilities is evident, which complements poor health and morbidity due to infections in urban areas in many ways (kundu, 2003). the central part of the kma, with intense commercial activities, affords higher informal occupations to slum dwellers as compared to the periphery. although the poor economic conditions compel the majority of family members to work, the number of children (age <18 years) engaged in earning was very less, and the latter were mostly associated with industrial activities. in kolkata, the very high association of informal settlements with green spaces and water bodies corroborated the higher suitability of such dwelling units under tree shades and water bodies, which provide a natural roof to solar insulation and rain, and base for constructing houses (gopal & nagendra, 2014; kohli, 2015). the blue-green zones are vacant land, primarily owned by the government, and are the least interference sites for informal dwellers. these zones are often vulnerable and overlooked sites; as evident in kma, most of them are the low-lying relief zones (<13m) that are prone to flooding caused by frequent cyclones and heavy rainfall (bose & ghosh, 2015; braun & aßheuer, 2011; jha & bairagya, 2013). on the other hand, the proximity of the blue-green zones to the water bodies, increases the risk of hygiene, triggering water-borne diseases like malaria, cholera etc. the high correlation of informal settlements with unplanned and planned settlements indicates the high plausibility of informal job and service opportunities, whereas the dwellers in proximity to commercial zones probably could not afford to bear daily transport charges to reach residential areas for job opportunities. the high physio-socio-economic vulnerability in central and northern parts of kma indicated the poor socio-economic condition of slum dwellers thereby affecting the local urban environment. it is crucial to understand the vulnerability of informal settlements, with complex informal social intersections, to any typical disaster or pandemic due to poor infrastructure and economic conditions. an example is evident during the outbreak of covid-19, which has disrupted the economy human health, and livelihood at local to global scales (lal et al., 2020). the cataclysm affected the dwellers of dharavi slum of mumbai during april-may 2020 with the meteoric upheaval of the number of positive cases in a very short period (pti, 2020). similarly, a few densely populated slums in north kolkata observed a rapid turn into covid-19 hotspots during april-may 2020, complemented by the lack of space (basu, 2020). the findings of the present study about the socio-economic health vulnerability of informal dwellers are crucial to understanding and mitigating covid-19 hotspots. the integration of high-resolution google earth imagery with the generic ontology of informal settlements in the present study based on geoinformatics and multi-criteria decision-making-analytical hierarchical process (mcdm-ahp), allows for a better understanding of the environment of informal settlements within the kolkata metropolitan area (kma). moreover, the study establishes a strong correlation between informal settlements and their proximity to environmental features, which highlights how land use and the natural environment impact the distribution and conditions of informal settlements. the study also highlights the influence of geography on settlement patterns and suggested a differentiated risk profile across the municipal wards. these findings offer valuable insights for urban planning and policymaking, especially in addressing the challenges of informal settlements and improving living conditions in vulnerable areas. 5. conclusion the present study aimed at analyzing the physio-socio-economic settings of the kolkata metropolitan area (kma) through a geoinformatics approach and applying a multi-criteria decision-making-analytical hierarchical process (mcdm-ahp) to further explain the living conditions of informal settlements (slum https://doi.org/10.14710/geoplanning.11.2.121-138 banerjee et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 121-138 doi: 10.14710/geoplanning.11.2.121-138 136 dwellers) and its influence on the local environment. in the present study, informal settlement clusters were delineated using ontological properties and were used to characterize the urban area (municipal wards) based on its physio-socio-economic condition using geoinformation. while the informal settlement concentration was observed mostly in the peripheral areas, the population density of informal settlement dwellers was concentrated in the central part of the city. the city core witnesses the lowest percentage of pucca houses in informal settlements and low per capita sanitation facilities, primarily in the wards lying along the hugli river in southern parts of the city. although the per capita income of informal settlement inhabitants was low, the level of literacy is moderately higher, and the percentage of children engaged in work is very low in many of the informal settlement clusters located in northern, eastern, and southern parts of kma. the present study asserts a high correlation of informal settlement clusters with the water bodies and green space. the finding was corroborated by the geographical locations of most of the informal settlements in the low-lying areas (<4 m). the study exhibited that the highly vulnerable zones in kma are in the central parts and northern parts, whereas the southern, eastern, and western peripheral areas were mostly low vulnerable zones. however, the study had some limitations, the use of a generic ontology for informal settlements, considering factors such as building density and characteristics, may overlook unique, location-specific factors that vary spatially. these generalized assumptions might not fully reflect the diversity of informal settlements across the kolkata metropolitan area (kma). the study also has not considered other significant parameters such as social networks, local governance, or environmental hazards like flooding, which can also impact living conditions. future studies may incorporate such factors and can also investigate the vulnerability of informal settlements to climate change, particularly considering flood risk, heatwaves, and sea-level rise, given that many of these settlements are in low-lying areas of kolkata. following the covid-19 pandemic, future research could assess how informal settlements have adapted to the challenges posed by health crises and what strategies have emerged to improve resilience against future pandemics or similar disruptions. this may also include studying the role of government and non-governmental organizations in crisis mitigation. future research needs to explore the integration of informal settlements in sustainable urban planning for more comprehensive policies, although improvements have been made. the study necessitates site-specific informal settlement redevelopment strategies to improve the conditions of informal settlement dwellers and the urban environment. it is recommended that measures to improve the status of slums should include raising awareness and increasing community participation wherever possible. in 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estimate the ecological loss at the mmnp after 2010 eruption based on rapid damage appraisal assessment. three steps were executed: (1) identifying and mapping damages in the mmnp using remote sensing data, (2) collecting in-situ information through field assessment, (3) estimating ecological loss assessment using economic valuation approach. two methods of economic valuation were used, namely the change in productivity (i.e. carbon stock loss) and replacement cost (i.e. land restoration cost). the results showed that approximately of 1,242 ha (19,37%) of mmnp area was heavily damaged, 1,208 ha (18,84%) was moderately damaged, and the rest was slightly damaged. the heavy damage and the moderate damage were occurred in the forest block of resort pakem-turi, cangkringan, srumbung, dukun, sawangan, selo and kemalang. the slight damage was occurred in the forest block of resort musuk-cepogo, kemalang and selo. the ecological loss was estimated in a total of 766 billion indonesian rupiah consist of 747.50 billion indonesian rupiah from carbon stock loss and 18.5 billion indonesian rupiah from land restoration cost. the total ecological loss was estimated approximately 38.3 trillion indonesian rupiah, which was a raw estimation and considered undervalued. abstrak: penilaian dampak kerugian ekologis oleh erupsi gunung merapi pada tahun 2010 di kawasan taman nasional gunung merapi (tngm) hingga saat ini belum pernah dilakukan. tulisan ini bertujuan untuk menaksir kerugian ekologis di tngm paska erupsi gunung merapi tahun 2010 dengan metode penaksiran kerusakan secara cepat. terdapat 3 tahapan yang dilakukan, yaitu : 1) mengidentifikasi dampak kerusakan kawasan di tngm dengan menggunakan teknik penginderaan jauh, 2) survey lapangan dan 3) menaksir kerugian ekologis yang ditimbulkan dengan pendekatan valuasi ekonomi lingkungan. terdapat 2 metode valuasi ekonomi lingkungan yang digunakan, yaitu perubahan produktivitas dan biaya pengganti. pendekatan perubahan produktivitas menggunakan satu komoditas yaitu potensi serapan karbon yang hilang. pendekatan biaya pengganti menggunakan taksiran biaya yang dibutuhkan untuk restorasi kawasan tngm. hasil identifikasi kerusakan di kawasan tngm menunjukkan bahwa ± 1.242 ha (19,37%) kawasan tngm mengalami kerusakan berat, ±1.208 ha (18,84%) mengalami kerusakan sedang, dan sisanya relatif utuh dengan hanya mengalami kerusakan ringan. kerusakan berat dan sedang terutama terjadi pada blok-blok hutan di rptn pakem-turi, rptn cangkringan, rptn srumbung, rptn dukun, rptn sawangan, rptn selo dan rptn kemalang. kawasan yang relatif utuh dan hanya mengalami kerusakan ringan terjadi di blok-blok hutan di rptn musuk-cepogo dan di sebagian wilayah rptn kemalang dan rptn selo. hasil penaksiran kerugian ekologis di kawasan tngm adalah sebesar ±766 milyar rupiah yang terdiri dari ±747,50 milyar rupiah dari biaya penurunan produktifitas serapan karbon dan ±18,5 milyar rupiah dari biaya restorasi. diperkirakan taksiran nilai total kerugian ekologis di kawasan tngm adalah 38,3 trilliun rupiah. nilai kerugian ekologis tersebut merupakan taksiran kasar dan dianggap dibawah nilai sebenarnya. copyright © 2015 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. article info: received: 7 july 2015 in revised form: 10 september 2015 accepted: 22 september 2015 available online: 31 october 2015 keywords: mount merapi national park, 2010 eruption, ecological loss assessment corresponding author: hero marhaento universitas gadjah mada, yogyakarta, indonesia email: marhaento@ugm.ac.id open access info artikel: diterima: 7 juli 2015 hasil revisi: 10 september 2015 disetujui: 22 september 2015 publikasi on-line: 31 oktober 2015 kata kunci: taman nasional gunung merapi, erupsi 2010, penilaian kerugian ekologis kontak penulis: hero marhaento universitas gadjah mada, yogyakarta, indonesia email: marhaento@ugm.ac.id http://dx.doi.org/10.14710/geoplanning.2.2.69-81 marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 70 | how to cite (apa 6th style): marhaento, h., & kurnia, a. n. (2015). refleksi 5 tahun paska erupsi gunung merapi 2010: menaksir kerugian ekologis di kawasan taman nasional gunung merapi. geoplanning: journal of geomatics and planning, 2(2), 69-81. doi:10.14710/geoplanning.2.2.69-81 1. pendahuluan gunung merapi merupakan salah satu gunung paling aktif di dunia. erupsi gunung merapi terjadi dalam siklus 4 – 6 tahun sekali (surono dkk., 2012). menurut van boekhold (1972) dan newhall dkk (2000), erupsi gunung merapi yang terdokumentasi pertama kali terjadi pada tahun 1786 – 1791. van bemellen (1942) dalam bukunya ‘the geology of indonesia’ menyampaikan bahwa pada tahun 1006 diduga pernah terjadi erupsi besar gunung merapi sehingga mengubur candi borobudur dan menghancurkan kerajaan mataram kuno (berpindah ke jawa timur). secara berurutan, sejak terdokumentasi pada tahun 1791 erupsi gunung merapi skala besar terjadi pada tahun 1822, 1872, dan 1930 (voight dkk., 2000). pada 10 tahun terakhir, tercatat 2 erupsi cukup besar yang terjadi pada tahun 2006, dan puncaknya pada tahun 2010 yang diperkirakan merupakan siklus ulang 100 tahunan gunung merapi (surono dkk., 2012). kronologi kejadian erupsi gunung merapi tahun 2010 dimulai pada tanggal 20 september 2010 dimana status gunung merapi ditingkatkan dari ‘normal’ menjadi ‘waspada’ (surat badan geologi no. 46/45/bgl.v/2010). pada 21 oktober 2010, status tersebut meningkat menjadi ‘siaga’ (surat badan geologi no. 393/45/bgl.v/2010). puncaknya pada tanggal 25 oktober 2010 saat status gunung merapi ditetapkan menjadi ‘awas’ (surat badan geologi no. 2048/45/bgl.v/2010). pada tanggal 26 oktober 2010, gunung merapi erupsi pertama kali dengan mengeluarkan awan panas (wedhus gembel) yang kemudian disusul letusan besar pada tanggal 5 november 2010. menurut data pusat informasi pengembangan pemukiman dan bangunan provinsi daerah istimewa yogyakarta (pip2bdiy), erupsi gunung merapi sejak tanggal 26 oktober 2010 telah menimbulkan korban jiwa sebanyak 346 orang (www.pip2bdiy.org). kerusakan yang diakibatkan oleh erupsi gunung merapi berdampak pada sektor permukiman, infrastruktur, telekomunikasi, listrik dan energi, serta air bersih. berdasarkan hasil penilaian kerusakan dan kerugian yang dilakukan oleh badan nasional penanggulangan bencana (bnpb) melalui metode dari economic commission for latin america and the caribbean (eclac) (www.eclac.cl), erupsi gunung merapi tahun 2010 telah menimbulkan kerusakan dan kerugian sebesar rp. 4,23 trilyun (www.bnpb.go.id). lebih rinci dijelaskan bahwa jumlah nilai kerusakan adalah rp. 1,138 trilyun (27%), sedangkan jumlah nilai kerugian adalah rp. 3,089 trilyun (73%). nilai kerusakan paling besar dialami oleh sektor perumahan yang mencapai 39% dari total nilai kerusakan, disusul oleh kerusakan sektor sumber daya air dan irigasi yang mencapai 13% dari total nilai kerusakan. kerugian terbesar dialami sektor pertanian dengan nilai kerugian mencapai rp. 1,326 trilyun atau 43% dari total nilai kerugian. disusul oleh kerugian sektor industri dan umkm sebesar rp. 382 milyar atau 12,4% dari nilai kerugian. secara keseluruhan sektor pertanian budidaya dan tanaman pangan tetap menjadi sektor yang paling terkena dampak dengan nilai total dampak rp. 1,326 trilyun yang merupakan 31,4% dari nilai total kerusakan dan kerugian. sektor perumahan senilai rp. 512,6 milyar yang merupakan 13% dari nilai kerusakan dan kerugian serta sektor industri dan umkm dengan nilai total dampak sebesar 415,4 milyar atau 11% dari total. perhitungan kerusakan dan kerugian akibat bencana erupsi gunung merapi oleh bnpb tersebut adalah hasil perhitungan aset rusak yang dimoneterisasi (nilai langsung), sementara kerugian tidak langsung dari dampak erupsi yaitu kerusakan ekosistem, keanekaragaman hayati dan penurunan fungsi ekologis jangka pendek dari kawasan gunung merapi belum dapat diukur nilai kerugiannya. hancurnya berbagai tipe vegetasi akibat awan panas berdampak pula pada kematian berbagai jenis satwa liar yang berhabitat di kawasan hutan gunung merapi (dove, 2008). sebagian satwa liar yang masih bertahan hidup pun rentan mengalami kematian karena keterbatasan sumber pakan yang diakibatkan rusaknya habitat. beberapa satwa liar yang dimungkinkan selamat juga mengalami ancaman kematian karena keterbatasan tempat pelarian (refugee) di sekitar kawasan gunung merapi. selain itu, kerusakan daerah tangkapan air akan mempengaruhi pasokan air ke wilayah hilir dan kerusakan hutan akan mengurangi potensi penyedia oksigen dan penyerap karbon (djuwantoko dkk., 2005). menurut uu no.5 tahun 1990 taman nasional adalah salah satu bentuk kawasan konservasi yang dicirikan dengan keberadaan ekosistem asli, dikelola dengan sistem zonasi yang dimanfaatkan untuk tujuan marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 | 71 penelitian, ilmu pengetahuan, pendidikan, menunjang budidaya, pariwisata, dan rekreasi. terdapat 3 fungsi utama dalam pengelolaan taman nasional, yaitu: (1) perlindungan sistem penyangga kehidupan, (2) pengawetan keanekaragaman jenis tumbuhan dan satwa beserta ekosistemnya, (3) pemanfaatan secara lestari sumber daya alam hayati dan ekosistemnya. indrawan dkk. (2007) menjelaskan bahwa paling tidak terdapat tiga alasan ditetapkannya suatu kawasan sebagai area konservasi yaitu adanya aspek keunikan (khas), keterancaman, dan kegunaan. taman nasional gunung merapi (tngm) merupakan kawasan konservasi yang unik. selain menyangga gunung api paling aktif di indonesia, ekosistem hutan di tngm berfungsi sebagai daerah tangkapan air kawasan provinsi jawa tengah dan daerah istimewa yogyakarta, habitat berbagai flora dan fauna yang dilindungi, kantong berbagai plasma nutfah yang potensial, dan fungsi sosial dan religius (djuwantoko dkk., 2005; dove, 2008). keberadaan gunung merapi yang dapat meletus sewaktu-waktu menyebabkan ekosistem di tngm memiliki tingkat kerapuhan yang tinggi (marhaento dan faida, 2015). tngm dikelola oleh balai taman nasional (btn) gunung merapi yang memiliki 2 seksi pengelolaan taman nasional (sptn), yaitu sptn i yang mencakup wilayah kabupaten magelang dan kabupaten sleman, dan sptn 2 yang mencakup wilayah kabupaten klaten dan kabupaten boyolali. sptn 1 mencakup 4 rptn yaitu rptn turipakem, rptn cangkringan, rptn srumbung dan rptn dukun, sedangkan sptn 2 mencakup 3 rptn yaitu rptn selo, rptn musuk-cepogo dan rptn kemalang. valuasi ekonomi lingkungan merupakan suatu instrumen ekonomi untuk mengestimasi nilai moneter dari produk barang dan jasa yang dihasilkan oleh sumber daya alam dan lingkungan (garrod dan willis, 1999). instrumen ini penting digunakan untuk mengukur potensi keuntungan/kerugian yang disebabkan oleh adanya perubahan pemanfaatan sumber daya alam (pramono, 2009). pada suatu ekosistem yang rapuh seperti halnya di tngm, valuasi ekonomi lingkungan dapat digunakan sebagai alat untuk menaksir potensi kerugian ekologis yang muncul apabila ekosistem yang ada di kawasan tersebut rusak. tanggal 26 bulan oktober 2015 ini akan menjadi peringatan 5 tahun kejadian erupsi gunung merapi yang menimbulkan ratusan korban jiwa dan kerusakan lingkungan yang luar biasa. tulisan ini bertujuan untuk mengidentifikasi kerusakan yang terjadi di kawasan taman nasional gunung merapi (tngm) paska erupsi gunung merapi tahun 2010 sekaligus menaksir kerugian ekologis jangka pendek yang ditimbulkan. kerugian jangka pendek yang dimaksud adalah potensi kerugian yang ditimbulkan akibat rusaknya sumber daya alam sesaat setelah terdampak erupsi. 2. data dan metode penaksiran kerusakan secara cepat (rapid damage appraisal rda) dipilih sebagai metode untuk mengidentifikasi dampak kerusakan ekologis dan untuk menaksir kerugian ekologis yang ditimbulkan paska erupsi di kawasan tngm. metode rda dilakukan dengan tujuan untuk memperoleh informasi yang dibutuhkan secara cepat, dapat dipertanggungjawabkan dan dapat segera digunakan sebagai masukan dalam proses pengambilan keputusan. terdapat 3 tahapan yang dilakukan dalam rda, yaitu : 1) mengidentifikasi dampak kerusakan kawasan di tngm dengan menggunakan teknik penginderaan jauh, 2) melakukan survey lapangan untuk melakukan validasi hasil interpretasi penginderaan jauh dan mengumpulkan informasi kondisi kawasan, 3) menaksir kerugian ekologis yang ditimbulkan dengan pendekatan valuasi ekonomi lingkungan. terdapat 2 metode valuasi ekonomi lingkungan yang digunakan, yaitu perubahan produktivitas (change of productivity) dan biaya pengganti (replacement cost). kedua pendekatan ini mengacu pada peraturan menteri negara lingkungan hidup republik indonesia nomor 14 tahun 2012 tentang panduan valuasi ekonomi ekosistem gambut. walaupun peraturan tersebut secara spesifik menyebut ekosistem gambut sebagai obyek valuasi, namun penulis berpendapat bahwa pendekatan yang ada di peraturan tersebut juga sesuai untuk diterapkan di tipe ekosistem yang lain. 2.1 klasifikasi kerusakan kawasan dengan penginderaan jauh citra landsat 7 path 120 row 65 perekaman tanggal 19 februari 2011 digunakan sebagai dasar klasifikasi kerusakan hutan akibat erupsi gunung merapi di kawasan tngm. untuk mengurangi gangguan citra yang diakibatkan oleh black-stripping dan tutupan awan, dilakukan rekonstruksi citra dengan marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 72 | menggunakan software frame and fill dari national aeronautics and space administration (nasa) (gsfc.nasa.gov). proses klasifikasi kelas kerusakan kawasan dilakukan secara visual dengan software arcgis 10.1. beberapa kombinasi citra komposit antara lain 321, 453 dan 742 dibuat untuk memudahkan proses klasifikasi. terdapat 4 klasifikasi kerusakan kawasan yang digunakan, yaitu : 1) kerusakan berat, adalah kondisi kawasan yang terkena dampak langsung dari awan panas sehingga tidak mensisakan vegetasi sedikitpun, dan jejak medan lava sangat jelas terlihat; 2) kerusakan sedang, adalah kondisi kawasan yang terkena dampak awan panas namun vegetasi masih tampak walaupun berupa sisa-sisa tonggak, sementara jejak awan panas dan abu vulkanik tampak jelas di permukaan tanah; 3) kerusakan ringan, adalah kondisi kawasan yang masih cukup bagus dengan penampakan vegetasi yang relatif utuh, hanya jejak abu vulkanik yang terlihat di permukaan tanah dan dedaunan; 4) tidak terdampak, adalah kondisi kawasan tidak tampak sedikitpun jejak erupsi gunung merapi. 2.2 pengukuran lapangan pengukuran lapangan bertujuan untuk memvalidasi klasifikasi kelas kerusakan kawasan dan mengumpulkan informasi kondisi fisik lahan paska erupsi 2010. pengukuran ini dilaksanakan oleh tim restorasi ekosistem tngm pada rentang waktu 4 – 13 mei 2011 dan 23 – 28 mei 2011 dengan melibatkan kurang lebih 30 orang yang terdiri dari mahasiswa fakultas kehutanan ugm dan staf tngm. selain memvalidasi klasifikasi kerusakan kawasan dan mengukur kondisi fisik lahan, tim restorasi ekosistem tngm juga melakukan identifikasi flora dan fauna di kawasan tngm paska erupsi 2010. namun demikian. hasil pengukuran potensi flora dan fauna tersebut tidak termasuk dalam skup tulisan ini. metode yang digunakan dalam pengukuran lapangan ini adalah sistematik blok dengan pengambilan titik pengamatan secara acak. sistematik blok dibuat dengan membagi seluruh kawasan tngm kedalam grid berukuran 1 km x 1 km (gambar 1). total jumlah grid adalah 37 unit. wilayah sekitar puncak gunung merapi dan beberapa wilayah lain di sekitar lereng selatan dan barat tidak dilakukan pengamatan dengan alasan risiko keamanan yang masih tinggi akan bahaya erupsi. peralatan yang dipergunakan selama di lapangan adalah: gps garmin 75csx, bor tanah, pita meter, dan alat tulis untuk pencatatan hasil. gambar 1. peta sebaran grid pengukuran lapangan di kawasan tngm (tngm, 2011) 2.3 menaksir kerugian ekologis metode yang digunakan untuk menaksir kerugian ekologis adalah dengan pendekatan perubahan produktivitas (change of productivity) dan pendekatan biaya pengganti (replacement cost). kedua marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 | 73 pendekatan ini saling melengkapi untuk dapat menaksir kerugian ekologis yang ditimbulkan oleh erupsi gunung merapi tahun 2010. kerangka berpikir yang digunakan adalah bahwa paska erupsi gunung merapi tahun 2010, kawasan tngm akan kehilangan produktivitasnya sehingga membutuhkan pemulihan kawasan (restorasi). sebagai catatan, metode valuasi ekonomi lingkungan yang digunakan dalam penulisan ini banyak menggunakan asumsi yang berlaku pada saat penulisan ini dilakukan. a.) pendekatan perubahan produktivitas teknik ini mengukur perubahan produktivitas lingkungan yang terjadi akibat erupsi gunung merapi tahun 2010. pendekatan perubahan produktivitas membutuhkan komoditas yang bisa diukur nilai pasarnya. kayu sebagai produksi utama kawasan hutan tidak bisa ditetapkan sebagai komoditas karena kawasan konservasi tidak mempoduksi kayu untuk kepentingan komersial. dalam tulisan ini, penulis menggunakan pendekatan faktor produksi serapan karbon yang berpotensi hilang akibat vegetasi yang rusak paska erupsi gunung merapi tahun 2010. serapan karbon dipilih sebagai komoditas untuk mengukur penurunan produktifitas kawasan tngm karena ketersediaan alat ukur dan informasi harga pasar di tingkat global dalam skema perdagangan karbon (brown, 1997). sedangkan penurunan produksi pada komoditas lainnya seperti penurunan debit air, kematian biodiversitas dan penurunan kualitas kesuburan tanah tidak diperhitungkan karena keterbatasan data dan informasi. stok karbon di tngm dihitung menggunakan neraca karbon yang direkomendasikan oleh intergovernmental panel on climate change (ipcc, 2006) berdasarkan konsep spl (sistem penggunaan lahan) sederhana. spl disusun berdasarkan peta penggunaan lahan dari peta rupa bumi indonesia skala 1:25.000 sebelum erupsi 2010. untuk meningkatkan akurasi perhitungan, kelas penggunaan lahan hutan didetilkan berdasarkan kelas kerapatan tegakan dengan transformasi normalized difference vegetation index (ndvi) citra aster wilayah gunung merapi perekaman 23 agustus 2009 (sebelum erupsi). terdapat 3 kelas kerapatan tegakan yang digunakan, yaitu kerapatan tegakan rendah (0 < ndvi ≤ +0.3), kerapatan tegakan sedang (+0,3 < ndvi ≤ +0,7) dan kerapatan tegakan tinggi (+0,7 < ndvi ≤ +1). penaksiran stok karbon yang hilang dilakukan di tiap spl yang terdampak erupsi (sedang dan berat) dengan menggunakan acuan pengukuran biomass yang dilakukan oleh marhaento dkk. (2010) di kawasan taman nasional gunung merbabu (tngmb) (lihat tabel 1). pada spl yang mengalami kerusakan berat, stok karbon dianggap hilang seluruhnya. sedangkan pada spl yang mengalami kerusakan sedang, stok karbon dianggap tersisa 10%. penentuan angka 10% berdasarkan pada kenampakan hutan pada kelas kerusakan sedang yang hanya mensisakan tonggak, dimana biomassnya diperkirakan sebesar 10% dari seluruh bagian pohon. pada spl yang mengalami kerusakan ringan dan tidak terdampak, tidak dilakukan perhitungan stok karbon yang hilang karena vegetasi masih berfungsi dengan baik sebagai penyerap karbon. dalam tulisan ini, dasar pemberian atribut harga pada potensi karbon yang hilang akibat erupsi adalah dengan skema imbalan cer (certified emission reductions) pada mekanisme pembangunan bersih (clean development mechanism). cer merupakan satuan penurunan emisi setara 1 ton co2 dalam bentuk sertifikat yang dapat diperdagangkan dalam pasar global. namun demikian, karena tujuan penulisan ini adalah untuk menaksir secara cepat kerugian ekologis di tngm paska erupsi 2010, maka proses penaksiran harga cer dilakukan secara sederhana tanpa mengikuti mekanisme yang sesungguhnya. untuk memahami proses mekanisme perdagangan karbon dengan cer, penulis menyarankan untuk mengakses worldbank (2012). tabel 1. taksiran potensi karbon di tiap sistem penggunaan lahan (spl) (marhaento dkk., 2010) spl stok karbon (mg/ha) total stok karbon (mg/ha) diatas permukaan dibawah permukaan rumput 31,9 22,3 54,2 semak 3.218,5 504,0 3.722,5 tegakan kerapatan rendah 119,5 19,9 139,4 tegakan kerapatan sedang 1.958,4 448,4 2.406,8 tegakan kerapatan tinggi 20.550,8 3.410,0 23.960,8 marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 74 | b.) pendekatan biaya pengganti teknik biaya pengganti mengukur nilai kerugian ekologis berdasarkan biaya yang harus dikeluarkan untuk mengembalikan fungsi ekologis yang hilang menuju keadaan seperti semula (restorasi) (turner dkk., 2004). komponen biaya yang ditimbulkan untuk restorasi gunung merapi disusun berdasarkan pengalaman kegiatan serupa, yaitu kegiatan rehabilitasi suaka margasatwa paliyan oleh pt.mitshui sumitomo. kegiatan tersebut dipilih sebagai acuan untuk menentukan komponen kegiatan restorasi kawasan tngm karena adanya kemiripan tujuan yaitu untuk melakukan upaya rehabilitasi di lahan kritis. perlu dicatat, asumsi penaksiran harga dalam penentuan biaya pengganti ini dilakukan secara umum, tidak merinci per komponen biaya, dan menggunakan standar harga yang disesuaikan dengan lokasi kawasan dan yang berlaku pada saat tulisan ini disusun. 3. hasil dan pembahasan 3.1 identifikasi kerusakan ekologis di tngm hasil klasifikasi kelas kerusakan kawasan dari citra landsat dan survey lapangan menunjukkan bahwa kawasan tngm mengalami 3 kelas tingkat kerusakan. kerusakan berat terjadi pada kawasan seluas ± 1.242 ha (19,37%), kerusakan sedang seluas ±1.208 ha (18,84%), kerusakan ringan seluas 2.544 ha (39,68%) dan sisa kawasan adalah medan lava dan lahar seluas 1.416 ha (22,11%) yang sudah ada sejak sebelum erupsi 2010 (gambar 2). tidak dijumpai kelas tidak terdampak erupsi karena seluruh kawasan tngm menunjukkan adanya jejak abu vulkanik sehingga kelas terdampak paling rendah adalah kerusakan ringan. persentase tingkat kerusakan di tiap wilayah kelola tngm tersaji pada gambar 3. gambar 2. peta distribusi kelas kerusakan kawasan di tngm (tngm, 2011) gambar 3. distribusi kelas kerusakan berat (kiri), kerusakan sedang (tengah), dan kerusakan ringan (kanan) di tiap rptn di tngm (analisis, 2015) marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 | 75 kawasan resort pengelolaan taman nasional (rptn) pakem-turi, rptn cangkringan, rptn srumbung, rptn dukun, rptn sawangan, rptn selo dan rptn kemalang merupakan kawasan yang mengalami kerusakan berat. pada kawasan yang mengalami rusak berat tersebut, tidak dijumpai lagi tegakan yang tersisa. salah satu kawasan yang mengalami kerusakan tingkat berat dan sedang adalah di resort turipakem dan resort cangkringan yang sebelum terdampak erupsi 2010 memiliki tutupan lahan berupa semak dan rumput di bagian utara yang berdekatan dengan kepundan gunung merapi dan hutan campur yang berada di bagian selatan (blok hutan kinahrejo, blok hutan alas gandok) (lihat gambar 4). gambar 4. kondisi kerusakan di kawasan tngm yang disebabkan erupsi 2010, gambar kiri adalah kondisi kerusakan berat di grid 8a (rptn cangkringan) dimana tidak dijumpai lagi sisa tegakan. gambar kanan adalah kondisi kerusakan sedang di grid 7a (rptn turi-pakem) dimana sisa-sisa tonggak tegakan masih terlihat (tngm, 2011) dampak yang ditimbulkan oleh rusaknya blok hutan tersebut adalah hilangnya potensi hutan sebagai penyedia oksigen, penyerap karbon, dan habitat berbagai flora dan fauna khas yang ada di gunung merapi. menurut tngm (2011), di kedua lokasi tersebut merupakan habitat dari berbagai jenis burung yang dilindungi, antara lain: elang jawa (spizaetus bartelsi), elang bido (spilornis cheela), elang hitam (ictinaetus malayensis), alap-alap sapi (falco moluccensis), betet (psittacula alexandri), dan serindit jawa (loriculus pusillus) yang masuk dalam lampiran ii cites. selain itu, di lokasi tersebut juga dijumpai (langsung maupun tidak langsung) berbagai jenis mamalia, antara lain : lutung jawa (trachypithecus auratus), babi hutan (sus scrofa), kijang (muntiacus muntjak), kucing hutan (prionailurus bengalensis), luwak (paradoxurus hermaphroditus), landak (hystix brachyura) dan macan tutul/kumbang (panthera pardus melas). kondisi relatif serupa juga terjadi di kawasan blok hutan ngasinan, gumuk dan petung di rptn kemalang, blok hutan lencoh, tlogolele di rptn selo dan blok hutan jaimin, bedengan, gejugan di rptn dukun yang mengalami kerusakan berat dan sedang. beberapa kawasan di tngm hanya mengalami kerusakan ringan yang dicirikan dengan penampakan vegetasi yang relatif utuh dengan jejak abu vulkanik yang terlihat di permukaan tanah dan dedaunan. kawasan ini terutama di bagian timur gunung merapi yang masuk wilayah kelola rptn musuk-cepogo dan sebagian wilayah rptn kemalang dan rptn selo. pada lereng selatan di wilayah blok hutan turgo (rptn pakem-turi) dan pada lereng barat di beberapa bagian blok hutan desa ngargosoko di rptn srumbung dan blok hutan gemer, kroyo (desa ngargomulyo) di rptn dukun (lihat gambar 5). marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 76 | gambar 5. kerusakan ringan di kawasan tngm yang disebabkan erupsi 2010, gambar kiri adalah kondisi di grid 4b (rptn kemalang) dan gambar kanan adalah kondisi di grid 5a (rptn turi-pakem) (tngm, 2011) 3.2 identifikasi kerusakan ekologis di tngm pendekatan perubahan produktivitas (change of productivity) dan pendekatan biaya pengganti (replacement cost) digunakan sebagai metode untuk menaksir kerugian ekologis di tngm yang ditimbulkan oleh erupsi 2010. a.) pendekatan perubahan produktivitas marhaento dkk. (2010) mengukur potensi stok karbon di kawasan taman nasional gunung merbabu (tngmb) yang memiliki kondisi lahan yang relatif mirip dengan tngm. penulis menggunakan acuan taksiran potensi karbon di tngmb (marhaento dkk., 2010) untuk mengukur potensi karbon di tngm. acuan tersebut kemudian digunakan untuk melakukan estimasi stok karbon yang hilang akibat erupsi di tiap sistem penggunaan lahan (spl). spl yang diukur sebagai potensi kehilangan stok karbon adalah kelas spl dengan tingkat kerusakan sedang dan kerusakan berat (gambar 6). gambar 6. peta kelas kerusakan kawasan pada sistem penggunaan lahan (spl) di tngm (analisis, 2015) dengan asumsi harga cer karbon adalah $12.8/ton (worldbank, 2012), dengan kurs rp. 9.500 per $1, maka taksiran perubahan produktivitas di tngm dapat ditaksir sesuai dengan yang disajikan pada tabel 2. marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 | 77 tabel 2. taksiran perubahan produktifitas karbon di kawasan tngm paska erupsi (analisis, 2015) sistem penggunaan lahan (spl) luas (ha) total potensi karbon hilang (mg) potensi kerugian (milyar rp) tegakan rapat rendah rusak berat 103,3 14.397,2 1,8 tegakan rapat sedang rusak berat 320,5 771.326,8 93,8 tegakan rapat tinggi rusak berat 3,0 72.926,8 8,9 padang rumput rusak berat 94,6 5.127,4 0,6 semak belukar rusak berat 659,7 2.455.574,5 298,6 hutan rapat rendah rusak sedang 125,0 15.683,9 1,9 hutan rapat sedang rusak sedang 418,1 905.735,9 110,1 hutan rapat tinggi rusak sedang 4,6 99.352,3 12,1 padang rumput rusak sedang 122,1 5.954,8 0,7 semak belukar rusak sedang 537,5 1.800.801,3 219,0 total 2.388,4 6.146.880,8 747,5 b.) pendekatan biaya pengganti biaya pengganti adalah biaya yang dibutuhkan untuk mengembalikan fungsi ekosistem (restorasi) gunung merapi seperti semula. dalam tulisan ini, upaya restorasi ekosistem dilakukan dengan penghutanan kembali kawasan-kawasan hutan yang rusak. pada kawasan yang sebelum erupsi gunung merapi adalah non hutan (rumput, semak) maka tidak dilakukan perlakuan penghutanan kembali. pada kawasan non hutan tersebut diasumsikan akan dibiarkan untuk mengalami suksesi secara alami. terdapat beberapa komponen biaya umum yang perlu diperhatikan dalam upaya restorasi ekosistem gunung merapi, antara lain : 1. pengadaan bibit jenis yang digunakan dalam upaya restorasi ekosistem gunung merapi harus jenis asli (native) yang ditentukan berdasarkan inventarisasi jenis-jenis tumbuhan yang ada di kawasan tngm. pengadaan bibit harus dipastikan dari pohon induk yang baik. kriteria bibit yang ditanam adalah tinggi minimal 50 cm, sehat, dan batang sudah berkayu. asumsi harga yang digunakan adalah rp7.500/bibit, sudah meliputi biaya transportasi bibit, biaya penampungan bibit sementara dan biaya pemeliharaan sebelum ditanam. 2. penyiapan dan pembersihan lahan kegiatan penyiapan dan pembersihan lahan diperlukan untuk menyiapkan lahan tanam dari bekas pohon yang bertumbangan. sistem penanaman dilakukan dengan cara membuat jalur tidak terputus dengan jarak antar jalur 5 m tegak lurus kontur. selanjutnya pada tiap-tiap jalur dibuat lubang-lubang tanam dengan jarak antar lubang tanam 5 m. pemilihan jarak tanam 5 x 5 m didasarkan pada petunjuk teknis rehabilitasi hutan bekas terbakar di areal hph (purnomo dkk., 1999). asumsi harga yang digunakan adalah harga per hektar, yaitu: 20 hok dengan biaya rp 65.000/hok. 3. penanaman penanaman dilakukan pada saat hujan mulai stabil dengan tujuan untuk mengurangi risiko kematian bibit. kegiatan penanaman meliputi pemberian pupuk, pemberian lapisan tanah atas pada tiap lubang tanam, pemberian mulsa, pembuatan acir, dan pembuatan “press block” yang merupakan metode menanam bibit tanaman di lahan kritis yang mampu mengatasi kondisi tapak berpasir yang memiliki daya cekam air yang sangat rendah. asumsi harga yang digunakan untuk kegiatan penanaman adalah rp20.000/bibit, sudah meliputi biaya tenaga tanam. 4. pemeliharaan dan penyulaman pemeliharaan tanaman dilakukan secara rutin setiap 2 bulan sekali pada tahun pertama dan 6 bulan sekali pada tahun kedua. setiap kegiatan pemeliharaan diikuti dengan kegiatan penggemburan marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 78 | tanah/pendangiran di sekitar tanaman pokok dan pemupukan. penyulaman adalah kegiatan yang dilakukan dengan cara mengganti tanaman pokok yang mati atau ‘hilang’ dengan tanaman yang baru. penyulaman dilakukan dengan bibit yang relatif sama dengan tanaman yang digantikan. asumsi biaya pemeliharaan dan penyulaman adalah 30% total biaya pada tahun pertama dan 15% total biaya pada tahun kedua. lokasi kegiatan restorasi pada kawasan hutan yang mengalami kerusakan sedang dan kerusakan berat adalah seluas 1.036,14 ha. total prakiraan biaya yang dibutuhkan untuk kegiatan restorasi ekosistem tngm adalah sesuai yang disajikan pada tabel 3. tabel 3. taksiran biaya pengganti di tngm paska erupsi (analisis, 2015) komponen biaya satuan harga volume luas kawasan (ha) biaya (milyar rp) pengadaan bibit ha 7,500 per bibit 400 1036.1 3.1 penyiapan dan pembersihan lahan ha 65,000 per hok 40 1036.1 1.3 penanaman ha 20,000 per bibit 400 1036.1 8.3 pemeliharaan dan penyulaman ha 30% + 15% total biaya 5.7 total biaya 18.5 biaya per hektar rp17,800,000 hasil dari rapid damage assessment (rda) dengan menggunakan citra penginderaan jauh dan survei lapangan menunjukkan bahwa 38,21% kawasan tngm mengalami kerusakan tingkat sedang hingga berat. kawasan yang mengalami kerusakan tersebut sebagian besar berada di sisi selatan, sisi barat dan sisi utara gunung merapi. pada sisi selatan, kerusakan terjadi di blok-blok hutan yang berada di sekitar kali woro (kec. kemalang, klaten), kali gendol dan kali kuning (kec. cangkringan, sleman). pada sisi barat, kerusakan terjadi di blok-blok hutan yang berada di sekitar hulu kali putih (perbatasan kec. dukun dan kec. srumbung, magelang), hulu kali senowo, hulu kali lamat dan hulu kali blongkeng (kec. dukun, magelang). pada sisi utara, kerusakan terjadi di blok-blok hutan di sekitar kali jengglung, hulu kali apu (kec. selo, boyolali). kerusakan blok-blok hutan tingkat sedang dan berat yang berada di sekitar lereng selatan, barat, dan utara gunung merapi ini sesuai dengan pergerakan awan panas erupsi gunung merapi tahun 2010 yang cenderung kearah tersebut (tngm, 2011; surono dkk., 2012). bukit turgo dan gunung bibi yang berada di sisi lereng selatan dan lereng timur gunung merapi merupakan kawasan hutan yang kondisinya relatif utuh paska erupsi 2010. menurut gunawan dkk. (2013), 44% perjumpaan dengan satwa liar yang tergolong mamalia penting antara lain monyet ekor panjang, lutung jawa, luwak, babi hutan, kucing hutan dan kijang di kawasan tngm paska erupsi tahun 2010 terjadi pada kawasan yang mengalami kerusakan ringan dan yang tidak terdampak sama sekali. keberadaan kedua kawasan hutan tersebut diduga menjadi tempat pelarian (refugee) bagi satwa liar yang ada di sekitar kawasan tngm ketika erupsi terjadi (marhaento dan faida, 2015). hal ini memberikan harapan bahwa fungsi ekologis di kedua kawasan hutan tersebut masih berjalan dengan baik. pada kawasan yang mengalami kerusakan tingkat sedang dan berat diduga memberikan dampak kerugian ekologis jangka pendek yang cukup tinggi. kerugian ekologis tersebut antara lain: kematian berbagai jenis vegetasi, rusaknya habitat satwa liar, hilangnya potensi penyedia oksigen dan penyerap karbon, gangguan pada suplai air, penurunan kualitas air dan tanah, dll. dalam penelitian ini digunakan pendekatan perubahan produktivitas dari komoditas serapan karbon karena ketersediaan alat ukur dan informasi harga pasar yang disepakati. nilai kerugian akibat kematian satwa liar, potensi penurunan debit air, penurunan kesuburan tanah, penurunan kualitas air dan udara, dll. tidak bisa disajikan dalam tulisan ini karena keterbatasan data dan informasi. selain itu, pendekatan biaya pengganti dilakukan dengan menggunakan acuan komponen biaya pada kegiatan serupa yaitu kegiatan rehabilitasi lahan kritis di suaka margasatwa paliyan yang dibiayai oleh pt. mitsui sumitomo (bksda yogyakarta, 2012). namun demikian, komposisi biaya dan taksiran harga disesuaikan dengan kondisi yang ada di kawasan tngm untuk mengurangi bias karena perbedaan lokasi. marhaento dan kurnia / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 69-81 doi: 10.14710/geoplanning.2.2.69-81 | 79 hasil penaksiran kerugian ekologis jangka pendek di kawasan tngm paska erupsi tahun 2010 adalah total sebesar ±766 milyar rupiah yang terdiri dari ±747,5 milyar rupiah dari biaya penurunan produktifitas serapan karbon dan ±18,5 milyar rupiah dari biaya restorasi. untuk menaksir total kerugian ekologis yang terjadi akibat erupsi tahun 2010, penulis merujuk pada penelitian van beukering dkk. (2003) yang menyebutkan bahwa penyerapan karbon memberikan kontribusi 2% dari total nilai ekonomi lingkungan di kawasan konservasi. apabila digunakan asumsi yang sama, maka total potensi kerugian ekologis yang ditimbulkan akibat erupsi tahun 2010 di tngm dapat diperkirakan adalah sebesar 38,3 trilliun rupiah. nilai kerugian ekologis tersebut merupakan taksiran kasar dan dianggap dibawah nilai sebenarnya (under estimate). salah satu upaya untuk memitigasi kerusakan dan kerugian lingkungan akibat erupsi gunung merapi adalah melalui penataan zonasi tngm berbasis risiko bencana. marhaento dan faida (2015) menyebutkan bahwa zonasi tngm yang ditetapkan pada tahun 2012 sudah cukup mampu merepresentasikan kebutuhan perlindungan terhadap potensi keanekaragaman hayati yang ada. namun demikian, zona inti yang ada saat ini lebih difokuskan pada aspek pengamanan dari aktivitas masyarakat dan tidak ada rencana kelola terkait ancaman erupsi gunung merapi. untuk itu, diperlukan adanya rencana kontingensi dalam pengelolaan zona inti untuk menghindarkan kematian dan kepunahan keanekaragaman hayati akibat bencana erupsi gunung merapi salah satunya dengan membuat kantong-kantong pelarian satwa liar (refugee) termasuk jalur (koridor) pelariannya (dowie, 2011). 4. kesimpulan hasil dari rapid damage assessment (rda) dengan menggunakan citra penginderaan jauh dan survei lapangan menunjukkan bahwa ± 1.242 ha (19,37%) kawasan tngm mengalami kerusakan berat, ±1.208 ha (18,84%) mengalami kerusakan sedang dan sisanya relatif utuh dan hanya mengalami kerusakan ringan. kerusakan berat dan sedang terutama terjadi pada blok-blok hutan di lereng selatan, barat dan utara gunung merapi yang masuk wilayah kelola resort pengelolaan taman nasional (rptn) pakem-turi, rptn cangkringan, rptn srumbung, rptn dukun, rptn sawangan, rptn selo dan rptn kemalang. sedangkan kawasan yang relatif utuh dan hanya mengalami kerusakan ringan terjadi di blok-blok hutan di lereng timur gunung merapi yang masuk wilayah kelola rptn musuk-cepogo dan sebagian wilayah rptn kemalang dan rptn selo. hasil penaksiran kerugian ekologis dengan pendekatan perubahan produktivitas (change of productivity) dan pendekatan biaya pengganti (replacement cost) di kawasan tngm paska erupsi gunung merapi tahun 2010 menunjukkan bahwa nilai kerugian adalah sebesar ±766 milyar rupiah yang terdiri dari ±747,50 milyar rupiah dari biaya penurunan produktifitas serapan karbon dan ±18,5 milyar rupiah dari biaya restorasi. dengan menggunakan asumsi bahwa serapan karbon hanya 2% dari total potensi jasa lingkungan di kawasan konservasi, maka taksiran nilai total kerugian ekologis di kawasan tngm paska erupsi gunung merapi tahun 2010 adalah 38,3 trilliun rupiah. nilai kerugian ekologis tersebut merupakan taksiran kasar dan dianggap dibawah nilai sebenarnya. salah satu upaya upaya untuk memitigasi kerusakan dan kerugian lingkungan akibat erupsi gunung merapi adalah melalui penataan zonasi tngm berbasis risiko bencana. 5. ucapan terima kasih tulisan ini merupakan bagian dari kegiatan “restorasi ekosistem taman nasional gunung merapi paska erupsi 2010” kerjasama antara balai taman nasional (btn) gunung merapi dan fakultas kehutanan ugm. penulis mengucapkan terimakasih kepada seluruh anggota tim kerja yang terdiri dari staf kantor btn gunung merapi, dosen dan mahasiswa fakultas kehutanan ugm. 6. daftar pustaka balai konservasi sumber daya alam (bksda) yogyakarta. 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(2012). state and trends of the carbon market 2012. sumber diakses melalui http://siteresources.worldbank.org/intcarbonfinance/resources/state_and_trends_2012_web _optimized_19035_cvr&txt_lr.pdf http://siteresources.worldbank.org/intcarbonfinance/resources/state_and_trends_2012_web_optimized_19035_cvr&txt_lr.pdf http://siteresources.worldbank.org/intcarbonfinance/resources/state_and_trends_2012_web_optimized_19035_cvr&txt_lr.pdf doi: 10.14710/geoplanning.2.2.69-81 keywords: mount merapi national park, 2010 eruption, ecological loss assessment corresponding author: hero marhaento universitas gadjah mada, yogyakarta, indonesia email: marhaento@ugm.ac.id kata kunci: taman nasional gunung merapi, erupsi 2010, penilaian kerugian ekologis kontak penulis: hero marhaento universitas gadjah mada, yogyakarta, indonesia email: marhaento@ugm.ac.id how to cite (apa 6th style): marhaento, h., & kurnia, a. n. (2015). refleksi 5 tahun paska erupsi gunung merapi 2010: menaksir kerugian ekologis di kawasan taman nasional gunung merapi. geoplanning: journal of geomatics and planning, 2(2), 69-81. doi:10.14710/geoplanning.2.2.69-81 pendahuluan gunung merapi merupakan salah satu gunung paling aktif di dunia. erupsi gunung merapi terjadi dalam siklus 4 – 6 tahun sekali (surono dkk., 2012). menurut van boekhold (1972) dan newhall dkk (2000), erupsi gunung merapi yang terdokumentasi pertama kal... kronologi kejadian erupsi gunung merapi tahun 2010 dimulai pada tanggal 20 september 2010 dimana status gunung merapi ditingkatkan dari ‘normal’ menjadi ‘waspada’ (surat badan geologi no. 46/45/bgl.v/2010). pada 21 oktober 2010, status tersebut mening... berdasarkan hasil penilaian kerusakan dan kerugian yang dilakukan oleh badan nasional penanggulangan bencana (bnpb) melalui metode dari economic commission for latin america and the caribbean (eclac) (www.eclac.cl), erupsi gunung merapi tahun 2010 tel... perhitungan kerusakan dan kerugian akibat bencana erupsi gunung merapi oleh bnpb tersebut adalah hasil perhitungan aset rusak yang dimoneterisasi (nilai langsung), sementara kerugian tidak langsung dari dampak erupsi yaitu kerusakan ekosistem, keaneka... menurut uu no.5 tahun 1990 taman nasional adalah salah satu bentuk kawasan konservasi yang dicirikan dengan keberadaan ekosistem asli, dikelola dengan sistem zonasi yang dimanfaatkan untuk tujuan penelitian, ilmu pengetahuan, pendidikan, menunjang bud... taman nasional gunung merapi (tngm) merupakan kawasan konservasi yang unik. selain menyangga gunung api paling aktif di indonesia, ekosistem hutan di tngm berfungsi sebagai daerah tangkapan air kawasan provinsi jawa tengah dan daerah istimewa yogyakar... valuasi ekonomi lingkungan merupakan suatu instrumen ekonomi untuk mengestimasi nilai moneter dari produk barang dan jasa yang dihasilkan oleh sumber daya alam dan lingkungan (garrod dan willis, 1999). instrumen ini penting digunakan untuk mengukur po... tanggal 26 bulan oktober 2015 ini akan menjadi peringatan 5 tahun kejadian erupsi gunung merapi yang menimbulkan ratusan korban jiwa dan kerusakan lingkungan yang luar biasa. tulisan ini bertujuan untuk mengidentifikasi kerusakan yang terjadi di kawas... data dan metode penaksiran kerusakan secara cepat (rapid damage appraisal rda) dipilih sebagai metode untuk mengidentifikasi dampak kerusakan ekologis dan untuk menaksir kerugian ekologis yang ditimbulkan paska erupsi di kawasan tngm. metode rda dilakukan dengan tu... terdapat 2 metode valuasi ekonomi lingkungan yang digunakan, yaitu perubahan produktivitas (change of productivity) dan biaya pengganti (replacement cost). kedua pendekatan ini mengacu pada peraturan menteri negara lingkungan hidup republik indonesia ... klasifikasi kerusakan kawasan dengan penginderaan jauh citra landsat 7 path 120 row 65 perekaman tanggal 19 februari 2011 digunakan sebagai dasar klasifikasi kerusakan hutan akibat erupsi gunung merapi di kawasan tngm. untuk mengurangi gangguan citra yang diakibatkan oleh black-stripping dan tutupan awan,... beberapa kombinasi citra komposit antara lain 321, 453 dan 742 dibuat untuk memudahkan proses klasifikasi. terdapat 4 klasifikasi kerusakan kawasan yang digunakan, yaitu : 1) kerusakan berat, adalah kondisi kawasan yang terkena dampak langsung dari a... pengukuran lapangan pengukuran lapangan bertujuan untuk memvalidasi klasifikasi kelas kerusakan kawasan dan mengumpulkan informasi kondisi fisik lahan paska erupsi 2010. pengukuran ini dilaksanakan oleh tim restorasi ekosistem tngm pada rentang waktu 4 – 13 mei 2011 dan ... metode yang digunakan dalam pengukuran lapangan ini adalah sistematik blok dengan pengambilan titik pengamatan secara acak. sistematik blok dibuat dengan membagi seluruh kawasan tngm kedalam grid berukuran 1 km x 1 km (gambar 1). total jumlah grid ada... gambar 1. peta sebaran grid pengukuran lapangan di kawasan tngm (tngm, 2011) menaksir kerugian ekologis metode yang digunakan untuk menaksir kerugian ekologis adalah dengan pendekatan perubahan produktivitas (change of productivity) dan pendekatan biaya pengganti (replacement cost). kedua pendekatan ini saling melengkapi untuk dapat menaksir kerugian ek... teknik ini mengukur perubahan produktivitas lingkungan yang terjadi akibat erupsi gunung merapi tahun 2010. pendekatan perubahan produktivitas membutuhkan komoditas yang bisa diukur nilai pasarnya. kayu sebagai produksi utama kawasan hutan tidak bisa ... stok karbon di tngm dihitung menggunakan neraca karbon yang direkomendasikan oleh intergovernmental panel on climate change (ipcc, 2006) berdasarkan konsep spl (sistem penggunaan lahan) sederhana. spl disusun berdasarkan peta penggunaan lahan dari pet... penaksiran stok karbon yang hilang dilakukan di tiap spl yang terdampak erupsi (sedang dan berat) dengan menggunakan acuan pengukuran biomass yang dilakukan oleh marhaento dkk. (2010) di kawasan taman nasional gunung merbabu (tngmb) (lihat tabel 1). p... dalam tulisan ini, dasar pemberian atribut harga pada potensi karbon yang hilang akibat erupsi adalah dengan skema imbalan cer (certified emission reductions) pada mekanisme pembangunan bersih (clean development mechanism). cer merupakan satuan penuru... teknik biaya pengganti mengukur nilai kerugian ekologis berdasarkan biaya yang harus dikeluarkan untuk mengembalikan fungsi ekologis yang hilang menuju keadaan seperti semula (restorasi) (turner dkk., 2004). komponen biaya yang ditimbulkan untuk resto... hasil dan pembahasan identifikasi kerusakan ekologis di tngm hasil klasifikasi kelas kerusakan kawasan dari citra landsat dan survey lapangan menunjukkan bahwa kawasan tngm mengalami 3 kelas tingkat kerusakan. kerusakan berat terjadi pada kawasan seluas ± 1.242 ha (19,37%), kerusakan sedang seluas ±1.208 ha (18... gambar 2. peta distribusi kelas kerusakan kawasan di tngm (tngm, 2011) gambar 3. distribusi kelas kerusakan berat (kiri), kerusakan sedang (tengah), dan kerusakan ringan (kanan) di tiap rptn di tngm (analisis, 2015) kawasan resort pengelolaan taman nasional (rptn) pakem-turi, rptn cangkringan, rptn srumbung, rptn dukun, rptn sawangan, rptn selo dan rptn kemalang merupakan kawasan yang mengalami kerusakan berat. pada kawasan yang mengalami rusak berat tersebut, ti... gambar 4. kondisi kerusakan di kawasan tngm yang disebabkan erupsi 2010, gambar kiri adalah kondisi kerusakan berat di grid 8a (rptn cangkringan) dimana tidak dijumpai lagi sisa tegakan. gambar kanan adalah kondisi kerusakan sedang di grid 7a (rptn tu... dampak yang ditimbulkan oleh rusaknya blok hutan tersebut adalah hilangnya potensi hutan sebagai penyedia oksigen, penyerap karbon, dan habitat berbagai flora dan fauna khas yang ada di gunung merapi. menurut tngm (2011), di kedua lokasi tersebut meru... beberapa kawasan di tngm hanya mengalami kerusakan ringan yang dicirikan dengan penampakan vegetasi yang relatif utuh dengan jejak abu vulkanik yang terlihat di permukaan tanah dan dedaunan. kawasan ini terutama di bagian timur gunung merapi yang masu... gambar 5. kerusakan ringan di kawasan tngm yang disebabkan erupsi 2010, gambar kiri adalah kondisi di grid 4b (rptn kemalang) dan gambar kanan adalah kondisi di grid 5a (rptn turi-pakem) (tngm, 2011) identifikasi kerusakan ekologis di tngm pendekatan perubahan produktivitas (change of productivity) dan pendekatan biaya pengganti (replacement cost) digunakan sebagai metode untuk menaksir kerugian ekologis di tngm yang ditimbulkan oleh erupsi 2010. marhaento dkk. (2010) mengukur potensi stok karbon di kawasan taman nasional gunung merbabu (tngmb) yang memiliki kondisi lahan yang relatif mirip dengan tngm. penulis menggunakan acuan taksiran potensi karbon di tngmb (marhaento dkk., 2010) untuk men... gambar 6. peta kelas kerusakan kawasan pada sistem penggunaan lahan (spl) di tngm (analisis, 2015) dengan asumsi harga cer karbon adalah $12.8/ton (worldbank, 2012), dengan kurs rp. 9.500 per $1, maka taksiran perubahan produktivitas di tngm dapat ditaksir sesuai dengan yang disajikan pada tabel 2. biaya pengganti adalah biaya yang dibutuhkan untuk mengembalikan fungsi ekosistem (restorasi) gunung merapi seperti semula. dalam tulisan ini, upaya restorasi ekosistem dilakukan dengan penghutanan kembali kawasan-kawasan hutan yang rusak. pada kawasa... terdapat beberapa komponen biaya umum yang perlu diperhatikan dalam upaya restorasi ekosistem gunung merapi, antara lain : pengadaan bibit jenis yang digunakan dalam upaya restorasi ekosistem gunung merapi harus jenis asli (native) yang ditentukan berdasarkan inventarisasi jenis-jenis tumbuhan yang ada di kawasan tngm. pengadaan bibit harus dipastikan dari pohon induk yang baik. kriteria... penyiapan dan pembersihan lahan kegiatan penyiapan dan pembersihan lahan diperlukan untuk menyiapkan lahan tanam dari bekas pohon yang bertumbangan. sistem penanaman dilakukan dengan cara membuat jalur tidak terputus dengan jarak antar jalur 5 m tegak lurus kontur. selanjutnya pada... penanaman penanaman dilakukan pada saat hujan mulai stabil dengan tujuan untuk mengurangi risiko kematian bibit. kegiatan penanaman meliputi pemberian pupuk, pemberian lapisan tanah atas pada tiap lubang tanam, pemberian mulsa, pembuatan acir, dan pembuatan “pr... pemeliharaan dan penyulaman pemeliharaan tanaman dilakukan secara rutin setiap 2 bulan sekali pada tahun pertama dan 6 bulan sekali pada tahun kedua. setiap kegiatan pemeliharaan diikuti dengan kegiatan penggemburan tanah/pendangiran di sekitar tanaman pokok dan pemupukan. penyu... lokasi kegiatan restorasi pada kawasan hutan yang mengalami kerusakan sedang dan kerusakan berat adalah seluas 1.036,14 ha. total prakiraan biaya yang dibutuhkan untuk kegiatan restorasi ekosistem tngm adalah sesuai yang disajikan pada tabel 3. hasil dari rapid damage assessment (rda) dengan menggunakan citra penginderaan jauh dan survei lapangan menunjukkan bahwa 38,21% kawasan tngm mengalami kerusakan tingkat sedang hingga berat. kawasan yang mengalami kerusakan tersebut sebagian besar ber... bukit turgo dan gunung bibi yang berada di sisi lereng selatan dan lereng timur gunung merapi merupakan kawasan hutan yang kondisinya relatif utuh paska erupsi 2010. menurut gunawan dkk. (2013), 44% perjumpaan dengan satwa liar yang tergolong mamalia ... pada kawasan yang mengalami kerusakan tingkat sedang dan berat diduga memberikan dampak kerugian ekologis jangka pendek yang cukup tinggi. kerugian ekologis tersebut antara lain: kematian berbagai jenis vegetasi, rusaknya habitat satwa liar, hilangnya... hasil penaksiran kerugian ekologis jangka pendek di kawasan tngm paska erupsi tahun 2010 adalah total sebesar ±766 milyar rupiah yang terdiri dari ±747,5 milyar rupiah dari biaya penurunan produktifitas serapan karbon dan ±18,5 milyar rupiah dari biay... salah satu upaya untuk memitigasi kerusakan dan kerugian lingkungan akibat erupsi gunung merapi adalah melalui penataan zonasi tngm berbasis risiko bencana. marhaento dan faida (2015) menyebutkan bahwa zonasi tngm yang ditetapkan pada tahun 2012 sudah... kesimpulan hasil dari rapid damage assessment (rda) dengan menggunakan citra penginderaan jauh dan survei lapangan menunjukkan bahwa ± 1.242 ha (19,37%) kawasan tngm mengalami kerusakan berat, ±1.208 ha (18,84%) mengalami kerusakan sedang dan sisanya relatif utu... hasil penaksiran kerugian ekologis dengan pendekatan perubahan produktivitas (change of productivity) dan pendekatan biaya pengganti (replacement cost) di kawasan tngm paska erupsi gunung merapi tahun 2010 menunjukkan bahwa nilai kerugian adalah sebes... ucapan terima kasih tulisan ini merupakan bagian dari kegiatan “restorasi ekosistem taman nasional gunung merapi paska erupsi 2010” kerjasama antara balai taman nasional (btn) gunung merapi dan fakultas kehutanan ugm. penulis mengucapkan terimakasih kepada seluruh anggot... daftar pustaka 71 geoplanning journal of geomatics and planning vol. 11, no. 1, 2024 original research tracking the temporal changes in land surface temperature, vegetation, and built-up patterns in rizal province, philippines using landsat imagery pauline angela sobremonte-maglipon1,2* anne olfato-parojinog1,2 king joshua almadrones-reyes3,4 james eduard limbo-dizon3,4 and nikki heherson a. dagamac1,2,3,4 1. department of biological sciences, college of science, university of santo tomas, españa, manila, 1008, philippines 2. the graduate school, university of santo tomas, españa, manila, 1008, philippines 3. research center for the natural and applied sciences university of santo tomas, españa, manila, 1008, philippines 4. advanced educational program, thai nguyen university of agriculture and forestry quyết thắng, thái nguyên, vietnam doi: 10.14710/geoplanning.11.1.71-84 abstract the rizal province was subjected to a series of natural and human-induced disturbances throughout the years. currently, the area is undergoing urbanization which in turn results in shifts in the extent of impervious surfaces that can intensify heat-related health concerns, increase energy consumption for cooling, and alter local weather patterns. this study uses remote sensing images from to quantify the various environmental considerations that remain undocumented and unmapped for areas caused by changes in land use and land cover from landsat collection 1level 1 (landsat 4-5 ™ c1level 1 & landsat 8 oli/ tirs c1 level 1) and calculated three parameters namely, (i) land surface temperature (lst), (ii) normalized difference vegetation index (ndvi), and (iii) the normalized difference built-up index (ndbi). the results showed the following: (i) an increase in the vegetation cover from 1993-2020 showed a decrease in lst from 29.34°c to 24.03°c, (ii) the relationship between lst and ndbi is directly proportional, whereas an inversely proportional relationship can be observed between lst and ndvi, and (iii) there is a fluctuating lst due to the changes in the land cover of the study site for almost three decades. this implicates the extensive shift in the ambient temperature of rizal which further emphasizes the effects of the modification in certain land use land cover classifications, especially in vegetation cover and urban development. this highlights how human-induced and natural factors significantly contribute to the release of heat and ambient temperature, thus, accentuating the need for sustainable urban planning. copyright © 2024 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction for the past several decades, urbanization in various megacities especially in developing countries such as the philippines has been steadily expanding in response to the increase in human population. as a result of this, urban development continuously ensues to meet the living demands of the rapidly growing population (doygun & alphan, 2006; ramachandra et al., 2015; buchori et al., 2022). unfortunately, the built up of urban areas has led to the increase in land surface temperature (lst) due to the accumulation of solar heat retention from infrastructural materials such as cement, asphalt, concrete, and steel, all of which are characterized to have high heat capacities (stempihar et al., 2012; uddin et al., 2022; morris et al., 2017). in addition, other forms of humaninduced heat emissions including fuel and biomass burning are contributing factors to the increase of lst e-issn: 2355-6544 received: 31 october 2023; accepted: 13 february 2024; published: 08 march 2024. keywords: lst, ndvi, ndbi, remote sensing *corresponding author(s) email: pmaglipon@gmail.com https://doi.org/10.14710/geoplanning.11.1.71-84 mailto:pmaglipon@gmail.com sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 72 (ekwurzel et al., 2017). this concept is otherwise known as the urban heat island (uhi) effect. the observed warming in urban areas corresponds to the land-use changes, usually that from vegetation to settlements (fang et al., 2011). the consequences of the uhi effect extend beyond mere temperature increases; it can significantly alter urban ecological systems and exert various ecological and environmental impacts on urban climates, hydrological situations, soil properties, atmospheric environments, biological habitats, material cycles, energy metabolism, and residents' health (yang et al., 2016). these changes can render urban areas more vulnerable to economic and socio-political perturbations (chase et al., 1999; vitousek et al., 1997; kasperson et al., 1995). the pioneering observation of the uhi phenomenon initially introduced by luke howard in 1818 indicated the global acknowledgment of a substantial urban climatic and environmental predicament (zhou & chen, 2018). uhi, widely prevalent across urban landscapes, encompasses elevated heat capacity, thermal conductivity, and predominantly impervious surfaces, which amplify heat absorption and retention relative to rural locales (gaur et al., 2018; naikoo et al., 2022). additionally, the proliferation of urban structures aggravates heat absorption by means of recurrent reflections and absorption, much as the presence of establishments and buildings reduce urban air circulation, further compounding heat accumulation (he et al., 2007; zhou & chen, 2018). because of this, megacities confront steadily escalating uhi magnitudes, exacerbating discomfort in the urban thermal setting, especially during warmer seasons, thereby establishing prolonged reliance on air conditioning by inhabitants, hence, giving rise to an extended levels of anthropogenic heat discharge (zhou et al. 2011; zhou & chen, 2018). similar studies highlight mitigating uhi implications to diminish the adverse effects on urban inhabitants’ health and well-being, advocating for a deeper understanding and definite interventions in urban development and design to foster a holistic approach towards sustainable and resilient urban planning. an analysis that can evaluate the uhi effect is the lst analysis, which is considered to be one of the most vital domains in assessing the physical systematic series of surface energy (chen et al., 2013). lst is the land surface’s radiative skin temperature measured by means of a remote sensor. additionally, it is approximated in distinction to geostationary satellites’ top-of-atmosphere brightness temperatures taken from their infrared spectral channels. information gathered by means of this analysis dispenses data regarding both temporal as well as spatial variations of the land (chen et al., 2013). this concept is generally used in a wide range of fields spanning critical concepts in environmental science such as evapotranspiration, hydrological cycle, urban climate, climate change monitoring, vegetation records, and other related studies (guha et al., 2017; chen et al., 2013). the acquisition of lst through multi-temporal remote sensing may be further supported by indices such as (1) the normalized difference vegetation index (ndvi) and (2) the normalized difference built-up index (ndbi). simply put, ndvi is used as an indicator for vegetation cover, whereas ndbi is for levels of urbanization. these are vital indices as they are used for correlation (bala et al., 2018; malik et al., 2019). the analysis of lst combined with ndvi and ndbi data can provide indications regarding the correlation among vegetation and build-up indices as well as surface temperature (chen et al., 2013). substantial alterations in terms of lulc are brought about by anthropogenic and natural factors impacting heat emission of lst (malik et al., 2018). the results that may be collated in this component of the study can illustrate the extensive shift in the ambient temperature of rizal, which can establish evidence of the implications of the changes in land use land cover (lulc) classifications in the area. several studies have been carried out in the philippines that utilize remote sensing to investigate, as well as address issues regarding urbanization and its effects. for instance, are the two recent studies that have been conducted in metropolitan manila. limbo-dizon and dagamac (2023) detect how the coastline of manila was affected by the rapid urban development over the past decades. almadrones-reyes and dagamac (2022) assessed lulc and lst changes in metropolitan manila using landsat imagery. similarly, tiangco et al. (2008), investigated the lst of metro manila, nonetheless, advanced spaceborne thermal emission and reflection radiometer (aster) was used. on the other hand, tinoy et al. (2019), generated spatiotemporal hot and cold spot occurrence maps by utilizing lst images in davao city. while numerous studies have explored urbanization and its impacts in the philippines, focusing primarily on metropolitan areas like manila, and davao, https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 73 research in semi-urban regions such as the province of rizal remains limited (limbo-dizon & dagamac, 2023; almadrones-reyes & dagamac, 2022; tiangco et al., 2008; tinoy et al., 2019). this particularly serves a significant purpose as semi-urban areas are known to be transitional zones between natural and human-modified landscapes (meeus & gulinck, 2008). with that, this study aims (i) to generate an lst map in rizal from the years 1993, 1997, 2014 and 2020; (ii) to calculate the ndvi and ndbi values of the said years; and (iii) to determine and interpret the relationship of lst to ndvi and ndbi in the study area. investigating abiotic dynamics in areas that have yet to be studied can potentially provide novel insights, in a regional aspect, into the processes driving socio-economic change and environmental impact, which, in turn, can contribute to sustainable development strategies. with these set objectives, research gaps may be bridged by elucidating the understanding of biological processes, particularly regarding abiotic factors such as vegetation and temperature. 2. data and methods 2.1. study area rizal (see figure 1), a rapidly growing province situated in region iv-a within the northern-central region of calabarzon in luzon, is recognized as one of the philippines' distinguished first-class provinces. it is specifically located west of metro manila and borders bulacan to the north, quezon to the east, and laguna to the southeast. notably, it stretches along the northern banks of laguna de bay, the largest lake in the philippines. characterized by a rugged terrain, rizal province is nestled within the western slopes of the southern section of the sierra madre mountain range and spans a total area of 1,182.65 square kilometers. the latitudinal and longitudinal extent of rizal are delineated by these coordinates: 14°28’69.79”n-14°89’24.46”n and 121°09’51.58”e-121°46’65.31”e. figure 1. map of the study area, rizal, philippines. the province consists of 1 city and 13 municipalities, with 8 of these municipalities classified as first-class, including angono, binangonan, cainta, pililla, rodriguez, san mateo, tanay, and taytay. in contrast, there are 2 second-class municipalities, morong and teresa, while cardona and baras represent the third and fourth class categories, respectively. this demarcation encompasses a total of 189 barangays, collectively shaping the province's administrative boundaries. serving as capital city of rizal, antipolo plays a pivotal role in anchoring this vibrant province. as per the 2020 census data from the national statistics office of the philippines, rizal province is now home to a substantial population, ranking as the fourth most populous province in the country. over the course of 117 years, it has experienced a remarkable demographic transformation, with its population burgeoning from 50,095 residents in 1903 to a significant 3,330,143 individuals by 2020. this remarkable growth signifies an increase of 3,280,048 people, reflecting an expansion rate of 3.07%. notably, this surge translates to an additional 445,916 individuals compared to the 2015 population count of 2,884,227. https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 74 classified under the koppen climate system as type 1, the study area features two distinct seasons: a dry season spanning november to april and a wet season from may to october. this climate type is conducive to both industrial and agricultural development. moreover, its strategic location makes rizal an attractive prospect for business ventures and settlements, including the establishment of ecotourism destinations, thereby drawing potential investments to the region. additionally, its proximity to metropolitan manila renders it susceptible to the effects of rapid urbanization, given its suitability for urban development. 2.2. data acquisition as a means to detect changes in lst in the study area, remotely sensed imageries obtained from the landsat collection 1-level 1 (specifically, landsat 4-5 ™ c1-level 1 and landsat 8 oli/tirs c1 level 1) accessible via the usgs earthexplorer were used. this resource offers an extensive catalog of satellite and aerial images that are readily accessible for comprehensive data analysis. the process of satellite imagery selection took into account several critical criteria to ensure maximum accuracy and reliability. these criteria encompassed the necessity for minimal land and scene cloud cover, both of which were stipulated to be below 10%, as well as the requirement that the imagery be captured during daylight hours. table 1. details of landsat 5 ™ c1level 1 and 8 oli / tirs c1 level imagery utilized for classification. satellite sensor acquisition date landsat product identifier source landsat 5 multispectral scanner and the thematic mapper 05-04-1993 lt05_l1tp_116050_1993050 4_20170119_01_t1 https://earthexplorer. usgs.gov/ landsat 5 multispectral scanner and the thematic mapper 10-22-1997 lt05_l1tp_116050_1997 1022_20161229_01_t1 https://earthexplorer. usgs.gov/ landsat 8 operational land imager and thermal infrared sensor 02-07-2014 lc08_l1tp_116050_2014 0207_20170426_01_t1 https://earthexplorer. usgs.gov/ landsat 8 operational land imager and thermal infrared sensor 12-24-2020 lc08_l1tp_116050_2020 1224_20210310_01_t1 https://earthexplorer. usgs.gov/ over a period spanning 27 years (1993, 1997, 2014, and 2020), a total of four remotely sensed images were procured (see table 1). these specific years were chosen due to their alignment with the additional criteria established for quality assurance. furthermore, to demarcate the boundaries of rizal province within the remotely sensed imagery, an administrative boundary dataset from the diva-gis database was layered and clipped. this comprehensive approach allowed satellite imageries that met stringent quality standards to be obtained, facilitating an accurate and reliable analysis of the study area's lst. the schematic diagram summarizing the major steps in data acquisition and processing is seen in figure 2. figure 2. methodological framework for assessing the land surface temperature in rizal 2.3. data processing prior to lst analyses of the remotely sensed data acquired, the images had also undergone image preprocessing through qgis 3.20 odense. atmospheric correction was employed since it is important to minimize the differences in surface reflectance (sr), which will enable to conduct a direct comparison between https://doi.org/10.14710/geoplanning.11.1.71-84 https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 75 variations of image dates and different sensors (nazeer et al., 2014). the semi-automatic classification plugin was utilized, wherein landsat data to top of the atmosphere (toa) reflectance and brightness temperature, with dos1 atmospheric correction. prior to the supervised classification of the lulc categories, an adjustment in brightness and contrast of the remotely sensed images is made through arcgis for more accurate classification. in addition, the area of the rizal province was delineated with the use of the administrative boundary shapefile acquired through the diva-gis database and extracted by mask through arcgis geoprocessing tools. 2.4. land surface temperature analysis considering that the data acquired from 1993-2014 are from landsat 5 tm and data from 2020 is from landsat 8 oli, the description, wavelength, and resolution of each landsat corresponds to the different designation of band number, hence, varying equations were used to retrieve certain variables, values of both indices, and the lst of each respective year. this may be due to the complexity that landsat 8 brings, seeing that it is the first of its kind, it has thermal infrared sensors (tirs) as well as an operational land imager (oli) incorporating spectral bands of 1 to 11 with bands of 10 and 11 being the thermal bands. on the other hand, landsat 5 uses a landsat thematic mapper (tm) sensor, and out of its 7 spectral bands, band 6 was used for the conversion of spectral radiance so as to acquire the lst (poursanidis et al., 2015). 2.5. retrieval of land surface temperature calculation from landsat 5 retrieval of lst initiated with obtaining the digital number (dn) image from band 6. the dn was converted to top-of-atmosphere (toa) radiance also known as the spectral radiance, which is denoted by the symbol 𝐿𝜆., 𝐿𝑚𝑖𝑛, and 𝐿𝑚𝑎𝑥𝜆 (which is obtained from the following calculation equation 1) represent the detected scaled spectral radiance to the minimum and maximum quantized calibrated pixel value, denoted by 𝑄𝐶𝐴𝐿, in digitized numbers, both of these spectral radiances are in w/(m2 ster m) (barsi et al., 2003; qin et al., 2001). 𝐿𝜆 = (𝐿𝑚𝑎𝑥𝜆−𝐿𝑚𝑖𝑛𝜆) (𝑄𝐶𝐴𝐿𝑀𝐴𝑋 − 𝑄𝐶𝐴𝐿𝑀𝐼𝑁)(𝑄𝐶𝐴𝐿 − 𝑄𝐶𝐴𝐿𝑀𝐼𝑁) + 𝐿𝑚𝑖𝑛𝜆……(eq.1) spectral radiance is, then, converted into temperature in kelvin. this is denoted by 𝑇 which represents the effective at-satellite brightness temperature of tm6 in the calibration constants 1 and 2, which are represented by the symbols 𝐾1 and 𝐾2. the calibration constant values for landsat 5 tm are as follows: 𝐾1 = 60.776 and 𝐾2 = 1260.56 (schneider & mauser, 1996). once 𝑇 is obtained, considering that the unit of temperature is kelvin, it must be converted into degree celsius by subtracting 273.15 (equation 2): 𝑇 = 𝐾2 𝐼𝑛( 𝐾1 𝐿𝑇 )+1 − 273.15………………………(eq.2) 2.6. retrieval of land surface temperature calculation from landsat 8 similar to that of landsat 5 tm, the initial step is to input a band, however, for landsat 8 oli the band number inserted into the application is band 10 for thermal infrared 1. the equation 3, below represents the first conversion formula for landsat 8 to obtain the top of atmospheric spectral radiance. 𝐿𝜆 = 𝑀𝐿 ∗ 𝑄𝑐𝑎𝑙 + 𝐴𝐿 − 𝑂𝑖 ………………(eq.3) the radiance multiplicative band number is denoted by ml. it is then multiplied to the pixel values or the dn of the quantized and calibrated standard product. 𝐴𝐿, on the other hand, is the additive rescaling factor that is distinct for each band. lastly, 𝑂𝑖 is the correction value for band 10 which is 0.29. once spectral radiance was converted into reflection, the step that followed was the conversion of atmosphere brightness temperature (𝐵𝑇) by the use of the equation below, which utilized the same thermal constants, only with varying values due to the difference in band number used (equation 4). the calibration constant values for landsat 8 oli band 10 are as follows: 𝐾1 = 774.89 mw and 𝐾2 = 1321.08 (guha et al., 2017). https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 76 bt = 𝐾2 𝐼𝑛( 𝐾1 lλ )+1 − 273.15………………..(eq.4) the two indices utilized in the study were the ndvi and ndbi, both of which are derived from three reflectance bands, namely: visible, near infra-red, and short-wave infra-red. in order to acquire the respective values of each index, computations were made possible by using the equation seen in table 2. table 2. retrieval of indices values index equation bands used in landsat 5 bands used in landsat 8 normalized difference vegetation index (ndvi) nir − red band 4 – band 3 band 5 – band 4 nir + red band 4 + band 3 band 5 + band 4 normalized difference builtup index (ndbi) nir – red band 5 – band 4 band 6 – band 5 nir + red band 5 + band 4 band 6 + band 5 it is of utmost importance to retrieve the two indices as it is the required values used to calculate the proportion of vegetation. a method in quantifying the vegetation proportion from wang et al. (2015), suggests that soil and vegetation ndvi values be included so as to fit to a more global context. however, considering the varying ndvi values of different geographical regions, in the case of the study it was best to simply use the minimum and maximum values of dn out of the ndvi image seeing that, generally, the ndvi value itself was quantified from the reflectivity of the spectral radiance (avdan & jovanovska, 2016). 𝑃𝑣 = ( 𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼𝑚𝑖𝑛 𝑁𝐷𝑉𝐼𝑚𝑎𝑥−𝑁𝐷𝑉𝐼𝑚𝑎𝑥 ) 2 …………..…….(eq.5) another aspect required to obtain an estimation of the lst is the land surface emissivity as it is used to indicate the emitted radiance (jimenez-munoz et al., 2006). the equation for the land surface emissivity is denoted by 𝜀 (equation 6). the value 0.004 presented is considered the surface roughness constant (sobrino & raissouni, 2000). on the other hand, since the obtained ndvi denotes that the majority of the land surface is covered by soil, therefore giving an ndvi value of 0 to 0.2, an emissivity value of 0.986 is designated in the equation (avdan & jovanovska, 2016; sobrino et al., 2004; guha et al., 2017). 𝜀 = 0.004 ∗ 𝑃𝑣 + 0.986…..…………….(eq.6) the determination of lst, in celsius, is conditionally quantified by using the equation below; equation 7 (stathopoulou & cartalis, 2007). the symbol ρ represents the wavelength of the emitted radiance, which is given an assigned value of 10.8 for band 10 in landsat 8. 𝑇𝑠 = 𝐵𝑇 {1+[ 𝜆𝐵𝑇 𝜌 ]inε} ………………………….(eq.7) 3. results and discussion four spatial distribution maps of the lst in rizal were generated from arcgis. the various thermal signatures indicated in each of the lst maps are results of the presence of different land cover classes constituting distinct thermal properties. fluctuations of temperature for these years are apparent. however, one aspect is shared among the retrieved data: relatively high temperatures are mostly centralized in the municipalities neighboring metro manila, where bare land, settlements, and population density are at their highest, causing higher concentrations of surface temperature. the spatial distribution of lst across the province of rizal reveals that cities located in the northern, eastern, and southern parts experience comparatively lower temperatures, whereas cities specifically in the western section of the region have higher temperatures. the descriptive statistic exhibiting the values of lst, ndvi, and ndbi is presented in table 3. these values, obtained from landsat 5 tm and landsat 8 oli and tirs satellites, range between 15.18°c and 40.31°c across the entire dataset. based on 1200 points selected from the calibrated satellite imagery, the average lst for 1993, 1997, 2014, and 2020 is 29.34°c, 23.91°c, 25.59°c, and 24.03°c, respectively. https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 77 figure 3. land surface temperature maps of (a) 1993, (b) 1997, (c) 2014, (d) 2020. as shown in figure 3a, the year with the highest surface temperature is 1993. this may be attributed to several biological phenomena that have taken place in rizal during that particular year. alongside this, it can also be observed that the pearson r value for buildup was at its highest. by 1997, the lst visibly decreased in response to the significant decline of barren lands and settlements. this is succeeded by a significant rise in lst for the year 2014. however, in the most recent lst map, the data exhibits yet another decline in surface temperature values. furthermore, it is from this year that the pearson r value for buildup was second to the lowest, followed by the pearson r value of 1997. though almost having the same average lst values from 2014, the pearson r value for ndbi decreased by 0.179. in comparison, the pearson r value for ndvi has increased by 0.186. the results obtained are not limited to this alone. correlations among lst, ndvi, and ndbi were additionally graphed (see fig. 4 and fig. 5). based on the normalized difference indices, it can be observed that a decrease in vegetation and an increase in buildups lead to a rise in surface temperature. the end-to-end results showed an overall increase in the vegetation cover from 1993-2020 in correlation with the decrease in lst simulated in the study. these observations are in coherence with similar studies (kumar et al., 2012; zhou et al., 2014; khandelwal et al., 2017; tran et al., 2017; peng et al., 2020; saha et al., 2021). 3.1. ndvi vs lst relationship the ndvi is the quantification of the amount and vigor of vegetation present at the land surface. this index is related to vegetation to the utmost degree seeing that it can immensely reflect the near-infrared portion of the spectrum (kumar et al., 2012). ndvi is susceptible to seasonal changes; therefore, its marginal variations can easily affect and modify the lst of an area. the relationship between lst and ndvi was determined to be inversely proportional through correlation analysis. in line with this, graphs were generated to further indicate the association between the two. as exhibited in figure 4, lst increases as ndvi value decreases. this can be https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 78 supported by the negative pearson correlation values of -0.754, -0.181, -0.568, and -0.353 for the 1993-2020 datasets, thus, this evidently designates how lst is negatively correlated with ndvi, further reinforcing the statement that areas with the highest amount of vegetation tend to have lower surface temperatures. figure 4. lst vs ndvi 2020 correlation graph of (a) 1993, (b) 1997, (c) 2014, (d) 2020 3.2. ndbi vs lst relationship one of the most established indices used in the processing and quantifying satellite data, especially that of monitoring the presence of build-up or settlements in each area, is ndbi (kumar et al., 2012). the pixel values of ndbi range between negative (-) 1 to positive (+) 1, with greater values indicating highly concentrated build ups. in this study, the relationship between ndbi and lst was assessed using the same methods for ndvi. however, in this analysis, the values of the pearson correlation coefficient were all positive values, indicating that there is a strong and directly proportional correlation between the two variables, which corroborates the premise that as build-up increases, so does the surface temperature (fig. 5). figure 5. lst vs ndbi 2020 correlation graph of (a) 1993, (b) 1997, (c) 2014, (d) 2020 this statement is further reaffirmed, as the acquired results indicate that the highest lst values are found within built-up areas. from this, it can be deduced that densely urbanized areas generate higher surface temperatures. hence, it is considered to be a primary factor in uhi, as previously confirmed by similar studies such as that of malik et al. (2019) and almadrones-reyes & dagamac (2022). the pearson r value for the years 1993, 1997, 2014, and 2020 are as follows: 0.799, 0.428, 0.757, and 0.571. though there may be visible fluctuations in these values, it overall indicates that there is a decline in buildup throughout the years. https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 79 there are numerous environmental parameters that heavily affect the modification and the transition of landscapes, and one such parameter is temperature. it is considered one of the major drivers of the productivity of vegetation in an area, which is directly interconnected with the concepts underlying ndvi and ndbi. in the case of this study, lst is strongly influenced by several factors such as solar incident radiation, angle of incidence of solar radiation, air temperature, and the expanse of vegetation; not to mention topography which encompasses several terrain conditions namely elevation, slope, and aspect. these are all linked to land surface properties like soil moisture and surface roughness (khandelwal et al., 2017; peng et al., 2020). table 3. descriptive statistics of lst, ndvi, and ndbi lst year minimum maximum mean sd 1993 15.1795 40.3119 29.3395 3.7753 1997 12.8362 34.4621 23.9105 1.8889 2014 18.1954 36.8866 25.5940 2.7182 2020 16.7025 35.2090 24.0299 2.6917 table 4. descriptive statistics of ndvi ndvi year minimum maximum mean sd pearson r 1993 -0.3953 0.7333 0.3565 0.1876 -0.7539 1997 -0.3704 0.7692 0.5488 0.1569 -0.1815 2014 -0.1874 0.5895 0.3616 0.1143 -0.5677 2020 -0.1937 0.6072 0.3914 0.1228 -0.3527 table 5. descriptive statistics of ndbi ndbi year minimum maximum mean sd pearson r 1993 -0.4074 0.4958 0.0456 0.1650 0.7981 1997 -0.4203 0.3797 -0.1097 0.0953 0.4284 2014 -0.4188 0.1826 -0.1762 0.0920 0.7567 2020 -0.3982 0.2038 -0.2195 0.0844 0.5710 it can be regarded that areas with relatively high settlements and patches of barren lands have garnered higher lst values. this assertion aligns with the results produced by this study (see fig. 3). this may be attributed to the lack of moisture from the soil surface in view of the natural disturbances that took place prior to 1993, such as a series of el niño events between the years of 1986-1992, which induced severe stress on water resources (hilario et al., 2009). another aspect that highlights the increase in surface temperature is the high amount of solar radiation received in the region. it is also worth noting that during the same period as the natural disturbances, the urban core areas of rizal, which are san mateo, cainta, taytay, angono, and binangonan, have significantly contributed to the intensification of the uhi effect by means of increasing the distribution of hotspots through the establishments of several medium scale residential areas, including subdivisions (regmi, 2017). in these highly concretized areas, ndvi values are at their lowest. the incremental consumption of energy, increase in built-up surfaces of concrete, asphalt, and the like, as well as a significant decline in both vegetation and water surfaces, all constitute the increase in lst (kumar et al., 2012), as it is very much susceptible to various heat discharges coming from increased surface coverage of urban development. this is in alignment with the research conducted by olfato-parojinog et al., which primarily focused on tracing the trends on the land use and land cover (lulc) in the province of rizal. their work that sheds light on the pattern of changes in lulc, and this study, set in the same study area and utilized data collected during the same years that ensures a directly comparable temporal and spatial context, corroborate each other's findings, providing robust evidence for lulc-lst dynamics. just as olfato-parojinog et al. highlighted the lulc https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 80 changes, our findings also emphasize the importance of lst as a key indicator of the various modifications in lulc classes through time. the consistent patterns observed between this study and the work of olfatoparojinog et al.46 affirm the broader relevance of lulc-induced effects on thermal regimes, it becomes evident that the changes in lulc is directly correlated with changes in lst consistently exhibited similar trends. extreme lack of precipitation, coming in the form of insufficiency of rainfall as well as protracted drought, gives off direct and indirect effects not only on the ecological system but also to humans. the el niño events that occurred in the years 2010 and 2014 have caused both meteorological as well as agricultural droughts. meteorological droughts entail a deficiency in precipitation that lasts for a few months or so. alterations in the pattern of atmospheric circulation bring about this phenomenon, which is primarily prompted by occurrences such as el niño-southern oscillation (enso) causing anomalous modification in sea surface temperature. on the other hand, agricultural drought is centered on deficiencies pertaining to soil water, therefore, resulting in low soil moisture and higher lst. these two droughts have significantly contributed to reduced growth of vegetation and decline of crop productivity, further aggravating reduced humidity, not to mention higher rates of soil evaporationresulting in drier environments. fortunately, even though major cities in the philippines, especially those found in metro manila, have undergone notable increase in lst may it be due to natural disturbances like drought or anthropogenic activities such as urban expansion (tiangco et al., 2008; almadrones-reyes & dagamac, 2022), effective measures were put in place to address the situation in rizal. the rejuvenation of vegetated areas may be attributed to the laguna lake development authority-tanay streambank rehabilitation project which is one of the reforestation campaigns conducted in rizal from 2004-2014 that aimed to reforest 70 hectares of private land and establish 25 hectares of agroforestry land to increase riparian forest cover (lasco et al., 2005). in line with this, the microwatershed occupying a notable portion of tanay and the flank of the sierra madre mountain range located east of rodriguez are prominent features found in the northern and eastern municipalities of rizal that also substantially induced a cooling effect due to the presence of dense vegetation as measured by the vegetation index (murdiyarso & skutsch, 2006). the increase in the amount of vegetation due to the enrichment of soil moisture demarcates lst through the continuous change of latent heat coming from the surface transferred through the atmosphere by way of evapotranspiration. furthermore, the decrease in patches of bare lands partakes in narrowing the discrepancies in radiant surface temperature (sun et al., 2020). both aforementioned statements give way to changes in the thermal responses and consequentially generate higher ndvi and lower ndbi values (yuan & bauer, 2007). the reforestation program implemented has further improved the state of rizal, in terms of its lst, as it has slowed down the rate of both agricultural and urban extensification, to a degree. moreover, it has also increased the forest cover, which lowered the study area’s albedo, and, in turn, portions of its absorbed solar radiation increased at the surface. because of this, shortwave radiation, especially during the daytime, is absorbed more, eliciting a warming effect. nevertheless, this is counteracted by a higher amount of latent heat loss by means of increased evapotranspiration, which is primarily influenced by vegetation activity and soil moisture status (peng et al. 2014). the positive modifications in the geophysical characteristics of rizal have somehow rectified the previous predicaments from which the region had previously suffered. evidently, by implementing effective and sustainable reforestation initiatives, it is feasible to progressively attain cooler temperatures. in essence, through integrating well-planned and environmentally responsible measures to restore forested areas, mitigating the effects of uhi, and contributing to the reduction of surface temperature is within the realm of possibility even for third-world countries, like the philippines. as the challenges of urban growth become more concerning throughout time, the significance of managing land resources is more pronounced in order to achieve sustainable development (un, 2015; unfcc, 2015; un habitat, 2018). thus, maximizing the available land resource through sustainable urban planning can achieve shortand long-term objectives given the prevailing land quality and socioeconomic factors in the locality (verheye, 1997; gebre et al., 2021). as the landscape has limited area for greeneries, giving priority for residential and industrial land use, the implementation of compact greenfields can minimize the effects of urban https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 81 heat islands. this can be in the form of establishment of street trees, forest parks, gardens, green walls and roofs, and green corridors and networks (phelan et al., 2018; maruna et al., 2019). residential lands are the most spatially extensive in the peri-urban province, giving limitations for opportunities for vegetation establishment, thus, the government efforts towards urban greening must employ more strengthened and clear land use regulations, for the potential and existing tree covers even in private properties. in addition, involving the community and addressing their inputs and engagement is vital for strategizing policies and development, instilling responsibilities not just for the government, but also for the stakeholders (phelan et al., 2018). since peri-urban vegetation, such as in the case of the province of rizal, plays a significant role in adaptation to climate change effect mitigation, looking further into the accurate and spatially explicit information on land suitability must be employed, involving stabilization of soils, reduction of pollutants, and overall environmental restoration (lagro jr., 2005; un, 2015). the findings from this study highlight the pressing necessity for a more sustainable and holistic environmental management strategy within the rizal province. as urbanization accelerates, temperature fluctuations are magnified, this supports previous studies related to the topic at hand (yang et al., 2016; yuan et al., 2007; zhou et al., 2014). the documented increase in vegetation, coupled with a concurrent decrease lst, suggests a potentially ameliorating effect on heat islands. however, the direct relationship between lst and the ndbi points to the potential worsening of urban heat islands, posing serious challenges to local climate resilience and human well-being, if left unchecked. furthermore, the inverse relationship between lst and the ndvi accentuate the ecological implications of urban expansion on vegetation. these shed light on the intricate dynamics of land use change, temperature regulation, and ecosystem health within rizal province. 4. conclusion this study sheds light on the dynamic interplay between land cover classes and lst in the rizal province. over the years, the region has undergone modifications brought about by natural and anthropogenic-driven disturbances that added to the complexity of its already unique landscape. by correlating lst to ndvi and ndbi, significant insights have emerged. the observed increase in vegetation cover from 1993 to 2020 has corresponded to a notable decrease in lst, pointing out the cooling effect of increased green spaces. additionally, this highlights the role of land cover in influencing temperature patterns. the study's documentation of fluctuating lst, attributed to nearly three decades of shifts in vegetation and built-up areas, accentuate the profound impact of both anthropogenic and natural factors on heat emissions and ambient temperature. these findings emphasize the continuous need for strict adherence of evidence-based land use and environmental policies to ensure a sustainable development towards urbanization and facilitate a harmonious coexistence with the environment in the rizal province, where the intricate relationship among various land cover classifications play a pivotal role in the lst of the area. given the significant role in assessing land cover and its influence on lst, future research should delve into the socio-economic drivers of the changes in the landscape and integration of the flows of urbanization in the province to address the mechanisms of urban expansion. this can be in the form of integration of socio-economic data such as population growth, income per capita, and even land tenure system for a deeper understanding of underlying conditions causing the landscape changes. additionally, looking into the qualitative studies of management strategies and landscape planning and monitoring can give a clearer insight into the possible implementations to sustainable development. community perceptions can also be assessed on the current land use strategies as the engagement of stakeholders is vital in formulating landscape strategies and policies to maintain the balance between urban development and environmental conservation. as the changes in the landscape are mainly due to infrastructure development, assessing the environmental impacts of these projects in relation to ecosystem integrity can give ways to strategize mitigation measures and minimization of urbanization effects. lastly, designing and implementing restoration and rehabilitation projects for degraded landscapes can serve to enhance ecosystem resilience. this can be conducted either with the use of remote sensing techniques or groundbased monitoring. by means of addressing the complex factors driving the changes in vegetation and temperature https://doi.org/10.14710/geoplanning.11.1.71-84 sobremonte-maglipon et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 71-84 doi: 10.14710/geoplanning.11.1.71-84 82 dynamics, policymakers can develop effective strategies for sustainable development and climate resilience in the province. 5. acknowledgements nhad acknowledges the dostphilippine council for agriculture, aquatic and natural resources research and development (pcaarrd) for the balik scientist grant. kjar and jeld acknowledges deutscher akademischer austauschdienst (daad) german academic exchange service for the in country/in region scholarship. pasm and aop would like to thank the dost – accelerated science and technology human resource development program (asthrdp) for the scholarship. the authors declare no conflicts of interest. 6. references almadrones-reyes, k. j., & dagamac, n. h. a. 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[crossref] https://doi.org/10.14710/geoplanning.11.1.71-84 https://doi.org/10.1080/01431169608948757 https://doi.org/10.1080/014311600210876 https://doi.org/10.1016/j.solener.2006.06.014 https://doi.org/10.3141/2293-15 https://doi.org/10.1016/j.jclepro.2020.120706 https://doi.org/10.1080/01431160701408360 https://doi.org/10.5194/isprs-archives-xlii-4-w19-433-2019 https://doi.org/10.1016/j.isprsjprs.2017.01.001 https://doi.org/10.21203/rs.3.rs-442136/v1 https://doi.org/10.1016/s0308-521x(96)00064-9 https://doi.org/10.3390/rs70404268 https://doi.org/10.1016/j.proeng.2016.10.002 https://doi.org/10.1016/j.rse.2006.09.003 https://doi.org/10.1016/j.landurbplan.2011.03.009 https://doi.org/10.1007/s10980-013-9950-5 https://doi.org/10.1016/j.scitotenv.2018.04.091 95 geoplanning journal of geomatics and planning geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 95 122 original research tourism potential zone mapping for the state of madhya pradesh, india using mcdm and machine learning models shrinwantu raha1*, sayan deb1 1. department of geography, bhairab ganguly college, belgharia, kolkata, india doi: 10.14710/geoplanning.12.1.95-122 abstract the rich and diverse tourism attractions of madhya pradesh have long been recognized, but the tourism potential zones (tpzs) have yet to be clearly identified. this research aimed to uncover these hidden potentials using a combination of multi-criteria decision making (mcdm) and machine learning techniques. tpz was predicted using a approaches, including analytic hierarchy process (ahp), linear model (lm), elastic net model (en), and k-nearest neighbors (knn). further, by combining the above models, a new ensemble model (ahp-ln-en-knn ensemble) was prepared. we followed the roc-auc (area under curve) and root mean squared error (rmse) as evaluation measures. the findings reveal a landscape of promise, with each model with accuracy levels ranging from 81.4% to 90.6%. the auc values for the models ranged from approximately 70% to 95%, while the rmse values ranged from 0.8 to 1.3. the ensemble model appeared with better accuracy (for training set 0.92 and for test set 0.88), higher auc value (for training set 94.5% and for test set 89.4%) and the lowest rmse (i.e., 0.71) value. on the other hand, the ahp was identified with higher combined rmse (i.e., combined rmse 1.08) and diminished auc (i.e., for training set 70.1% and test set 70.2%). the northern, south-western, and middle regions emerge as high-potential areas, whilst the south-western edges languish with less promise. meanwhile, the north-western expanse offers a scene of moderate potential. these findings not only inform, inspire, laying a foundation for madhya pradesh's long-term tourist growth. copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction tourism, which is hard to define, may promote a region's environmental, social, and economic growth (telfer & sharpley, 2015). however, its’ enigmatic character requires practicality, precision, and efficacy to fulfill its’ revolutionary potential (harianto et al., 2020). tourism's dynamic and unpredictable tapestry is hidden between exquisite delicacy and cultural sanctity (smith, 2015). (atun et al, 2019) describe tourism potential as a complex web of social, cultural, economic, and infrastructural factors. this initiative attracts tourists with its’ appealing melody of accessibility and lofty quest of guardianship over multiple valuable resources, producing a tapestry of attraction that captivates the adventurous soul (saner et al., 2019). underutilization persists across sectors and paradigms, awaiting its’ transformational potential (ramírez-guerrero et al., 2021). tourism potentiality goes beyond asset accumulation to turn sites into wanderlust hangouts (raha & gayen, 2022a). it nurtures a tourist-environment relationship by curating and protecting a destination's soul (sarker, 2018). the evaluation of tourist potential embodies complex calculation that defines a full mosaic of criteria within the authority of the united nations world tourism organisation (unwto) (trukhachev, 2015). within this e-issn: 2355-6544 received: 09 march 2024; revised: 12 may 2025; accepted: 18 may 2025; available online: 18 may 2025; published: 26 may 2025. keywords: tourism potential zone (tpz), k-nearest neighbors model, analytic hierarchy process *corresponding author(s) email: raha@gmail.com https://doi.org/10.14710/geoplanning.12.1.95-122 mailto:raha@gmail.com raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 96 framework, geology, relief, aspect, and water proximity influence landscape appeal all interact to shape the landscape's attraction (yamin et al., 2021). the complex accessibility network, depicted by road and railway density measures vs distance metrics, controls accessibility dynamics. demographic variables including sex ratio, literacy, population, and growth rates suggest societal vitality, whereas tourism density enhances the region's appeal (stępniak et al., 2019). a complex tapestry of these factors defines visitor potentiality, symbolizing the unwto's holistic approach to tpz evaluation. in fact, exploring the potential of tourism becomes an appeal for countries to develop in unison, strengthening the fabric of culture and way of life. it is an enlightening journey, a pilgrimage to protect the integrity of our nature and our shared past. the indian planning commission considers tourism the nation's second-largest business since it creates lowand intermediate-skill jobs (rajan, 2018). india's business lacks infrastructure and coordination despite its’ historical and natural charms (mamun & mitra, 2012). in madhya pradesh, tpzs are poorly defined (aijaz, 2022). due to efficiency and simplicity, gis and multi-criteria decision-making approaches, notably the analytic hierarchy process (ahp), are increasingly employed for this purpose (raha & gayen, 2022a, 2022b). statistical and machine learning models are also emerging alongside mcdm models. the linear model (lm) is a statistical regression approach that performs well with gis. the supervised learning algorithms along with the lm model are widely used in the landslide prediction (dong et al., 2011), ground water potentiality assessment (naghibi et al., 2015), eco-tourism potential assessment (mirsanjari & mirsanjari, 2012; zhang et al., 2024), forest fire site detection (tien bui et al., 2016), and other fields. the elastic net (en) model reduces overfitting and multicollinearity. en model has been also widely used in the hazard mitigation (suchting et al., 2019), landslide prediction (zhu et al., 2023), flood prediction (al-areeq et al., 2023), non-linear tourist behaviour prediction (brida, 2018), picture categorization (soomro et al., 2016), and more. the knn algorithm swiftly categorizes fresh data (okfalisa et al., 2017). k-nearest neighbour (knn) has three advantages: 1) it is fast to compute, 2) it predicts better than other models, and 3) its’ output is simple to read (okfalisa et al., 2017). after storing all data, the knn model classifies fresh data points using distance functions. the knn model is widely used for the analysis of credit rankings, (chen et al., 2011) pattern recognition (mir & nasiri, 2018), data mining (mohanapriya & lekha, 2018), intrusion detection (aburomman & ibne reaz, 2016), face recognition (nugrahaeni & mutijarsa, 2016; sugiharti et al., 2020), and for the analysis of health related data (mittal et al., 2019). varied models have varied accuracy levels, hence integrated ensemble models are needed to explain real-world events. out of these methods, the ahp have been applied widely for the tpz identification (e.g., raha et al., 2024, sahani, 2019). however, the en, knn and lm methods have been applied rarely for the prediction of tpzs. moreover, their performance has not also been compared in literature. a new ensemble model was also prepared in this research by taking average of ahp, lm, en, and knn models. further, till now, the tourism potentiality of the state of madhya pradesh is unexplored. therefore, the objectives of the present research are as follows: to evaluate and compare the performances of one mcdm technique (i.e., ahp technique), 3 machine learning models (i.e., lm, knn and en model) in the prediction of tpz, and to prepare one ensemble model to improve the performances of the input algorithms (i.e., ahp, lm, en and knn models) for the prediction of tpz for the state of madhya pradesh. the tpzs were explored through an integrated 9step process in this research. the receiver operating characteristic (roc) curve and root mean squared error (rmse) have been adopted to evaluate and compare the machine learning and mcdm models. this research beautifully utilized the gis platform to spatially illustrate the input data as well as the tpz. 2. data and methods 2.1. study area a rich cultural and historical history exists in madhya pradesh. tourism studies favor madhya pradesh, the "heart of india," in the midst of the indian subcontinent for its historical, cultural, and natural splendor. bhopal, indore, gwalior, ujjain, and jabalpur are major cities in madhya pradesh. north, southeast, south, west, https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 97 and north-west boundaries of madhya pradesh are uttar pradesh, chhattisgarh, maharashtra, gujrat, and rajasthan figure 1. source: authors, 2023 figure 1. location map madhya pradesh has unesco world heritage sites including sanchi stupa and khajuraho. for historians, archaeologists, and cultural anthropologists, the area is an engaging case study because of these archaeological wonders, which offer insightful information on the historical importance and architectural history of the region. madhya pradesh is a dynamic laboratory for comprehending the dynamic changes in infrastructure, accommodations, transportation, and tourism legislation . the state comprises a unique scenario for its’ ecological and wildlife tourism through its’ wealth of natural beauty and biodiversity. kanha national park and bandhavgarh national park are just two of the many national parks and animal sanctuaries that madhya pradesh is home to. the existence of tigers and leopards, two iconic megafaunas, has drawn a lot of attention from the fields of wildlife conservation and tourist management. the region is also very rich with the tribal traditions and indigenous populations. many different tribal communities (e.g., bhil, sahariya, kharwar, munda,kol, sora, baiga, kolba, andh etc.), with unique customs (e.g. ghotul), traditions, and artistic expressions (i.e., mandana art etc), are found in madhya pradesh. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 98 there are many studies on tourism in madhya pradesh, including resource development (pandey et al., 2014), social media's impact on tourism (gohil, 2015), art and craft tourism (kumar et al., 2023), and tourism's economic effects (sharma, 2019), role of mass tourism (chandravanshi & jain, 2023; gohil, 2015) development of sustainable tourism sector (kishnani, 2022), eco-tourism (ahmad & pandey, 2016) for the state of madhya pradesh, but hardly any research work is available on tourism potential zone identification on this tract. therefore, the tourism potential zone identification for the state of madhya pradesh is a noble attempt. 2.2. methodology the methodological framework was marked in the figure 2. the 9-step methodology was used in this research to demarcate the tpz of the madhya pradesh state. source: authors, 2023 figure 2. the methodology 2.2.1. data acquisition the first step was to acquire all the data to be used in this research. all of the secondary data sources were illustrated in the table 1. here, the geology (gl), relief (rl), aspect (as), distance from river (dr), road and railway density (rrd), distance from road and railway (drrd), sex ratio (sr), literacy rate (lr), total population (tp) and growth rate (gr) were used. the geological data was downloaded from the usgs. this is a compiled data, which includes petroleum geology, geological provinces, oil and gas fields of the southasia. the data was available in a shapefile (.shp format) format, a part of the u.s. geological survey world energy project. for the efficient management and analysis of enormous amount of data; the world was classified into 8 energy regions and those are further subdivided into geologic provinces, on the basis of the natural geologic entities. these may often include a dominant structural element or a number of contiguous elements. unesco world geologic maps and other tectonic geologic maps helped to delineate the boundaries of major geologic provinces. those shapefiles were amalgamated by the usgs from the unesco. the geologic maps of south and east asia of 1976 and 1990 having scales of 1:10,000,000 and 1:5,000,000 were used here. the relief and aspect maps were prepared from the digital elevation model (dem), synthesized by the shuttle radar topography mission (srtm) having the spatial resolution of 30m. srtm dem was downloaded from usgs earth explorer. river, road and railway shapefiles (prepared from the maps of india online portal) were required for the preparation of drrd and dr layer. euclidian distance method equation.1 was utilized to estimate the drrd and dr spatial layer: https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 99 𝑑 = √(𝑥2 − 𝑥1)2 + (𝑦2 − 𝑦1)2……………. (eq.1) where, d euclidian distance, (𝑥2, 𝑥1) is the point exist on the river or road, (𝑦2, 𝑦1) is the closest point of the previous. the euclidian distance tool was used to estimate the distance from each cell in the raster to the closest source. the sr, lr, tp and gr were collected from the census data prepared by directorate of census operations madhya pradesh, ministry of home affairs, govt. of india (2011). the tourist spots were marked with the help of google earth and non-participant observation technique (banik & mukhopadhyay, 2022) (figure 21). all of the secondary data sources used here contain an open-access licence agreement; which specify that any data can be used for the academic purposes by citing their original sources (ruda, 2016). table 1. data sources for analyse the tourism potentiality 2.2.2. checking of prerequisites a. checking of multicollinearity the correlation matrix was prepared to check whether any multicollinearity exist within the acquired data. for the efficient processing of machine learning and mcdm models, the basic prerequisite is the independent nature of the variable (vairetti et al., 2024). if the correlation coefficient value comes >0.8 then the sl no . criteria sources of data nature of data relationship with tourism references 1 geology (gl) https://pubs.er.usgs.gov/publication/ofr 97470c open access (input/independe nt variable) rutherford et al. (2015) 2 aspect (as) https://earthexplorer.usgs.gov/ (201409-23) resolution (30 meters * 30 meters) open access (input/independe nt variable) inverse woźniak et al. (2018) 3 relief (rl) https://earthexplorer.usgs.gov/ (201409-23) resolution (30 meters * 30 meters) open access (input/independe nt variable) inverse sahabi abed and matzarakis (2018) 4 distance from river (dr) https://www.researchgate.net/post/ho w-to-get-a-river-basin-shape-files-ofindia-kindly-suggestany/615150d9584a141e805d5a83/citati on/download open access (input/independe nt variable) inverse woźniak et al. (2018) 5 road & railway density (road and railway shapefile) (rrd) https://grpbhopal.mppolice.gov.in/railw ay-map & https://www.mapsofindia.com/maps/m adhyapradesh/madhyapradeshroads.htm open access (input/independe nt variable) proportional wang et al. (2018) 6 distance from road & railway (drrd) https://grpbhopal.mppolice.gov.in/railw ay-map & https://www.mapsofindia.com/maps/m adhyapradesh/madhyapradeshroads.htm road and railway shapefile open access (input/independe nt variable) inverse raha et al. (2022b) 7 sex ratio (sr) directorate of census operations madhya pradesh ministry of home affairs, govt. of india (2011) open access (input/independe nt variable) proportional 8 literacy rate (2011) (%) (lr) proportional natalia et al. (2019) 9 total population (2011) (tp) proportional trukhachev (2015) 1 0 growth rate (2011) (%) (gr) proportional banerjee (2014) 1 1 density of tourist spots (ts) non-participant observation technique, google earth, govt. reports and relevant websites dependent variable proportional raha et al. (2022a) https://doi.org/10.14710/geoplanning.12.1.95-122 https://pubs.er.usgs.gov/publication/ofr97470c https://pubs.er.usgs.gov/publication/ofr97470c https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://grpbhopal.mppolice.gov.in/railway-map https://grpbhopal.mppolice.gov.in/railway-map https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm https://grpbhopal.mppolice.gov.in/railway-map https://grpbhopal.mppolice.gov.in/railway-map https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm https://www.mapsofindia.com/maps/madhyapradesh/madhyapradeshroads.htm raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 100 variables are considered as the potentially correlated and dependent. if it is less than 0.8 the variables are considered as the independent and then the models could be applicable on the collected data sets. b. checking of residuals vs. fitted plot, normal q-q plot, and residuals vs. leverage plot before applying the lm model, the residuals vs. fitted plot, normal q-q plots, scale location plot and residuals vs. leverage plot were checked. residuals vs. fitted plot help to detect the non-linear nature, variances (equal/unequal) of error, potential biases and possible outliers (chavan & momin, 2017). normal q-q plot help display the theoretical distribution of associated data sets (i.e., whether normal, exponential or gaussian distribution etc.) determining if two data sets originate from populations with a similar (run-of-the-mill) distribution is another benefit of it. there may be cases, where the results are affected due to the extreme data points, which are influential. the cook’s distance was applied in the residuals vs. leverage graph to calculate the measure the reasonable range beyond which the data points might be influential (zhao et al., 2020). 2.2.3. data normalization the normalization of data is required to remove the redundancy of data, minimize the modification errors, and simplify the query process (ayesha et al., 2020) of the data. the normalization of the data was done in the third step of the research. 2.2.4. data partitioning in the fourth step, total 471 data points were extracted from the each of the raster layers. for the efficient analysis, those were subdivided into the training and test set. 70% of the total data points (i.e., 330 data points) were included in the training set and the 30% of the total data points were included in the test set (i.e., 141 data points) figure 3. source: authors, 2023 figure 3. location of training and test points 2.2.5. setting the input and target variables the tourist spots were added in the gis platform and using the inverse distance weightage (idw) tool, the density of tourist spots (ts) layer was created. this raster layer was considered as the target (dependent) variable which was assumed to be influenced by the other independent variables in this research. therefore, apart from the ts raster variable, other variables were considered as input variables. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 101 2.2.6. employ the analytic hierarchy process (ahp), linear model (lm), elastic net (en) model and k-nearest neighbors (knn) model a. the ahp model the ahp is a widely recognized multicriteria decision making tool (saaty, 1980), which was used in this research to put the weightage of each thematic layer. ahp is an objective mathematical procedure that allows the incorporation of subjective and objective choices. ahp's broad application is a result of its ease of use, readiness, and high degree of flexibility (sahani, 2019). in decision making, the ahp approach incorporates a hierarchical structure of different criteria in a pairwise comparison method (saaty, 1980). ahp matrices display the uniform number of rows and columns (raha & gayen, 2022a). each criterion is rated against the other criteria by assigning a relative priority scale of 1 (equal importance), 3 (moderate importance), 5 (very strong) and 9 (extreme importance) to construct a pair-wise comparison matrix. 2,4,6 and 8 were the intermediate values. relative priority scale was assigned to each of the spatial layer and their classes using the recommendation of 5 expert panel. each expert had more than 5 years of experience in the field of travel and tourism. the experts signed a separate informed permission letter confirming that their replies would be utilised solely for academic reasons without revealing their identities. for example, in the table 2, sr and gr are less important than gl and rl. therefore, sr and gr were coded as 2 and the gl and rl were coded as 9 and 8 respectively. similarly, rrd is less important than gl. therefore, the rrd was coded as 3 and the gl was coded as 9. here, gl was identified with the highest priority (23.8% weightage) followed by the rl, as (18.4% weightage), dr (11% weightage), drrd (8.2% weightage), rrd (5.7% weightage), tp (4% weightage), lr (3% weightage), sr (2.5% weightage), and gr (1.8% weightage). all subclasses of each thematic layer was rated based on the causative factors on which the tourism phenomena triggers. here, higher rating indicates higher tourism potential value. the weightage was estimated by the ahp using the following formula equation. 2 0 < 𝑤 < 1; ∑ 𝑤𝑖𝑗 𝑛 𝑖=1 = 1……………. (equation 2) where, consistency ratios (crs) were calculated to determine, whether pairwise comparisons were consistent or inconsistent. it was calculated as follows equation. 3 : 𝐶𝑅. = 𝐶.𝐼. 𝑅.𝐼 ……………. (equation 3) where, 𝐶. 𝐼. = 𝜆𝑚𝑎𝑥−𝑛 𝑛−1 ……………. (equation 4) where, c.i. is the consistency index; r.i. is the random index. if the cr is <0.1 the index was considered as the consistent (saaty, 1980). in this research, consistency ratios of all matrices table 4 were <0.1 so, all of the matrices were appeared as consistent. b. linear model (lm) the linear model (lm) is expressed here as follows equation. 5: 𝑦 = 𝑎0 + 𝑎1𝑥1 + 𝑎2𝑥2 + 𝑎3𝑥3 + ⋯ … … … … . . +𝑎𝑛𝑥𝑛……………. (equation.5) where, y is the target variable; which is the density of tourist spot raster. 𝑥1, 𝑥2, 𝑥3 … … . . 𝑥𝑛 are the variables in the linear model specified in the table 1. 𝑎1, 𝑎2, 𝑎3 … … . . 𝑎𝑛 are the coefficients. 𝑎0 is the parameter of the model. the parameters a and b are estimated using the ordinary least square (ols) procedure. the ols method is implemented by minimizing the actual and predicted value. c. elastic net model (en) the en model is an enhanced iteration of the machine learning-based regression model that incorporates both lasso and ridge regression. equation. 6, equation. 7, equation. 8: modifying the equation. 5, we can write https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 102 𝑆𝑆𝐸𝑅𝑖𝑑𝑔𝑒 = ∑ (𝑥𝑖 − �̅�𝑖)2𝑛 𝑖=1 + 𝜆(𝑎1 2 + 𝑎2 2 + 𝑎3 2 + ⋯ … … . +𝑎𝑛 2) ……………. (equation 6) 𝑆𝑆𝐸𝑙𝑎𝑠𝑠𝑜 = ∑ (𝑥𝑖 − �̅�𝑖)2𝑛 𝑖=1 + 𝜆(⌈𝑎1⌉ + ⌈𝑎2⌉ + ⌈𝑎3⌉ + ⋯ … … . +⌈𝑎𝑛⌉) ……………. (equation 7) 𝑆𝑆𝐸𝐸𝑁 = ∑ (𝑥𝑖 − �̅�𝑖)2𝑛 𝑖=1 + 𝜆[(1 − 𝛼) ∑ 𝑎2 + 𝛼|𝑎|𝑛 𝑖=1 ] ……………. (equation 8) where, equation. 6, equation 7, and equation 8 are known as the ridge, lasso and elastic net regression model (en) respectively. 𝑥𝑖 is the observed model variable; �̅�𝑖 is the predicted model; sse is the sum of the squared error. 𝜆 is the regularization parameter, that controls the amount of regularization applied. by adding the regularization term, the magnitude of the regression coefficients (a) are penalized by the ridge, lasso and en models. if the α value tends to 0 the equation 8 is transformed into equation 6 ; and when the α value tends to 1 then equation 8 is transformed to equation 7. for the effective use of the en model; the fraction deviance plot and log lambda plot were used. the fraction deviance plot illustrates that how the model coefficients varies with the increase or decrease of the fraction deviance. it helps to determine the larger or smaller coefficients with the changing nature of fraction deviance value. the log lambda plot is essential to know the coefficient scores as a function of log (𝜆). the top numbering of the plot indicates the number of predictors (variables) the model. d. k-nearest neighbors algorithm (knn) model the k-nearest neighbors algorithm, sometimes referred to as knn or k-nn, is a supervised learning classifier that employs proximity to produce classifications or predictions about the grouping of a single data point (boateng et al., 2020). although it may be applied to classification or regression issues, it is commonly employed as a classification method since it relies on the idea that comparable points can be discovered close to one another (zhang et al., 2017). it generalizes well to multi-class problems and can learn complex decision boundaries when combined with ample data. additionally, as it does no training step beyond caching the dataset, it's very effective in situations where training speed is essential and memory resources are ample. here, the euclidian distance metric was used to determine the distance between the given point and query point. the partitioning of various category datasets is one of the decision boundaries that this knn model aids in determining (bansal et al., 2019). 2.2.7. ensemble model (ahp-lm-en-knn model) the ensemble models were prepared by taking average of ahp, lm, en, and knn models (huang et al., 2024). these models were prepared using following equations equation 9: 𝐸𝑛𝑠𝑒𝑚𝑏𝑙𝑒_𝑚𝑜𝑑𝑒𝑙 = 𝐴𝐻𝑃 𝑚𝑜𝑑𝑒𝑙+𝐿𝑀 𝑚𝑜𝑑𝑒𝑙+𝐸𝑁 𝑚𝑜𝑑𝑒𝑙+𝐾𝑁𝑁 𝑚𝑜𝑑𝑒𝑙 4 ……………. (equation 9) before proceeding with the ensemble model preparation, we evaluated the inter-model correlation coefficient. if the correlation coefficient exceeds 0.8, suggesting a high level of correlation between the models, ensemble approaches may not be appropriate (cankurt & subasi, 2022). 2.2.8. weighted sum the variable importance plot (vip) was used to determine the weightage of each criterion utilized in ahp, lm, en and knn models. vip is a popular global method to rate the importance of criteria involved in a model by rating the criterion 0 to 100 based on the priorities in the model. 0 means the lowest priority and 100 indicates the highest priority. those weightages are used to determine the weighted sum model; which is marked as the tpz equation 10. (mitra et al., 2022): 𝑇𝑃𝑍 = ∑ 𝑤𝑖 𝑇𝑃𝑍𝑛 𝑖=1 ……………. (equation 10) where, tpz is the tourism potential zone; 𝑤𝑖 𝑇𝑃𝑍 is the criteria for the tpz identification. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 103 2.2.9. validation of models validation is one of the most critical steps in ensuring the accuracy of any model (mitra et al., 2022, sofaer et al., 2019). there are several ways to validate a model and here, the overall accuracy, roc-auc and rmse metric were used here. the roc-auc curve renders the trade-off between the false positive rate and true positive rate. the false positive rate is shown on the x axis of the roc (e.g. a two-dimensional graph), while the true positive rate is shown on the y axis. equation 11 and equation 12 represents the attributes of x and y axis respectively: 𝑥 = 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 = 1 − 𝑇𝑁 𝑇𝑁+𝐹𝑃 ……………. (eq.11) 𝑦 = 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 = 𝑇𝑁 𝑇𝑁+𝐹𝑃 ……………. (eq.12) 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (𝑇𝑁+𝑇𝑃) (𝑇𝑁+𝑇𝑃+𝐹𝑁+𝐹𝑃) ……………. (eq.13) where, tn, fp, tp, fn, and fp represent true negative, false positive; true positive; false negative; and the false positive values. the auc (i.e., the area under the roc curve) was applied to evaluate the performance of the ahp, lm, elastic net and knn models. the proposed tourism potential map was verified high (code 1) and moderate to low tourism potential points (code 0). further, the tourism potential map was validated using root mean squared error (rmse). lower rmse depicts higher accuracy of the model. in this research, the rmse was estimated using the following formula equation 14: 𝑅𝑀𝑆𝐸 = √ ∑ (𝑥𝑖−𝑦𝑖)2𝑛 𝑖=1 𝑛 ……………. (eq.13) where, 𝑥𝑖 is the observed value; 𝑦𝑖 is the predicted value. the number of observations is denoted as n. the arcgis 10.4 version, r and python were used to process the data in this research. 2. result and discussion 3.1 analysis of criterion (input variable) a. geology (gl) overall, the madhya pradesh has nine geological units, which are carboniferous sedimentary rocks, water, cretaceous sedimentary rocks, quaternary sediments, tertiary and cretaceous sedimentary rocks, paleocene cretaceous extrusive rocks, tertiary igneous rocks, lower triassic to upper carboniferous sedimentary rocks, and undivided precambrian rocks figure 4a. as the scenic beauty of water bodies attract tourists; it creates smooth visual ambience on tourists (36.7% weightage, code 9). on the other hand, the undivided precambrian rocks hold least or equal importance (9.6% weightage, code 1). the tertiary and cretaceous sedimentary rocks are noticed with moderate importance (14.4% weightage, code 3). b. aspect (as) the aspect was reclassified here into four classes; and as the class value decreases, the tourism potentiality is expected to increase and vice-versa. following this, the lowest class was gained very strong to extreme importance (code 8, 55% weightage). on the contrary, the highest class achieved the equal to moderate importance (code 2, 9.4% weightage) figure 4b. other classes were marked with strong to very strong importance and moderate to strong importance. c. relief (rl) the relief of the madhya pradesh fluctuated from 56 metre to 1333 metre. the northern, north-eastern and south-eastern sections of the study area were marked with lower relief (i.e., 56.00 -300 metre). the narmada river is flowing from south-western to north-eastern section and hence this portion attains a moderate to low https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 104 relief (i.e., 300 metre 400 metre). the north-western and south-eastern portions were marked with a higher relief value (400.01metre1333 metre) figure 5a. low relief is suitable for tourism activity (ovreiu et al., 2018) and thus the lowest relief was noticed with higher priority and vice-versa. source: authors, 2023 figure 4 a) geological map; b) aspect map source: authors, 2023 figure 5. a) relief b) distance from river d. distance from river (m) (dr) the dr in the madhya pradesh varied from 0 to 0.170-meter figure 5b. riverine beauty increases the scenic beauty of a particular territory (pouya & başkaya, 2018). therefore, tourism potentiality is positively enhanced by the scenic beauty of the river. here, dr was classified into 4 classes; and as the class value increases; priority decreases and vice-versa. hence, the lowest class (i.e., 0 to 0.042m) was marked with the highest weightage (48.7% weightage, code 8) and the highest class (i.e., 0.128 to 0.170m) was marked with the lowest priority (5.5% weightage, code 2). the lowest class was identified here with the highest areal coverage (%). e. road and railway density (rrd) the rrd fluctuated in the madhya pradesh from 0 to 453 km./sq. km figure 6a. excessively high rrd decreases tourism potentiality but the moderate to low rrd increases the value of tourism potentiality (acharya et al., 2022). here, the rrd has been subdivided into 4 classes and the highest class was identified with the highest priority and vice-versa. f. distance from road and railway (drrd) the drrd varied from 0 to 0.200 meter within the study area figure 6b. if the distance from road and railway increases; tourism potentiality decreases and vice-versa (dedík et al., 2022). here, the drrd was divided into 4 classes and the highest class was marked with the lowest weightage and the lowest class was marked with the highest weightage. the highest class was coded with 2 (equal to moderate importance) and lowest class was coded with 8 (very strong to extreme importance). https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 105 g. total population (tp) the tp varied from 46768 to 3272335 within the study area (figure 10a). the tp is comparatively low in the datia, sheora, ashoknagar, panna, shahdol, dindori, anupur, hadra, burhanpur, jhabua, alirajpur and neemuch districts. higher tp (i.e., 2090307 to 3272335) was marked in the satna, rewa, sagar, jabalpur, bhopal, indore and dhar districts figure 7a. comparatively low tp accelerates the scenic beauty and thus motivates the tourists to visit the madhya pradesh. here, the tp was classified into 4 class, and as the class value decreases, priority increases and vice-versa. source: authors, 2023 figure 6. a) road and railway density b) distance from road and railway source: authors, 2023 figure 7. a) total population b) growth rate h. population growth rate (pgr) low pgr value (i.e.,12.30% to 16.10%) was marked for the chhindwara, hoshangabad, betul, warshinghpur, jabalpur, balaghat, anuppur, ujjain, mandasur, and neemuch districts. the pgr is higher (i.e., 20.21% to 32.70%) for the guna, bhopal, singrauli, indore, barwani, jhabua, dhar, khargone, khandwa, sehore, rajgarh, shivpuri, ashoknagar, gwalior, sheopur, morena, katni, umaria, dindori and sidhi districts figure 7b. remaining districts are marked with 16.11% to 20.20% pgr. higher pgr degrades the environment and thus deteriorates the tourism potentiality (raha & gayen, 2022b). here, the pgr was reclassified into 4 groups and as the different class value of pgr decreases; tourism potentiality increases and vice-versa. i. sex ratio (sr) the sex ratio (sr) of the madhya pradesh varies from 838 to 1021. lower sr (i.e., 838 to 870) occurred for the morena, bhind and gwalior districts. higher sex ratio (i.e., 976 to 1021) was identified for the jhabua, alirajpur, barwani,seoni,belaghat, mandia and dindori districts. remaining districts were noticed with 871 to 975 sex ratios figure 8a. the higher sex ratio helps to flourish the tourism potentiality (rahman, 2021) and therefore as the class value of sr increases; priority increases and vice-versa. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 106 j. literacy rate (lr) the literacy rate of the study area fluctuates from 37.20% to 82.50%. comparatively low literacy rate (i.e., 37.20% to 68.40%) was marked for the jhabua, alirajpur, barwani, sheopur, shivpuri, guna,ashoknagar, rajgarh, ratlam, dhar, khargone, burhanpur, khandwa, tikmagarh, chhatarpur, panna, mandla, dindori, shahdol, umaria, sidhi, and singrauli districts. higher lr was observed for the datia, morena, vidisha, anuppur,sahajapur,sehore,dewas, ujjain, neemuch, raisen, betul, damoh, chhindwara, seoni, katni, satna, rewa, bhind, gwalior, bhopal, indore, hoshangabad, sagar, narsinghpur, balaghat, and jabalpur districts figure 8b. as the tourism potentiality is positively vibrated by the lr (raha & gayen, 2022a); the higher class of lr was marked with the higher priority and vice-versa. source: authors, 2023 figure 8. a) sex ratio b) literacy rate 3.2 density of tourist spots (ts) (target variable) density of tourist spots is a good indicator of tourism potential (chen et al., 2021; marrocu & paci, 2013). the intensification of tourist spots increases the tourist availability and in turn creates destination loyalty. many tourist spots create ample opportunity for tourists to identify many destinations within a single time budget friendly time frame. total 487 tourist spots were identified in the madhya pradesh state; and those were categorized under 11 categories. bhimbetka rock shelter is one of the most popular archaeological tourist sites explored here. apart from it, the bhind fort (castle), maharana pratap square (memorial), matageswar temple (monument), rani roopmati mahal (monument), hanuman statue (monument), tribal museum, darya khan’s tomb (ruins) were explored. the region is very rich in several artwork (e.g, raja bhoj and bhagawan kala kendra etc.), theatre centres (e.g. davy auditorium, bharat bhawan amphitheatres), several attractive scenic beauty places (e.g., purva falls, udaigiri cavesraneh waterfalls viewpoint etc.), water tower, budhist (e.g. southern gate, western gate etc.), christian (e.g. nun monestry), hindu (e.g. nilkanth shiva temple, iscon https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 107 temple), muslim (e.g. jama masjid) and sikh (e.g. gurudwara) tombs. café (e.g. dominos vijay nagar, cafe coffee day cafe kava, chaifeteria etc.), restaurants (e.g. mediterraneo restaurant, my kitchen restaurant, food land restaurant, balaji family restaurant and dhaba etc.), community centres (e.g., akshat garden, relax garden, nani maa ki dharamshala, ravindra bhavan, hindi bhavan, gandhi bhavan, shahpura community hall, sindhu bhavan etc.), giftshop (e.g. handmade items), guesthouse (e.g. sharma guest house, madai forest guesthouse), hotel (e.g., gem palace, royal garden, red maple, pramad palace etc.), motel, park (e.g., kanha national park, rani park, saket park, dravid nagar colony park, naveen nagar park, laxman sing gaur udyan etc.) picnic site (e.g., ekta park, siddha ghat etc.), sports centre, stadium (e.g., railway stadium, cricket stadium, ashbagh stadium, dr bhim rao amedkar stadioum etc.) and several tourist information centres (e.g., ticket counter man-singh-palace, orchah nature reserve, ticket counter, asi counter, government of india tourist office, ticket office, m.p. tourism office, kanha national parkkisli gate etc.),supermarket (e.g. vishal mega mart, aparti super market, aprooti super market) were identified and listed with latitude and longitude (with the help of a gps). overall, the region is dominated by guesthouses and hotels, followed by café, religious places, archaeological sites, giftshops and mall, park, community centres, tourist information centres, and several tourist viewpoints figure 9b. the density of tourist spots is comparatively high at the northern, south-western and middle south-north stretches. on the other hand, the south-western portions were marked with the relatively low density. overall, 31.56% area of the madhya pradesh was marked as the highly dense with the popular tourist spots; and 68.44% area was identified as the low to moderately dense with several popular tourist spots figure 9a. source: authors, 2023 figure 9. a) density of tourist spots b) frequency of different categories of tourist spots 3.3 analysis of prerequisites a. assessment of multicollinearity the correlation matrix was portrayed in the figure 4. it is evident that here the correlation coefficient value fluctuated from -0.49 to +0.17. as all of the correlation coefficient value comes below 0.8; it can be stated that no significant correlation coefficient value exists in the dataset. it was further verified from the scatter plot https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 108 in figure 10. in most of the cases, the points are coalesced in different portions of the plot section. therefore, all variables used in this research is independent and machine learning models can be applied on collected data without any hesitation. further, before proceeding to prepare the ensemble model, the multi model intercollinearity was checked in table 3. here, the correlation coefficient fluctuated from 0.55 to 0.74. therefore, models are not highly inter-correlated with each other and ensemble method could be applicable in determination of tpzs. table 3. correlation matrix between each model models ahp lm en knn ahp 1 lm 0.6 1 en 0.59 0.69 1 knn 0.55 0.73 0.74 1 source: authors, 2023 figure 10. correlation matrix b. residuals vs. fitted plot, q-q plot, scale location plot, residuals vs. leverage plot, fraction deviance plot and log lambda vs. coefficients plot the red line seems to be fitted with the dashed line (parallel to the x-axis) in the residuals vs. fitted plot figure 11a. in the q-q plot. figure 11b the standardized residuals fit with the theoretical quantiles. here all of the data points are exactly aligned over 45°line in the q-q plot. in the scale location plot figure 11c, the residuals spread wider along with the x-axis. moreover, the red line is almost aligned with the dashed line. that means the spread is random. there is no influential case found in the residual vs. leverage plot figure 11d. here, all data points are well inside the cook’s distance line. the coefficients are large in both side of the axis, whenever the fraction deviance value is increasing figure 12a. the coefficients are fluctuating from 0 to -0.25. the coefficients are increasing with increasing the log (𝜆) figure 12b. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 109 source: authors, 2023 figure 11. different plots a) residual vs. fitted; b) normal q-q; c) scale location d) residual vs. leverage source: authors, 2023 figure 12. different plots a) fraction deviance; b) log lambda c. best tuning results after the 5 repetition and 10-fold cross validation, here, optimal 𝜆 and α values were found. in this research, after the best tune the 𝜆 and α value was obtained as 0.0136 and 0.861 respectively. 3.4 estimation of weightages by each model for the ahp; the gl and rl were marked with the highest weightage (23.8% and 21.6% weightages) and the growth rate (gr) (1.8% weightage) was marked with the lowest weightage. for the ahp, the as was marked with 18.4% weightage; dr was marked with 11% weightage, drrd was identified with 8.2% weightage; rrd was identified with 5.7% weightage; tp, lr and sr were marked with 4%, 3% and 2.5% weightages respectively table 2. the layers were reclassified as 9, 8, 7,6,4,3,3,2 and 2 respectively. the gl and rl were marked with the highest code as those two layers were marked with the highest priority. similarly, the sr and gr were identified with the lowest code (code 2) as those were identified with the lowest priority. for the lm, knn, and en model, the rl was marked with the highest priority, and the lr was identified with the lowest priority. for the lm model, drrd, dr, as, gr, tp, sr, gl, were marked with 32.01, 18.80, 17.45, 11.70, https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 110 7.09, 4.98, 2.90, and 0.76 weightages. for the en model, drrd, dr, gr, as, sr, gl, and rrd were identified with 23.14, 13.64, 12.07, 10.80, 5.56, 4.17, 0.57, and 0.02 weightages. similarly, for the knn model, sr, gr, tp, lr, rrd, gl, dr, drrd, and as were identified with 42.91, 15.80, 10.31, 7.47, 4.09, 1.77, 0.50, and 0.16 weightages. for the ensemble model, gr, lr, sr, tp, as, dr, drrd, rrd, rl, and gl, were marked with 6.40, 1.60, 8.40, 4.20, 7.20,6.80, 9.80, 1.60, 4.96 and 4.50 weightages figure 13. the detailed weightages for the ahp and other models were portrayed in the table 2. table 2. estimation of priority of different indicators through ahp source: authors, 2023 figure 13. variable importance plot of different models 3.5 tourism potential zone (tpzs) for all models, the tpzs were classified into the high, moderate and low tpzs. using the ahp model; 31.92% area of the madhya pradesh was identified as the low tourism potentiality; 58.69% area was delineated under the moderate tpz and 9.39% area was marked with high tourism potentiality figure 14a. in case of the lm model, 18.71% area was marked with the low tourism potentiality, 58.20% area was marked with moderate tourism potentiality, and 23.09% area was demarcated as the high tourism potentiality figure 15a. for the en model, 12.83% area, 66.66% area and 20.51% area were demarcated as the low, moderate and high tourism potentiality respectively figure 16a. with the help of the knn model, 15.56% area was marked as the low tourism potentiality, 57.16% area with the moderate tourism potentiality and 27.28% area was identified as with high tourism potentiality figure 17a. for the ensemble model, 23.19% area was demarcated as the low tpz, 65.11% area as the moderate tpz, and 11.70% area was delineated as the high tpz figure 18a. for each case, the northern, south-western and middle south-north stretches were identified with the high tourism potentiality. on the other hand, the south-western portions in each case were marked with the relatively low tourism potentiality. the north-western portions are demarcated with the moderate tourism potentiality in each case figure 14a, 15a, 16a, 17a. parameters gl rl as dr drrd rrd tp lr sr gr priority (%) weightage gl 1 1 2 3 5 4 5 6 7 7 23.8% (9) rl 1 1 1 2 4 5 6 7 7 8 21.6% (8) as 0.5 1 1 2 3 4 5 6 7 8 18.4% (7) dr 0.33 0.5 0.5 1 2 2 3 4 5 6 11% (6) drrd 0.2 0.3 0.3 0.5 1 1 3 4 5 7 8.2% (5) rrd 0.25 0.2 0.3 0.5 1 1 1 2 3 4 5.7% (4) tp 0.2 0.2 0.2 0.33 0.33 1 1 1 2 3 4% (3) lr 0.17 0.1 0.2 0.25 0.25 0.5 1 1 1 2 3% (3) sr 0.14 0.1 0.1 0.2 0.2 0.33 0.5 1 1 2 2.5% (2) gr 0.14 0.1 0.1 0.17 0.14 0.25 0.33 0.5 1 1 1.8% (2) https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 111 in case of ahp based tpz; the high tourism potentiality dominated at chambal (34.37% area); gwalior (15.46% area), indoor (13.02% area), rewa (11.84% area) and sagar (16.77% area) subdivisions. for the lm model; the high tpz was trounced at the chambal (81.65% area), gwalior (27.94% area), indore (37.56% area), narmadapuram (17.81% area). rewa (32.11% area), and sagar (32.75% area) subdivisions. similar feature exists for the knn and elastic net models also; but the percentage area differs slightly. for the knn model, chambal, gwalior, indore were identified with the higher tourism potential with 94.07%, 33.26% and 32.58% area respectively. narmadapuram, rewa and sagar were marked with 29.11%, 23.48% and 56.84% area respectively under the high tpz. for the elastic net model, chambal, gwalior, indore, nardapuram, rewa, and sagar were marked with 80.63%, 27.64%, 37.45%, 16.24%, 23.27% and 27.48% area figure 14b, 15b, 16b, 17b. for the ensemble model, 18% districts were marked under the high tpz category. for the bhopal, gwalior and rewa each, 2 districts were categorized under the high tpz figure 18b, 18c. these sections have moderate relief, and a better accessibility and connectivity network through high road-railway density. apart from it, the distance from the river, the total population and population growth rate are relatively less. the sex ratio and literacy rate are also high in these sections of the study area. source: authors, 2023 figure 14. a) tpz by the ahp technique b) sub-division wise tpz in case of the ahp based model; the low tpz (ltpz) dominated for the jabalpur (47.83% area), narmadapuram (48.43% area), rewa (24.33% area), sagar (24.75% area), shadal (36.23% area), bhopal (35.94% area), gwalior (22.57% area) and indore (24.41% area) subdivisions. the chambal district are identified with the lowest areal coverage (4.09% area) of ltpz. for the lm model; the low tpz subjugates for the bhopal (17.85% area), gwalior (17.44% area), indore (12.52% area), jabalpur (37.68% area), narmadapuram (40.39% area), shahdol (22.45% area) and ujjain (10.43% area) districts. the chambal district are identified with the lowest areal coverage (1.63% area) of ltpz. for the elastic net model, jabalpur (31.02% area), narmadapuram (37.71% area), shadol (19.95% area) and bhopal (8.09% area) districts were marked with higher ltpz. for the knn model, indore (11.56% area), jabalpur (64.04% area), narmadapuram (36.78% area), and shadol (17.13% area) were noticed with higher areal (%) coverage of ltpz figure 14b, 15b, 16b, 17b. remaining districts were marked with less than 5% area in this category. for the ensemble model, 32% districts of the study area were categorized under the low tpz. approximately 2 to 4 districts of bhopal, indore, jabalpur, narmadapuram, and shadol subdivisions were marked with low tpz figure 18b, 18c. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 112 source: authors, 2023 figure 15. a) tpz by the lm model b) subdivision wise tpz source: authors, 2023 figure 16. a) tpz by en model b) sub-division wise tpz https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 113 source: authors, 2023 figure 17. a) tpz by knn model b) sub-division wise tpz in case of the ahp based model; the lowest moderate tpz (mtpz) was marked (46.97% area) for the narmadapuram district. other districts were noticed with 58% to 65% areal coverage. for the lm, knn and elastic net model, the chambal district was marked with the lowest areal coverage (16.71% area; 5.93% and 19.25% area). remaining districts were identified with 40% to 80% areal coverage figure 14b, 15b, 16b, 17b. for the ensemble model, approximately 50% area was demarcated under the moderate tpz. approximately, 2 to 6 districts from each subdivision (except, bhopal and shadol) fall under this category figure 18b, 18c. source: authors, 2023 figure 18. a) tpz by ensemble model b) share of number of districts under different categories of tpz in different subdivisions c) percentage share of number of districts under tpz https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 114 3.6 accuracy assessments for the ahp model; the overall predictive accuracy for the training and test set were marked as 83.1% and 87.3% respectively. the auc for the roc curve for both training and test set were marked as 73.1% figure 19a and 73.4% figure 19b respectively. for the lm model; the overall accuracy of the model was 90.6% for the training data and 81.4% for the test set. for the lm model, the auc for the roc curve for both training and test set were marked as 93.9% figure 19c and 85.1% figure 19d respectively. the overall accuracy for the elastic net model were identified as the 90.3% and 86.5%, respectively for the training and test sets. for the elastic net model, the auc for the roc curve for both training and test set were marked as 93.3% figure 19e and 85.5% figure 19f respectively. for the knn model, 83.5% and 85.7% accuracy were marked for the training and test data respectively. for this model the auc were marked as 90.8% figure 19g and 88.3% figure 19h respectively. for the ensemble model, the overall accuracy was attained as 91.2%for the training set and 89.1% for the test set respectively. rmse for the ensemble model was achieved as 0.33 and 0.46 for training and test set respectively. the auc was marked as 94.5% area figure 20a for the training set and 89.4% area for the test set figure 20b. the combined rmse was lowest for the ensemble mode (i., e., 0.79); whereas it was higher for the lm models. for the ahp; the combined rmse value was moderate table 4. further, the combined auc value was the highest for the ensemble model; followed by knn, lm, en and ahp model table 5. therefore, the ensemble model outperformed the others for the tpz identification in this region. source: authors, 2023 figure 19 auc-roc measurement for different models https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 115 table 4. rmse for each model model rmse for the training set rmse for the test set combined rmse lm 0.36 0.96 1.32 ahp 0.37 0.71 1.08 knn 0.37 0.46 0.83 elastic net 0.37 0.5 0.87 ensemble 0.33 0.46 0.79 table 5. auc for each model roc-auc value model name auc for the training set auc for the test set combined level of accuracy auc ensemble 94.50% 89.40% 183.90% highest knn 90.80% 88.30% 179.10% lm 93.90% 85.10% 179% to en 93.30% 85.50% 178.80% ahp 70.10% 70.20% 140.30% lowest source: authors, 2023 figure 20 auc-roc for the ensemble model a) training set b) test set source: authors, 2023 figure 21 tourist spot identification by non-participant observation technique and google earth imagery https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 116 3.7 detailed discussion and implication of the research the research has several implications via following significant aspects at first, theoretically this research provides an engaging real-world application of leiper's tourism system theory (leiper, 1990) and michael porter’s diamond model (porter, 1998). the tourist, the generating region, the transit route, the destination region, and the tourism industry are the five main components that make up leiper's model, which describes tourism as a dynamic system. the study successfully maps and quantifies these interrelated components throughout madhya pradesh utilising multi-criteria decision making (mcdm) approaches and machine learning algorithms, offering a methodical examination of how tourism operates as an integrated whole. further this research implicates the diamond model of tourism by evaluating and enhancing the regional competitiveness of tourism destinations through a structured, data-driven approach. the diamond model, adapted from michael porter’s original framework (porter, 1998), comprises four key determinants—factor conditions, demand conditions, related and supporting industries, —that collectively influence a destination's tourism competitiveness. through the application of multi-criteria decision making (mcdm) methods and machine learning algorithms, this study quantifies and spatially analyzes these determinants across the diverse regions of madhya pradesh. factor conditions, including geology, relief, aspect, distance from river, cultural such as density of tourist spots, sex ratio, literacy rate, total population and infrastructural such as road and railway density, distance from road and railway are analyzed through geospatial and socio-economic information, determining areas with high tourism value. demand conditions are indirectly considered by taking into account accessibility and connectivity, affecting tourist movements from domestic and international markets. through the integration of these factors into a complete map of tourism potential zones, the research not only strengthens the theoretical model of the diamond model but also makes it more applicable to regional tourism planning. the inclusion of machine learning provides predictive and adaptive functions to the model, allowing stakeholders to foresee shifts in tourism demand and infrastructure requirements. therefore, this study closes the gap between theory and practice by converting the conceptual dimensions of the diamond model into a working tool for destination development, competitive positioning, and strategic investment in madhya pradesh's tourism industry. further, there are many studies on tourism in madhya pradesh, including resource development (pandey et al., 2014), social media's impact on tourism (gohil, 2015), art and craft tourism (kumar et al., 2023), and tourism's economic effects (sharma, 2019), role of mass tourism (chandravanshi & jain, 2023; gohil, 2015) development of sustainable tourism sector (kishnani, 2022), eco-tourism (ahmad & pandey, 2016) for the state of madhya pradesh, but hardly any research work is available on tourism potential zone identification on this tract. therefore, the tourism potential zone identification for the state of madhya pradesh is a noble attempt. in case of ahp based tpz; the high tourism potentiality dominated at chambal (34.37% area); gwalior (15.46% area), indoor (13.02% area), rewa (11.84% area) and sagar (16.77% area) subdivisions. for the lm based tpz; the high tpz was trounced at the chambal (81.65% area), gwalior (27.94% area), indore (37.56% area), narmadapuram (17.81% area). rewa (32.11% area), and sagar (32.75% area) subdivisions. similar feature exists for the knn and en models also; but the percentage area differs slightly. for the ensemble model, 18% districts were marked under the high tpz category. for the bhopal, gwalior and rewa each, 2 districts were categorized under the high tpz. higher tourism potential in any region is boosted by the low aspect (vijay et al., 2016), moderate to low relief (li et al., 2024), and closer proximity (small distance) to river water (maaiah et al., 2023). further, this higher tourism potential is amplified by moderate to low road and railway density (sang et al., 2022) and the closest proximity (small distance) to road and railway (rolando & scandiffio,2022). these portions also marked with comparatively low total population (chen et al., 2019), population density (raha et al., 2021) but higher literacy rate (chen & li, 2023; raha et al., 2021). chambal, gwalior, indoor, rewa and sagar are identified with a popular tourist circuit (olivelle, 2006). the starting point for this travel circuit is indore. the holkar kings' seat was this thriving trading town. from delhi and mumbai, it has excellent air, rail, and road connections. the rajwada, the palace, and the cenotaphs of the holkar kings are some of its intriguing features. it is known as the "mini mumbai" due to the https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 117 significance of its business activity (the market research division, department of tourism, 2003). chambal is well-known for the canoeing safari or white-water rafting (kohli, 2002). gwalior features a tall citadel with 14th-century ad rajput residences and mediaeval monuments (sijatha, 2017). an historic fort and cave provide a touch of heritage to rewa's national park which is known as the bandhavgarh national park (lahiri et al., 2022). it is the greatest location to witness tigers in their native environment (lahiri et al., 2023). sagar becomes extremely attractive in the monsoon because of scenic beauty (rakhra, 2023). those above-mentioned regions bear the state's cultural heritage, are dominated by handlooms. the madhya pradesh tourism development corporation also took necessary steps to popularise these portions such as, development of hotels, lodges and dormitories for providing accommodation to tourists, facilities of accessibility and connectivity through efficient transport networks, developing the tourist information centres, and advertising and marketing of tourist places (ministry of tourism govt. of india, 2023). additionally, the study used an integrated 9-step technique to identify tourist potential zones in the state of madhya pradesh. to the best of our knowledge, this study is a cutting-edge effort in the use of decisionmaking and machine learning models for the accurate prediction of tourist potential zones. the research gives a detailed and analytical view on the on-the dynamics of tourism in the region by examining the spatial distribution of tourist potential levels, classifying them as high, moderate, and low. we performed a thorough evaluation and comparison of both traditional multiple criteria decision making (mcdm) methods, such as the analytic hierarchy process (ahp), and modern machine learning models such as elastic net (en), linear regression (lm), and k-nearest neighbours (knn). it's important to highlight that although the analytic hierarchy process (ahp) is well-acknowledged, the adoption of machine learning models en, lm, and knn for prediction of tourism potential zone identification has been widely appreciated by scholars worldwide. this research not only brings to the forefront the effectiveness of these various methodologies but also offers valuable perspectives on their real-world usefulness in forecasting tourism potential zones in the state of madhya pradesh. furthermore, the intrinsic variety in ensemble model in this research acts as a buffer against the vagaries of uncertainty, protecting decision-making frameworks from the negative influence of outliers and noise. the ensemble model provides decision-makers with a more comprehensive and nuanced understanding of the underlying dynamics governing a given domain by leveraging the collective wisdom distilled from a variety of algorithmic perspectives, empowering them to make informed and judicious decisions amidst the tumult of uncertainty. moreover, the synergistic interplay of constituent algorithms (i.e., ahp, lm, en and knn models) improve ensemble models' ability to infer complicated correlations contained within datasets, allowing for a more in-depth knowledge of underlying phenomena. this collective intelligence, created by the harmonic merger of many algorithms, generates a greater range of ideas, hence increasing the effectiveness of decision-making processes. 3. conclusion madhya pradesh is a very prominent and well-known tourist destination in india. the tpz of madhya pradesh was explored in this research with the help of one mcdm (i.e., ahp), three machine learning (en, lm and knn models) and one ensemble models. the methodology was implemented here through 9 steps. first of all, total 11 layers (i.e., gl, rl, as, dr, drrd, rrd, sr, lr, tp, gr, and ts) was collected and prepared the raster layer in the gis platform. next, the multicollinearity was checked which is one of the basic prerequisites. further residuals vs. fitted plot, normal q-q plot, residuals vs, leverage plot and scale location plot were checked. in each case, the trend line (red) was fitted with the dashed line, parallel with x axis. the residuals vs. leverage plot shows that there exist no influential significant extreme points exist in the dataset. at the third step, the data was normalized to 0 to 1. the data was partitioned into training and test set in a 70:30 ratio at the fourth step. the ts was set as the target variable and the others were set as input variables in the fifth step. next, the ahp, lm, en and knn models were applied in demarcation of tpzs. the ensemble model was prepared at seventh step by combining the ahp, lm, en and knn models. the proposed tourism potential maps were validated through the auc-roc curve and rmse value. https://doi.org/10.14710/geoplanning.12.1.95-122 raha et al. / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 95 – 122 doi: 10.14710/geoplanning.12.1.95-122 118 the ensemble model appears as the best model as it was noticed with a low rmse and higher auc value. the northern, south-western, and middle regions emerge as high-potential areas, whilst the south-western edges were appeared with less potential. meanwhile, the north-western expanse offers a scene of moderate potential. however, by adding more variables, the tourism potential map forecast accuracy may be further ameliorated. furthermore, any alteration to the natural environment brought about by human activity or any changes in the natural phenomena such as, the relief, aspect, or distance from river, may alter the area's current status of tourism potentiality. therefore, the tourism potential zone map should be updated annually by incorporating all the changes. the tpz map can be used as the base data to support the planning and developmental activities in the state. to the best of our knowledge, this research is the first to highlight the tourism potentiality of the madhya pradesh using the decision making and machine learning models. this intricates the novelty of the research. this research holds significant value in advancing sustainable tourism development by integrating multicriteria decision-making (mcdm) techniques and machine learning to identify and prioritize regions with high tourism potential. this innovative approach enhances spatial planning and resource allocation by combining expert-driven criteria assessment with data-driven predictive capabilities, offering a more accurate and dynamic mapping of tourism zones. the study not only aids policymakers and tourism stakeholders in making informed decisions but also contributes to regional economic growth, heritage conservation, and balanced tourism distribution across the state, aligning with broader goals of sustainable development and digital governance.through the integration of machine learning and multi-criteria decision-making (mcdm) methodologies, the research effectively advances sustainable tourism development by identifying and prioritising regions with high tourism potential. by fusing data-driven prediction capabilities with expert-driven criterion assessment, this novel method improves spatial planning and resource allocation while providing a more dynamic and accurate mapping of tourism zones. the study supports balanced tourism distribution throughout the state, historical preservation, and regional economic growth in addition to helping policymakers and tourism stakeholders make well-informed decisions. these outcomes are in line with the larger objectives of sustainable development and digital governance. the tpz map can be used as the base data to support the planning and developmental activities in the state. to the best of our knowledge, this research is the first to highlight the tourism potentiality of the madhya pradesh using the decision making and machine learning models. this intricates the novelty of the research. future researchers should consider expanding the spatial and thematic resolution of datasets to improve model precision and scalability. they should integrate dynamic datasets such as real-time tourist footfall, social media sentiment, environmental change indicators, and transportation network updates to better capture evolving tourism patterns. incorporating participatory gis and crowd-sourced local knowledge can further enhance model accuracy and stakeholder relevance. comparative analyses between different mcdm techniques (e.g., topsis, promethee) and ml algorithms (e.g., random forest, gradient boosting, neural networks) should be systematically conducted to identify the most robust and context-sensitive combinations. researchers are encouraged to explore ensemble modeling approaches to minimize uncertainty and enhance predictive validity. future studies should also address the interpretability and explainability of ml outputs to facilitate practical implementation by policymakers and tourism planners. furthermore, integrating sustainability indicators—ecological, cultural, and socioeconomic—into the tpz framework can ensure that development strategies align with long-term conservation goals. finally, establishing a temporal component in tpz models to analyze seasonal variations and long-term trends could significantly improve planning effectiveness and the adaptability of tourism strategies in madhya pradesh. 4. references aburomman, a. a., & ibne reaz, m. b. 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revised: 15 may 2025; accepted: 15 may 2025; available online: 14 may 2025; published: 28 may 2025. keywords: urban morphology, kernel density estimation, cultural heritage, mae hong son *corresponding author(s) email: oin_s@su.ac.th https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 70 mae hong son is recognized as a historic city under the `prime minister's office regulation on rattanakosin and old towns (2021) by the office of natural resources and environmental policy and planning (onep). mae hong son has a long history, evolving from a trading outpost along the mae hong son river to its current status as a significant urban center. its heritage includes tangible and intangible elements such as ancient monuments, architectural styles, artistic works, traditional lifestyles, and cultural practices. this research explores the development and transformation of mae hong son through historical documents, field surveys, and spatial analysis using kernel density estimation within a geographic information system (gis). the aim is to gain a deeper understanding of the city's urban development and the cultural and social dimensions associated with these changes. the findings will contribute to recommendations for sustainable conservation and development of mae hong son as a historic city, ensuring its cultural identity is preserved for future generations. the importance of studying historical cities and urban morphology lies in the preservation of culture and history, particularly in maintaining local identity, which is often affected by urban and economic development. research such as urban morphology and conservation in china (whitehand et al., 2011) (xie et al., 2020) and urban morphology and historical urban landscape conservation and management (zhang & li, 2022) highlights the critical role of urban morphology in managing historical urban areas through sustainable development strategies. similarly, studies like urban morphological analysis framework for conservation planning and management (mohamed et al., 2018) emphasize the importance of using urban morphology as a tool for conservation planning and management. spatial analysis techniques are widely applied in urban morphology studies to analyze urban forms. examples include urban build-up building change detection using morphology based on gis (moe & sein, 2016) and urban road change detection using morphological processing (win, 2021), which employ change detection techniques to study urban morphological changes. other research utilizes kernel density estimation to analyze the distribution and transformation of urban structures and economic activities, as seen in studies by zehul et al. (2021), and king et al. (2015). these techniques have been extensively used to study urban morphology across various regions. understanding urban morphology contributes to contextual insights into the development of historical cities, ultimately leading to urban conservation (whitehand, 2015). for instance, studies on the conservation of kiruna, sweden (jennie & erik, 2020), and the preservation of urban and architectural heritage (umar et al., 2019) demonstrate the interplay between different factors and urban development impacts on historical areas. this review underscores the need for urban morphological studies that balance economic growth with cultural heritage preservation. it provides a foundation for investigating the urban morphology and development of mae hong son to achieve a harmonious balance between economic development and the preservation of cultural identity. 2. data and methods 2.1. study area mae hong son (figure 1), a province in the northern of thailand, is notable for its unique geographical features, cultural diversity, and multi-ethnic population. mae hong son’s old city is situated along the mae hong son river, covering an area of approximately 6 square kilometers at an elevation of 250–400 meters above sea level. the city is located within a mountainous basin, flanked by two streams: the mae hong son river to the south and the bu stream to the north. these streams converge at ban sop pong before merging with the pai river. historically, the original city was oval-shaped and relatively compact, with initial settlements covering an area of about 0.4 square kilometers near the riverbank. over time, the community expanded along roadways, resulting in urban growth. nong chong kham, a central feature of the area, is located to the south of the early settlement. the city's diameter extended to about 1 kilometer, encompassing an area of approximately 1.5 square kilometers. evidence indicates that the city's ancient moat was completed in 1885 (be 2428). however, remnants https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 71 of the moat no longer exist today, with some sections transformed into drainage channels. this historical and geographical context underscores the significance of mae hong son's old city as a cultural and historical landmark. source: author, 2025 figure 1. study area: the ancient city of mae hong son 2.2. data and methodology in this study, the researchers utilized aerial photographs and high-resolution satellite imagery from 1971 to 2023 within the wgs 1984 utm zone 47q coordinate system. the data included black-and-white aerial photographs at a scale of 1:15,000 from the royal thai survey department in 1971 and 1984, color aerial photographs at a scale of 1:25,000 from the ministry of agriculture and cooperatives in 2002, and highresolution quickbird satellite imagery from 2023. the selection of the time period was based on the availability of aerial photographic data and the major urban development periods of mae hong son, with 1971 being the pre-urban expansion period prior to major infrastructure development in 1984, reflecting initial growth influenced by road expansion, 2002 corresponding to significant urban expansion under regional tourism initiatives, and 2023 being the latest development period for comparative analysis. the digitization process was performed to extract building structures and road networks from these images. these data were then analyzed to study the distribution and density patterns of built-up areas using change detection techniques (lu et al., 2004; coppin et al., 2004) and kernel density analysis. these methods were employed to examine the spatial patterns and expansion dynamics of urban development over time. 2.3. change detection and kernel density estimation change detection is a process used to identify changes in data by comparing two distinct time periods. it is commonly applied in the context of satellite imagery or spatial data to analyze changes in landscapes, environments, or infrastructure (picard, 1985; ghaderpour & vujadinovic, 2020). a widely used technique in this process is classification comparison, which involves comparing the classification results of imagery from two different time periods (mas, 1999). kernel density estimation (kde) is a method for analyzing point pattern distributions, falling under the principles of quantitative geographic analysis (maurizio et al., 2007). it is a non-parametric statistical technique used to estimate the probability density function (pdf) of a random variable. the method aims to approximate https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 72 the pdf of a dataset based on existing sample data. spatial data points are analyzed using geographic information systems (gis), and the results are typically presented as a raster grid. kde was chosen over other spatial density methods (e.g., ripley’s k-function or spatial autocorrelation indices like moran’s i) due to its ability to produce continuous surface density maps that visually represent urban intensity. kde is particularly effective for identifying urban cores, expansion zones, and development hotspots (figure 2). its flexibility in setting bandwidth and kernel function makes it suitable for analyzing urban forms in compact, topographically constrained cities. the principle of kde involves calculating the radius for each data point and connecting it with other points using a specified bandwidth to determine density. this approach can improve the accuracy of predictive models. the density function (equation 1) is expressed as follows (hastie et al, 2001): 𝑓 (𝑥) = 1 𝑛ℎ ∑ 𝐾 𝑛 𝑖=1 ( 𝑥 − 𝑥𝑖 ℎ ) … … … … … . (𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛. 1) where: 𝑓 (𝑥) – the density of data at position 𝑥, 𝑛 – number of samples, ℎ– bandwidth, 𝐾 – kernel function, 𝑥𝑖 – sample data values source: author, 2025 figure 2. conceptual framework 3. result and discussion the study revealed that, based on historical aerial photographs and current satellite imagery, the city of mae hong son has undergone urban expansion over time (figure 3). the city has grown outward from its original center, which historically served as a trading hub for travelers. this area has since transformed into a residential zone, while still maintaining its historical significance. it continues to reflect the multicultural interactions of settlers, particularly the tai yai (shan) community, who have preserved the city's history, architecture, and landscape. these characteristics provide a foundation for promoting cultural tourism in the region. key historical sites include sai yut market, the old market (pok kad kao), the city pillar shrine, and nong chong kham. these landmarks highlight the city's rich cultural heritage and its potential for sustainable tourism development. 1971 1981 2002 2023 source: author, 2025 figure 3. aerial photographs and high-resolution satellite imagery from 1971 to 2023 https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 73 when examining the urban morphology, it was observed that between 1971 (figure 4) and the present (figure 5), the city has significantly expanded outward from its original center (table 1). the most notable growth occurred in the northern area near the airfield, due to the flat terrain located within a valley. in contrast, expansion in other directions has been limited by natural barriers such as mountains and rivers, which have constrained urban development in those areas. source: author, 2025 figure 4. aerial photograph and urban morphology of mae hong son city in 1971 source: author, 2025 figure 5. aerial photograph and urban morphology of mae hong son city in 2023 table 1. comparison of the number of buildings and the area size of mae hong son city before 1971 1971 1981 2002 2023 building 1,328 2,855 6,002 10,053 11,948 area (km2) 0.47 1.22 3.58 8.31 9.71 source: author, 2025 when examining the building patterns, it was found that mae hong son city has a concentration of buildings in the center of the community, particularly along main road, covering an area of approximately 0.45 https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 74 square kilometers (figure 6 and table 2 & 3). by 1984, the city had expanded to the northern outskirts along the road to the north of mae hong son airport, and to the west along the road leading south. in 2002, the city continued its expansion northward, reaching the area north of mae hong son airport. by 2013, development had extended further south and west. in terms of building characteristics, the old city area of mae hong son primarily consists of one to two storey buildings clustered in the city center along the main roads. taller buildings, ranging from four to five storeys, are located near the hospital, situated to the east of the area. source: author, 2025 figure 6. building usage types and number of floors of buildings in mae hong son city in 2023 table 2. number of floors of buildings in mae hong son city between 1971 – 2023 year building floor 1 2 3 4 5 6 frequency total 1971 1,918 844 83 7 2 1 2,855 2,855 1984 2,483 600 54 8 1 1 3,147 6,002 2002 3,692 348 7 3 1 4,051 10,053 2023 1,747 146 1 1 1,895 11,948 total 9,840 1,938 145 19 4 2 11,948 source: author, 2025 table 3. building usage types in mae hong son city between 1971 – 2023 ty building use building 1971 1984 2002 2023 total residential 2,186 2,418 3,455 1,653 9,712 commercial 28 50 6 2 86 industrial 17 16 4 37 mixed use 2 2 4 public utilities 543 580 383 93 1,599 public facilities 37 47 23 1 108 cultural heritage 1 1 2 agricultural 19 24 86 136 265 others 23 12 95 5 135 total 2,855 3,147 4,051 1895 11,948 source: author, 2025 when analyzed using kernel density estimation (kde) to assess the distribution of buildings, it was found that between 1971 and 1984, the city expanded predominantly to the north and south (figure 7). from https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 75 1984 to 2020, the expansion continued at an increased rate, with more buildings being constructed across a broader area. however, during the period from 2002 to 2023, the expansion slowed down considerably, and the city underwent minimal change compared to earlier periods. this suggests that urban development in mae hong son became more stable, with limited growth and fewer new construction areas. this trend indicates a potential saturation of available land or a shift towards more sustainable urban planning strategies in the city. (a) (b) (c) (d) source: author, 2025 figure 7. comparison of urban expansion analysis using kernel density estimation (kde) technique. 4. discussion this analysis shows the changes in the urban morphology of mae hong son city from the past to the present, utilizing data from aerial photographs and satellite imagery, along with analysis using kernel density estimation and change detection techniques. this analysis results show the changes in morphological characteristics of mae hong son city from the past to the present using data from aerial photographs and satellite images, along with analysis using kernel density estimation and change detection techniques, which is consistent with the study by guan et al. (2024) which examined three decades of urbanization in qingdao city, china, as well as the research by patel et al. (2024), which employed high-resolution satellite imagery to analyze land use changes in ahmedabad city, india. the findings reveal that mae hong son city has undergone significant physical changes and urban expansion from the 1970s to the present. in the past, urban growth primarily concentrated around the commercial center and gradually expanded into surrounding areas, https://doi.org/10.14710/geoplanning.12.1.69-78 o-in / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 69 78 doi: 10.14710/geoplanning.12.1.69-78 76 particularly to the north, due to geographic constraints such as surrounding mountains. the city's expansion is characterized by a combination of traditional buildings and modern architecture, while still maintaining the unique identity of mae hong son city. this transformation reflects the interaction between urban development and external forces such as transportation, economy, and tourism. the results of this study align with previous research on ethnic settlement patterns (teerarojanarat, 2012), studies on the urban development of ancient cities in grevana, greece (apostolou et al., 2024), and research on the settlement development of batu, indonesia (witjaksono et al., 2023), all of which employed gis and remote sensing techniques to study urban expansion. additionally, this study is consistent with research on settlement distribution using kernel density analysis in andean, argentina (lazzari et al., 2024), land-use classification within cities (brandes, 2024), and spatial pattern analysis of rural towns such as pingnan in fujian, china (chen et al., 2024). these studies highlight the significant contribution of this research in enhancing understanding of historical urban development and emphasize the importance of preserving the cultural and archaeological identity of the historic city of mae hong son. the findings are in line with those of li et al. (2023), who utilized gis to delineate protection zones in response to increasing tourist pressure at the wulingyuan world heritage site in china; amato et al. (2017), who employed maps and aerial photographs to guide cultural heritage conservation amid urban expansion in altamura, italy; and li et al. 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[crossref] https://doi.org/10.14710/geoplanning.12.1.69-78 https://doi.org/10.1016/j.habitatint.2019.102098 https://doi.org/10.1080/10095020.2021.1978276 187 geoplanning journal of geomatics and planning geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 187 198 original research spatial upgrading of riverbank slums towards sustainability of watershed infrastructure hakimatul mukaromah1,2*, chrisna t. permana1,3, zumrotus sa’adah4 1. urban and regional planning program, universitas sebelas maret, indonesia 2. center for information and regional development, board of research and community services universitas sebelas maret, indonesia 3. center for environmental research, board of research and community services universitas sebelas maret, indonesia 4. department of environmental engineering, universitas diponegoro, indonesia doi: 10.14710/geoplanning.12.2.187-196 abstract due to limited land availability, riverbanks are frequently the preferred location for the establishment of slums or squatters. the expansion of these areas can diminish the capacity and sustainability of urban drainage system. it is envisaged that the upgrading of slum settlements on riverbanks will not only enhance livelihood levels but also contribute to the watershed's sustainability as primary drainage. the research study area is kampong mojo, a pilot project for slum upgrading along the bengawan solo river. this article seeks to determine how slum upgrading and infrastructure can contribute to the sustainability of the bengawan solo watershed’s supporting infrastructure. in this study, qualitative and spatial analysis were utilized, with data support provided by field observations, interviews, and document research. furthermore, data and information will be analyzed in three stages: (1) mapping the land use change of infrastructure and settlement along the river; (2). identification of settlement riverside upgrading models; and (3). analyzing the relevance of settlement planning on the sustainability of the watershed infrastructure. the findings of this study indicate that, for a river to function optimally as a primary drainage and flood control system, it is essential to promote the development of watershed-supporting infrastructure by strategically structuring land use along the river and enhancing the community’s capacities. this study highlights the significance of an integrated approach to slum management, thereby facilitating the government's capacity to implement more inclusive and sustainable riverbank management. copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction the urban population is increasing annually and is expected to reach 67.1% in 2045 (bps, 2018). if rapid growth in urban areas is not properly managed, it will present a number of problems that hinder the achievement of existing development goals (sagala et al., 2022). limited land for proper settlements with infrastructure support is one of the problems that often arise. as a result, informal settlements have grown in critical urban areas, such as riverbanks. the emergence of informal settlements with the use of semi-permanent physical materials is also caused by social and economic factors and does not require land ownership (pramantha et al., 2021). the riverbank area is one of the locations that people are interested in because the riverbank area is considered to provide economic and cultural value (sultana & alam, 2023), as a source of livelihood, and even as a recreational space (hawa et al., 2023). therefore, the implementation of land-use planning strategies in riparian areas is crucial to promote environmental sustainability (buchori et al., 2015; pihui et al., 2024). e-issn: 2355-6544 received: 22 may 2024; revised: 08 may 2025; accepted: 21 may 2025; available online: 31 october 2025; published: 31 october 2025. keywords: drainage system, gis, riverbank slum, sustainable infrastructure, watershed *corresponding author(s) email: hakimatul.m@staff.uns.ac.id https://doi.org/10.14710/geoplanning.12.2.187-196 mailto:hakimatul.m@staff.uns.ac.id mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 188 on the other hand, slum settlements that develop along riverbanks can affect the river’s capacity and function as part of the drainage system. the diminished capacity of rivers can increase the vulnerability of areas along riverbanks to flooding. the area along the watershed eventually lost the riparian areas that should have been used as a location for river infrastructure development, such as the construction of inspection roads and others. as part of the urban drainage system, river and their supporting drainage infrastructure (parapet embankments, water pumps, etc.) must be managed in a systematic and sustainable manner so that they can function optimally. with the state of riverbank slums, the vulnerability to floods rises. this is exacerbated by the lack of drainage, sanitation, and open space in slum areas. this vulnerability also increases when there is no attempt to mitigate flood conditions (nasution et al., 2022). surakarta is a city that is intersected by tributaries of bengawan solo river. the historical development of residential along the riverbank has been significantly shaped by confluence of geographic advantages, including land availability, accessible to city center, and other socio-economic factors. semanggi is a highpriority slum neighbourhood due to the fact that several sections of the slum area are located in critical areas along the banks of the bengawan solo and premulung rivers. the concept of structuring slum settlements in the semanggi area, especially in mojo urban village, with illegal land differs significantly from other slum upgrading locations (meilasari-sugiana et al., 2018; bawole et al., 2020; taylor, 2015; widyaningsih & van den broeck, 2021) is labelled as the structuring without demolition. this phenomenon also occurs in other cities in indonesia such as jakarta and cagayan de oro in southeast philippines. in jakarta, providing land for the construction of apartments as a resettlement location is a challenge for the jakarta provincial government during the resettlement of kampung pulo squatters. not only are there substantial expenditures associated with providing this land, but there are also negotiations involved in picking a place that fits the qualities and interests of the community (meilasari-sugiana et al., 2018). if the site decision is inappropriate, it can frequently lead to numerous other issues. several slums with eviction solutions (jakarta, indonesia and cagayan de oro in southern philippines) frequently create new problems, with residents losing not only home possessions, but also their self-esteem, cultural identity, social networks, and place connections (widyaningsih & van den broeck, 2021), failure of housing projects in terms of occupancy rates or abandonment of properties, destruction of livelihoods and community relationships, and fragmented settlements (carrasco & dangol, 2019). as was the case in nanga bulik, kalimantan, indonesia, the partial relocation of slum communities on riverbanks is also a viable solution. those without land rights are relocated, while those with land rights can improve the quality of their houses and its environment (purwanto et al., 2017). the concept of structuring slum settlements without resorting to demolition but instead focusing on enhancing their infrastructure can serve as an all-inclusive solution. following this concept, efforts to improve infrastructure aim to raise environmental quality and reduce flood risks (michiani & asano, 2019). this concept can be implemented if disaster risk can still be managed without complete or partial relocation (dangol & carrasco, 2019). enhancing the quality of infrastructure and the surrounding environment can lessen the level of vulnerability to flooding (nasution et al., 2022). the construction of inspection roads and long embankments on the riverbank is also considered capable of enhancing the environmental quality of slum settlements (pusporini et al., 2021). in addition to improving the physical element, a social and economic approach is required through strengthening or empowering the community to ensure the sustainability of slum settlement (hawa et al., 2023). moreover, the participation and cooperation of each stakeholder can raise the level of social capital, reducing disaster risk (buchori et al., 2022; rustinsyah et al., 2021). the case study of the concept of structuring slums in kampong mojo is an example of an integrative planning effort with a macro-drainage system in urban areas that is distinct from both the structuring of slums and the management of drainage systems in other locations (dangol & carrasco, 2019; purwanto et al., 2017; widyaningsih & van den broeck, 2021). it is envisaged that upgrading slum settlements on riverbanks will not only enhance livelihood levels but also contribute to the watershed's sustainability as primary drainage (hawa et al., 2023; pihui et al., 2024). many strategies for addressing issues related to flood and watershed sustainability are community-based or community-initiated (auliagisni et al., 2022). however, there has been limited discourse https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 189 on riverbank governance that integrates with the surrounding land use, particularly through a slum upgrading scheme that involves a more prominent role for the government. consequently, the objective of this article is to introduce a novel concept for slum upgrading that encompasses enhancements in both spatial and social dimensions, can contribute to the sustainability of the bengawan solo watershed's supporting infrastructure. 2. data and methods 2.1. study area the case study area is the location of the slum settlement along the banks of the bengawan solo river in kampong mojo, surakarta city. this location is one of the locations identified as a slum area by the decree of the mayor of surakarta number 413.21/38.3/1/2016 in 2016 about the determination of the location of housing and slum environmental areas in the city of surakarta. since 2017, slum settlement arrangements have been carried out. the concept of structuring slum settlements in the semanggi area, especially in kampong mojo, with illegal land differs significantly from other slum upgrading locations (meilasari-sugiana et al., 2018; bawole et al., 2020; widyaningsih & van den broeck, 2021) and labelled as the structuring without demolition. it is expected that the case study can offer a novel approach to riverbank governance by incorporating the sustainability of watershed infrastructure and organizing land use along the river. figure 1 displays the map of the research locations. source: cnes/airbus image recording, 2023 figure 1. the map of kampung mojo as research area 2.2. method the case study method is employed to examine contextual conditions that are pertinent to the phenomenon of the study. case studies cover the logic of design, data collection, and specific approaches to data analysis (yin, 2009). this method was utilized due to its appropriateness in attaining the research objectives, specifically in the exploration of concepts through relevant and contextual case studies. data collection was carried out using semi-structured interviews, focused group discussion (fgd), field observations, and documentary reviews. semi-structured interviews and fgd methods involved the key persons from the representatives of the slum upgrading project; kotaku (cities without slums organisation); government https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 190 unit of housing and settlement; government unit of public works, head of mojo urban village, and head of neighbourhood 01 (rw.01). the selection of respondents was determined based on their level of importance and influence in the management of watershed infrastructure and slum upgrading projects. the purpose of semistructured interviews and fgd are to investigate the expansion and characteristics of settlement along the river, the vulnerability of settlement to flooding, and the historical process of watershed infrastructure and slum upgrading. a documentary review and field observation method were conducted to review the implementation of slum upgrading planning, the spatial changes of riverbanks, and flood-related data. spatial data is used to describe changes in land cover that occurred before and after the parapet was built and changes in the settlements around it. the spatial data utilized is obtained from satellite imagery (copyright 2023 cnes/airbus, image recording 2013 – 2023). the analysis techniques used are qualitative analysis and spatial analysis. spatial analysis utilizes geographic information systems (gis) to discern alterations in land cover throughout a predefined temporal interval. there are three stages used in analyzing the data obtained: 1. mapping the land use change of infrastructure and settlement along the river; 2. identifying the process of settlement riverside upgrading models; and 3. analyzing the relevance of settlement planning on the sustainability of the watershed infrastructure. the research diagram can be seen on figure 2. in the end, it can be seen how the slum upgrading and the infrastructure can contribute to the sustainability of the bengawan solo watershed's supporting infrastructure. figure 2. research method diagram 3. result and discussion the case study is discussed in three stages. first, we explain the vulnerability of watershed infrastructure and the expansion of settlement along it by mapping infrastructure and settlement land use changes along the river. second, we outline the riverbank slum upgrading process. finally, we examine the importance of riverside slum upgrading to the long-term viability of watershed infrastructure. 3.1. the vulnerability of watershed infrastructure and settlement growth in kampong mojo the case study area, kampong mojo, is located on the banks of the bengawan solo river and partially on the banks of the premulung river. this area is included in the flood-prone category because its elevation is below the river’s surface; consequently, it continues to experience flooding each year, particularly during the rainy season. therefore, a 320-meter-long parapet was constructed in 2017 to prevent the overflow of river water into residential areas during the rainy season. parapet construction is an enormous investment and is expected to have a service life of up to twenty years. in addition to constructing parapets, it is important to develop other supporting infrastructure such as inspection roads, drainage belts, and water pumps in order to maximize parapet function, control parapet quality, and regulate river capacity (interview with kotaku, 2023). https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 191 source: field observation and cnes/airbus image recording, 2013 2018 figure 3. growth of riverbank’s settlement before slum upgrading program table 1. the land use change of kampong mojo land use year 2013 2016 2017 2018 commercial area 1,026.68 3,037.71 3,037.71 3,037.71 housing & settlement 32,685.11 32,919.06 32,446.29 31,696.85 riverbanks 19,410.33 17,618.70 17,618.70 18,491.10 retaining wall 0 0 535.29 348.66 empty ground 1,026.68 0 510.81 574.48 roads 1,454.20 1,454.20 1,454.20 1,454.2 total area 55,603.00 55,029.67 55,603.00 55,603.00 *area in square meters source: field observation and cnes/airbus image recording 2013 – 2018 on the other hand, riverside settlements continue to expand and develop. this may be observed in figure 3 and table 1. which depicts land use changes along the riverbank from 2013 to 2018 (before the arrangement of slum settlements). from 2013 to 2016, the number of settlements located along the riverbanks and occupying the remaining green open spaces increased. in 2017, construction of parapets began as part of annual flood mitigation. nonetheless, there are a number of houses recognized as belonging to the river boundary zone that are prohibited from becoming housing or settlements. because the land on the riverbanks belongs to the bengawan solo river basin center (balai besar wilayah sungai bengawan solo or bbws bengawan solo) a) 2013 d) 2018 c) 2017 b) 2016 https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 192 under the ministry of public works, it is banned for the area to be developed for housing, as it could disrupt the sustainability or service life of the existing parapets. this is due to the fact that the buildings of these illegal settlements tend to utilize parapet walls as part of their dwellings. in addition, inadequate facilities and infrastructure to support decent settlements, such as a lack of public space, might lead to the use of river banks as a place for interaction (government unit of settlement surakarta, 2022). the development of supporting infrastructure for primary drainage is also necessary, including the construction of drainage belts or secondary drainage, the installation of water pumps, and the arrangement of drainage in the settlement area. however, the high housing density and existing road structures cause the situation challenging to construct the supporting infrastructure. a comprehensive arrangement of slum settlements should be carried out so that the overall development of the existing drainage system can operate and function optimally to reduce the flood risk. 3.2. process of riverbank’s slum upgrading kampong mojo is one of the five sections in the semanggi slum area that lies in a total area of 3.72 hectares, concentrated in rw 01. this area is inhabited by 192 families occupying 178 residential units, 72 families of whom live in 63 building units on state-owned land or on the banks of the premulung and bengawan solo rivers. the inhabitants of the river’s riparian area are local migrants from surakarta and outside of the city. the process of structuring slum settlements is motivated by multiple issues, including the fact that these settlements are located in flood-prone areas, have limited facilities and infrastructure for proper settlements, have building irregularities, and have formed informal settlements that are expanding and occupying illegal land on riverbanks. environmental conditions before an arrangement can be seen in figure 4. to address various existing issues, the focus of the arrangement includes four primary activities: 1) arrangement of illegal settlements along the border of the premulung river and bengawan solo river; 2) the construction of roads and drainage along riverbanks; 3) the construction of parapets and flood pump houses; and 4) the improvement of the settlement environment’s quality. the arrangement of illegal settlements began with outreach and determination by the affected residents; a total of 63 dwellings had to be demolished for realignment. in kampung mojo, unlike other slum settlement locations (such as in the philippines and nepal), an institution at the community level is formed as a representative to negotiate with the government (carrasco & dangol, 2019). only 56 of the 72 household were able to re-occupy land in the area and later acquire land rights. source: cnes/airbus image recording, 2018 figure 4. kampong mojo before slum upgrading program https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 193 the community must have a surakarta city identity card and does not legally own a house in another location. the transfer of land ownership rights from bbws bengawan solo to the community involves multiple stages including technical recommendations from the mayor of surakarta. the process is conducted in accordance with the ministry of finance regulation no. 111/pmk.06/2016 article 93. the transfer of rights by grant from state-owned land to community ownership differs significantly from the case studies conducted in other locations. the arrangement of settlements is also accompanied by the construction and improvement of basic infrastructure, including the arrangement of roads and drainage. as observed in figure 5, the road structure, drainage arrangements, construction of pump houses, and construction of sanitation channels have been modified. the provision of infrastructure, particularly drainage-related infrastructure, is a component of urban drainage system planning as a form of mitigation against the annual flooding that has occurred in this area in the past. source: government unit of settlement surakarta, 2023 figure 5. kampong mojo after slum upgrading program 3.3. the relevance of riverbank’s slum upgrading on the sustainability of the watershed infrastructure the solution to the challenge posed by slum settlements situated in riverbank areas with illegal land status across various locations is eviction rather than relocation (cook et al., 2019). the relocation model is employed with the expectation that the affected community will secure a legal residence. however, upon implementation, following the relocation to the designated housing, the community faced challenges in covering the management costs associated with the area. this predicament arose from the absence of an accompanying economic and social capacity-building program (khan, 2021). this top-down approach is necessitated by the fact that the land occupied by the community does not belong to them. such situations frequently arise in strategically significant locations, including railway banks, riverbanks, and other pivotal areas within the urban center. river banks are considered areas that may not be designated as settlements. the determination of river border lines in indonesia refers to the regulation of the minister of public works and public housing of the republic of indonesia number 28/prt/m/2015 concerning the determination of river border lines and lake border lines. the boundary line of the river embankment in urban areas is determined to be at least 3 meters from the outer edge of the foot of the embankment along the river channel. riverbanks are restricted to activities that do not interfere with the function of the river, such as the construction of facilities related to water resources, electricity, telecommunication lines, and others (ministry of public works and public housing of the republic https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 194 of indonesia, 2015). the arrangement of slum settlements in kampung mojo is conducted beyond the delineated riverbank boundaries although the land is owned by the bbws. the arrangement of slum settlements in kampung mojo with the on-site upgrading model, particularly for the 63 houses that are on state-owned or illegal land, is a policy that the city government has never adopted before. the city government faces two options, the riverbank residents and those occupying state land can be relocated, or they can remain where they are. however, the city government decided to retain its 56 household’s citizens despite the fact that they were located on state land. “the city mayor stated that the community was present prior to the implementation of the current regulations. as a result, the mayor has opted to pursue a discretionary petition to facilitate their continued presence.” (interview with kotaku, 2023) this policy differs from those typically used by regional government, such as in kalimantan, indonesia, which opted for partial relocation (purwanto et al., 2017). flood control or the loss of livelihood is often considered an option in structuring slum settlements on riverbanks (cook et al., 2019). it turns out that these two elements can be obtained with the support of all stakeholders, with political will from regional leaders or city governments being the most crucial factor. furthermore, the successful implementation of improving the environment has been made possible through the cooperation of the government, local communities, and support from third parties, such as the private sector. the government and local communities contribute significantly to fostering dialogue within the slum upgrading planning process, facilitating the formulation of actionable plans for implementation. the private sector is also integral, particularly in financing housing development through corporate social responsibility (csr) initiatives. this involvement is necessitated by the fact that housing development occurs concurrently with the land transfer process, while municipal government funds are restricted from being allocated for revitalization efforts in the area, given that the land transfer process remains incomplete. this has been achieved without the necessity of resettlement programs (wahyuni et al., 2021). slum upgrading initiatives, founded on robust collaborations between the community and local government, combined with proactive measures to anticipate future risks, render this case a prime example of comprehensive community-led upgrading with a focus on resilience (satterthwaite et al., 2020). concerning watershed infrastructure, these slum settlements not only affect the quality of the housing environment but also pose a risk to the durability of the existing drainage infrastructure (parapet) and impede the establishment of a secondary drainage system in riverfront residential areas. integration between the arrangement of slum settlements, including community empowerment, can simultaneously be an effort to maintain the sustainability of the constructed drainage infrastructure. communities have a sense of belonging to their area and anticipate being able to participate in the operation and maintenance of existing drainage infrastructure. economically, the utilization of empowered communities enhances the cost-effectiveness and efficiency of infrastructure development (sedyowati et al., 2020). the surakarta city government could have evicted the slum dwellers, but instead it improved the infrastructure and utilized these empowered communities to transform the watershed into a tourism and cultural destination. in addition to physical upgrading, social and economic factors are required to strengthen community institution capacity in order to increase commercial business productivity and can further contribute to the sustainability of settlements so that they don't revert to slums (hawa et al., 2023). the following step is to provide shared space and facilities as a complement that can form an identity and reveal the visual charm of the location (michiani & asano, 2019). this arrangement can also be utilized as a means of generating collective action from all stakeholders, particularly the community, in order to maintain the revitalized urban environment (meilasari-sugiana et al., 2018), in this case the infrastructure along the riverbanks. https://doi.org/10.14710/geoplanning.12.2.187-196 mukaromah et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 187-198 doi: 10.14710/geoplanning.12.2.187-196 195 4. conclusion this study aims to propose a novel concept for slum upgrading that encompasses enhancements in both spatial and social dimensions, contributing to the sustainability of the bengawan solo watershed's supporting infrastructure. kampong mojo is situated along the banks of the river, with its terrain positioned at a lower elevation than both the bengawan solo river and the premulung river. as a result, this area is susceptible to flooding, particularly during the rainy season. to mitigate flooding, the government constructed embankments and enhanced the drainage system in settlements along the river. improvement of the drainage system in slum settlements is carried out comprehensively with the concept of slum upgrading, including in slum areas that occupy state land. the city government elected to arrange residential areas on site, including the transfer of housing land ownership from the state to affected communities. this case study indicates that for a river to function optimally as a primary drainage and flood control system, it is essential to promote the development of watershed-supporting infrastructure through the regulation of land use along the river and the enhancement of community capacity. the arrangement of illegal slum settlements by on site upgrading is not a well-liked strategy. city governments and urban planners frequently face challenging decisions regarding flood management and the arrangement of slum settlements. the adoption of an appropriate settlement strategy can effectively address both issues concurrently and sustainably. the approach implemented in kampung mojo, which integrates physical enhancements with the reinforcement of land ownership security, significantly contributes to the sustainability of drainage infrastructure while improving the quality of life and fostering a sense of ownership among residents. the proximity of residences to the river boundary facilitates community involvement in the maintenance of the drainage support infrastructure, thereby mitigating the risk of new illegal settlements emerging along the riverbank. this engagement is driven by the community's awareness that suboptimal functioning of the drainage system could lead to flooding that directly impacts their homes. initiatives that were undertaken to transform the watershed, which was formerly a slum, into a destination for tourism and culture aligned with the area's potential through spatial or physical improvement and community empowerment. in practice, the findings of this research highlight the importance of an inclusive approach to addressing the riverbank slum problem. implementing on-site upgrading instead of eviction or relocation requires a long-term commitment from the local government. policies that focus on land tenure security, longterm infrastructure investment, and leveraging community capacity can foster the development of a more resilient and equitable urban environment. further research is required to evaluate the sustainability of the area following the implementation of slum upgrading initiatives 5. acknowledgement this research is funded by non-tax state revenue of universitas sebelas maret with contract number: 228/un27.22/pt.01.03/2023. this paper is also part of research done by sustainable urban region research group, universitas sebelas maret. 6. references auliagisni, w., wilkinson, s., & elkharboutly, m. 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(2009). case study research: design and methods (vol. 5). sage publications, inc. https://doi.org/10.14710/geoplanning.12.2.187-196 https://doi.org/10.1016/j.ijdrr.2019.101195 https://doi.org/10.1080/13562576.2019.1667764 https://doi.org/10.1016/j.ijdrr.2019.101156 https://doi.org/10.3390/su15118974 https://doi.org/10.47772/ijriss.2021.5824 https://doi.org/doi.org/10.5719/hgeo.2018.122.4 https://doi.org/10.1016/j.foar.2019.03.005 https://doi.org/10.1016/j.ijdrr.2022.103407 https://doi.org/10.13189/cea.2024.120310 https://doi.org/10.1088/1755-1315/916/1/012012 https://doi.org/10.3390/su9071261 https://doi.org/10.13189/cea.2021.090307 https://doi.org/10.1016/j.ijdrr.2020.101963 https://doi.org/10.1088/1755-1315/986/1/012055 https://doi.org/10.1016/j.oneear.2020.02.002 https://doi.org/10.24425/jwld.2020.134214 https://doi.org/10.1016/j.crsust.2023.100216 https://doi.org/10.1177/0956247815594532 https://doi.org/10.24425/jwld.2021.137113 https://doi.org/10.1111/sjtg.12370 | 19 geoplanning vol 4, no. 1, 2017, 19-26 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.1.19-26 coordinate transformation using featherstone and vaníček proposed approach a case study of ghana geodetic reference network y. y. ziggah a, b, j. ayer c, p. b. laari d, e. frimpong b a department of surveying and mapping, china university of geosciences, wuhan, 430074, p.r. china. b department of geomatic engineering, university of mines and technology, tarkwa, western region, ghana. c department of geomatic engineering, kwame nkrumah university of science and technology, kumasi, ashanti region, ghana. d department of environment and resource studies, university for development studies, tamale, ghana. abstract: most developing countries like ghana are yet to adopt the geocentric datum for its surveying and mapping purposes. it is well known and documented that nongeocentric datums based on its establishment have more distortions in height compared with satellite datums. most authors have argued that combining such height with horizontal positions (latitude and longitude) in the transformation process could introduce unwanted distortions to the network. this is because the local geodetic height in most cases is assumed to be determined to a lower accuracy compared with the horizontal positions. in the light of this, a transformation model was proposed by featherstone and vaníček (1999) which avoids the use of height in both global and local datums in coordinate transformation. it was confirmed that adopting such a method reduces the effect of distortions caused by geodetic height on the transformation parameters estimated. therefore, this paper applied featherstone and vaníček (fv) model for the first time to a set of common points coordinates in ghana geodetic reference network. the fv model was used to transform coordinates from global datum (wgs84) to local datum (accra datum). the results obtained based on the root mean square error (rmse) and mean absolute error (mae) in both eastings and northings were satisfactory. thus, a rmse value of 0.66 m and 0.96 m were obtained for the eastings and northings while 0.76 m and 0.73 m were the mae values achieved. also, the fv model attained a transformation accuracy of 0.49 m. hence, this study will serve as a preliminary investigation in avoiding the use of height in coordinate transformation within ghana’s geodetic reference network. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): ziggah, y. y, et al. (2017). coordinate transformation using featherstone and vaníček proposed approach a case study of ghana geodetic reference network. geoplanning: journal of geomatics and planning, 4(1), 19-26. doi:10.14710/geoplanning.4.1.19-26 1. introduction the last decade has witnessed the ascendancy in the application of global navigation satellite systems (gnss) for geospatial works in developed and developing countries. however, most developing countries like ghana which is yet to migrate onto a geocentric datum cannot apply directly gnss positional measurement without transforming the data into its local coordinate system. in line with this, several research works have been carried out to ascertain the applicability and capability of transformation methods such as three parameter, bursa-wolf, molodensky-badekas, standard molodensky, iterative abridged molodensky, veis, 3d projective, 12 parameter linear affine in ghana geodetic reference network (ayer & fosu, 2008; ayer & tiennah, 2008; ayer, 2008; dzidefo, 2011; poku-gyamfi & schueler, 2008; ziggah et al., 2013a; ziggah et al., 2013b). however, the general insight gathered from these studies in ghana showed varying coordinate transformation results and accuracy among the various authors even though the same dataset is utilized. upon careful observation, it is noticed that this phenomenon of inconsistencies in the results could mainly be attributed to the estimated local geodetic height used in the coordinate transformation process. this is because the iterative abridged molodensky technique utilized to article info: received: 30 july 2016 in revised form: 18 september 2016 accepted: 26 january 2017 available online: 26 march 2017 keywords: coordinate transformation, global navigation satellite systems, geodetic reference network, featherstone and vaníček model corresponding author: yao yevenyo ziggah geomatic engineering department, faculty of mineral resource technology, university of mines and technology, tarkwa, western region, ghana email: yyziggah@umat.edu.gh open access http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 20 | estimate the ellipsoidal height correction and in return used to estimate the ellipsoidal height is difficult to reach convergence for the ellipsoidal height correction factor. in so doing, researchers in ghana applied different geodetic height correction values to estimate the local geodetic height thus obtaining different coordinate transformation results. it therefore confirms the assertion made by dzidefo (2011) and kotzev (2013) that there exist no ideal transformation parameters to be utilized in ghana. hence, this has contributed to the users’ adoption of different transformation parameters in the gnss data processing in ghana. it is important to note that the effect of height distortions in horizontal geodetic datum transformation has been duly investigated (featherstone & vanicek, 1999; vaníček & steeves, 1996). in order to minimize these distortions, vaníček and steeves (1996) proposed a four-parameter transformation model in which geodetic height in both global datum and local datum is not used and only three translations and one rotation parameter are needed to carry out coordinate transformation. hence, the transformation parameters determined are free from geodetic height distortions. similarly, featherstone and vanicek (1999) extended the four-parameter model to six-parameters on the premise that the orientation of the local geodetic system with respect to the geocentric system could possibly be done without using the local astronomic system at the origin of the network. the proposition thereof lies in coordinate transformation containing either four or six parameters. on the basis of the above related issues, the present authors were motivated to apply for the first time the featherstone and vanicek (1999) coordinate transformation model (fv model) in ghana’s geodetic reference network. we deem it appropriate to apply such a method because ghana’s national coordinate system is a two-dimensional projected grid coordinates. applying the fv model will most importantly eliminate distortions related to the height component in the coordinate transformation of ghana. this study will serve as guide on the importance of not using the local geodetic height since our national coordinate system is a two-dimensional projected grid coordinates. 2. data and methods a secondary data of 19 geodetic common point coordinates was acquired from the ghana survey and mapping division of lands commission. these data sets were in world geodetic system 1984 (wgs84) and war office 1926 ellipsoid. the war office 1926 is the reference ellipsoid for accra datum. the obtained data sets covered five out of ten regions in ghana. these regions form the ghana geodetic reference network known as the golden triangle. figure 1 shows the study area and common point’s distribution. figure 1. area of study showing data distribution http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 | 21 2.1. conversion of geodetic coordinate to cartesian coordinate the forward conversion of geodetic coordinate to cartesian coordinate was the first step employed in the coordinate transformation process. to accomplish this task, the standard forward equation (heiskanen & moritz, 1967) defined in equation (1) was used.    sin]h)2e(1[nz sinλcosh)(ny cosλcosh)(nx    [1] where , λ and h is the geodetic latitude, geodetic longitude and ellipsoidal height while x, y, z is the cartesian coordinates to be estimated. n in equation (1) is the radius of curvature in the prime vertical defined by equation (2) as 22 sine1 a n   [2] here, e is the first eccentricity expressed in eq. (3) as a 2b2a e   [3] where a and b are the semi-major axis and semi-minor axis. on the basis of the concept proposed by featherstone and vanicek (1999); vaníček and steeves (1996) the heights for wgs84 and war office 1926 should not be used. hence, equation (1) was modified into equation (4) as    )sin2en(1z sinλncosy cosλncosx    [4] equation (4) was then applied to convert all the 19 geodetic coordinates of common points designated in this study as wgs84λ),( and warλ),( into cartesian coordinates. the transformed cartesian coordinates for wgs84 and war office 1926 are represented in this study as (x, y, z)wgs84 and (x, y, z)war respectively. 2.2. coordinate using featherstone and vaníček (fv) model the featherstone and vaníček (fv) model is a six parameter transformation that combines three rotation axis and three origin-shifts in a mathematical model which presents a relationship between points in two different cartesian coordinate systems (featherstone & vanicek, 1999). this study applied the fv model to determine three rotational and three translational parameters for transforming coordinates from global datum (wgs84) to ghana local geodetic datum (war office 1926). the fv model (equation (5)) (featherstone & vanicek, 1999) could be represented mathematically as trimwgsqwarq  [5] here, qwar is the cartesian (x, y, z)war coordinates, qwgs is the cartesian (x, y, z)wgs84, m is a three-bythree matrix containing each qwgs positions, r is the rotation matrix and t is the translation vector. m in equation (5) is defined by equation (6) as               0ixiy ix0iz iyiz0 m , i = 1,…,n [6] where n is the number of observation points. equation (5) could be rewritten in a simplified form expressed in equation (7) as http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 22 | piaiδq:n1,i   [7] where  im,ia  is the design matrix, i is the identity matrix, iδq is the observation vector and  tt,rp  is the transpose of the vector of the unknown transformation parameters to be determined. in this study, the least squares approach defined by equation (8) was used to estimate the unknown parameters. δq 1 δqcta1a) 1 δqct(ap   [8] where at is the transpose of the design matrix, warqc wgsqcδqc  represent the sum of the variance covariance matrix of the cartesian coordinates of wgs84 and war office 1926 system. 2.3. estimating new war office cartesian coordinates here, the six transformation parameters calculated in section 2.2 was used to transform the wgs84 cartesian coordinates into the war office 1926 system to obtain new war office cartesian coordinates denoted as (xnwar, ynwar, znwar). these coordinates were obtained by rewriting equation (7) as equation (9). p)*i(awgsqwarq  [9] 2.4. converting cartesian coordinates to geodetic coordinates the (xnwar, ynwar, znwar) (section 2.3) was converted into geodetic coordinates. this conversion was necessary so that the obtained geodetic coordinates could be projected on to the transverse mercator to obtain two-dimensional projected grid coordinates which is the national mapping coordinate system utilized for surveying and mapping purposes in ghana. to achieve this, paul’s method (paul, 1973) was used in the reverse conversion. the choice of this method was based on a study carried out by kumi-boateng and ziggah (2016) where the paul’s method performed slightly better than other six reverse conversion techniques evaluated for the ghana geodetic reference network. in that respect, having (xnwar, ynwar, znwar), the geodetic latitude ( ) was obtained using equation (10) given as 2 z ςptan  [10] where 2y2xp  and 1t4 zα 1t 2 β 4 2z 1tς  . to get ς , the variables ς),1t,1uq,β,,( should be calculated in an orderly manner using equation (11) to (15) respectively. 2e1 4e2a2p α    [11] 2e1 4e2a2p β    [12] 22 22 )z2(β β)(α27z 1q    [13]         3 12qq3 12qq 2 1 1u [14] 6 β 12 z u 6 2zβ t            [15] detailed derivation of the paul’s method can be found in paul (1973). in this study, the conversion from ellipsoidal coordinates to plane coordinates (transverse mercator projection); equations given in dzidefo (2011) were used. http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 | 23 2.5. accuracy assessment the accuracy of the fv model utilised was analysed using statistical indices. this was done by quantifying the residuals obtained when the fv model results were subtracted from the existing projected grid coordinates. the statistical indices used are the root mean square error (rmse), mean absolute error (mae), horizontal position error (he) and standard deviation (sd). they are defined by equation (16) to (19) respectively as    n 1i 2)ipi(o n 1 rmse [16]    n 1i ipio n 1 mae [17] 2δn2δehe  [18]      n 1i 2)e(e 1n 1 sd [19] where n is the number of observations, oi and pi represents the existing projected grid coordinates and computed projected grid coordinates. e is the error between oi and pi with e as its mean. 3. results and discussion 3.1. transformation parameters determined table 1 shows the transformation parameters and their related standard deviations for transforming coordinates from wgs84 (us department of defense, 1984) to war office 1926 system using featherstone and vaníček (1999) model. with reference to table 1, δx, δy, δz is the translation parameters while rx, ry and rz represent the rotational parameters. these translation parameters (table 1) signify the degree of shift in origins of wgs84 and war office 1926 along the three axes in three-dimension space. the rotational parameters around each of the x, y and z axes relate the orientation of the wgs84 and war office 1926 systems. table 1. parameters from fv model parameters value δx (m) 164.585 ± 0.098 δy (m) -4.781 ± 1.326 δz (m) -21.902 ± 1.003 rx (arc seconds) -1.613e-06 ± 1.180e-06 ry (arc seconds) -4.736e-05 ± 1.56e-07 rz (arc seconds) 4.335e-06 ± 1.55e-07 3.2. analysis of transformed coordinates results the shifts in coordinates (∆e, ∆n) between the existing coordinates and transformed coordinates by featherstone and vaníček (1999) (fv model) are presented in table 2. the sd values and he for the control points are also shown. these results (table 2) indicate the extent at which each of the transformed coordinates produced by the fv model varies with respect to the existing coordinates. figure 2 gives an illustration on how the residuals generated oscillate along the ideal zero residual (horizontal line) with respect to the observation points. from figure 2, a fairly consistent rise and fall was noticed for the easting coordinates whereas a sharp rise and fall was observed for the northing coordinates respectively. overall analysis of figure 2 indicates that the residuals in northings were higher than the eastings. these residuals incurred by the fv model clearly depict the limitation in most mathematical models that they could only produce results approximating the existing data. although the height component was not applied in this transformation, the residuals obtained suggest that the fv model could not completely absorb the horizontal coordinate distortions mainly contributed by the astro-geodetic network (war office http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 24 | 1926) system. these factors however, have contributed to the inability of the fv model to notice its potential of providing higher (sub-metre or even sub-centimetre) accuracy even though it is a rigorous model. hence, the ideal condition of obtaining zero residuals could not be achieved in this study. in order to mitigate these residual effects, we are proposing that distortion modelling should be carried out after coordinate transformation in ghana geodetic reference network. however, the fact still remains that the fv model will serve as a preliminary step that will facilitate a viable consensus in selecting applicable transformation parameters in ghana. this could be achieved because the height component which has created such inconsistencies in the transformation results among researchers in ghana is not applicable in the featherstone and vaníček (1999) model. table 2. deviation of transformed coordinates from existing coordinates point id ∆e ∆n he 1 0.20 0.56 0.59 2 0.34 0.45 0.56 3 0.25 -2.23 2.24 4 0.67 1.15 1.33 5 0.21 -0.93 0.95 6 0.25 -0.74 0.78 7 0.92 -0.84 1.25 8 0.64 0.36 0.73 9 0.79 0.23 0.82 10 0.80 -0.43 0.91 11 -0.36 -0.10 0.37 12 -0.91 -0.50 1.04 13 -0.04 1.74 1.74 14 -1.08 1.22 1.63 15 0.45 0.76 0.88 16 -0.85 0.99 1.30 17 -0.62 -0.47 0.78 18 -0.56 0.00 0.56 19 -1.08 -1.24 1.64 sd 0.68 0.98 0.49 figure 2. residuals in easting and northing coordinates http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 | 25 in order to ascertain the horizontal positional accuracy of the transformed coordinates this study applied equation (18). the obtained estimated he values (table 2) show that a maximum horizontal uncertainty of 2.24 m was observed for point 3. this high he value for point 3 was mostly contributed by the northing coordinate which had an error of -2.23 m while an error of 0.25 m was gotten for easting. hence, because the he is dependent on both values to get its estimates the value for point 3 became higher. nonetheless, this could be attributed to observational error or the point is located in a mountainous region. the fv model produced 0.374 m as the minimum he. a graphical illustration of the he (table 2) is shown in figure 3. the sd value of 0.49 m (table 2) realised for he indicate the transformation accuracy of the fv model utilised. figure 3. horizontal errors of the transformed coordinates the validity of the fv model was further assessed using the rmse, mae and sd respectively. in relation to the rmse (table 3), 0.66 m and 0.96 m were obtained for the easting and northing coordinates. these rmse values quantify how close the fv model transformed coordinates differs from the observed data. that is, the fv model deviates from the most probable value (zero) by not more than 0.66 m and 0.96 m in eastings and northings respectively. moreover, the mae (table 3) in eastings and northings were 0.58 m and 0.79 m respectively. this gives an indication on the magnitude of how close the fv model transformed coordinates is to the existing coordinates on average. the sd values (table 3) for the coordinate differences in easting and northing show how wide the transformed coordinates are dispersed from the most probable value. hence, signifying the precision of the data used. table 3. model performance assessment statistical indicators eastings (m) northings (m) rmse 0.66 0.96 mae 0.76 0.73 sd 0.68 0.98 4. conclusion coordinate transformation is an active research area especially in countries that still use astro-geodetic datum for their surveying and mapping purposes. it is well understood that astro-geodetic networks were established as a horizontal datum based on local astronomical coordinates and thus lacked ellipsoidal height. however, in ghana most coordinate transformation has been done by including height estimated using the abridged molodensky model to the horizontal positions (latitude and longitude). conversely, this estimated height for the non-geocentric datum is irrelevant in horizontal geodetic datum transformation. http://dx.doi.org/10.14710/geoplanning.4.1.19-26 ziggah et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 19-26 doi: 10.14710/geoplanning.4.1.19-26 26 | rather, the height introduces more distortions into the local geodetic network. for the purpose of avoiding the use of height in coordinate transformation process, this study applied featherstone and vaníček model for the first time in ghana geodetic reference network. the results revealed a rmse error of 0.66 m and 0.96 m in the eastings and northings with their corresponding mae values at 0.76 m and 0.73 m respectively. a transformation accuracy of 0.49 m realised by the fv model showed that it could be utilized to transform coordinate in ghana geodetic reference network. in line with the results, it was noticed that although the height component was not used in the transformation process the residuals produced between the existing and some fv transformed coordinates were high. the conclusion drawn here was that ghana geodetic network is highly inherent with distortions that could not be more absorbed by the fv model. hence, we agree with featherstone and vaníček (1999) that distortion modelling should be carried out right after transformation is done. this will help improve the coordinate transformation results because most distortions will be modelled out. hence, for future studies in ghana, we recommend that the distortion modelling should be adopted as part of the transformation process. 5. acknowledgment the authors would like to thank the anonymous reviewers for their time and effort to improve the quality of this paper. our sincere appreciation also goes to the ghana survey and mapping division of lands commission for providing us with the necessary data set. 6. references ayer, j. 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(2013b). accuracy assessment of centroid computation methods in precise gps coordinates transformation parameters determination-a case study, ghana. european scientific journal, 9(15), 200–220. http://dx.doi.org/10.14710/geoplanning.4.1.19-26 https://doi.org/10.4314/just.v28i1.33084 http://ir.knust.edu.gh/bitstream/123456789/298/1/thesis.pdf https://doi.org/10.1080/00050326.1999.10441920 https://doi.org/10.12691/jgg-4-1-1 https://doi.org/10.1007/bf02522075 https://doi.org/10.1007/bf00867152 volume 1, no 2, 2014, 56-64 http://ejournal.undip.ac.id/index.php/geoplanning | 56 open access geoplanning e-issn: 2355-6544 model spasial statistik kepemilikan sepeda motor di kecamatan banyumanik, kota semarang s.y.ardiansyaha, a.r.rakhmatullahb a universitas diponegoro, indonesia, email: shepta_yudha@yahoo.co.id b universitas diponegoro, indonesia, email: anita.ratnasari.r@gmail.com abstract: semarang is a fast forward and fast growing city viewed from its economic growth. however, the increased population along by increased activity gives a new difficulty, that is congestion. the existence of urbanization encourages people tend to prefer a suburban area to the center of the city. with the growing movement of transportation activity, it has pushed the level of ownership of private vehicles, especially motorcycles in the suburbs. banyumanik sub-district as a suburb of the semarang city is functioned as settlements development direction. banyumanik sub-district is the highest number of motorcycle ownership in semarang sub urban with the amount of 20.428 units. for understanding the motorcycle ownership concentration, it is required the study of distribution and spatial aspects, so that it can be seen spreading evenly or not. the method used in this research is quantitative descriptive and spatial modeling approach named spatial statistics analysis. the analysis methods used in this study is using the method of analysis descriptive statistics. the analytical tool uses a gis (geographic information system). according to the research results, it is revealed that the movement system of the motorcycle user community in banyumanik subdistrict reaches until 37% toward the center of the city, 58% banyumanik stir around the area, and a 5% move toward semarang regency. as a results of the analysis of spatial patterns, it is showed that the distribution of the motorcycles ownership forms a cluster pattern with a high concentration level (high cluster). the highest concentrations of motorcycle ownership exist in the surrounding area perumnas banyumanik, while the lowest ownership concentration is in the region such as the bukitsari residence, and graha estetika residence. abstrak: kota semarang merupakan kota cepat maju dan cepat tumbuh dilihat dari pertumbuhan ekonominya, namun demikian peningkatan jumlah penduduk yang disertai dengan meningkatnya aktivitas memberikan permasalahan baru yakni kemacetan. adanya urbanisasi mendorong masyarakat cenderung lebih memilih kawasan pinggiran kota untuk tinggal. dengan meningkatnya pergerakan aktivitas terhadap transportasi telah mendorong tingkat kepemilikan kendaraan pribadi terutama sepeda motor di kawasan pinggiran. kecamatan banyumanik sebagai kawasan pinggiran kota semarang difungsikan sebagai arahan pengembangan kawasan permukiman. kecamatan banyumanik merupakan kecamatan yang memiliki jumlah kepemilikan kendaraan terbanyak di kawasan pinggiran semarang dengan jumlah sebesar 20.428 unit. untuk memahami konsentrasi kepemilikan sepeda motor diperlukan penelitian dari distribusi dan dari aspek spasialnya, agar dapat dilihat persebarannya merata atau tidak. metode yang dipakai dalam penelitian ini adalah deskriptif kuantiatif dan pendekatan pemodelan spasial yakni spatial statistic analysis. metode analisis yang digunakan dalam penelitian ini menggunakan metode analisis statistik deskriptif. alat analisis yang digunakan menggunakan bantuan gis (geographic information system). berdasarkan hasil penelitian diketahui bahwa pola pergerakan masyarakat pengguna sepeda motor di kecamatan banyumanik 37% menuju pusat kota semarang, 58% begerak di sekitar kawasan banyumanik, dan 5% bergerak menuju ke kabupaten semarang. sedangkan untuk hasil analisis pola spasial, didapatkan hasil bahwa distribusi atas kepemilikan sepeda motor membentuk sebuah pola klaster dengan tingkat konsentrasi tinggi (high cluster). konsentrasi kepemilikan sepeda motor tertinggi berada di sekitar kawasan perumnas banyumanik, sedangkan konsentrasi kepemilikan terendah berada di wilayah seperti perumahan bukitsari dan graha estetika. info artikel; diterima: 5 september 2014 hasil revisi : 15 september 2014 disetujui: 17 september 2014 publikasi on-line: 1 oktober 2014 kata kunci: kecamatan banyumanik, kepemilikan sepeda motor, pola spasial, gis article info; received: 5 september 2014 in revised form: 15 september 2014 accepted: 17 september 2014 available online: 1 october 2014 keywords: banyumanik subdistrict, motorcycle ownership, spatial patterns, gis mailto:shepta_yudha@yahoo.co.id mailto:anita.ratnasari.r@gmail.com geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 57 1. pendahuluan kota semarang sebagai salah satu kota besar di indonesia dan sebagai ibu kota provinsi jawa tengah, setiap harinya terdapat 852.496 kendaraan yang melintas lalu lalang di jalan raya. tingginya angka kepemilikan kendaraan pribadi membuat kota semarang kian hari kemacetan semakin bertambah. jumlah sepeda motor di tiap kecamatan berbeda-beda dan jika ditelusuri akan terdapat karaketristik yang menarik untuk diteliti. dengan karakteristik yang berbeda tersebut peneliti berusaha mengidentifikasi pola spasial yang terbentuk dari kepemilikan kendaraan sepeda motor di kawasan pinggiran kota semarang. seiring bertambahnya penduduk di pusat kota, penduduk akan lebih memilih tempat tinggal yang lebih tenang yakni di kawasan pinggiran. bertambahnya jumlah kepemilikan sepeda motor dapat menyebabkan permasalahan turunan dalam bidang transportasi. fenomena yang menarik tentang keterkaitan antara perilaku perjalanan dan pola spasial adalah dalam hal bangkitan perjalanan. jika ditinjau dari segi spasialnya, maka akan membentuk distribusi persebaran, bangkitan dan pola tersendiri dari kepemilikan sepeda motor yang berbeda-beda di pusat kota ataupun di pinggiran kota semarang. tujuan dari penelitian ini adalah untuk mengidentifikasi distribusi dan pola spasial yang terbentuk berdasarkan kepemilikan sepeda motor di pinggiran kota semarang yakni kecamatan banyumanik. dengan penggunaan pola spasial diharapkan masyarakat dapat lebih mengerti bagaumanakah kepemilikan kendaraan bermotor terkonsentrasi terutama di kawasan pinggiran kota semarang. pertimbangan pemilihan wilayah studi di kecamatan banyumanik adalah karena kepemilikan sepeda motor di banyumanik merupakan yang tertinggi jika dibandingkan dengan kecamatan pinggiran lain yang jauh dari pusat kota. perkembangan kawasan banyumanik yang pesat yakni banyaknya perumahan-perumahan baru juga ikut bertambahnya jumlah penduduk yang tinggal di kecamatan ini dan menggunakan sepeda motor. 2. data dan metode 2.1 travel behavior terdapat indikator statistik dalam travel behavior yakni: travel rate (tingkat perjalanan) merupakan frekuensi perjalanan rata-rata penduduk setiap hari utuk melakukan aktivitasnya. tingkat perjalanan dipengaruhi oleh gender seseorang, untuk wanita biasanya dalam melakukan aktivitasnya cenderung lebih sedikit daripada pria. selain itu faktor usia juga mempengaruhi tingkat perjalanan masyarakat. masyarakat yang berumur >60 tahun cenderung relatif kecil pergerakannya karena dipengaruhi oleh karakteristik fisiknya. travel time consumption merupakan waktu perjalanan yang dihabiskan untuk menyelesaikan perjalanan mereka, atau yang biasa disebut dengan waktu tempeh perjalanan. travel purpose tujuan perjalanan tidak hanya berbasis kepada permintaan perjalanan, tetapi menunjukkan tentang kegiatan yang akan dilakukan setelah sampai tujuan. aktivitas yang dilakukan dapat berupa bekerja, belanja, sekolah, dll. travel mode moda perjalanan merupakan alat transportasi yang digunakan untuk melakukan aktivitasnya. moda dapat berupa kendaraan bermotor seperti bus, sepeda motor dan mobil atau juga bersepeda atau berjalan kaki. 2.2 pola guna lahan pola guna lahan dalam bangkitan perjalanan dan awal pergerakan masyarakat berasal dari perumahan. menurut (suparno & marlina, 2006) dalam perumahan, jenis rumah dikategorikan berdasarkan tipe rumah. jenis rumah tersebut terdiri atas: a. rumah sederhana rumah sederhana merupakan rumah bertipe kecil, yang mempunyai keterbatasan dalam perencanaan ruangnya. rumah tipe ini sangat cocok untuk keluarga kecil dan masyarakat yang geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 58 berdaya beli rendah. rumah sederhana merupakan bagian dari program subsidi pemerintah untuk menyediakan hunian yang layak dan terjangkau bagi masyarakat berpenghasilan rendah. pada umumnya rumah sederhana mempunyai luas rumah 22 m2 s/d 36 m2, dengan luas tanah 60 m2 s/d 75 m2. b. rumah menengah rumah menengah merupakan rumah bertipe sedang. pada tipe ini, cukup banyak kebutuhan ruang yang dapat direncanakan dan perencanaan ruangnya lebih leluasa dibandingkan pada rumah sederhana. pada umumnya rumah menengah ini mempunyai luas rumah 45 m2 s/d 120 m2, dengan luas tanah 80 m2 s/d 200 m2. c. rumah mewah rumah mewah merupakan rumah bertipe besar. biasanya dimiliki oleh masyarakat berpenghasilan dan berdaya beli tinggi. perencanaan ruang pada tipe ini lebih kompleks karena kebutuhan yang dapat direncanakan dalam rumah ini banyak disesuaikan dengan kebutuhan pemiliknya. rumah tipe besar pada umumnya tidak hanya digunakan sebagai simbol status, simbol kepribadian, dan karakter pemilik rumah, ataupun simbol kebanggaan. pada umumnya rumah mewah memiliki luas lebih dari 120 m2 dengan luasan tanah lebih dari 200 m2. 2.3 spasial statistik & hot-spot spasial statistik adalah alat analisis dalam sistem informasi geografis yang berfungsi untuk menganalisis distribusi spasial, pola spasial, proses, dan hubungan spasial. meskipun mungkin terdapat kesamaan antara spasial dan non-spasial (tradisional) statistik dalam hal konsep dan tujuan, spasial statistik lebih unik dikembangkan khususnya digunakan untuk data yang menyangkut geografis spasial. spasial statistik menggabungkan ruang yakni jarak, area, konektivitas dan hubungan spasial langsung menggunakan matematika (esri). analisis statistik juga digunakan untuk mengidentifikasi dan mengkonfirmasi bentuk pola spasial, seperti pemusatan kelompok, mengetahui tren arah, atau apakah terbentuk suatu klaster. fungsi statistik yakni menganalisis data yang mendasari dan memberikan ukuran yang dapat digunakan untuk mengetahui keberadaan dan kekuatan pola. dalam spasial statistic terdapat analisis klaster yang memperhitungkan tentang pola klaster yang terbentuk. hot spot (titik panas) merupakan konsenstrasi dari kejadian dengan batas area geografis yang muncul dari waktu ke waktu. hot spot dapat digunakan pula untuk menilai konsentrasi spesifik dari guna lahan atau antara aktifitas dan guna lahan (block & c.r, 1995). hot spot mungkin tidak ada dalam kehidupan nyata, tetapi hot spot merepresentasikan dimana ada konsentrasi dari kegiatan atau kasus tertentu sehingga daerah tersebut dapat dicap sebagai daerah konsentrasi tinggi. terdapat puluhan teknik statistik analisis untuk mengidentifikasi hot spot (everiit, 1974). sebagian besar teknik analisis statistik digunakan yang biasa disebut dengan analisis klaster. ini adalah teknik mengelompokkan kasus bersama-sama dalam kelompok yang relatif koheren. semua tetap bergantung kepada bagaimana cara mengoptimalkan kriteria yang dipakai untuk diidentifikasi. karena hot spot adalah sebuah konstruksi persepsi, teknik yang digunakan harus mendekati bagaimana seseorang memahami wilayah studi. berikut adalah beberapa tipe metode hot spot/cluster anaysis (everitt, 1974 dan megbolougbe, 1996) : 1. point location 2. hierarchical techniques 3. partitioning techniques 4. density techniques 5. clumping techniques 6. risk-based techniques 7. miscellanous techniques 2.4 klaster dan pola spasial menurut tobler dalam bukunya “hukum pertama geografi” mengungkapkan bahwa semua hal selalu berkaitan dengan semua hal lain, tetapi sesuatu yang lebih dekat lebih memiliki pengaruh daripada sesuatu yang jauh (tobler, 1970). distribusi spasial dengan nilai-nilai yang ada di lokasi geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 59 tertentu menunjukan hubungan dengan lokasi lain disebut dengan autokorelasi spasial. klaster spasial adalah autokorelasi spasial positif ketika ada nilai yang mirip mengelompok jadi satu, sedangkan kebalikannya jika terdapat nilai yang terpisah-pisah disebut dengan autokorelasi spasial negative (boots et al., 1988). dengan adanya klaster spasial dapat membantu pemahaman proses geografis yang mendasari hubungan dengan fenomena yang diteliti. berdasarkan klaster spasial yang telah ada maka akan terbentuk sebuah pola spasial (spatial pattern) yang berbeda-beda. spatial pattern atau pola spasial adalah sesuatu yang menunjukkan penempatan atau susunan benda-benda di permukaan bumi (lee & wong, 2001). pola spasial akan menjelaskan bagaimana fenomena geografis terdistribusi dan bagaimana perbandingannya dengan fenomena-fenomena lain. pola spasial dapat berupa titik (point) maupun luasan (polygon), dan mereka dapat membentu pola yang bergerombol, tersebar, serta acak. 3. metodologi penelitian metode analisis yang digunakan dalam penelitian ini menggunakan metode analisis statistik deskriptif. dengan menggunakan metode statistic deskriptif, penyajian data dan analisis akan lebih mudah dipahami. terdapat tiga tahap yang dilalui dalam penelitian ini yakni: 1. tahap 1 a. identifikasi karakteristik travel behavior masyarakat b. identifikasi karakteristik kepemilikan sepeda motor 2. tahap 2 a. analisis hot spot kepemilikan sepeda motor b. analisis klaster dan pola spasial kepemilikan sepeda motor 3. tahap 3 identifikasi karakteristik distribusi spasial untuk mengetahui persebaran dan hubungan antara kepemilikan sepeda motor dan penggunaanya terhadap ruang yang ada. 4. hasil dan pembahasan 4.1 analisis perilaku perjalanan pola pergerakan masyarakat pengguna sepeda motor di kecamatan banyumanik didasarkan terhadap perilaku perjalanan masyarakat. setiap kelurahan di kecamatan banyumanik memiliki pola pergerakan yang berbeda-beda dan menunjukkan adanya hubungan antara pergerakan dengan guna lahan yang ada. selain itu juga ditemukan kelurahan yang paling banyak terdapat komuter yang tinggal di kecamatan banyumanik. berikut adalah gambar hasil pola pergerakan tiap kelurahan di kecamatan banyumanik. gambar 1. pola pergerakan penggunaan sepeda motor di kecamatan banyumanik (hasil analisis peneliti, 2014) geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 60 gambar 2. prosentase pergerakan masyarakat kecamatan banyumanik ke dalam dan luar kota semarang (hasil analisis peneliti, 2014) gambar 3. prosentase pergerakan masyarakat kecamatan banyumanik ke pinggiran, pusat dan luar kota (hasil analisis peneliti, 2014) berdasarkan gambar 1 menunjukkan bahwa anak panah menggambarkan tentang arah tujuan bekerja masyarakat banyumanik, dan sumber panah digambarkan berada pada pusat kecamatan banyumanik. ketebalan panah menunjukkan banyaknya arus pergerakan tujuan ke lokasi bekerja, semakin tebal panahnya maka akan semakin banyak masyarakat yang menuju lokasi tersebut. berdasarkan gambar 2 terlihat bahwa prosentasi masyarakat yang bekerja di kecamatan banyumanik yang bekerja di dalam kota semarang terbesar yakni mencapai 94% sedangkan 6% bekerja di wilayah kabupaten semarang yakni di ungaran. jika pembagian wilayah dalam dan luar kota semarang terbagi lagi menjadi tipologi kawasan maka akan terbentuk 3 kawasan baru yakni kawasan yang menuju ke pusat kota (27%), kawasan pinggiran kota (67%) dan kawasan yang berada di luar kota semarang (6%). 4.2 analisis kepemilikan sepeda motor berdasarkan hasil survey maka didapatkan prosentasi hasil jumlah kepemilikan sepeda motor dalam rumah tangga sebagai berikut: gambar 4. prosentase jumlah kepemilikan sepeda motor dalam rumah tangga (hasil analisis peneliti, 2014) geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 61 dari grafik diatas menunjukkan bahwa jumlah terbesar kepemilikan sepeda motor dalam 1 rumah tangga adalah sebanyak 2 buah sepeda motor dengan prosentase 51%. selanjutnya mencapai prosentase 19% untuk kepemilikan 3 motor dan 17% untuk kepemilikan berjumlah 1 motor. namun terdapat masyarakat yang tidak memiliki sepeda motor dengan kemungkinan pertama bahwa masyarakat tidak mampu membeli sepeda motor dan lebih memilih jalan kaki atau masyarakat mampu membeli sepeda motor namun tidak membelinya. dalam fakta dilapangan ditemukan bahwa sejumlah 2% masyarakat yang tidak memiliki sepeda motor adalah masyarakat yang bergolongan mampu namun tidak membeli sepeda motor. hal ini dikarenakan sepeda motor dirasa kurang aman dalam berkendara dan kurang nyaman. masyarakat yang tidak memiliki sepeda motor lebih cenderung berlokasi di kawasan perumahan atau perumahan mewah di kecamatan banyumanik seperti di graha estetika, taman setiabudi, villa aster dll. 4.3 analisis hot spot analisis hot spot pada kepemilikan sepeda motor atribut yang digunakan adalah rasio antara jumlah sepeda motor yang dimiliki dengan jumlah anggota tiap keluarga dalam rumah tangga. untuk menganalisis hot spot menggunakan tools analisis dalam arc map yang bernama “hot spot analisys (getis-ord-gi’)”. selain atribut diatas dibutuhkan jarak maksimal tiap rumah tangga dalam 1 kecamatan, berdasarkan analisis sebelumnya hasilnya adalah 1.450 meter. berdasarkan data yang telah diinput pada aplikasi arc map didapatkan hasil hot spot sebagai berikut. gambar 5. peta hot spot (titik panas) kepemilikan sepeda motor (hasil analisis peneliti, 2014) berdasarkan gambar diatas titik panas menunjukkan bahwa dimana konsentrasi kepemilikan sepeda motor tiap rumah tangga terjadi. terdapat 2 warna utama yakni merah dan biru, dimana warna merah menunjukkan konsentrasi kepemilikan sepeda motor per rumah tangga tinggi untuk warna biru sebaliknya semakin rendah. geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 62 4.4 analisis klaster dan pola spasial pola spasial kepemilikan sepeda motor ditunjukkan dengan peta konsentrasi jumlah kepemilikan sepeda motor di kawasan tertentu. pola spasial yang terbentuk adalah berupa klaster. berdasarkan hasil analisis didapatkan bahwa perumnas banyumanik memiliki kepemilikan sepeda motor yang paling besar, hal ini dikarenakan perumnas banyumanik merupakan kawasan permukiman padat dan bertipe rumah menengah. untuk kepemilikan sepeda motor yang paling rendah berada pada kawasan perumahan mewah seperti perumahan graha estetika dan perumahan bukitsari. hal ini dikarenakan masyarakatnya yang cenderung lebih memilih menggunakan mobil daripada sepeda motor. kemungkinan memiliki sepeda motor di kawasan ini ada, namun sangatlah kecil dengan rasio 0,2. gambar 6. peta klasifikasi pola spasial kepemilikan sepeda motor (hasil analisis peneliti, 2014) 4.5 analisis karakteristik spasial untuk mengidentikasi karakteristik spasial tentang kepemilikan sepeda motor dilakukan pembandingan tiap kelas klaster kepemilikan sepeda motor terhadap lokasi permukiman tempat masyarakat tinggal. dengan melakukan pembandingan dan verifikasi kelas terhadap citra, maka akan didapatkan gambaran tentang bagaimana hubungan karakteristik spasialnya. berikut adalah tabel identifikasi karakteristik spasial. geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 63 tabel 1. identifikasi karakteristik spasial (hasil analisis peneliti, 2014) berdasarkan tabel diatas menunjukkan tentang lokasi dan status kelas kepemilikan sepeda motor di kecamatan banyumanik. didapatkan hasil bahwa kepemilikan sepeda motor tertinggi terdapat pada kawasan perumnas banyumanik dan sekitarnya yang merupakan perumnas terbesar di kecamatan banyumanik. kepemilikan sepeda motor cukup tinggi berada pada kawasan komplek rumpun diponegoro dan kampong temugiring yang berada di sekitar komplek militer. kepemilikan sepeda motor sedang terdapat di kelurahan srondolo kulon yang berada di daerah belakang ada swalayan dan sekitarnya. selain itu kepemilikan sepeda motor sedang juga terdapat di pudak payung asri yang merupakan kawasan perumahan menengah. untuk kepemilikan sepeda motor cukup rendah berada pada komplek villa pudak payung dan komplek pertamina pudak payung yang juga merupakan perumahan menengah berada di pinggir jalan raya. sedangkan untuk status kepemilikan sepeda motor rendah berada pada kawasan perumahan bukit sari ngesrep dan perumahan graha estetika yang mana merupakan kawasan perumahan mewah. geoplanning 2014,vol: 1, no: 2, 56-64 ardiansyah dan ratnasari | 64 5. kesimpulan jika dilihat secara keseluruhan kecamatan banyumanik, akan terdapat 37% masyarakatnya yang bergerak menuju ke pusat kota semarang, 58% masyarakatnya bergerak dan melakukan aktifitasnya di sekitar kecamatan banyumanik dan 5% masyarakatnya bergerak keluar kota yakni ke kabupaten semarang. pada tingkat kelurahan akan terdapat karakteristik pergerakan yang berbeda-beda pula. berdasarkan analisis pola spasial yang tebentuk dari kepemilikan sepeda motor, di dapatkan hasil bahwa terbentuk pola klaster. jenis klaster yang terbentuk merupakan high cluster (klaster tinggi), yang berarti distribusi dan konsentrasi kepemilikan sepeda motor tidak acak dan cenderung memusat. klaster kepemilikan sepeda motor dikategorikan menjadi 5 kelas yakni kepemilikan tinggi, kepemilikan cukup tinggi, kepemilikan sedang, kepemilikan cukup rendah dan kepemilikan rendah. berdasarkan hasil analisis dan pemetaan didapatkan hasil bahwa kepemilikan sepeda motor tertinggi berada pada kawasan perumnas banyumanik, hal ini dikarenakan perumnas banyumanik merupakan kawasan permukiman padat terbesar dan termasuk perumahan menengah. untuk kepemilikan sepeda motor yang paling rendah berada pada kawasan perumahan mewah seperti perumahan graha estetika dan perumahan bukitsari. hal ini dikarenakan masyarakatnya yang cenderung lebih memilih menggunakan mobil daripada sepeda motor. kemungkinan memiliki sepeda motor di kawasan ini ada, namun sangatlah kecil dengan rasio 0,2. untuk studi persebaran distribusi dan pola spasial kepemilikan sepeda motor di kecamatan banyumanik adalah membentuk sebuah klaster kepemilikan. namun untuk berbagai kasus yang berada di kecamatan lain, perlu studi lebih lanjut untuk kaitan antara pola spasial dan pola distribusi atau pergerakan. 6. daftar pustaka block, r.l., and c.r. block (1995). space, place, and crime: hot spot areas and hot places of liquor-related crime. in: j.e. eck and d. weisburd, eds., crime and place. monsey, ny: criminal justice press; and washington, dc: police executive research forum, pp. 145-184. bps, 2013. kota semarang dalam angka tahun 2012. kantor bps kota semarang. boots, b.n., and getis, a., (1988), point pattern analysis. newbury park, ca: sage publications. everitt, b., (1974). cluster analysis. london: heinemann education books. everitt & megbolugbe, i. (1996). the geography of underserved mortgage markets. paper presented at the american real estate and urban economics association meeting. lee, j. and wong, d.w.s., (2001). statistical analysis with arcview gis. new york: john wiley & sons inc. suparno, m., & marlina, e. (2006). perencanaan dan pengembangan perumahan. yogyakarta: andi. tobler w., (1970) "a computer movie simulating urban growth in the detroit region". economic geography, 46(2): 234-240. yudohusodo, s. 1991. tumbuhnya pemukim-pemukim liar di kawasan perkotaan. jakarta : yayasan padamu negeri. model spasial statistik kepemilikan sepeda motor di kecamatan banyumanik, kota semarang abstrak: kota semarang merupakan kota cepat maju dan cepat tumbuh dilihat dari pertumbuhan ekonominya, namun demikian peningkatan jumlah penduduk yang disertai dengan meningkatnya aktivitas memberikan permasalahan baru yakni kemacetan. adanya urban... keywords: banyumanik subdistrict, motorcycle ownership, spatial patterns, gis 1. pendahuluan 2. data dan metode berdasarkan klaster spasial yang telah ada maka akan terbentuk sebuah pola spasial (spatial pattern) yang berbeda-beda. spatial pattern atau pola spasial adalah sesuatu yang menunjukkan penempatan atau susunan benda-benda di permukaan bumi (lee & wong... 3. metodologi penelitian 4. hasil dan pembahasan 5. kesimpulan 6. daftar pustaka 23 geoplanning journal of geomatics and planning vol. 10, no. 1, 2023 original research an application of cellular automata (ca) and markov chain (mc) model in urban growth prediction: a case of surat city, gujarat, india kaushikkumar p. sheladiya1*, chetan r. patel1 1. urban planning section, department of civil engineering, s. v. national institute of technology,surat,gujarat,india doi: 10.14710/geoplanning.10.1.23-36 abstract the main purpose of this study is to detect land use land cover change for 1990-2000, 2000-2010, and 2010-2020 using multispectral landsat images as well as to simulate and predict urban growth of surat city using cellular automata-based markov chain model. maximum likelihood supervise classification was used to generate lulc maps of the years 1990,2000,2010, and 2020 and the overall accuracy of these maps were 90%, 95%, 91.25%, and 96.25%, respectively. two transition rules were commuted to predict the lulc of 2010 and 2020. for validation of these lulc maps, the area under characteristics curve was used, and these maps' accuracy was 95.30% and 86.90%. this validation predicted lulc maps for the years 2035 and 2050. transition rules of 2010-2035 showed that there will be a probability that 36.33% of vegetation area and 40.27% of the vacant land area will be transited into built-up by the year 2035, and it will be 49.20 % of the total area. also, 57.77% of the vegetation area and 60.24% of the built-up area will be transformed into urban areas by the year 2050, almost 62.60 %. analysis of lulc maps 2035 and 2050 exhibits that there will be abundant growth in all directions except the south zone and southwest zone. therefore, this study helps urban planners and decision-makers decide what to retain, where to plan for new development and type of development, what to connect, and what to protect in coming years. copyright © 2023 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction the current demographic transition from rural to urban areas is the most considerable shift of this century, bringing planning and development policy for micro and macro development (chaudhuri & clarke, 2019). this evaluation of the demographic transition process starts with the formation of towns and cities, and then it takes the size of metropolitan and urban agglomerations (deep, 2014; sahana et al., 2018). urban sprawl has become a worldwide problem, especially for a developing nation. an indication of imbalance between urban spatial expansion and underlying population can characterize urban sprawl. reasons behind the urban sprawl are high population growth, high accessibility to urban areas from suburban areas, and choice of people to live near periurban areas. urban sprawl usually covers vegetation land, resulting in biodiversity loss and the heat island effect. so, assessing the impact of different land use planning schemes and development policies is essential to optimize the loss of natural land (gao et al., 2020; jokar arsanjani et al., 2013). town planners generally used zoning to differentiate land use as a method of guiding and controlling the growth of urban areas. in the early development phase, this concept was applied in the planning of developed countries and is now in developing countries (he et al., 2018). developing strategies for evaluating various urban development scenarios about potential implications for land use and the advancement of existing spatial plans and policies is vital for urban and regional planners (al-ahmadi et al., 2009). stakeholders, such as those involved e-issn: 2355-6544 received: 06 february 2023; accepted: 31 october 2023; published: 31 october 2023. keywords: cellular automata(ca), markov chain(mc), urban growth modelling, geographic information system, surat city *corresponding author(s) email: chetanrpatel@rediffmail.com https://doi.org/10.14710/geoplanning.10.1.23-36 mailto:chetanrpatel@rediffmail.com sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 24 in research, modeling, forecasting, and policymaking related to planning for sustainable urban growth, are also concerned about the effects of piecemeal planning in large cities. however, the urbanization and urban development phase worldwide does not follow a uniform pattern. in developed countries, the concentration of population in medium-sized cities has risen dramatically. most small and medium-sized cities in india are expected to be of regional significance by 2030. as a result, these cities will strengthen their socioeconomic conditions and infrastructure and serve the more extensive hinterland. land use land cover change is a complex and dynamic system resulting from spatial interaction between different land uses over some time. this has become a key process in optimizing land use in a step towards the development of sustainable smart cities (deep, 2014; shu et al., 2020). the urban system requires integrated tools that help guide and forecast urban growth because of its complex and dynamic nature. this time series dynamic process has complex interactions between land use, transportation, population, economy, river, topography, growth policies, culture, and politics. planners have tried to project and guide urban growth using several simulation models (gharaibeh et al., 2020; mustafa et al., 2017; thapa & murayama, 2020; tripathy & kumar, 2019). in urban planning, many researchers and decision-makers have applied different methods and models to simulate and predict urban sprawl. remote sensing (rs) and geographic information systems (gis) are some of the most used to detect spatial and temporal changes (deep, 2014; gharaibeh et al., 2020; sahana et al., 2018; tripathy & kumar, 2019). it can be analyzed by modeling urban growth using different models like cellular automata (ca) (mustafa et al. 2017; xu & gao, 2019), logistics regression (okafor et al., 2020), markov chain technique (lu et al., 2018; mosammam et al., 2017), sleuth (slope, land use, exclusion, urban extent, transportation, and hill shade), fuzzy, genetic algorithm (li et al., 2008), ahp (analytical hierarchy process) (aburas et al., 2016), ann (arithmetic neural network) (gharaibeh et al., 2020), weights of evidence, conversion of land use and its effects (clue), entropy optimization (gao et al., 2020) and land use transformation model to predict urban growth. out of all models, the ca model is used widely because of its flexibility, ability to integrate the spatial and temporal dimensions of the process, and to model complex dynamic systems (aburas et al., 2016). ca model is a discrete, repetitive, and dynamic system in which the state of each cell depends on previous conditions as well as the condition of the neighborhood (lagarias, 2012). ca model can simulate and predict urban growth based on the assumption that past urban growth and local and regional interactions of different land uses. it is most suitable for the simulation of a spatial pattern. however, it does not help interpret urban growth because it cannot predict and simulate spatial changes (hu & lo, 2007). markov chain (mc) model can quantify temporal changes in land use classes from one stage to another using a transition probability matrix but not spatial changes. in contrast, the ca automata model can predict and simulate spatial changes. mc model doesn't consider the effect of surrounding cells but only considers the states of cells at a different two-time period while quantifying land-use changes (okafor et al., 2020). the ability to articulate time shifts from one time to another makes mc the best tool to model land-use changes and thus provides a framework for forecasting future changes. to overcome the limitation of both the mc and ca model, it is better to integrate the ca and mc model to identify spatial-temporal changes and quantify those changes for simulation and projection of urban growth. the integrated ca – mc model has been the most common method of simulating transition in urban development and land use over the last 10 years, perhaps because it does not demand a considerable amount of data, and the model itself is user-friendly, even for users who are not specialists (milad et al., 2016). surat city has experienced unprecedented urban growth in the last three decades. due to commercial and business activities, many workers migrated to surat from surrounding states like rajasthan, uttar pradesh, madhya pradesh, maharashtra, and bihar. it steered its natural resources like agricultural land and water bodies. https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 25 therefore, this study was carried out to detect spatial changes in land use land cover from the year 1990 to 2020 to analyze the urban growth direction of the city as well as to predict urban growth for the years 2035 and 2050. this paper comprises five sections. section 2 discusses the functionality of the ca-based markov chain model, followed by section 3, which is about the study area profile and data used for the study. section 4 comprises an analysis of maps and results of actual and predicted lulc maps. section 5 concludes this study's applicability and future scope with advanced machine learning models. 2. data and methods 2.1. study area profile surat is well-known for its major diamond polishing industries and the textile hub of gujarat state of india. the city is located between latitudes 21º03' and 21º19' north and longitudes 72º41' and 73º00' east, having an area of 326.53 sq.km, as shown in figure 1. it is 13 m above the mean sea level. surat city has a population of 4.46 million and population density was 13,680 persons per sq.km as per census 2011. the city is located in the southern part of gujarat state in western india. it lies near the mouth of the tapti river in the gulf of khambhat (cambay). the city has grown on both sides of river tapti. surat city is considered one of the fastestgrowing cities in india. surat city has a high migration rate from different parts of gujarat and other states of india because of the diamond and textile industries. it is well connected by the ahmedabad-mumbai corridor. surat has experienced rapid urbanization and expansion of urban boundaries due to its fast-growing population. thus, future growth should be confined within the specified urban form delimited by urban growth boundaries to preserve agricultural areas and prevent urban sprawl. figure 1. the map of surat city 2.2. methodology of cellular automata (ca) and markov chain (mc) model four satellite images were used to extract the land use maps for surat city as part of the materials and methodology used in this study. the maximum likelihood classification technique, a supervised classification method, was used to classify images. accurate polygons were chosen as training and study areas in order to classify the images. four classes were used: built-up, agriculture, vacant land and water. therefore, the resampling step was conducted after image classification. consequently, the analysis, simulation and future land use change prediction were conducted in the idrisi-selva software environment. specifically, the number of land use classes and their changes in periods (1990, 2000, 2010 and 2020) was calculated using cross-tabulation analysis. afterward, the ca–markov model https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 26 was applied to simulate and predict future land use changes in surat city, as shown in the flowchart presented in figure 2. 2.2.1. cellular automata (ca) model generally, ca models aim to simulate the actual nature regulations. land use change modeling using the ca technique is a preferred method because it gives explicit spatial modeling results based on a defined transition rule (ward et al., 2000; white & engelen, 2000). moreover, ca automata model types are suitable to represent, analyze and forecast geographic processes due to the relationships among a raster grid (mitsova et al., 2011). the ca automata are a practical tool in urban system simulations since population and land use change can be presented together. furthermore, cells of the cellular lattice can be aggregated efficiently with economic and transportation data. thus, the urban areas can be effectively simulated by using proper neighborhoods of cells on the cellular grid. moreover, theories of the urbanization process can be examined based on used spatial models (mitsova et al., 2011). the ca model has been used increasingly in land use change and urban expansion modeling (he et al., 2006). it is worth mentioning that the time and space in ca model are considered as discrete units, and the space is considered as a regular grid (lattice) in two dimensions. the main aspect of ca model is the local interactions which reflect the dynamicity of system evolution (wang et al., 2012). ca models are able to simulate stochastic, nonlinear and spatial processes. many studies have illustrated that ca models have the potential to model the complex spatiotemporal process of land use change, urban systems and its patterns in an understandable manner (barredo & demicheli, 2003; he et al., 2006; wang et al., 2012; xian & crane, 2005). the significant components of ca models are as follows: (a) cells, (b) cell neighborhoods and (c) transition rules, i.e., the cell is the fundamental element of the automation system, i.e., the cell are organized in a lattice. the transition rule that defines the state of each cell for the coming time step depends on the current state of that cell and its surrounding neighborhood cells. after that, a land use change suitability map is required and the dynamics should be defined in the system. the primary expression of the ca model can be expressed as: 𝑆(𝑡, 𝑡 + 1) = 𝑓(𝑆(𝑡), 𝑁)……………………. eq. (1) where s is the states of discrete cellular, t is the time instant, t +1 is the coming future time instant, n is the cellular field and f is the transition rule of cellular states in local space. 2.2.2. markov chain (mc) model markov chain model is based on the progression of the formation of markov stochastic process systems for predicting one status being changed to another. the markov chain model is commonly used to model and simulate changes, dimensions and trends of land use/cover (sang et al., 2011; weng, 2002). markov chain model analyses and summarizes the change in land use by a number of probabilities transition areas from one status to a different status over a specified period. additionally, the produced probabilities transition areas can be used to predict and discover the probable scenarios of future land use change and urban growth patterns. on the other hand, the markov chain cannot model and simulate the changes in spatial distribution. nevertheless, it is an effective and powerful model that can estimate and predict the quantity of land use change (xin et al., 2012). the prediction of future land use changes can be calculated based on the conditional probability formula by using the following equation: 𝑆(𝑡 + 1) = pij x 𝑆(𝑡)……………..eq. (2) pij = ( 𝑃11 𝑃12 𝑃1𝑛 𝑃21 𝑃22 𝑃2𝑛 𝑃𝑛1 𝑃𝑛2 𝑃𝑛𝑛 )…………..eq. (3) https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 27 and ( 0 ≤ pij < 1 and ∑ 𝑃𝑖𝑗 𝑁 𝑗=1 = 1, (i, j = 1,2,……..n) where s (t) is the state of the system at time t, s (t +1) is the state of the system at time (t +1); pij is the matrix of transition probability in a state. 2.2.3. validation of ca-mc model by receiver operating characteristic curve (roc) relative operating characteristics is an excellent method to assess the validity of a model that predicts the location of the occurrence of a class by comparing a suitability image depicting the likelihood of that class occurring. this technique compares predicted results with actual results and plot percentages of true positives against the percentage of false positives at a predefned threshold value (hu & lo, 2007; sarkar & chouhan, 2020). the roc calculates the area under the curve (auc), which threshold value lies in between 0 to 1, where 1 denotes a perfect match and 0 denote complete miss-match. by using this validation technique, lulc maps for year 2035 and 2050 were forecasted. 2.3. preparation of landuse maps and land use land cover change (lulc) analysis to perform landuse land cover change analysis, landsat 8 satellite images were downloaded from the portal of the united states geological survey (https://earthexplorer.usgs.gov/) of years 1990,2000,2010 and 2020 at no cost as shown in table 1. after doing spatial, radiometric, and spectral corrections of satellite images, they were applied for lulc analysis. to supervise image classification, an image classification tool was used to generate a training sample of each land cover, which includes built-up, agriculture, vacant land, and water bodies. then, final land use land cover raster maps were generated using the maximum likelihood classification tool for the years 1990,2000,2010, and 2020, as shown in figure 3. for ground truth verification, twenty-five points were considered for each land cover using google earth pro software to check the accuracy of classified lulc images. from that, the confusion matrix was generated by cross-verifying twenty-five stratified random points of each land cover with ground truth points. afterward, user accuracy, producer accuracy, and kappa coefficient were calculated from the confusion matrix for lulc maps at 0.87, 0.93,0.88, and 0.95, respectively, for the years 1990,2000,2010, and 2020. the overall accuracy of maps was 90%, 95%, 91.25%, and 96.25%, respectively, which indicates that these lulc maps can be used for further processing of land use assessment. figure 2. flow chart of applied ca markov model https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 28 table 1. metadata of satellite images year dataset sensor path/row 1990 landsat 5 tm 148/05 2000 landsat 5 tm 2010 landsat 5 tm 2020 landsat 8 oli/tirs figure 3. land use land cover maps for the years 1990, 2000, 2010 and 2020 in 1990, 20.77 sq. km area was covered by built-up, 125.51 sq. km by vegetation, 155.62 sq. km by vacant land, and 17.23 sq. km by water bodies. in 2000, there was a gain in the built-up area by 140.25 % and it became 49.9 sq. km, while a loss in vegetation area by 11.26% and vacant land area by 15.58%. the built-up area was further increased to 35.27 %, which was 67.5 sq. km, vegetation area decreased by 20.18 %, which became 88.9 sq. km and vacant land area was increased by 8.99 % and it was 143.19 sq. km in the year 2010. a huge development took place in surat city area from 2010 to 2020, including the outer ring road in the surat urban development authority area, the starting phase of surat metro and diamond burge in khajod area, good public transport infrastructure in terms of bus rapid transit system (brts) and sitilink. these steps attracted many workers from the surrounding area for employment in the textile and diamond industries. due to this, the builtup area increased by 78.66 % and became 120.6 sq. km, vegetation area again decreased by 39.65%, and vacant land area decreased by 21.13 % in 2020. 3. result and discussion 3.1 transition rules the transition probability matrix records the number of pixels expected to change from each land cover type to each other over the specified number of time units. the transition areas matrix was calculated using the https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 29 markov model of idrisi selva for the years 1990-2000, 2000-2010 and 2010-2020. there is a probability that 3.29% of agriculture areas will be expected to change into built-up areas, 32.14% of areas into vacant land and 4.33% of areas into water bodies, while 60.23% of areas expect to persist in the year 2000. in vacant land, 17.97% of the areas will be transformed to build up, 4.92% of areas into water, while 55.44% expect to remain as it is in the year 2000. regarding water bodies, 13.15% will convert into vacant land in 2000, as shown in table 2. table 2. transition probability of changing land use land cover class from 1990-2000 land use/ land cover built-up agriculture vacant land water built-up 99.20% 0.60% 0.63% 0.11% agriculture 3.29% 60.23% 32.14% 4.33% vacant land 17.97% 21.67% 55.44% 4.92% water 1.18% 10.63% 13.15% 75.04% there is a probability that 5.17% of agriculture areas will be expected to change into built-up areas, 45.04% of areas into vacant land and 1.59% areas into water bodies. in comparison, 48.20% of areas are expected to persist in 2010. in vacant land, 16.47% of areas will be transformed to build up, 1.82% of areas into the water, while 61.98% expect to remain as it is in 2010. regarding water bodies, 27.03% will be converted into vacant land in 2010, as shown in table 3. table 3. transition probability of changing land use land cover class from 2000-2010 land use/ land cover built-up agriculture vacant land water built-up 97.58% 1.38% 1.03% 0.01% agriculture 5.17% 48.20% 45.04% 1.59% vacant land 16.47% 19.73% 61.98% 1.82% water 1.94% 12.90% 27.03% 58.13% there is a probability that 25.49% of agriculture areas will be expected to change into built-up areas, 32.39% of areas into vacant land and 2.70% areas into water bodies. in comparison, 39.43% of areas are expected to persist in 2020. in vacant land, 29.37% of areas will be transformed to build-up, 3.05% of areas into water, while 49.93% expect to remain as it is in 2020. in the case of water bodies, 7.34% will convert into vacant land and 9.48% into agriculture in 2020, as shown in table 4. table 4. transition probability of changing land use land cover class from 2010-2020 land use/ land cover built-up agriculture vacant land water built-up 97.03% 0.34% 2.53% 0.10% agriculture 25.49% 39.43% 32.39% 2.70% vacant land 29.37% 17.64% 49.93% 3.05% water 8.16% 9.48% 7.34% 75.02% results should be clear and concise. the results should summarize (scientific) findings rather than provide data in great detail. please highlight differences between your results or findings and the previous publications by other researchers. for tables, they are sequentially numbered with the table title and number above the table. tables should be centered in the column and fit to the window. 3.2 prediction of land use land cover maps ca-mc is a combined cellular automata and markov chain land cover prediction procedure that adds an element of spatial contiguity and knowledge of the likely spatial distribution of transitions to markov chain analysis. the transition areas matrix calculated using the markov model was integrated into the ca -based markov model to predict land use land cover maps for 2010 and 2020. https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 30 3.2.1 predicted land use land cover map of the year 2010 to predict the lulc map for year 2000, the lulc map of 1990 is considered the base map. the transition probability calculated in table 2 was considered as changing probability from one land cover category to another for the year 1990-2000. it was assumed that the same probability will take place to change particular land use. the actual and predicted lulc map of the year 2000 are shown in figure 4. figure 4. predicted land use land cover maps of year 2010 3.2.2 predicted land use land cover map of the year 2020 to predict the lulc map for the year 2020, the lulc map of 2010 is considered a base map. the transition probability calculated in table 3 was considered as changing probability from one land cover category to another for the year 2000-2010. it was assumed that the same probability will take place to change particular land use. the actual and predicted lulc map for the year 2020 is shown in figure 5. figure 5. predicted land use land cover maps of the year 2020 https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 31 3.3 model predication accuracy of predicted lulc figure 6. shows the 95.30% probable accuracy that predicted lulc will be concentrated in the actual lulc map of the year 2010. similarly, for the year 2020 (figure 7.), an area under the curve is 86.90%. it shows that there will be 86.90% of areas that are available in the actual lulc map of the year 2020. figure 6. model prediction accuracy of predicted lulc 2010 figure 7. model prediction accuracy of predicted lulc 2020 3.4 predicted map of lulc 2035 and lulc 2050 to predict lulc for 2035, transition areas (transition probabilities) were calculated using markov chain analysis from 2010 to 2035, as shown in table 5 and the predicted lulc map in figure 8. it was assumed that the transition probability of lulc between 2010 and 2020 will remain the same for the next 15 years, also considering 2020 as a base year. there is a probability that 36.33% of agriculture areas will be expected to change into built-up areas, 33.50% of areas into vacant land and 3.66% areas into water bodies. in comparison, 26.51% of areas are expected to persist in 2020. in vacant land, 40.27% of areas will be transformed to build up, 3.97% of areas into the water, while 37.54% expect to remain as it is in 2020. regarding water bodies, 10.64% will be converted into vacant land and 11.67% into agriculture in 2020. figure 8. predicted lulc map of 2035 https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 32 table 5. transition probability of changing land use land cover class from 2010-2035 land use/ land cover built-up agriculture vacant land water built-up 95.86% 0.64% 3.34% 0.17% agriculture 36.33% 26.51% 33.50% 3.66% vacant land 40.27% 18.23% 37.54% 3.97% water 12.94% 11.67% 10.60% 64.79% to predict lulc for 2050, transition areas (transition probabilities) were calculated using markov chain analysis from 2010 to 2050, as shown in table 6 and predicted lulc map 2050, as shown in figure 9. it was assumed that the transition probability of lulc between 2010 and 2020 will remain the same for the next 30 years, also considering 2020 as a base year. there is a probability that 57.77% of agriculture areas will be expected to change into built-up areas, 23.30% of areas into vacant land and 4.62% of areas into water bodies. in comparison, 14.31% of areas are expected to persist in 2020. in vacant land, 60.24% of areas will be transformed to build up, 4.69% of areas into water, while 22.55% expect to remain as it is in 2020. regarding water bodies, 14.80% will be converted into vacant land and 12.44% into agriculture in 2020. figure 9. predicted lulc map of 2050 table 6. transition probability of changing land use land cover class from 2010-2050 land use/ land cover built-up agriculture vacant land water built-up 93.54% 1.39% 4.64% 0.43% agriculture 57.77% 14.31% 23.30% 4.62% vacant land 60.24% 12.52% 22.55% 4.69% water 29.45% 12.44% 14.80% 43.31% 3.5 discussion over thirty years, the city has experienced vast urbanization as the built-up area increased by 480.64% at the expense of vegetation loss by almost 97.16% from 1990 to 2020. which proximity factors like national and state highways (li et al., 2018), central business district (thapa & murayama, 2020), the airport (zhou et al., 2020), the railway station (yang et al., 2020), the bus stations (bharath et al., 2018), brts and sitilink, https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 33 development of outer ring road and metro; government interventions like town planning schemes, development plan (sheladiya, 2023), educational and health facilities, parks (lu et al., 2018), low land price in outer skirt, diamond and textile industries (sheladiya, 2023) as socioeconomic factors played significant role in urbanization of surat city. the resulting value by the aoc curve indicated that the prediction accuracy of forecasted lulc maps of 2010 and 2020 was 95.30% and 86.90%, which was relatively higher and good compared to the ca-mc model used by kallvetty and bandopadhyay (2018) and rahnama (2021). it was found that the incorporation of gis and land use/ cover maps derived from remote sensing data with the ca– markov model was capable of modeling and simulating spatial and temporal land use change efficiently for the years 2035 and 2050 (kamusoko et al., 2009; mitsova et al., 2011; myint & wang, 2006) therefore, the predicted lulc maps for the year 2035 and 2050 showed that city should have 48.16% and 61.28% concrete jungles because of rapid urbanization and mega infrastructure projects like metro, dedicated industrial freight corridor, diamond burge (sheladiya, 2023). taubenböck et al. (2012) monitor the rate of urbanization for twenty-seven mega cities across the world but not analyzed and modeled that how dynamic structure of lulc changes over the period. however, our study on surat city focusing towards changes in dynamic structure of lulc with transition probability over the period of thirty years. yin et al. (2011) carried out lulc analysis from 1979 to 2009 but not used mathematical modelling like ca-mc. it becomes vital when we are dealing with large scale temporal datasets to determine the accuracy of resulted maps. the reliability of land use change modeling methods can be improved by combining two or more simulation techniques to integrate the advantages of each model (xin et al., 2012). it is worth mentioning that ca–markov model has been used recently in dynamic spatial phenomenon simulation and future land use change prediction (wang et al., 2012). moreover, the integrated ca–markov chain model takes advantage of the markov chain of land use change quantities prediction and dynamic explicit spatial simulation of the ca model. thus, the ca–markov model can be appropriate for spatial modeling of land use change (xin et al., 2012). consequently, the incorporation of gis and land use/ cover maps derived from remote sensing data with ca– markov model is capable of modeling and simulating spatial and temporal land use change efficiently (kamusoko et al., 2009; mitsova et al., 2011; myint & wang, 2006). moreover, the ca–markov model provides reliable land use change simulation results and overcomes the lack of socioeconomic, statistical, and historical data (sang et al., 2011). in the ca–markov modeling process, the temporal changes of land use classes are directed in the markov chain process based on produced transition matrices, whereas the spatial changes are controlled by transition potential maps, configuration of neighborhoods, and local transition rule during ca model process (guan et al., 2020; white & engelen, 2000). town planners generally used zoning to differentiate land use as a method of guiding and controlling the growth of urban areas. in the early development phase, this concept was applied in the planning of developed countries and is now in developing countries (he et al., 2018). therefore, this study will provide deep insights to urban and regional planners in developing strategies for evaluating various urban development scenarios about potential implications for land use and the advancement of existing spatial plans and policies in the indian context (al-ahmadi et al., 2009). 4. conclusion augmentation of classified land use land cover maps of 1990, 2000, 2010 and 2020 into ca–markov chain model for surat city area were modelled and simulated successfully. the overall modeling success was 95.30 % for the projected land use map 2035 and 86.90 % for the predicted land use map 2050. one good advantage of the applied ca–markov chain model is that the model needs limited data to simulate and predict any future land use change explicitly, i.e., at least two land use maps in different time instants. on the other hand, the ca– markov chain model cannot analyze and explain urban land use change driving factors, such as biophysical and socioeconomic factors, which are very important to manage, guide current situations and prepare wise plans for future demands. the land use change analysis of the period 1990-2020 demonstrated a continuous decrease in vegetation and vacant lands. https://doi.org/10.14710/geoplanning.10.1.23-36 sheladiya and patel/ geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 23-36 doi: 10.14710/geoplanning.10.1.23-36 34 additionally, the annual decline rate of vegetation areas has increased in the last decade. the predicted land use situations in 2035 and 2050 reveal alarming accelerated loss of vegetation lands in the study area. furthermore, based on predicted results, the future urban area would expand in a much-dispersed mode. these findings indicate that the situation will worsen in the future. for that, controlling increased urban growth and protecting agricultural areas is necessary to promote rational land use and a sustainable urban environment. however, to better understand land use changes and their driving forces, it's necessary to incorporate biophysical and socioeconomic data in the ca–markov chain model. this aim can be achieved by integrating another model involving these factors, like the logistic regression model, ahp model and other data mining approaches. 5. acknowledgments thanks to all staff in the urban planning section and cartographic lab of the department of civil engineering for allowing us to conduct research. 6. references aburas, m. m., ho, y. m., ramli, m. f., & ash’aari, z. h. 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[crossref] https://doi.org/10.14710/geoplanning.10.1.23-36 file:///d:/new%20folder/%23vol%2010.%20no.01/kelar/23-36_52320%20-%20sheladiya/rev/%20https:/doi.org/10.1007/s10661-010-1660-8 https://doi.org/10.1016/j.scs.2020.102045 23 geoplanning journal of geomatics and planning vol. 8, no. 1, 2021 original research slum upgrading spatial model based on level of vulnerability to climate change in coastal area of semarang city khristiana d. astuti 1*, p. pangi 1, reny yesiana 1, intan m. harjanti 1 1. diponegoro university, indonesia doi: 10.14710/geoplanning.8.1.23-40 abstract slum settlement is one of the significant global problems which requires special concern in the discussion agenda of sustainable development goals (sdgs) of 2016-2030. the sustainable development summit held in new york in september 2015 formulated that one of sdgs goals is to build inclusive, safe, resilient, and sustainable cities and settlements. in indonesia, the achievement of this goal is stated in national medium-term development plan 20152019, i.e. creating 0% urban slum settlement which is supported by policies expected to accommodate the achievement of national development targets. semarang mayor decree no. 050/801/2014 concerning the determination of the location of housing environment and slum settlements in semarang city has been issued as the basis to identify slum settlements scattered throughout semarang city, in terms of location, physical condition, and social conditions. this study was conducted by case studies on slum settlements in trimulyo village and mangkang wetan village, semarang city, central java province, indonesia, to formulate a slum upgrading model based on the resilience level of coastal communities towards climate change. the analysis included identifying the characteristics of slum settlements, scoring analysis to determine the resilience level possessed by coastal communities, and analysis of pentagon assets used to formulate slum upgrading models. the results of the study showed that these two research areas had a moderate level of vulnerability, with several different characteristics of asset ownership, particularly those related to human and social assets. increasing the quality of human resources and social relations in the community was more intensified in the environment and community in trimulyo, while improving the physical quality of the environment through housing improvements was carried out in mangkang wetan. copyright © 2021 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction trend on population growth is dominated by urban areas which not only function as centers for development activities but also as centers of population growth (carter et al., 2015). the increasing number of urban residents throughout the years results from various socio-economic backgrounds and some of them come to cities without a clear purpose (olthuis et al., 2015). based on data from the un urbanization prospect projection, it was estimated that more than 54% of the world’s population in 2016 lived in urban (population divisions department of economic and social affairs, 2018). such a high population in urban areas is often insufficiently balanced by the readiness of urban system plans which may accommodate the developments. consequently, the increasingly diverse activities but not integrated with the planned urban activity system cause many implications for the emergence of other various urban problems. the need for housing in urban areas is an essential basic need with high demand; when housing prices are nevertheless not affordable for the poor living in cities, this certainly will worsen the conditions in urban areas, one of which is the increasing number of developing slum settlements (saad et al., 2019). e-issn: 2355-6544 received: 9 april 2020; accepted: 5 may 2021; published: 30 july 2021. keywords: slum upgrading, coastal community, vulnerability. *corresponding author(s) email: khristiana.dwiastuti@live.undip.ac.id https://doi.org/10.14710/geoplanning.8.1.23-40 mailto:khristiana.dwiastuti@live.undip.ac.id astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 24 the increasing population growth in the east asia and pacific (eap) region over time has caused this region as the largest slum population in the world. there are approximately 75 million people (out of 250 million people) in eap who live in slum areas living under the poverty level with income below us$ 3.10/day. cities with the highest number of urban poor people are located in china, indonesia, and the philippines, while the highest rates of urban poverty are in the pacific island countries of papua new guinea and vanuatu, indonesia, and the lao people's democratic republic (baker & gadgil, 2017). this issue does not only occur in one or two countries, but it has been a global issue. thus, managing slum areas has been one of the global agendas contained in sustainable development goals (sdgs) 20162030 (infid, 2019; meredith & macdonald, 2017). the handling of slum settlements in the sdgs is the 11th of the 17 goals and 169 targets expected to be achieved by 2030. the 11th goal describes "building inclusive, safe, resilient and sustainable cities and settlements", with the first target is to ensure access for all communities to decent, safe, and affordable housing, including slum management and access to basic urban services. as an effort to achieve this goal, un-habitat formulated participatory slum upgrading program (psup) in 2008 as a result of discussions conducted with several representative countries of africa, the caribbean, and the pacific (acp), and the european mission (ec). un-habitat assists countries to develop and implement housing policies, strategies, and programs aimed at increasing access to adequate housing, improving the living conditions of slum inhabitants, and preventing the proliferation of new slum settlements (de schutter, 2014). the implementation of psup has been seen in various countries such as the asia coalition for community action (acca) program targeting an increase in inclusive slum settlements for the urban poor in thailand (baker & gadgil, 2017) and the kenya slum upgrading program (kensup) which seeks to improve the quality of life for people living in slum neighborhoods in kenya by improving housing quality, community income, providing tenure security, and improving infrastructure (meredith & macdonald, 2017). moreover, under the supervision of housing and urban renewal authority inc. (hura), a slum upgrading program is carried out in the philippines by updating or rebuilding damaged slums and other urban communities, developing resettlement sites, and mostly by improving and promoting urban development (minnery et al., 2013). in indonesia, improving the quality of slum upgrading in urban areas is one of the national priority programs towards cities without slums (kotaku) based on the national medium term development plan (local term: rpjmn) 2015-2019 (bappenas, 2017). kotaku program is an effort to accelerate the handling of slum settlements to support the achievement of the "100-0-100" target, i.e. 100% access to drinking water, 0% slum areas, and 100% access to proper sanitation by 2019 (public works office/dinas pekerjaan umum, 2017). according to the central statistic bureau (local term: biro pusat statistik/bps) data stating that by the end of 2013, access to drinking water achieved in urban areas was 67%, 11.6% for slum areas, and 59% for access to proper sanitation. since the trend for population growth continues to increase (bps, 2017), it is necessary to create integrated efforts to achieve the targets of this program involving the central government, local governments (provincial, regency or city, villages), and the community. therefore, it is fundamentally reasonable that the government should gradually reduce the areas of slum settlement and increase the achievement of settlement infrastructure, specifical access to drinking water and sanitation for urban communities (dirjen cipta karya, 2015a). kotaku program is nationally implemented in 34 provinces involving over 268 regencies/cities in 11,067 villages (desa and kelurahan) (dirjen cipta karya, 2015b). in addition, the target achievement of kotaku program has been carried out moderately from 2015 until 2019. moreover, at the end of 2015, urban slum areas were targeted to be reduced to 8%; around 6% in 2016; and respectively reduced to 4% and 2% for the following years 2017 and 2018; until the target of 0% was achieved by the end of 2019. semarang city is one of the targets for the implementation of kotaku program in indonesia. there are some slum settlements scattered in this city of which are located in coastal areas. this definitely affects the characteristics of the slum settlements in the area as seen physically from the building condition, the existence of environmental infrastructure in the settlement, as well as disaster threat like rob (seawater flooding) which frequently becomes an obstacle in managing slum areas problems. furthermore, it can also be non-physically identified from the social life pattern of the community, how they earn money (livelihoods), and people’s daily living habits. the characteristics of the people living in coastal areas certainly cannot be https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 25 separated from the current global phenomena which are closely related to climate change that directly or indirectly has implications for the lives of coastal communities (sariffuddin et al., 2017) including those who live in the slum settlement at the coastal area. accordingly, the efforts to improve the conditions of the slum environment must be holistically concerned both physically and non-physically regarding the characteristics of the community in dealing with the conditions they own. thus, community participation can be considered as a significantly integral approach that needs to be implemented to achieve the goals of kotaku program (yu et al., 2016). to support the realization of the '100-0-100' program, the handling and management of slum settlements specifically in coastal areas, therefore, needs to be carried out integratively through a slum upgrading model considering the resilience level of coastal communities toward climate change which differs from one region to others. 2. data and method 2.1 study area the scope area of this research covered mangkang wetan village (local term in semarang city: kelurahan) located in mangkang village and trimulyo village, a part of genuk subdistrict (figure 1). both villages (figure 2) are included in the administrative local government of semarang city. these two areas were selected as the research areas because these villages are listed in the decree of semarang city mayor no. 050/801/2014 about the determination of the location of slum housing and settlements environment of semarang city, and became part of the pilot projects to improve coastal community resilience by strengthening mangrove ecosystem services and developing sustainable livelihoods in semarang city in collaboration between semarang city government and ngo (mercy corps indonesia) with funding assistance from the rockefeller foundation. figure 1. location of study area https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 26 figure 2. map of mangkang wetan and trimulyo villages 2.2 method the method used in this study was quantitative. the method was used to analyze the vulnerability level of the community toward the climate change in the coastal area and formulate the slum upgrading strategy based on-field characteristics. data gathering was performed using questionnaires and observation, also document assessment. samples were taken using simple random sampling, with 18 respondents from mangkang wetan village and 14 respondents from trimulyo village. there were 3 stages to answer the aim of the study: a. slum settlement characteristic identification slum settlement characteristic identification was based on few aspects which had been determined by dirjen cipta karya which consists of: building condition, road condition, drainage condition, water supply condition, wastewater management, waste management, and fire extinguish system condition. those aspects and criteria could be seen in table 1. b. vulnerability level of community in the coastal area the analysis technique used to identify the vulnerability level of the community in the coastal area was scoring. this analysis technique was performed to identify the score of every sub-variable (table 2) in each exposure variable, sensitivity variable, and adaptation capability variable (boer, 2012). the highest score from each sub-variable was different, the score was based on how much the indicator used for the scoring. in the exposure variable, the highest score was 5 points, while in the sensitivity variable the highest score was 3 and 4 points for the adaptation capability variable. however, the lowest score from every variable was all the same as, i.e. 1 point. thus, after counting each variable, there would be 3 scores for each respondent. the formula for each respondent could be seen at equation (1) and (2): as explained previously, the vulnerability was affected by the exposure level, sensitivity, and adaptation capability. the vulnerability formula could be seen below: variable cumulative score = sub variable score amount ……………………………… (1) sub variable highest score amount vulnerability rate = exposure score x sensitivity score ……………………………………... (2) adaptation capability score https://doi.org/10.14710/geoplanning.8.1.23-40 18465-46665-1-sm%20new.docx#boer2012 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 27 table 1. slum settlements criteria (dirjen cipta karya, 2015a) no aspects criteria 1. building condition building regularity (dimension, orientation, footprint, and building form) building compactness building technical requirement (structure system, lighting safety, weather control, lighting, sanitation, and building material) 2. road condition service area road condition 3. drainage condition inundation presence (inundation duration, inundation frequency) service area 4. water supply condition technical requirement (pipe network, non-pipe network) service area 5. wastewater management technical requirement (personal wastewater management, communal and center wastewater management) service area 6. waste management technical requirement (warehouse, sorting, gathering, and management) service area 7. fire extinguish system condition water supply to extinguish the fire (natural resource: pond, lake, river, deep well; artificial resource: water tank, pool, water reservoir, water tank car, hydrant) the road for the fire truck table 2. sub variable and scoring that affected vulnerability level (analysis, 2018) no variable sub variable weight no variable sub variable weight exposure variable adaptation capability variable 1 occupation fishpond owner 5 1 group program exist 2 fishpond worker 4 nothing 1 fisherman 3 2 group program involvement often 4 processing fishery products 2 sometimes 3 don’t have a job 1 never 2 2 fishpond ownership yes 2 not active 1 no 1 3 skill training involvement yes 2 3 fishpond productivity not productive 2 no 1 productive 1 4 training advantages yes 2 4 fishpond is stricken by flood yes 2 no 1 no 1 5 training result utilization for income resource alternative yes 2 5 home is stricken by flood yes 2 no 1 no 1 6 routine meeting involvement yes 2 6 flood frequency in every house in a month. > 2 x 2 no 1 < 2 x 1 7 routine meeting frequency once a week 3 7 flood duration in every house > 2 hour 3 once a month 2 1-2 hour 2 more than once a month 1 < 1 hour 1 8 routine meeting benefits exist 2 8 source of clean water well 3 nothing 1 artesian well 2 9 media information (group meeting, meeting in rt/rw, radio, sms) >4 of media 3 pdam 1 2-3 of media 2 9 family member amount >4 people 2-3 people 3 <2 of media 1 2 10 group meeting very effective 4 < 2 people 1 effective 3 number of exposure 24 less effective 2 not effective 1 sensitivity variable meeting (rt/rw) very effective 4 1 monthly income < idr 1,500,000 3 effective 3 idr 1,500,000idr 2,000,000 2 less effective 2 > idr 2,000,000 1 not effective 1 2 monthly outcome > idr 2,000,000 3 radio very effective 4 idr 1,500,000idr 2,000,000 2 effective 3 < idr 1,500,000 1 less effective 2 3 asset ownership (land, house, boat, fishpond, etc.) < 1 asset 3 not effective 1 1-3 assets 2 sms/ wa very effective 4 > 4 assets 1 effective 3 4 capital access/ loan no 2 less effective 2 yes 1 not effective 1 number of sensitivity 11 11 information media usage yes 2 no 1 12 weather information needs yes 2 no 1 13 media used to spread the information >4 of media 3 2-3 of media 2 <2 of media 1 number of adaptation capability 45 https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 28 c. spatial analysis of community in the coastal area spatial analysis was carried out on the results of the questionnaire in two research locations. in addition to using random sampling to determine the number of samples, the location of the sample is determined by spatial sampling. spatial sampling is a sampling activity based on geographic/coordinate locations (buchori & pangi, 2015; thompson, 1997). respondents are marked based on their location coordinates. the results of the questionnaire answers were mapped based on sample locations and continued with interpolation analysis. the results of the interpolation analysis of the questionnaire data were used to perform spatial analysis based on the vulnerability criteria. 3. results and discussion 3.1 slum settlements characteristic the slum settlements area used in this study was the settlements in mangkang wetan village and trimulyo village. mangkang wetan village is part of the tugu subdistrict which is located in the western part of semarang city, while trimulyo village is one of the coastal areas located in genuk subdistrict, semarang city. a. mangkang wetan village the location distribution of slum settlements in mangkang wetan village is in rw 5, 6, and 7 with a total slum settlements area of 13.59 hectares. the distribution of slum locations in mangkang wetan village can be seen in figure 3 and appendix 1. figure 3. map of slum settlements distribution in mangkang wetan village (bkm, 2017) https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 29 there were approximately 388 units of houses in the slum settlements area. some of them were permanent units with plaster (cemented floor) or ceramic made floor, brick or wood mass wall and the roof was constructed by using the tin roof and/or wavy tile roof. the rest, 136 units, were semi-permanent buildings and 46 units were non-permanent buildings. generally, the status of the houses in the slum settlements was personal ownership. based on the identification results during the field research, infrastructure in the slum area in mangkang wetan includes a road network in the form of footpaths and some of which were already damaged, especially in rw 05 and rw 07. the drainage system in the slum settlements area was in the form of the open canal and close canal. the drainage system of the settlements mostly had got shallower and some of the canals were already damaged. the inundation from flood location or the area with the high frequency of precipitation was the effect of this suboptimal drainage system. in addition, the clean water supply system is fulfilled through drilled wells because the pdam (drinking water regional company – a company owned by the city government) piping system has not yet reached the location. meanwhile, concerning wastewater treatment infrastructure (sanitation), approximately 92% of the houses already had sanitation facilities/toileting; however, 8% of the community which does not possess the toileting yet still use the communal sanitation facilities. b. trimulyo village the location distribution of slum settlements area in trimulyo village is in rw 3, particularly in rt 3 and rt 4 with 3.55-hectare total slum settlements area. the distribution of slum locations in trimulyo village can be seen in figure 4 and appendix 2. figure 4. map of slum settlements distribution in trimulyo village (bkm, 2017) the houses in slum settlements area of trimulyo village were entirely permanent units with plaster/ceramic made floor, brick or wood mass wall and the roof was constructed by a tin roof and wavy tile roof. the status of the houses in the slum settlement was personal ownership. the road network was in the form of local environmental roads and they were generally in good condition and usable, while the drainage network was dominated by soil and masonry construction but less optimal function due to silting. as the supply of clean water in mangkang wetan village, trimulyo has not been reached by the local drinking water company (pdam) network; thus, the community meets their clean water needs by using drilled wells. however, for sanitation, all of the people in trimulyo village already have their own sanitation facilities. https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 30 3.2 vulnerability level analysis of coastal area community in mangkang wetan village and trimulyo village as one of the global warming effects, the rise of sea surface has caused a change in sea current and also made the land near the seashore logged, and thus it often affects the settlements in the coastal area (susandi et al., 2008). the settlements near the shore/river are commonly more susceptible than any other settlements far from the shore/river. it could happen since the land far from the shore/river or any waters would spend a longer duration to get logged (wulandari et al., 2013). a damaged mangrove ecosystem is another effect of the rising sea surface. therefore, if mangrove existence cannot be revived anymore, the abrasion of the land will occur more frequently than before because there is no backup when the wave comes. this phenomenon will affect the economic condition of the community in the coastal area; thus the coastal community will be more susceptible the climate change. in concordance, the relative escalation of sea surface would bring some consequences toward the coastal area, such as littoral area would be logged (1 cm escalation of the sea surface would decrease 1 cm of the shore), erosion would occur more frequently, the salt concentration of the soil will increase so it would no longer suitable for human to consume, and also the escalation of the sea in estuary area (suhelmi & prihatno, 2014). this study aimed to assess the vulnerability level of coastal areas in semarang city by considering mangkang wetan (west coastal area) and trimulyo genuk (east coastal area) as the location of this case study. there were 3 variables analyzed in this study, i.e. exposure, sensitivity, and adaptation capability. based on the data acquired in the field study, the result of those variables can be described as follow: a. exposure gallopin (2006) stated that exposure level represents the level, duration, and/or chance of a system to contact with disturbance or shock (boer, 2012). the exposure level in trimulyo and mangkang wetan villages was categorized as high, i.e. 76% and 74% respectively. moreover, the exposure level in mangkang wetan village was affected by the occupation of the people living mostly as pond owners. since the expense of pond maintenance was quite high, when the abrasion came it would bring more disadvantages toward the people. meanwhile, the vulnerability of trimulyo village was affected by the community occupation as fishermen. trimulyo community had no land to make a pond and there were only 2-3 people of trimulyo village whose pond for fish cultivation. however, the land used as a pond was a government-owned one. in addition, the majority of the community worked as fishermen. furthermore, another indicator of vulnerability level was that there were more houses damaged by the flood in trimulyo village than those in mangkang wetan village. nevertheless, in some points, flood duration in mangkang wetan village was much longer than in trimulyo village. the exposure levels are depicted spatially in figure 5. the spatial characteristics of exposure levels between mangkang wetan and trimulyo village are different since mangkang wetan exposure level is higher than that of trimulyo (appendix 3). b. sensitivity sensitivity was the internal condition of the system representing the vulnerability level toward any disasters (boer, 2012). the sensitivity level of trimulyo village was lower than that of mangkang wetan village (figure 6). moreover, mangkang wetan village’s sensitivity level was affected by higher monthly income gained by the community than that in trimulyo village. furthermore, mangkang wetan village income ranged from idr 1,874,931, while trimulyo village was only ranging from idr 1,438,889,-. therefore, the community in mangkang wetan village had generally more assets than trimulyo village’s community. the assets were mostly in the form of land, house, boat, fishpond, etc. in addition, the sensitivity level of these two villages is illustrated in appendix 4. https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 31 figure 5. map of exposure level in mangkang wetan and trimulyo villages figure 6. map of sensitivity level in mangkang wetan and trimulyo villages https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 32 c. adaptation capability adaptation capability represents the ability of a system to adapt toward the climate change phenomenon to reduce the negative effect and to maximize the positive effect or in other words, it could handle the consequences of climate change (boer, 2012). the approach conducted to fishing groups was by doing some attempts to increase the community's adaptive capability toward disaster vulnerability such as training to improve skills in processing fishery products and mangrove tourism entrepreneurship as alternative sources of income for the community. regular meetings and the use of information media are some means used by the community to respond to disasters as they live in coastal areas. appendix 5 describes the level of adaptation capability found in this study. map of adaptation capability in both of study area can be seen in figure 7. based on the result of three variables, the vulnerability level of both villages was then being counted. vulnerability level was categorized in 3 groups: high vulnerability (>0.6), moderate vulnerability (0.3-0.6), and low vulnerability (<0.3). table 3. result of vulnerability calculation in mangkang wetan dan trimulyo villages (analysis, 2018) vulnerability variable village mangkang wetan trimulyo exposure (k) 76% 74% sensitivity (s) 54% 49% adaptation(a) 74% 69% vulnerability level (kxs/a) 0.56 0.53 the level of vulnerability in mangkang wetan and trimulyo villages was categorized in moderate, i.e. 0.56 and 0.53 respectively (tabel 3). however, the vulnerability level of mangkang wetan village showed a higher rate (74%) than trimulyo village (69%). this result showed that the vulnerability of mangkang wetan community was stronger than that of trimulyo village. in addition, the vulnerability level represents the survival rate of the community in handling the climate change phenomenon. map of vulnerability in both of study area can be seen in figure 8. figure 7. map of adaptation capability in mangkang wetan and trimulyo villages https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 33 figure 8. map of vulnerability in mangkang wetan and trimulyo villages 3.3 slum upgrading spatial model the formulation of the slum upgrading model is based on the characteristics of the slum settlements in mangkang wetan and trimulyo villages by paying attention to the level of community vulnerability to climate change. this is done considering that both villages (local term: kelurahan) are coastal areas that are at risk from the impacts of climate change. this is in line with the implementation of the asian cities climate change resilience network (acccrn) program which seeks to make semarang a resilient city through increasing attitudes and behavior as well as knowledge and skills capabilities, building networks to increase knowledge and skills for the community (sariffuddin et al., 2017). vulnerability and risks to climate change are influenced by the existence of social relations between the community and its surrounding environment, both related to their physical, social and economic conditions (folke, 2006). various asset limitations and conditions in accessing existing resources become obstacles in improving the quality of slum settlements (olotuah, 2012). therefore, strategic efforts as a solution to improve the quality of the slum environment are needed to involve all relevant stakeholders. slum upgrading, which is an effort to improve the quality of the slum environment, is realized through the community's active participation and involvement. this is because slum upgrading is realized through a social program and a series of democratic activities with a clear direction of communication (deliberation for instance) to accommodate the aspirations of the community, as well as to increase the capacity of its human resources. improving the quality of the environment in slum settlements is also inappropriate if it is carried out through a top-down approach by ignoring the role of the community (meredith & macdonald, 2017; olotuah, 2012; un habitat, 2016). the pattern of activities in the community is influenced by asset ownership and community livelihoods (singh & gilman, 2000). in this study, asset ownership was identified from 5 components: human capital, physical capital, social capital, finance capital, and natural capital, which were obtained through observations and questionnaires. human capital represents the ability of someone in acquiring better access to their lifestyle (nugroho et al., 2017). human capital assessment in this study was measured by education, involvement, and creativity inside the group, using the training result as an income alternative (singh & gilman, 2000). in addition, social capital was identified through social connections or relations among the https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 34 community to support their social lives. in this study social capital was being assessed using 6 indicators: group program, group program involvement in both villages, routine meeting involvement, group meeting, and media usage for transferring information in daily life, especially for the matters related to coastal area community adaptation toward the vulnerability of each region also how much the network of stakeholders involved in giving training in every village. slum settlement area of the coastal area had a physical characteristic which may be seen from their dwelling or the infrastructure. the physical capital in this study was identified using 5 indicators, i.e. house condition assessed from the permanence of their building, road condition seen from the damage of the road itself, drainage system, clean water accommodation, and also sanitation system. then, financial capital was related to income or salary acquired by the individuals of the community each month, monthly outcome, and access to get capital or loan for their business or their asset ownership. that financial capital can encourage community participation in improving the quality of their lives (das, 2015). natural capital assessed in this study was based on 5 indicators: fishpond productivity, fishpond damaged by flood, house damaged or stricken by flood, flood duration in every house, and flood frequency in a month. table 4. result of pentagon asset assessment (analysis, 2018) no indicator mangkang wetan trimulyo no indicator mangkang wetan trimulyo 1 human capital 0.88 0.53 4 finance capital 0.69 0.75 education 0.26 0.11 monthly income 0.13 0.13 involvement inside the group 0.22 0.20 monthly outcome 0.13 0.13 creativity (using the training result as income alternative) 0.18 0.11 capital access/ loan 0.06 0.06 participation in skill training 0.22 0.11 fishpond ownership 0.13 0.06 2 social capital 1.00 0.78 land ownership 0.06 0.13 group program 0.17 0.11 house ownership 0.13 0.13 group program involvement 0.17 0.17 boat ownership 0.06 0.13 routine meeting involvement 0.17 0.17 5 natural capital 0.12 0.15 group meeting 0.17 0.17 fishpond productivity 0.17 0.08 media usage for information transfer in daily life 0.17 0.11 fishpond damaged by flood 0.17 0.08 network (government and ngo) 0.17 0.06 damaged houses because they were stricken by flood, 0.08 0.17 3 physical capital 0.80 0.73 flood duration in every house 0.10 0.25 house condition 0.20 0.20 flood frequency in every house in a month. 0.08 0.17 road condition 0.20 0.13 drainage system 0.07 0.07 clean water accommodation 0.13 0.13 sanitation system 0.20 0.20 based on vulnerability analysis of the community in the coastal area, mangkang wetan village and trimulyo village both had moderate vulnerability levels. mangkang wetan village represented the coastal area of west semarang, while trimulyo village represented the coastal area of north semarang. although both villages were categorized as slum settlement areas with similar vulnerability levels, the slum upgrading model for these two villages was different. the result of the pentagon asset analysis (figure 9) showed that human capital and social capital aspects in mangkang wetan village were higher than those in trimulyo village, while the other capital conditions were almost similar both in mangkang wetan and trimulyo villages. the result of pentagon asset assesment can be seen in table 4. https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 35 figure 9. analysis of pentagon asset in mangkang wetan and trimulyo villages (analysis, 2018) slum upgrading strategy in slum settlement of study area can be seen in table 5. table 5. slum upgrading strategy in slum settlements of mangkang wetan and trimulyo villages (analysis, 2018) no. pentagon asset mangkang wetan trimulyo 1. human capital training improvement should be maintained to support the creativity of mangkang wetan community. increasing the training and skill among the community in trimulyo village. this program was aimed to increase the creativity of the people to take every benefit from the training activity. 2. social capital innovation in group programs so that people are not bored and increasingly interested in engaging in the program increasing the government network involvement and ngo in the village was necessary. along with the improvement of the network, it could open the access to increase the training process toward the community. 3. physical capital  building regulation and physical condition refinement of the house  reducing inundation spots and increasing the capacity also the quality of drainage in the settlements.  house system improvement to access decent sanitation.  fire hydrant availability for fire safety.  road quality and access improvement.  fire hydrant availability for fire safety.  reducing inundation spots and increasing the capacity also the quality of drainage in the settlements 4. financial capital capital access improvement. capital access improvement. 5. natural capital pond protection using apo (wave breaker equipment) house protection through mangrove cultivation on the side shore. 4. conclusion slum upgrading as a program to improve the environmental quality of slum settlements area should consider various aspects such as physical, social, and economic aspects. however, besides these three aspects, it is necessary to observe human resource quality in the area and also natural resource availability which could be used for the community interests. pentagon asset analysis was used in this study to assess those five aspects based on the existing condition in the research location. based on the analysis result, it showed that there was a difference in the characteristic of mangkang wetan village in mangkang village and trimulyo village in genuk subdistrict. the vulnerability level of the coastal area community toward climate change, social capital, and human capital in mangkang wetan village was relatively higher than trimulyo village. this result was affected by various programs which had been performed to handle the effect of climate change along with intensive involvement of the community in mangkang wetan village. https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 36 therefore, slum upgrading can be conducted by concerning the improvement of human resource quantity and quality and making social relation in the community more intensively to the community of trimulyo village. physical quality improvement of the environment may be established by repairing and refining houses in mangkang wetan village as there are still 30% non-permanent houses. meanwhile, drainage quality network improvement is necessary for both villages since the drainage function is not optimal yet because of the silting which causes inundation formation. previous studies have discussed more the resilience of coastal communities, but have not yet been concerned about how the resilience of these communities affects the efforts to improve slum settlements. however, through this research, the output of the analysis shows that differences in community characteristics, including the community's response to the disasters they face in coastal areas, will affect the efforts to improve the quality of life. 5. acknowledgments the authors would like to express their gratitude to undip vocational school which has funded this research. 6. references baker, j. l., & gadgil, g. u. 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[crossref] https://doi.org/10.14710/geoplanning.8.1.23-40 https://doi.org/10.1016/j.aej.2019.03.001 https://doi.org/10.1088/1755-1315/55/1/012047 https://doi.org/10.1016/j.proenv.2016.09.026 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 38 appendices appendix 1. the location distribution of slum settlements in mangkang wetan village (bkm, 2017) no. slum area (rw/rt) slum wide (ha) population poor population house building* household people household people number slum 1. rw.05 1 3.81 74 252 43 171 35 4 2 62 216 31 123 35 6 3 49 177 18 70 38 3 4 44 162 13 51 30 2 5 44 162 13 52 19 2 2 rw.06 1 4.34 39 147 8 32 23 1 2 43 159 12 47 22 3 3 41 153 10 40 20 1 4 36 138 5 18 25 1 5 43 159 12 46 40 3 6 42 156 11 42 27 3 3 rw.07 1 5.43 41 153 10 39 22 5 2 48 174 17 67 23 4 3 39 177 8 32 27 2 4 44 162 13 51 23 3 5 52 186 21 83 31 6 6 57 201 26 104 39 18 7 61 213 30 119 39 8 8 49 177 21 85 30 11 9 40 150 9 37 24 7 total 13.59 948 3474 331 1309 572 91 *) slum building is a house that is not a decent place to stay review by the main building construction (roof, floor, and building wall) appendix 2. the location distribution of slum settlements in trimulyo village (bkm, 2017) no. slum area (rw/rt) slum wide (ha) population poor population house building* household people household people number 1. 03/03 0.64 138 138 19 100 33 2. 03/04 0.68 136 136 23 103 47 total 3.55 415 415 66 286 117 https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 39 appendix 3. exposure level of mangkang wetan and trimulyo villages (analysis, 2018) no indicator mangkang wetan trimulyo 1 occupation 20.83% 12.50% 2 fishpond ownership 6.55% 4.17% 3 fishpond productivity 5.65% 8.33% 4 fishpond is stricken by flood 7.44% 4.17% 5 houses are stricken by flood 5.95% 8.33% 6 frequency of houses stricken by flood 3.27% 8.10% 7 duration of houses stricken by flood 7.74% 9.49% 8 source of clean water 7.44% 7.41% 9 family member amount 11.31% 11.34% number of exposure 76 % 74% appendix 4. sensitivity level of mangkang wetan and trimulyo villages (analysis, 2018) no indicators mangkang wetan trimulyo 1 monthly income 12.34% 13.64% 2 monthly outcome 19.48% 15.66% 3 asset ownership (land, house, boat, fishpond, etc.) 5.56% 2.72% 4 capital access/ loan 16.88% 17.17% number of sensitivity 54% 49% https://doi.org/10.14710/geoplanning.8.1.23-40 astuti et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 23-40 doi: 10.14710/geoplanning.8.1.23-40 40 appendix 5. adaptation capability level of mangkang wetan and trimulyo villages (analysis, 2018) no indicators mangkang wetan trimulyo 1 group program 2.22% 2.22% 2 group program involvement 7.46% 8.89% 3 skill training involvement 3.33% 2.47% 4 training advantages 3.65% 2.47% 5 training result utilization for income resource alternative 3.17% 2.22% 6 routine meeting involvement 3.17% 1.98% 7 routine meeting frequency 4.29% 4.07% 8 routine meeting benefits 6.03% 4.44% 9 information media (group meeting, meeting rt/rw, radio, sms) 4.29% 4.44% 10 information media 4.29% 4.44% 11 group meeting 6.67% 8.64% 12 meeting (rt/rw) 7.46% 6.67% 13 radio 2.22% 2.22% 14 sms/whatsapp text messaging 4.44% 2.47% 15 information media usage 4.29% 4.44% 16 weather information needs 4.44% 4.44% 17 media used to spread the information 2.38% 2.22% number of adaptation capability 74% 69% https://doi.org/10.14710/geoplanning.8.1.23-40 | 9 geoplanning vol 4, no. 1, 2017, 9-18 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.1.9-18 a gis based evaluation of land use changes and ecological connectivity index p. indrayani a, b, y. mitani a, i. djamaluddin c, h. ikemi a a department of civil engineering, kyushu university, fukuoka 819-0395, japan b fajar university, indonesia c hasanuddin university, indonesia abstract: recently, the makassar region is a significant land use planning and management issue, and has many impacts on the ecological function and structure landscape. with the development and infrastructure initiatives mostly around the urban centers, the urbanization and sprawl would impact the environment and the natural resources. therefore, environmental management and careful strategic spatial planning in landscape ecological network is crucial when aiming for sustainable development. in this paper, the impacts of land use changes from 1997 to 2012 on the landscape ecological connectivity in the makassar region were evaluated using geographic information system (gis). the resulted gis analysis clearly showed that land use changes occurring in the makassar region have caused profound changes in landscape pattern. the spatial model had a predictive capability allowing the quantitative assessment and comparison of the impacts resulting from different land use on the ecological connectivity index. the results had an effective performance in identifying the vital ecological areas and connectivity prior to development plan in areas. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): indrayani, p., et al. (2017). a gis based evaluation of land use changes and ecological connectivity index. geoplanning: journal of geomatics and planning, 4(1), 9-18. doi:10.14710/geoplanning.4.1.9-18 1. introduction indonesia is one of the fastest urbanizing countries in asia. the level of population growth in urban areas approximately reaches 2.75% per year or higher than the level of national population growth (1.49% per year). in 2015, population growth in urban areas was estimated to reach 59.35% of the total population (parasati, 2013). assuming that population growth in urban areas approximately reaches 2.75% per year; the indonesian population living in urban areas in 2045 will reach 82.37% of the total population. in recent years, the impact of population growth on urban sprawl in many major cities in indonesia has become a major issue. as the fifth fastest growing city in indonesia, the urbanization of the makassar region has followed a model characterized by a low density of built-up area, which has revealed itself as tremendously negative for natural habits (turner, 2005). in many areas with increasing urban sprawl, fragmentation has turned out to be virtually inevitable (dupras et al., 2016). generally, high ecological connectivity and a well-designed green network are assumed to better facilitate flows of energy, materials, and species, and thus are important for environmental conservation in developing landscapes (crooks & sanjayan, 2006). similarly, green infrastructure (gi) is used emphasizing on a system of natural areas as a backbone of landscape ecology (mathey et al., 2015; richter & weiland, 2011). the definition of green infrastructure is strategically planned and managed networks of natural lands, working landscapes and other open spaces that conserve ecosystem values and functions also provide associated benefits to human populations (benedict et al., 2012; breuste et al., 2015). therefore, based on literatures, green infrastructures in the united states and the europe are best achieved through an integrated approach to land use management and strategic planning in ecological network. article info: received: 31 august 2016 in revised form: 8 september 2016 accepted: 29 november 2016 available online: 8 march 2017 keywords: land use change, landscape pattern, ecological connectivity, gis, urban planning corresponding author: poppy indrayani kyushu university, japan email: poppy@doc.kyushu-u.ac.jp open access http://dx.doi.org/10.14710/geoplanning.4.1.9-18 http://dx.doi.org/10.14710/geoplanning.4.1.9-18 mailto:poppy@doc.kyushu-u.ac.jp indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 10 | ecological landscape theory has provided set quantitative methods namely landscape metrics to characterize landscape pattern. however, there is a lack of quantitative methods to effectively assess ecological connectivity at regional scale. marulli and mallarach (2005) proposed a methodological approach used in quantitative landscape ecology, allowing to turn current theories into useful spatial analysis tools for regional land use planning. most of the previous research in the land use changes of makassar area (figure 1) has been carried out using a remote sensing technique. moreover, the spatial scales of the research settings deal with the metropolitan region. figure 1. geographic location of the makassar region and tallo river area this study aims to evaluate land use changes using a topographical map of 1:50,000 scales with geographic information system (gis) grid system analysis dealing with the makassar city scale and assess its impacts into the landscape ecological connectivity index. in order to effectively evaluate the respective goal, land use maps using a 50 m grid mesh were developed from availability of the digital topographic maps. land use changes during the period from 1997 to 2012 were simulated based on 50 m grid mesh calculation, then a number of landscape properties were identified. adopting the ideas and methodologies in landscape ecology, the study provided an assessment regarding the impact of land use changes on landscape ecological connectivity index. several landscape metrics were calculated, and the evolution of the landscape ecological connectivity was assessed using gis. 2. data and methods 2.1. land use planning of the makassar region makassar is the largest city in eastern indonesia and the capital of the province of south sulawesi (figure 1). in 2016, the population counts about 1.7 million, with an average density of 8000 inhabitants per square kilometer. makassar covers total area of nearly 177 km2 which is divided into 14 districts. the mamminasata metropolitan area (covering the city of makassar and the regencies of gowa, maros, takalar) has a population of approximately 2.5 million which is expected to grow to 2.9 million in 2020. recently, the government of makassar city proposes to create tallo river area where the city can strategically manage urban development in the currently undeveloped land. moreover, the large area on downstream of tallo river is near the city center. it has been endorsed in the makassar city’s spatial plan (2012) identifying the tallo river as a special development area. therefore, an evaluation of the impact of land use changes on the ecological landscape pattern of tallo river area is indispensable to ensure that the strategic land use planning can be optimized to protect the environment. 2.2. land use maps in 1997 and 2012 in this research, the land use maps in 1997 and 2012 were developed from the availability of digital topographic maps of 1:50,000 scales provided by the regional development planning agency of makassar shown in figure 2 (indrayani et al., 2016). land use division was determined based on land use boundary line of the topographic maps. the land use divisions for 1997 and 2012 were reclassified into 10 categories. http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 | 11 2.3. land use changes between 1997 and 2012 in gis process, the land use polygons from the topographic maps were intersected with 50 m grid mesh from grid division analysis. the grid-mesh has orientation of the world grid system. intersected land use polygons were calculated to obtain the maximum area in each grid feature. land use value for each grid was dissolved based on maximum area analysis in gis. the 50 m grid mesh based scale for land cover values can be obtained (zhou et al., 2014); accordingly the spatial matrix analysis of gis for land use changes can be done. by utilizing the developed land use maps, an analysis of land use changes based on 0.25 hectares area size was performed. land uses for the period of 1997-2012 have shown important changes, urban surface has grown up from 4,805 to 8,098 hectares which represents almost twice the urban expansion of 15 years period. in the land use map of 1997, the existence of crop field, fishpond, and swamp field has not been clearly visible. it is revealed that urban, paddy field, garden field, and mangrove forest occupied 27%, 27.8 %, 26.7%, and 11.9% of the makassar region, respectively. in the eastern part of makassar, the land area was bordered by the sea in which some of the sea areas were assumed as future reclaimed land. in 2012, urban has increased to 45.6 % of the makassar region, in contrast that of paddy field, garden field, and mangrove forest has decreased to 16.3%, 9.3%, and 2.6%, respectively. the increasing phenomenon of urban area from 1997 to 2012 have caused the land cover of fishpond (13.5%) and swamp field (2.1%) existed in 2012. moreover, a large area of fishpond was developed due to the conversion of mangrove forest for local peoples to practice fishery in recent years, the impact of regular flooding could be potentially increased in the tallo river basin as a natural drainage catchment area, supported the local peoples to create the fishpond as a livelihood. in addition, the occurrence of swamp field and fishpond counted as 15.6% from the makassar region, has transformed the pattern of mangrove forest, shrubs field in the vicinity of tallo river area. figure 3 shows the comparison of the area (hectares) of land use change values in the makassar region from 1997 to 2012. figure 2. development of land use maps of the makassar region (1997) (2012) figure 3. comparison of land use change values from 1997 to 2012 http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 12 | 3. results and discussion 3.1. identification of ecological functional areas the decision about the ecological functional areas that needs to be connected is essential for assessing the ecological connectivity (girvetz et al., 2008; li et al., 2013). using the land use maps, a topological analysis of the land cover categories was performed (table 1). based on a review of existing literatures, depending on each land cover, simple ecological functional areas are defined by a minimum surface (sr = 50 hectares) (bender, contreras, & fahrig, 1998). the areas that could not be considered as simple ecological functional areas can be grouped into forest, agricultural or agroforest mosaics (forman, 1995). the gis spatial analysis obtained six types of ecological functional areas, as shown in table 2. forest is considered as a relative small spectrum size (less than 50 hectares), thus it is not included in the ecological functional areas. all remaining areas were considered fragmented areas. results of the ecological functional areas in the makassar region are shown in figure 4. as expected, the ecologically functional areas c’r (55.42%) of the total area in 2012 was lower than the ecologically functional areas c’r (87.79%) in 1997. the largest ecologically functional areas in 1997 were paddy field covering 98.0% of the total area, followed by garden field and mangrove forest covering 86.9% and 77.1%, respectively. in 2012, paddy field, garden field and mangrove forest decreased into 79.5%, 38.2% and 50.7% of the total area, respectively. in particular, small swamp field, which hold the vertebrate biodiversity (13.7% of the total area) were the important habitat type, and therefore the most vulnerable. to deal with the landscape ecological connectivity analysis, the following sections described the barrier effect index (bei) and the ecological connectivity index (eci). table 1. land cover categories code land cover b1 urban b2 water b3 fishpond c1 forest c2 mangrove forest c3 swamp field c4 paddy field c5 garden field c6 shrub field n1 crop field table 2. results of topological analysis showing the ecological functional areas 3.2. calculation of the barrier effect index (bei) urban development often hinders the movement of ecological processes. barriers include all artificial land uses that create obstacles to the flow of energy, information, or matter across the matrix, or in other words, the landscape resistance. to reflect the barrier effects in measuring ecological connectivity, a group of artificial attributes were designated with different weights on each attribute depending on the relative influence on the entire landscape. the maximum level of weight was given to the built-up areas comprised of high and medium-density residential development because for the most time the built-up areas are impermeable to movement of many species (fahrig, 2003). since this study does not consider water body code land cover total area (ha) sr (ha) 1997 2012 1997 2012 ha % ha % c'1 forest 8.58 44.52 50 0 0 0 0 c'2 mangrove forest 2,088.57 471.69 50 1,610.20 77.10 239.13 50.70 c'3 swamp field 0 382.74 50 0 0.0 52.40 13.70 c'4 paddy field 4,805.79 2,795.54 50 4,711.80 98.0 2,222.43 79.50 c'5 garden field 4,705.47 1,663.63 50 4,088.34 86.9 0 634.93 38.20 c'6 shrub field 520.2 476.58 50 237.47 45.60 84.73 17.80 total 12,128.61 5,834.70 10,647.81 3,233.62 http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 | 13 related species, water bodies such as rivers, lake and fishpond were counted as medium-level barriers. the ecological connectivity model is primarily based on the least-cost analysis considering the ecological functional areas and an impedance surface which incorporates the barrier effect and the potential affinity matrix. to calculate the effects of artificial barriers on ecological and landscape connectivity, a barrier effect index (bei) was defined (marulli & mallarach, 2005) as follows: bei = yi/ymax [1] where, yi: the value of the barrier effect in a pixel, ymax: the maximum value of the barrier effect calculated on a given area. the bei was based on the weight that each barrier type (table 3), the affected land use class, and the distance from the barrier, according to the assigned potential impact matrix and logarithmic relationship with distance. thus, it reflects an impedance surface, where ai corresponds to the maximum significantly affected distance for each type of barrier, and ai corresponds to the potential impact value for each type (table 4). based on marulli and mallarach (2005), it was assumed that the effect of a single barrier from a given point is logarithmic and decreasing as distance increases, according to the following expression: ys = bs-ks1.ln(ks2(bs-d’s)+1) [2] where, bs: the weight of each barrier type, ks1 and ks2 are constants for logarithmic decreasing function d’s: the adapted cost distance per barrier type. the bei model applies the cost distance analysis using gis and requires two gis layers (a source layer and a friction resistance layer) as the input of the model: one is origin surface for each barrier type and second is impedance surface from the potential impact matrix. the principal algorithm underlying the cost distance model is the least-cost method. in this way, the cost value in each cell represents the distance to the source, measured as the least effort (lowest cost) in moving over the resistance layer. the cost distance model individually calculates the barrier effect ys for each sub-class type. thus, the entire barrier effect in the landscape is defined as the addition of the effects of all barrier types on a given area. bei is relative index to give values within an ordinal scale from 1 to 10, as shown in table 5. the application of the bei shows that in 1997 at least 61.08 % of the makassar region was under negative impact from urban area, and increased at least 83.54% in 2012 (figure 5). the maps resulting from the application of the bei confirmed the distribution of areas in 2012 slightly affected by barrier in the tallo river area (red-dotted lines), in contrast with the pervasive impacts spreading in the eastern part of the makassar region. figure 4. distribution of the ecological functional areas in the makassar region (own analysis, 2016) (1997) (2012) http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 14 | table 3. weighted value system for the calculation of the bei code type weight (bs) ks1 a ks2 a b1 urban b1= 100 k11=55.52 k21=0.051 b2 water b2= 60 _b _b b3 fishpond b3= 60 _b _b a constants for a logarithms decreasing function (α=0.3) b for s = 2 there is not surrrounding spatial affectation; y2 = b2 table 4. impact matrix for the calculation of the barrier effect index (own analysis, 2016) code type classes includeda affectation coefficient (a1)b affectation value (a1) v1 neutral n1 a1=1000m a1=0.10 v2 agriculture c4, c5 a2=750m a2=0.13 v3 "natural" c1, c2, c3, c6 a3=500m a3=0.20 v4 barrier b1, b2, b3 a4=250m a4=0.40 (an=b1/an) a class description in previous table b a1 defines the maximum significantly affected distance by each type table 5. ranking of the barrier effect in the landscape (modified from marulli and mallarach (2005)) barrier effect index effect type of barriers 0 non existent lack of anthropogenic barriers. total permeability of matter, energy and information 1 low impact small and scattered barriers, such as isolated farms 2 high ecological permeability remains 3 medium impact low density residential areas 4 medium ecological permeability remains 5 high impact scattered urban, commercial or industrial areas 6 low ecological permeability 7 very high synergic combination of urban areas 8 very low ecological permeability 9 critical impact synergic combination of large, high density urban areas 10 minimum ecological permeability 3.3. evaluation of the ecological connectivity index (eci) ecological connectivity refers to the functional aspects of the actual connection between the different elements of the landscape (pino & marull, 2012). an ecological connectivity index (eci) was defined based on a least-cost model that considers the different functional ecological areas and an impedance surface which incorporates the barrier effect and a potential affinity matrix for all the land use types. the model applied the cost distance analysis in gis using two input data: one is origin surface for each type of ecological functional area and second is impedance surface resulting from the application the effect of the barriers. finally, to transform the continuous values of the cost distance to discrete values based on a decimal scale, the eci was calculated, according marulli and mallarach (2005) to the following expression: eci = 10-9.ln(1+(xi-xmin))/ln(1+(xmax-xmin))3 [3] where, xi is the adapted cost distance value in a pixel, xmax is the maximum and xmin is the minimum cost distance values on a given area. it is considered that this index reflects a kind of general ecological connectivity, since its computation includes all the ecological functional areas. thus, it is a generic approach untied to specific indicator species. an interesting propriety of the eci is that it has a relativistic distribution of values, always giving values between 0 and 10. this feature is useful to compare different alternatives. ranking distribution of eci allows the identification of areas of low absolute value as the only viable way of connecting existing ecological functional areas. http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 | 15 figure 5. maps resulting from the application of the bei on the makassar region (own analysis, 2016) (1997) (2012) forman (1995) described landscape connectivity as a degree of spatial connectedness among landscape elements such as patches, corridors, and matrix. patch connectivity focuses on amount and arrangement of habitat patches, and thus effective distance between the patches becomes an important issue (broquet et al., 2006). corridor connectivity identifies linear features to promote dispersal through connectivity restoration (graves et al., 2007). matrix connectivity evaluates overall landscape mosaic, including landscape matrix to maintain maximum landscape continuity of non-built areas (levin et al., 2007). various methods were developed from general landscape ecological principles to measure landscape connectivity. although there are a wide range of proposed connectivity measures and geometric analyses from very simple to highly sophisticated (selman, 2006), the approaches were categorized into four groups (connectivity metrics, least-cost analysis, empirical models and graph-based models). the use of least-cost analysis has been increased in recent landscape and ecological connectivity research because it calculates effective distance, a measure for distance modified with the landscape resistance (adriaensen et al., 2003). this method can be implemented in gis efficiently and effectively. comparing to the previous similar study, the results of gis analysis were able to evaluate each of the land use changes and its effects on the value of landscape connectivity index, spatially and temporally. the gis methodology employed in this study to assess ecological connectivity in the makassar region revealed spatial processes, such as land use changes based on 50 m grid mesh analysis, and assess spatial differentiation of ecological connectivity index from urban expansion between 1997 and 2012. the analysis result observably showed that land use changes between 1997 and 2012 which occurred in the makassar region have in turn caused profound changes on landscape pattern. while in 1997, the makassar region was mainly a paddy field, garden field and mangrove forest, recently it is an urban area where urban spaces occupy most of the area. there has been a large decrease in ecological connectivity in the entire makassar region due to the high fragmentation produced by urban sprawl (figure 6). from a landscape ecological point of view, one of the main trends in the makassar area during the last fifteen years has been the rapid fragmentation and transformation of the natural landscape, creating numbers of patches of habitat, increasingly smaller and disconnected. conversion of each land use value into urban area such as paddy field to urban, garden field to urban, mangrove forest to urban and shrubs field to urban showed different levels of impact on the eci (figure 7). the distribution differentiation of eci due to conversion of each land use value (1997) changed into urban (2012) using analysis of gis showed that the shrub and mangrove forest in the vicinity of tallo river area has relatively high impact levels to the eci (figure 8). therefore, a development plan is currently being considered in the tallo river area and if appropriately designed it could mitigate the loss of ecological connectivity. http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 16 | figure 6. comparison of eci for ecological functional areas (own analysis, 2016) (1997) (2012) figure 7. (a) land use values changed into urban; (b) differentiation of the eci (own analysis, 2016) (a) (b) figure 8. differentiation of eci resulting from the conversion of each land use (own analysis, 2016) http://dx.doi.org/10.14710/geoplanning.4.1.9-18 indrayani et al. / geoplanning: journal of geomatics and planning, vol 4, no 1, 2017, 9-18 doi: 10.14710/geoplanning.4.1.9-18 | 17 moreover, there is an urgent need for green infrastructure strategies that facilitate the protection and restoration of environment, especially the remaining few mangrove forest, swamp field and agricultural field in the tallo river area. study on the gis based green infrastructure planning coordinated with ecological connectivity analysis is essential (mao et al., 2012). there is an urgent need for strategies that facilitate the protection and restoration of ecologically connected landscapes (parcerisas et al., 2012; tscharntke et al., 2012). 4. conclusion an evaluation of land use changes and landscape connectivity index was conducted using gis. the ecological functional areas were identified by topological analysis using the developed land use maps of 1: 50,000 scales. landscape metrics method proposed by marulli and mallarach (2005) was adopted to calculate the bei and eci in the makassar region. the impact of the conversion of each land use value in 1997 into the urban area in 2012 on the differentiation of the ecological connectivity index level was evaluated. it is shown that the gis has important function to proceed each step of spatial analysis for land use changes and landscape pattern analysis model. 5. acknowledgments this work was supported in part by indonesian government scholarship program (dikti), indonesia. 6. references adriaensen, f., et al. 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[crossref] http://dx.doi.org/10.14710/geoplanning.4.1.9-18 https://doi.org/10.1061/(asce)up.1943-5444.0000275 https://doi.org/10.1016/j.envsci.2012.08.002 https://doi.org/10.1016/j.landusepol.2011.11.004 https://books.google.co.id/books?id=tmh-agaaqbaj https://doi.org/10.1016/j.biocon.2012.01.068 https://doi.org/10.1146/annurev.ecolsys.36.102003.152614 https://doi.org/10.1007/s10980-013-9950-5 | 101 geoplanning vol 5, no. 1, 2018, 101-114 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.101-114 insight analysis on dyke protection against land subsidence and the sea level rise around northern coast of java (pantura) indonesia h. andreasa , h. z. abidina , d. a. sarsito a , d. pradipraa a geodesy research group, bandung institute of technology, indonesia abstract: land subsidence and the sea level rise is newly well-known phenomenon around northern coast of java indonesia (pantura). the occurrence of land subsidence at least recognizes at the first of the city or urban area development, while the sea level rise was recognized from several last decades corresponds to the global warming. following the both phenomena, tidal inundation (in javanese they call it “rob”) is now becoming another newly well-known phenomenon along pantura. in the recent years the tidal inundation comes not only at a high tide but even at the regular tide in some area. sea level rise and the land subsidence are considered as the causes deriving the occurrence of tidal inundation. dykes have been built against tidal inundation around pantura (e.g. in jakarta, blanakan, pekalongan, semarang, and demak). nevertheless, since the land subsidence and the sea level rise are believed to be continuing through times, insight analysis on these dyke’s “protector” is necessary. how long the dyke would effectively protect the land area would be highlight in this paper. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): andreas, h. et al. (2018). insight analysis on dyke protection against land subsidence and the sea level rise around northern coast of java (pantura) indonesia. geoplanning: journal of geomatics and planning, 5(1), 101-114. doi: 10.14710/geoplanning. 5(1), 101-114 1. introduction northern coast of java indonesia is famous with the local name called pantura. it started from merak in northern part of west of java province crossed to about 1000-kilometer length to banyuwangi in east java province (figure 1). pantura existed along with the java sea. many big cities develop along pantura such as jakarta, cirebon, pekalongan, semarang, and surabaya. despite of many features that can be found in pantura, land subsidence, sea level rise, and tidal inundation are there as very interesting features. land subsidence and the sea level rise is newly well-known phenomenon around pantura. the occurrence of land subsidence at least recognizes at the first of the city or urban area development, while the sea level rise was recognized from several last decades corresponds to the global warming. as the consequences of land subsidence and the sea level rise phenomena, tidal inundation (in javanese they call it “rob”) is becoming another newly well-known phenomenon along pantura. in the recent years the tidal inundation comes not only at a high tide but even at the regular tide in some area. land subsidence by definition is a lowering the ground level from the reference height system such as geoids or the sea level. excessive of groundwater abstraction in combination with natural compaction of sediments and probably tectonic deformation, land setting/reclamation, loading from construction of new buildings, oil and gas extraction, underground mining, drainage of peat lands, etc. are considered as possible causes of the land subsidence. meanwhile, the increasing temperature of the earth has been brought ice to melt in the north and south antarctica and made volume of water larger. as the consequences the sea level rise exists. the tidal inundation simply explains as the flood coming from the sea because the land is lower than the sea level. the land subsidence is making the low land area along the coast become lowering through times than the sea level. tidal inundation is become a disaster for several open access article info: received: 06 august 2017 in revised form: 10 dec 2017 accepted: 30 january 2018 available online: 30 april 2018 keywords: geodesy, geography, geomatics, civil, urban and regional planning corresponding author: heri andreas geodesy research group, bandung institute of technology, indonesia email: heriandreas49@gmail.com https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 mailto:heriandreas49@gmail.com andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 102 | places around the coastal area like pantura. many of urban and other areas like farming area, fishpond, etc. have been suffered tidal inundation and becoming worse in times. first it was only few centimetres of inundation and come only at a high tide, but now it can be more than a half of meter and coming at regular tide, and even has comes permanently in certain places. dykes have been built against tidal inundation disaster around pantura (e.g. in jakarta, blanakan, pekalongan, semarang, and demak). nevertheless, as we will see on the paper, since the land subsidence and the sea level rise are believed to be continuing through times, insight analysis on these dyke’s “protector” is necessary. how long the dyke would effectively protect the land area would be highlight in this paper along with others analysis. figure 1. the map of pantura. red color represents urban area (source: deltares) 2. data and methods 2.1. land subsidence for building the dyke along pantura we absolutely need to understand beforehand about the land subsidence characteristic. it is most probably the dyke will be sinking due to subsidence. in this case, knowing the rate of the subsidence and the longevity of the subsidence are important parameters for dyke design, etc. as for pantura, the land subsidence is clearly taking places in jakarta, pondok bali blanakan, cirebon, pekalongan, semarang, demak and surabaya (figure 2). the evidence for subsidence was based on repeated levelling measurements, gps surveys, insar measurements, extensometer, etc. levelling is a very conventional of geodetic technique measuring height from reference point connected to the mean sea level (msl). repeated measurement of levelling in the same point in some are may revealed land subsidence information. gps (global positioning system) is a passive, all-weather satellitebased navigation and positioning system, which is designed to provide precise three dimensional positions and velocity, as well as time information on a continuous worldwide basis (hofmann-wellenhof et al., 2008; abidin et al., 2008). insar is satellite base imagery that can provide better understanding in spatial variation of land subsidence, which become the weakness of gps method. some researchers have been conducted to perform land subsidence monitoring by insar, such as (chaussard et al., 2013; koudogbo et al., 2012; kuehn et al., 2009), etc. the occurrence of land subsidence in jakarta was recognized at least in the early the development of the city. according to some publications (e.g. h. abidin et al., 2010; h z abidin, 2005; h z abidin et al., 2004; hasanuddin z abidin et al., 2008; hasanuddin z abidin et al., 2011; chaussard et al., 2013; koudogbo et al., 2012; d murdohardono & sudarsono, 1998; dodid murdohardono & tirtomihardjo, 1993; ng et al., 2012; rajiyowiryono, 1999) the yearly value of jakarta’s subsidence generally ranging from 1 to 10 centimeter per-year and may reach 20-26 centimeter in certain place, especially in northern part of jakarta for the recent years. magnitude of subsidence of 4 meter and even more has been recognizing around pluit area north of jakarta. ground water abstraction is one very dominant factor causing land subsidence in jakarta. p a n t u r a https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 | 103 pondok bali blanakan area is not a city but mostly farming and fishpond area while pekalongan is a development of urban area. it is quite surprising that the land subsidence is happening in these areas with significant rate per year (1-10 centimeters). tidal inundation is regularly flooded the areas. as being investigated, preliminary conclusion said that land subsidence in pondok bali blanakan area is due to exploitation of oil and gas. in the area there are more than fifteen oil and gas platform facilities which are actively exploiting oil and gas in the area. meanwhile pekalongan is a center of batik industry. the need of huge amount of water for washing batik has predicted influence the land subsidence in the area. preliminary result concludes that many batik industry is taking groundwater in deep aquifer and as the consequences 1-10 centimeters of land subsidence per year existed in the area. land subsidence is not a new phenomenon for semarang. some report said the subsidence in semarang probably is occurring for more than 100 years. the impact of land subsidence in semarang can be seen in several forms, such as the wider expansion of (coastal) flooding areas, cracking of buildings and infrastructure, and increased inland sea water intrusion. it also badly influences the quality and amenity of the living environment and life (e.g. health and sanitation condition) in the affected areas. in the case of semarang, comprehensive information on the characteristics of land subsidence is applicable to several important planning and mitigation efforts, such as effective control of coastal flood and seawater intrusion, spatial-based groundwater extraction regulation, environmental conservation, design and construction of infrastructure, and spatial development planning. according to some publications (chaussard et al., 2013; kuehn et al., 2009; lubis et al., 2011; marfai & king, 2007; d murdohardono et al., 2009; sutanta et al., 2005) the yearly value of semarang’s subsidence generally ranging from 1 to 17 centimeter per-year. figure 2. map of subsidence along pantura (modified from chaussard et al., 2013, abidin et.al, 2008; 2010; 2011) table 1 shows average rate of land subsidence in jakarta, pondok bali blanakan, pekalongan, semarang, demak, surabaya area, and other places around pantura. high rate of subsidence is taking places in jakarta and semarang. hugh development on the city, especially in jakarta has created such rate. if the rate is continuing, in combining with the sea level rise, they will derive tidal inundation in larger area. that is a very potential disaster to pantura. https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 104 | table 1. average rate of land subsidence in jakarta, pondok bali blanakan, pekalongan, semarang, demak, surabaya, and other places around pantura no area average rate of subsidence (meter/year) 1. jakarta 0.010 – 0.150 2. bekasi cikarang 0.010 – 0.100 3. pondok bali blanakan 0.010 – 0.100 4. cirebon 0.005 – 0.020 5. pekalongan 0.010 – 0.100 6. semarang & demak 0.010 – 0.150 7. surabaya 0.010 – 0.030 2.2. sea level rise the increasing temperature of the earth has been brought ice to melt in the north and south antarctica and made volume of water larger (figure 3). as the consequences the sea level rise exists. figure 4 show graph of sea level rise that is happening in the ocean worldwide from recorded of tide gauges (1970-2005) and satellite altimetry data (1992-2010) reported by ipcc. generally the sea level rise is ranging in order millimetre to few centimetres per year. only few places in the world are experiencing in larger magnitude, but it is not more than a few decimetres per year. this sea level rise even in the small magnitude in theory can also influence the tidal inundation respectively. the sea in indonesia especially around northern part of java is experiencing sea level rise in order of few millimetres per year (nurmaulia et al., 2010). satellite altimetry is satellite base geodetic technique that has good capability in sensing sea level rise from the satellite. by using electromagnetic pulse we can measure the range from sea surface (footprint) to the satellite with accuracy in order of few centimetres. the satellite is equipped with the gps (global positioning system) that can provide height of either satellite or sea surface above ellipsoid reference system. in the future the total magnitude of sea level rise may reach meters level. in this case, for low land coastal area it would be prone to inundation. dyke is one solution to protect land from inundation. another possibility is to higher the land through reclamation. as for the case of combination of land subsidence and the sea level rise, we won’t wait another longer to establish the dyke since the inundation comes earlier. as explained previously what happens in pantura is combination of them. figure 3. courtesy of google satellite image taken in 1984 and 2011 around antarctica showing decreasing in ice volume https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 | 105 figure 4. recorded of tide gauges (1970-2005) and satellite altimetry data (1992-2010) indicating sea level rise (in centimeter scale) as the consequences of global clime change (modified from ipcc graph) 2.3. tidal inundation time series of high-resolution satellite image from all available google data archives (year 2000recent years) together with any others similar available sources have been used to see tidal inundation. generally, the pixels resolution for most of the images were below 1 meter. this is good enough to clearly interpret the inundation, etc. if the images uncovered by the clouds. by this approach we may identify all of tidal inundation areas along pantura. we used the newest until the oldest satellite image available data one by one. from the images, we delineate the old predicted of coast line with new probable coastline (either sharp inward of abrasion, far inland of tidal inundation or toward the sea of sedimentation), and then we made polygon area to represent the area that at least being inundated, suffering abrasion or, expanded due to sedimentation (see example in figure 5). as the cased of tidal inundation that would be the focus in this research, to delineate the exact line we have limitation on the image colors and tones interpretation since in the dense housing area or images timing being taken in the low tide, and therefore tones or colors of inundation was unclear. in order to make sure the true existence of tidal inundation area, in several place we done in-situ measurements. during the past years we conducted expedition to visit coastal area of northern java. place that we visited included tanggerang, jakarta, bekasi, pondok bali, blanakan, pekalongan, kendal, semarang, demak, gresik, and surabaya. from all high resolution satellite image data that we have been collected, processed, and analyzed, we can see the tidal inundation, sedimentation, and also abrasion are taking place at least in tanggerang, jakarta, bekasi, pondok bali, blanakan, indramayu, cirebon, brebes, tegal, pemalang, pekalongan, batang, kendal, semarang, demak, gresik, surabaya, and sidoarjo. if we add the results from the site visit and internet surfing, we would find many others area being inundated especially the temporary one. figure 5 shows several figures of tidal inundation in most locations along northern coast of java as result of above works. since mentioned places are suffering tidal inundation, planning and execution in development dyke should be exist in these areas. how severe the inundation will influence how well the dyke should be design and establish. 2.4. dyke protection dyke to protect land from tidal inundation, sea level rise and perhaps the subsidence has been establishing in many part of pantura (figure 6 and figure 7). in jakarta we can find dyke along kamal muara, pantai indah kapuk, muara angke, muara karang, pantai mutiara, muara baru, ancol, tanjung priok, and maruda. the heights can be 1 up to 2 meters. meanwhile, in pondok bali blanakan we can find almost 1.5-meter dyke established to protect some village in the area. few temporary dykes can be found along coastal area of pekalongan. in semarang and demak area we can find dyke along pantai marina, tanjung mas, tambak loro, kaligawe, sayung, and worosari. many small dykes may be also found in others https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 106 | place around pantura from tanggerang, cirebon, tegal, and up to surabaya area. occasionally the dyke can be seen created using the sand bag. figure 5. tidal inundation, abrasion and sedimentation in most locations of pantura from all highresolution satellite images data and others supported data being collected and analized for sometimes, these dykes are quite effective on protecting the land from tidal inundation, sea level rise, and perhaps the subsidence. there was a heavy tidal inundation in jakarta back at 2007 where access to the airport was closed for one day leaving flight schedule in chaos. now it seems like a history. in 2008 and 2009 almost half of north semarang was experiencing severe tidal inundation. the water has reached even more than 2 kilometer inward the coastline. now it seems dry. nevertheless, on chapter result and discussion we will understand the real situation after done insight analysis to the dyke. the hint is that the dyke has longevity and it is not a final solution against the land subsidence and the sea level rise. figure 6. picture on the left shows temporally dyke around pluit area jakarta using sand bag just after heavy tidal inundation in 2007, while on the right shows one-meter new dyke establish in 2008 https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 | 107 figure 7. dyke to protect land from tidal inundation, sea level rise and perhaps the subsidence has been establishing in many parts of pantura dyke program in bigger scale has been initiated in jakarta. first initiated by the jakarta coastal defense study (jcds), the scenario of “giant sea wall” is establishing along coastal of jakarta city. the city would be closed from the sea by more than six-meter-high of the giant wall located few kilometers from the sea shore. this giant wall will be equipped by pumping station and inner lake as retention area. jcds was transformed into ncicd (national capital integrated coastal development). formed by the central government this ncicd will be executing the giant sea wall project. a shape of “great garuda” on the giant livable dyke will be created along coastal area of jakarta (figure 8). around 600 trillion rupiah will be spent through scheme public private partnership (ppp). infrastructures such as highway, railway including airport will be develop on the dyke. central of business and housing will be part of this livable dyke as well. recently the plan is still under tight discussion. responding the problem of tidal inundation on the north east area of semarang and demak in 2016 where national road access connecting semarang demak and to the eastern pantura has been closed few times and made such a traffic jam, another dyke program in the bigger scale has been initiated. soon, there will be tanggul laut dan trase jalan tol in between semarang and demak (figure 9). it means that the central government will built the high way which is act also as the “giant sea wall” to protect the area from tidal inundation, sea level rise and perhaps the subsidence. figure 8. the scenario of “giant sea wall” is establishing along coastal of jakarta city as respond to the tidal inundation, sea level rise, and land subsidence (image courtesy of jcds) pondok bali blanakan semarang demak pekalongan tanggerang jakarta https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 108 | figure 9. the scenario tanggul laut dan trase jalan tol in between semarang and demak as respond to the tidal inundation, sea level rise, and land subsidence 3. results and discussion excessive of groundwater abstraction in combination with natural compaction of sediments and probably tectonic deformation, land setting/reclamation, loading from construction of new buildings, oil and gas extraction, underground mining, drainage of peat lands, etc. are believed to derive land subsidence. meanwhile the increasing temperature of the earth has been brought ice to melt in the north and south antarctica and made volume of water larger and further on made the sea level rising. both subsidence and the sea level rise when is occurring around coastal area, after some time can derive tidal inundation. this tidal inundation may come not only at a high tide but even at the regular tide in some area. for some reason in fact the inundation comes permanently. from all high resolution satellite image data that we have been collected, processed, and analized for pantura, we can see the tidal inundation and also sedimentation, abrasion are taking place in tanggerang, jakarta, bekasi, pondok bali, blanakan, indramayu, cirebon, brebes, tegal, pemalang, pekalongan, batang, kendal, semarang, demak, gresik, surabaya, and sidoarjo. this tidal inundation indeed has become a disaster for some places in pantura. one adaptation and mitigation are to build the dyke. in jakarta we can find dyke to the heights of about 1 up to 2 meters. meanwhile, in pondok bali blanakan we can find almost 1.5-meter dyke established to protect some village in the area. few temporary dykes can be found along coastal area of pekalongan. in semarang and demak area we can find 1or 1.5-meter dyke along the coast. many small dykes may be also found in others place around pantura from tanggerang, cirebon, tegal, and up to surabaya area. occasionally the dyke can be seen created using the sand bag. dykes are supposed to be protecting the land permanently or in a very long period of time from tidal inundation, sea level rise, and perhaps the subsidence, but at the same time they are sinking quite fast in some location and failed to protect. figure 10 shows picture of 1 meter of dyke which was failed to protect pluit north of jakarta from tidal inundation in 2007, and after raised 1 meter more in 2008, it is slowly sinking and about to failed again to protect the area after 2013. for the recent years, indeed, it has been elevated again for more than 1 meter. figure 11 shows illustration where dyke has been increased two times from the first development, each for one-meter height, around luar batang north of jakarta. before being increased the dyke failed to halt the tidal inundation comes to the area. https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 | 109 figure 10. illustration of dyke “sinking” in pluit area north of jakarta. in 2007, around 1 meter of dyke failed to protect the land, while 2008 another 1-meter elevated dyke would soon after 2013 would probably fail again figure 11. illustration where dyke has been increased two times from the first development, each for onemeter height, around luar batang north of jakarta figure 12 shows dykes that failed to protect the land from tidal inundation around north and the north east of semarang area. as explain earlier problem of tidal inundation on the north east area of semarang and demak area quite severe. almost a half of northern semarang was experiencing tidal inundation in 2008 and 2009. in 2016 the tidal inundation has made national road access connecting semarang demak and to the eastern pantura has been closed few times and created such a traffic jam. everybody agree that they were a disaster. some adaptation and mitigation are necessary for avoid more disaster. many dykes are created around semarang, but fact in figure 12 shows the dyke is sinking and at the certain moment failed to protect the land from tidal inundation. https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 110 | as mentioned above, responding the problem of tidal inundation on the north east area of semarang and demak in 2016 where national road access connecting semarang demak and to the eastern pantura has been closed few times and made such a traffic jam, another dyke program in the bigger scale has been initiated. soon, there will be tanggul laut dan trase jalan tol in between semarang and demak. it means that the central government will built the high way which is act also as the “giant sea wall” to protect the area from tidal inundation, sea level rise and perhaps the subsidence. we have a question whether that is a final solution or not? it is very well explained that as long as the land subsidence is continuing; therefore, the dyke is also sinking. if we built 1-meter dyke and we have 10 centimeter per year the rate of subsidence it will make the dyke can protect the land for only 10 years. figure 12. illustration how is dykes has failed to protect the land from the tidal inundation around northern and the northern east of semarang area figure 13 shows mangrove and dyke that supposed to protect the land from tidal inundation in pondok bali blanakan area, nevertheless through times the area has suffering again tidal inundation. it is interesting to note that some people are still mixing up the tidal inundation phenomenon with the abrasion, and the land subsidence; they though mangrove can protect the land from the ‘phenomenon’ but turnout it cannot. unfortunately, many places along pantura have programs of buildup mangrove area against tidal inundation induced by the land subsidence and the sea level rise. this approaches absolutely need to reevaluated. figure 13. mangrove and dyke that supposed to protect the land from tidal inundation in pondok bali blanakan area, nevertheless through times the area has suffering again tidal inundation surprisingly the land subsidence in pantura is continuing with the linier trend (figure 14). the rates vary mostly between 1 -15 centimeters per year. the yearly value of jakarta’s subsidence generally ranging from 1 to10 centimeter per-year and may reach 20-26 centimeter in certain place, especially in northern part of jakarta for the recent years. the land subsidence in pondok bali blanakan and pekalongan are quite significant with rate per year for about 1-10 centimeters. the value of semarang’s subsidence is generally ranging from 1 to 17 centimeter per-year. in 5 or 10 year from now we believed the subsidence will remain if no serious action done in dealing with the land subsidence. so, we have to pay attention to the longevity of dykes from the subsidence. https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 | 111 figure 14. the linear trend of land subsidence around monitoring point in pantura in the table 2 we try to summarize the rate of sinking on dyke in some places around pantura (e.g. pantai mutiara jakarta, muara baru jakarta, ancol jakarta, tanjung mas semarang, tambak loro semarang, and kaligawe semarang). along with the rate we try to calculate magnitude after 10 and 20 years. in this case we can have an idea when we can increase the dyke. nevertheless, keep increasing the dyke is not smart solution after certain times. it can add a risk of dyke failure. figure 15 shows illustration on how the risk is increasing when we increase the dyke. the higher the dyke the more potential to collapse since the stress is increasing from the sea volume pressure. in other hand the higher dykes will higher the risk of flooding in inner area since the water cannot easily pumped to the sea. we need very high capacity which is not easy to build and it will be so expensive. table 2. rate and magnitude of dyke sinking in some places around pantura no area rate sinking (meter/years) magnitude after 10 year (meter) magnitude after 20 year (meter) 1. pantai mutiara jakarta 0.10 1.00 2.00 2. muara baru jakarta 0.12 1.20 2.40 3. ancol jakarta 0.05 0.50 1.00 4. tanjung mas semarang 0.08 0.80 1.60 5. tambak loro semarang 0.10 1.00 2.00 6. kaligawe semarang 0.10 1.00 2.00 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 112 | figure 15. illustration on how the risk is increasing when we increase the dyke 4. conclusion dykes have been built against tidal inundation around pantura (e.g. in jakarta, blanakan, pekalongan, semarang, and demak). nevertheless, since the land subsidence and the sea level rise are believed to be continuing through times, the dyke is also subsiding through times. therefore, dyke program concluded not to be a final solution. if we take a closer look, we would realize that the protection is not answering directly to the sources of subsidence and the sea level rise. if the subsidence and the sea level rise is continuing with the same rate or even accelerating, as the consequences the dyke is also subsiding and sinking. examples above shows prove dyke is subsiding and sinking (e.g. in jakarta and semarang). in year 2007 the sea water overtopping the old dyke that was develops in the early 2000. in order to avoid another overtopping, one-meter height of new dyke develops above the old dyke. what happen few years later seem the sea is “rising” and about to overtopping again sometimes in the near future. that is proves the dyke is subsiding and sinking. if we even built the giant sea wall “great garuda” or “great high way sea wall” they won’t guaranty that the sea wall is not subsiding and sinking if the area is continuing to suffer land subsidence and the sea level rise. so, along with counter measure or protection of sea wall project or dyke program, etc. we need to understand also how to stop or at least reduce the subsidence, and forget about the mangrove and the sea level rise for this purpose. first thing to do is how to carefully found the causes of the subsidence. as being explained earlier, the land subsidence can be caused by several factors, divided into tectonic, geotechnique, and geo-hydrology factor. tectonic given the value to subsidence in a way of plate interaction and fault activities; geo-technique play role to subsidence from load of buildings and constructions, natural consolidation of alluvium soil, soil setting/reclamation, etc.; meanwhile the geo-hydrology given consequences to subsidence from excessive groundwater extraction. geo-technique and geo-hydrology strongly correlate each other through effective stresses and compaction processes, and both influenced much by soil properties. excluding the tectonic and natural consolidation factors, all others define as anthropogenic causes. https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 | 113 5. acknowledgments many thanks and appreciation give to the students who helped the investigation in the field and especially to the local people for kindly sharing the information and experience relating tidal inundation since indeed most of them are victims now from this disaster. 6. references abidin, h., andreas, h., gamal, m., gumilar, i., napitupulu, m., fukuda, y., riawan, e. (2010). land subsidence characteristics of the jakarta basin (indonesia) and its relation with groundwater extraction and sea level rise. in iah selected papers on hydrogeology (pp. 113–130). [crossref] abidin, h z. (2005). suitability of levelling, gps and insar for monitoring land subsidence in urban areas of indonesia. gim int, 19(7), 12–15. abidin, h z, djaja, r., andreas, h., gamal, m., hirose, k., & maruyama, y. (2004). capabilities and constraints of geodetic techniques for monitoring land subsidence in the urban areas of indonesia. geomatics research australia, 81, 45–58. abidin, hasanuddin z, andreas, h., djaja, r., darmawan, d., & gamal, m. (2008). land subsidence characteristics of jakarta between 1997 and 2005, as estimated using gps surveys. gps solutions, 12(1), 23–32. [crossref] abidin, hasanuddin z, andreas, h., gumilar, i., fukuda, y., pohan, y. e., & deguchi, t. (2011). land subsidence of jakarta (indonesia) and its relation with urban development. natural hazards, 59(3), 1753–1771. [crossref] abidin, hasanuddin z, djaja, r., darmawan, d., hadi, s., akbar, a., rajiyowiryono, h., … others. (2001). land subsidence of jakarta (indonesia) and its geodetic monitoring system. natural hazards, 23(2–3), 365– 387. chaussard, e., amelung, f., abidin, h., & hong, s.-h. (2013). sinking cities in indonesia: alos palsar detects rapid subsidence due to groundwater and gas extraction. remote sensing of environment, 128, 150–161. [crossref] hofmann-wellenhof, b., lichtenegger, h., & wasle, e. (2008). gnss--global navigation satellite systems: gps, glonass, galileo, and more. springer science & business media. koudogbo, f. n., duro, j., arnaud, a., bally, p., abidin, h. z., & andreas, h. (2012). combined xand l-band psi analyses for assessment of land subsidence in jakarta. in c. m. u. neale & a. maltese (eds.), remote sensing for agriculture, ecosystems, and hydrology xiv. [crossref] kuehn, f., albiol, d., cooksley, g., duro, j., granda, j., haas, s., … murdohardono, d. (2009). detection of land subsidence in semarang, indonesia, using stable points network ({spn}) technique. environmental earth sciences, 60(5), 909–921. [crossref] lubis, a. m., sato, t., tomiyama, n., isezaki, n., & yamanokuchi, t. (2011). ground subsidence in semarangindonesia investigated by alos palsar satellite sar interferometry. journal of asian earth sciences, 40(5), 1079–1088. [crossref] marfai, m. a., & king, l. (2007). monitoring land subsidence in semarang, indonesia. environmental geology, 53(3), 651–659. [crossref] murdohardono, d, & sudarsono, u. (1998). land subsidence monitoring system in jakarta. proceedings of symposium on japan-indonesia idndr project: volcanology, tectonics, flood and sediment hazards, 243–256. murdohardono, d, sudradjat, g. m., wirakusumah, a. d., kühn, f., & mulyasari, f. (2009). land subsidence analysis through remote sensing and implementation on municipality level; case study: semarang municipality, central java province, indonesia. bgrgai-ccop workshop on management of georisks “the role of geological agencies in government practice of risk reduction from natural disaster, 23– 25. murdohardono, dodid, & tirtomihardjo, h. (1993). penurunan tananh di jakarta dan rencana pemantauannya. proceedings of the 22nd annual convention of the indonesian association of geologists, bandung, 6–9. ng, a. h.-m., ge, l., li, x., abidin, h. z., andreas, h., & zhang, k. (2012). mapping land subsidence in jakarta, https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.1201/b10530-11 https://doi.org/10.1007/s10291-007-0061-0 https://doi.org/10.1007/s11069-011-9866-9 https://doi.org/10.1016/j.rse.2012.10.015 https://doi.org/10.1117/12.974821 https://doi.org/10.1007/s12665-009-0227-x https://doi.org/10.1016/j.jseaes.2010.12.001 https://doi.org/10.1007/s00254-007-0680-3 andreas et al./ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 101-114 doi: 10.14710/geoplanning.5.1.101-114 114 | indonesia using persistent scatterer interferometry (psi) technique with alos palsar. international journal of applied earth observation and geoinformation, 18, 232–242. [crossref] nurmaulia, s. l., fenoglio-marc, l., & becker, m. (2010). long term sea level change from satellite altimetry and tide gauges in the indonesian region, paper presented at the egu general assembly 2010, 2--7 may. vienna, austria. rajiyowiryono, h. (1999). groundwater and land subsidence monitoring along the north coastal plain of java island. ccop newsletter, 24(3), 19. sutanta, h., rachman, a., & sumaryo, d. (n.d.). predicting land use affected by land subsidence in semarang based on topographic map of scale 1: 5.000 and leveling data. proceedings of the map asia 2005 conference, 22–25. https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.14710/geoplanning.5.1.101-114 https://doi.org/10.1016/j.jag.2012.01.018 | 97 geoplanning vol 4, no. 1, 2017, 97-108 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.1.97-108 model of climate and land-use changes impact on water security in ambon city, indonesia r. a. barkey a, m. f. mappiasse a, m. nursaputra a a research center for natural heritage, biodiversity and climate change, hasanuddin university, indonesia abstract: ambon city is the center of national activities in maluku province, established under presidential decree 77 issued in 2014 about spatial planning of maluku islands. ambon is a strategic region in terms of development in agriculture and fisheries sectors. development of the region caused this area to be extremely vulnerable to the issues on water security. seven watersheds which are air manis, hutumury, passo, tulehu, wae batu merah, wae lela and wae sikula affect the water system in ambon city. therefore, this study was conducted to determine the impact of climate and land use change on water availability in seven watersheds in ambon city. the analysis was performed using a soil and water assessment tool (swat) model in order to analyze climate changes on the period of 1987-1996 (past), of 2004-2013 (present) and climate projection on the period 2035s (future) and equally to analyze land use data in 1996 and 2014. the results of the research indicated that land use in the study area has changed since 1996 to 2014. forest area decreased around 32.45%, while residential areas and agriculture land increased 56.01% and 19.80%, respectively. the results of swat model presented the water availability amount to 1,127,011,350 m3/year on the period of 1987-1996. during the period of 2004-2013, it has been reduced to 1,076,548,720 m3/year (around 4.48% decrease). the results of the prediction of future water availability in the period of 2035s estimated a decrease of water availability around 4.69% (1,026,086,090 m3/year). land use and climate change have greatly contributed to the water availability in seven watersheds of ambon city. ambon city is in need of land use planning especially the application of spatial plan. the maintenance of forest area is indispensable. in built-up areas, it is essential to implement green space and water harvesting in order to secure water availability in the future. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): barkey, r. a., mappiasse, m. f., & nursaputra, m. (2017). model of climate and land-use changes impact on water security in ambon city, indonesia. geoplanning: journal of geomatics and planning, 4(1), 97-108. doi:10.14710/geoplanning.4.1.97-108 1. introduction at present, water resources become an important issue in many regions in indonesia. this has procreated the encouragement of efficient and effective water use. the objective is that the water resources existing today can meet various needs in order to improve the welfare and prosperity of all human beings in all aspects of life. in general, two causes of water insecurity such as the low capacity of water resources management and the disruption of water resource itself are present. the high use of the land on catchment areas, particularly in upstream region triggers disruptions in water resources. a change on natural factors such as climate related to water resources was the impact of high human activity in the use of land such as in the case of land built up correspondingly generate. deforestation has equally led to a transformation of area from having cold to warm temperatures. the csiro (2012) has reported that scientific evidence of climate change was real. the study of csiro australia showed average temperature rise of 0.29-0.39°c in the six developed climate models. since the climate change could change water availability which will forwardly affect all aspects of human life, its diversity should be considered in regional planning. article info: received: 20 november 2016 in revised form: 13 january 2017 accepted: 28 march 2017 available online: 1 april 2017 keywords: climate change, land use change, water security, land use planning corresponding author: roland a. barkey research center for natural heritage, biodiversity and climate change, hasanuddin university, indonesia email: rolandbarkey@gmail.com open access http://dx.doi.org/10.14710/geoplanning.4.1.97-108 http://dx.doi.org/10.14710/geoplanning.4.1.97-108 mailto:rolandbarkey@gmail.com barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 98 | based on data from the ministry of research, technology and higher education, about 70% of drinking water sources in indonesia have decreased in quality and quantity. the water crisis causes disruption of the water availability for the community. people with difficult water access are obliged to reach distant water source and yet with poor quality of water. in indonesia, the drinking water supply is a consistent and complex problem. (lokollo, 2000) suggested that the decrease of minimum discharge increases of maximum discharge, rainfall can be considered as random factor. meanwhile, the increase of runoff coefficient is a limiting factor that leads to the decline in the water availability in a region due to the spatial planning for unsustainable water resources. spatial planning for water resources conducted with the aim of managing the land area by providing the proper place for the water in order to fulfill the maximum into the soil through infiltration, thereby reducing the high surface runoff. land use change is an important driver to change the hydrological response of a watershed (niraula, meixner, & norman, 2015; sarwuan, beka, & ogbole, 2016). study of changes in land use has been frequently used in assessing the hydrological response in some watersheds in the larger islands in the world, especially in the basin with rapid human development. land use is the study of the specific unit in a watershed due to its impact to the increase in population and economic growth of a region and due to its role as a component of the watershed that can be managed. the results of the study by nie et al. (2011), obtained information that transformation of land use into residential areas in the period 1973, 1986, 1992 and 1997 in san pedro watershed, arizona has decreased groundwater flow and increased evapotranspiration, thereby decreased of water yield in the region. the use of land for agriculture correspondingly resulted in the increase of runoff from the period 1956 to 2010 in the heihe watershed, china nian et al. (2014). the decrease of forest area by 16.3% in the catchment area of the be river, vietnam could increase discharge flow by 0.2-0.4%, sediment by 1.8-3.0% and runoff by 4.8-10.7% (khoi & suetsugi, 2014). conversion of land in the catchment area has led to increased runoff index of each watershed, as well as the rapidly increasing flow concentration time. change of water system in the watershed, causing a phenomenon with negative impact for community such as intensity of floods and droughts has exacerbated (chen & yu, 2015; memarian, tajbakhsh, & balasundram, 2013; pervez & henebry, 2015; schilling et al., 2014; zhang et al., 2016), as is the case today in the ambon city (putuhena, 2013). compared to islands in indonesia, ambon city as a center of growth in maluku province has a characteristic of small island with mountainous contours leading to the formation of many streams in this region. thus, it causes rapid fluctuations in the flow from upstream to downstream in the region due to its narrow, short and steep width stream. characteristics of river flow in this region are extremely different from the characteristics of the river flow in watersheds in other places, especially on a large island with a large watershed. the habitant in these areas consumes water from the springs and the river. this characteristic of the ambon city has also generated a lack of flat areas for the development of an ideal settlement area. in fact, the increase of population growth in the city of ambon requires land to reside. thus, the areas of settlements were then reached the mountainous contours, part of upstream on catchment areas for rivers located in the ambon city. these conditions resulted in the destruction of water catchment areas, especially in the upstream region. thus, spatial planning for water resources in watersheds in the ambon city is indispensable. the role of forests in the upper stream of the watershed is accordingly vital considering forested land can increase water infiltration into the soil. the results of the study by jacob (2009) on sub watershed batu gantung (wae batu merah watershed) stated that the decrease in forest area in the region has raised the surface flow, so that it needs at least 30% of the forest area in the sub watershed batu gantung to reduce the surface flow. to solve the problem of water security in the city of ambon, sustainable resource management is required. planning and management should be based on the carrying capacity of ecosystems and land on water security in the future. the concept of carrying capacity should be used as the framework for the operation of natural resource and sustainable environmental management. therefore, this study was conducted in order to determine the level of water security affecting the ambon city due to changes in land use and climate impacts on watersheds. http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 | 99 2. data and methods 2.1. study area the study was conducted in the ambon city in the objective to analyze the level of water resistance face to changes in land use and climate. ambon city, the center of national activities in maluku province, indonesia was established under presidential decree 77 of 2014 on spatial planning maluku islands as a region in terms of development of agriculture and fisheries sectors. ambon city has an area of 359.45 km2, with five districts administrative namely nusaniwe, sirimau, baguala, teluk ambon and south leitimur. the land area in ambon city is affected by seven watersheds that regulate the water system in the region namely air manis, hutumury, passo, tulehu, wae batu merah, wae lela and wae sikula. spatial distribution of watersheds that affect the city of ambon is presented in figure 1. figure 1. map of watershed in ambon city the area of ambon city is mostly hilly and steep slopes above 20 degree with an area of about 73% and plain areas with a slope of less than 20 degree with an area of about 17%. the influence of geographical conditions where the composition of the steep slopes area more dominant, have led to insufficient space for settlement development. however, with the increase of population growth in the city of ambon, the need for the region for land settlements has equally increased especially in unsuitable locations. this has led to the opening of steep forested land into land settlements affecting the region of upstream watersheds. logically, this has also resulted to an imbalance of the water system on seven watersheds affecting ambon city as shown by water shortages in the dry season and floods in the rainy season happening every year. the problems of the water system on the watersheds in ambon city are most closely related to the fulfillment of the water availability in the present and the future. 2.2. data acquisitions for the swat model to simulate, this study used a simple model in order to describe the condition of hydrological watersheds that affect water security in ambon city. the swat (soil and water assessment tool) model was used to generate information on the availability of water every watershed. swat model is designed to predict the impact of land use and management practices of land to water, sediment, and agricultural chemicals that enter the river or body of water in a watershed having the characteristics of soil types, land use and management of complex in the long term. swat could directly model the processes of physics and chemistry using hydrologic response unit (hru) analysis in each analyzed sub-watershed (arnold et al., 2013). for the operationalization of hydrological models using a swat model, arcswat software was utilized. it was then connected with the application of esri (arcgis) as an additional extension are based http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 100 | gui (graphical user interface) (olivera et al., 2006). in the simulation of swat models, some data were analyzed and classified into spatial and text data. the data was prepared in the swat model simulation, including: a. digital elevation model (dem), which has been made with gis approach through topo to raster interpolation method using boundary data of the study area (watersheds boundary in ambon city), contours data at intervals of 5 meters were extracted from the aster dem with 30-meter resolution (http://gdex.cr.usgs.gov/gdex/), the river network data and point elevation data from topography maps of indonesia (http://portal.ina-sdi.or.id/). b. land cover data on 1996 and 2014 were obtained from the time series of land cover data ministry of environment and forestry and interpretation results landsat 8 scale of 1: 50.000, then the data were exported into a data with raster format for the swat model simulation. c. soil type data from reppprot map (regional physical planning project for transmigration), which was equipped with physical and chemical parameters of the land acquired from land information database from reppprot map and web soil usda natural resource conservation service. data type of soil was then exported into a data with raster format. d. the daily global climate data were obtained from the global weather (http://globalweather.tamu.edu/) in the period 1987-1996 (past) and the period 2004-2013 (actual), includes data rainfall (mm), temperature (°c), humidity (fraction), solar radiation (mj/m2) and wind speed (m/s). 2.3. swat model processing the use of swat models to analysis the water security in the watershed was instrumental in generating information on the conditions of the water system. this information could be used as reference and guidance for stakeholders in the development management giving an overview of a region presently and utterly managed. the swat model resulted in hydrological response unit or hru overlaying land use maps, soil maps and detailed with physical and chemical information, and class slopes map. firstly, the swat model simulation watersheds in ambon city began with the formation of the hydrological boundaries, which were analyzed using dem data to extract the networking data of watersheds, sub-watersheds and river. secondly, the hru (hydrology response unit) was formed by overlaying land use data (land use in 1996 and 2014), soil type data and slope class data extracted from dem data. thirdly, a re-write database after climate data at the input was carried out. fourthly, the swat model was run with time adjusting in accordance to the analysis of selected climatic period. the period of analysis used in this study was the period from 1987 to 1996 (past) and the period from 2004 to 2013 (present). the results of this analysis generated information on the water availability in each watershed. water availability information for each watershed in the ambon city was furtherly compared with the level of the water demand of the community to illustrate the level of water security in this region due to changes in land use and climate factor. analysis of water demand in the ambon city was calculated based on the sni.6728.1 2015 on the preparation of spatial balance water resources (bsn, 2015). figure. 2 mechanism of swat model http://dx.doi.org/10.14710/geoplanning.4.1.97-108 http://portal.ina-sdi.or.id/ barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 | 101 swat model output on each watershed affecting ambon city produced water availability data for the period of 1987-1996 (past) and the period of 2004-2013 (present). the outputs of the change rate were analyzed to obtain data water availability on period 2035s (future). analysis of the water availability is a stage to define the output generated from the simulation swat model. in the swat model, water availability is termed as the water yield obtained in a watershed (arnold et al., 2013). water yield was obtained from the following formula: wyld = surq + latq + gwq – tloss – pond abstractions [1] explanation: wyld : the net amount of water leaving the sub basin and contributing to steam flow (mm) surq : surface runoff contributing to streamflow (mm) latq : lateral flow contributing to streamflow (mm) gwq : groundwater contributing to stream flow (mm) tloss : transmissions losses/amount of water loss from reach (mm) after the output of water availability was defined, the value range of water availability level in each watershed was generated in order to determine the level of water availability in the region. with the ability to produce hydrological sub-watershed information in the watershed, swat data could be a reference in the control of natural resources and environmental management of watersheds. sub-watershed with a problem can be identified and evaluated so that environmental improvement efforts-based watersheds could be undertaken. simulation of watershed improvement can be done by arranging multiple land use management plans or action plans for implementation by involved stakeholders in the watershed. the management plan was then simulated using swat models to obtain the best management plan. 3. results and discussion 3.1. land use and climate change analysis the results of this study showed that the rate of land conversion from 1996 until 2014 was high. there were a type of land use that has the addition and reduction in area. land use that has the reduction area was secondary dry forest and open land. while, land use that has the addition area was pasture, settlements, dry land agricultural and mix dry land farming with shrubs and bushes. conditions of land use change in ambon city was dominated by forest land (14.3%) into settlement area (6.2%), agricultural lands (5.1%) and other land (pasture; shrubs and bushes) (3%). the details of the changes in land use are presented in table 1 and figure 2. table 1. land use condition in ambon city no land use on 1996 on 2014 rate of changes (ha) area (ha) percentage area (ha) percentage 1 airport 92.8 0.3 92.8 0.3 0.0 2 primary dryland forest 438.0 1.4 438.0 1.4 0.0 3 secondary dryland forest 13,473.2 44.0 9,099.8 29.7 -4,373.4 4 secondary mangrove forest 30.3 0.1 30.3 0.1 0.0 5 pasture 213.4 0.7 +213.4 6 settlement 1,501.3 4.9 3,413.5 11.1 +1,912.2 7 dryland agriculture 484.8 1.6 842.9 2.8 +358.1 8 mix dryland agriculture 7,365.9 24.0 8,562.3 27.9 +1,196.5 9 scrub and busses 7,185.0 23.5 7,924.7 25.9 +739.7 10 bare land 68.1 0.2 21.5 0.1 -46.6 total 30,639.0 100.0 30,639.0 100.0 http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 102 | the table illustrates that the process of change was caused by human factors where most of the land cover converted into settlements and dry land agriculture. this is due to the increasing population from year to year superior to the birth rate or population resettled from other areas. consequently, the land conversion for farming is inevitable in which it has been the biggest income of population in ambon city. land use change from vegetated land into built-up area almost occurred in all watersheds in the world, not just in the large watershed but in small watersheds such as in ambon correspondingly with the increased human activities in the region. the results of the research by butt et al. (2015) in simly watershed, pakistan with an area of about 5,000 ha (small watershed classification) has shown that from the period analysis of 1992-2012 there has been a decline in forest and agricultural land into residential land for 38.2% and 74.3%, respectively. monitoring of land use on oben area, niger river, nigeria equally gave an overview of high change of land use in some watersheds in the world with the transfer function of the protected forest area into built-up area for industry in a period of 28 years (1987-2015) (sarwuan et al., 2016). most of the problems of changes in land use in watersheds in the world were caused by the activity of the community, therefore proper land use planning for the development of effective strategies in sustainable watershed is required (iqbal et al., 2012). figure 2. (a) map of land use in 1996; (b) map of land use in 2014 a b http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 | 103 the impact of the land use change in ambon city caused changes in natural factors such as climate. community activities that convert forests into non-forested land has transformed climate change in this region generating as a random rain and increased temperatures. climate in ambon city is a tropical sea and season, because the location of ambon island surrounded by the sea. therefore, the ambon climate is strongly influenced by the ocean and coincides with the climatic seasons namely west season (north) and east season (southeast). change of seasons always is punctuated by a transition season. west season generally runs from december to march, while in april a transition period to east season lasts from may to october, followed by a transition period in november to the west season. based on analysis of climate data from global weather for ambon city area, a trend of change was shown in figure 3. figure 3. rainfall condition on period 1987-1996 (past) and period 2004-2013 (actual) in ambon city comparison of precipitation obtained from climate data global weather indicated a decline in the actual period of rainfall in west season between december and march. while in april and early may which is transition to the east season, a decline in rainfall was detected. but, the east season in june and july predicted the increase of rainfall. in connection with this research, the future challenges related to climate change in ambon city is expected to have a negative impact on natural systems and human activities, especially related to the elasticity of water resources. the risk of vulnerability of water resources in ambon city should be anticipated in the period from december to april and during the transition period of april and may. during this period, there would be a decrease in rainfall, so the estimated reserves of surface water and groundwater in the future will be reduced, which is accompanied by increased evapotranspiration in the dry seasons. 3.2. water availability simulation based on the results of the simulation model of swat the description of the hydrological watersheds condition in ambon city was marked by an increase in average surface flow and a decrease in average groundwater flow (table 2 and 3) on seven watersheds that flow into the gulf of ambon and into the banda sea. the watersheds that flow into the gulf of ambon including air manis, passo, wae lela, wae sikula, as well as several streams of hutumury and wae batu merah located in the north of ambon city, while for the watersheds that flow in the banda sea is a tulehu, as well as several streams of hutumury and wae batu merah located in the southern of ambon city. http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 104 | table 2. surface flow condition on watersheds in ambon city no watershed surface flow (mm) rate of changes (mm) surface flow prediction on 2035s (mm) past condition actual condition 1 air manis 40,201.60 54,881.22 733.98 69,560.84 2 hutumury 25,479.33 53,669.72 1,409.52 81,860.11 3 passo 28,681.52 54,350.41 1,283.44 80,019.30 4 tulehu 1,167.60 1,986.33 40.94 2,805.06 5 wae batu merah 79,139.51 124,858.67 2,285.96 170,577.83 6 wae lela 26,446.54 50,449.33 1,200.14 74,452.12 7 wae sikula 37,096.29 61,639.21 1,227.15 86,182.13 total 238,212.39 401,834.89 8,181.12 565,457.39 table 3. base flow (ground water) condition on watersheds in ambon city no watershed ground water (mm) rate of changes (mm) ground water prediction on 2035s (mm) past condition actual condition 1 air manis 13,952.92 9,167.30 -239.28 4,381.68 2 hutumury 42,484.55 29,314.56 -658.50 16,144.57 3 passo 29,154.99 22,153.52 -350.07 15,152.05 4 tulehu 1,192.78 687.35 -25.27 181.92 5 wae batu merah 42,796.23 43,624.92 41.43 44,453.61 6 wae lela 36,529.00 31,228.88 -265.01 25,928.76 7 wae sikula 18,396.50 17,184.80 -60.59 15,973.10 total 184,506.97 153,361.33 -1,557.28 122,215.69 the increase in surface flow illustrates the flow rate fluctuations during the rainy season and the deficit flow in the dry season. the total precipitation that falls in each watershed almost entirely becomes surface runoff, thus lowering the water flow laterally and groundwater potentially supply water into river in the dry season. hydrologic conditions such as surface flow, groundwater flow and water availability in each watershed that affect water resources in ambon city during the period of 1987-1996 (past condition) were compared with the period of 2004-2014 (present condition). information and phenomenon during the period of analysis, in addition to the results spatial plan of ambon city should give better results on water availability. however, some of the land uses in the watershed were found not suited to its purpose. if the spatial plan in ambon city will be implemented and simulated using swat models, the water availability in some of the watershed will be lower at end year of planning period. the adoption of the spatial plan will greatly affect the future of water availability and water demand in the community of ambon city. based on the actual conditions in 2015, the analysis of the level of water demand in the ambon city reached 79,448,856.99 m3 the details as presented in table 4. the total water demand in the ambon city is shown in table 4. the level of water availability is shown in table 5. all seven watersheds in ambon are continuously providing water resources. however, the calculation of water availability cannot be carried out for all watersheds, but based a watershed that supplies water for each district. table 4. water demand per district in ambon city no district water demand (m3) 1 baguala 17,685,666.51 2 leitimur selatan 10,219,713.26 3 teluk ambon 34,881,443.91 4 nusaniwe 7,806,268.52 5 sirimau 8,855,764.79 total 79,448,856.99 http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 | 105 tabel 5. water yield condition on watersheds in ambon city no watershed water yield (m3) rate of changes (mm) water yield prediction on 2035s (m3) water yield based on spatial plan (m3) past condition actual condition 1 air manis 162,636,020.0 129,287,000.0 -1,667.45 95,937,980.0 143,613,290.00 2 hutumury 235,634,860.0 206,655,470.0 -1,448.97 177,676,080.0 201,306,830.00 3 passo 140,156,930.0 138,339,820.0 -90.86 136,522,710.0 131,455,880.00 4 tulehu 3,907,230.0 3,255,630.0 -32.58 2,604,030.0 2,927,470.00 5 wae batu merah 315,314,660.0 325,133,070.0 490.92 334,951,480.0 319,546,060.00 6 wae lela 130,874,700.0 132,080,880.0 60.31 133,287,060.0 127,716,520.00 7 wae sikula 138,486,950.0 141,796,850.0 165.50 145,106,750.0 136,029,640.00 total 1,127,011,350.0 1,076,548,720.0 1,026,086,090.0 1,062,595,690.00 the prediction of water availability on 2035s in the ambon city was analyzed using temporal data changes in the activity of land (land use) using the rate of change water availability for a period of 20 years (the periods 1987-1996 and 2004-2013). the prediction has resulted to the potential significant decrease of the conditions of water supply in ambon city. this negative change will occur, if the spatial plan for the development of ambon city having seven watersheds, in particular in the gulf of ambon do not pay attention to the aspects of the hydrological balance in particular by reducing the high rate of surface runoff during the rainy season (figure 4). figure 4. (a) map of water yield period 1987-1996, (b) map of water yield period 2004-2013 (c) map of water yield period 2035s (prediction) (d) map of water yield base on spatial planning a b c d http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 106 | the level of reduction in water availability was not affecting all districts in ambon city. however, one district, baguala will have water deficit in the future. district baguala is an urban center and trade which have an imbalance affecting the region between water needs with the availability of water supplied from the watershed. the deficit was due to only 10% of current water available in the sub-basins watershed. this deficit affected clearly the district baguala (figure 5). figure 5. map of deficit rate of water yield in ambon city even though water availability is on deficit level, the overall water demand is still in line with the water supply. however, if the land use changes carry on without regarding the rules of soil and water conservation, the rate of changes of water availability will create negative impacts in the future. in particular, the concerned impact can already be observed in actual period by the decreased rainfall intensity in ambon city. this condition will aggravated the scarce of water resources in the ambon city. thus, spatial planning is necessary to improve the condition. without water, all activities cannot be optimum and sustained affecting the decrease of society’s welfare. 3.3. land utility plan in ambon city preventing a decrease in water availability needs to be done to achieve water security in the future. however, it should be noted that the achievement of water security will be more complex in regard to the context of population growth and rapid urbanization, the increasing demand for water, food and energy. climate change and variability correspondingly raise additional concerns, which will be explored in more detail in the rehabilitation efforts in the ambon city. one of the efforts that could be made is to maintain forested lands and to improve the application of spatial pattern plan for all areas. creating infiltration wells and water harvesting in residential areas and office buildings, education, business area and regional trade will be required. water harvesting with water storage and manufacture of wetlands/swamp artificial can be made artistic implementing agroforestry systems for appropriate designated region. banuwa (2013) explained that vegetation that could reduce the runoff and erosion was affected by canopy height, vegetation density and density of roots. it is also stated by asdak (1995), that vegetation has a role as a http://dx.doi.org/10.14710/geoplanning.4.1.97-108 barkey, mappiasse, and nursaputra / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 97-108 doi: 10.14710/geoplanning.4.1.97-108 | 107 protective layer or buffer between the atmosphere and the soil to reduce erosion. vegetation can affect interception of rainwater, runoff, rain destructive power and runoff on the ground. the utility plan directives for land use and spatial plan control in ambon city are presented in table 6. table 6. utility plan directives for land use and spatial plan control in ambon city no land-use and spatial plan utility plan directives 1 forest area maintained condition, in order to remain forest 2 spatial plan directives for forest area but the condition is not forest modified condition, become vegetated land (rehabilitation) 3 dryland agriculture attempts to apply the concept of agroforestry 4 the status of spatial plan not to forest area and built up area area with the basic building coefficient (40%) and open green spaces (60%) 5 public zone (commerce, office, education, business) attempts are green open space (minimum 20%) in the region and are equipped with infiltration wells or biopori 6 wetland agriculture preserved as agriculture wetlands with implementing soil and water conservation techniques 7 green belt area follow the rules that have been determined 4. conclusion there are seven watersheds with an area of 30,638 ha affecting ambon city. watershed management in the ambon city had a relatively high degree of difficulty due to the form of watershed with generally small and elongated and steep topography. simulation of water availability in the period 1987-1996 has calculated the value of as much as 1,127,011,350 m3 and the period 2004-2013 has generated the value of as much as 1,076,548,720 m3. the second period illustrated the decrease in water availability in ambon due to the capacity of particular sub-basins in which the decrease of water yield as the impact of development was the source of changes in land cover. regulation for the city development is required to ensure the water availability in the ambon city and should be maintained in the future. analysis of spatial pattern plan for the ambon city indicated a deficit of water availability at end year of planning period. therefore reconsideration for sustainability development should be extended. 5. acknowledgments the author would like to thank the center of development control in eco region sulawesi and maluku, ministry of environment and forestry, indonesia for their support in terms of funding, data collection, and field survey for the completion of the study. 6. references arnold, j. g., et al. (2013). soil & water assessment tool: input/output documentation. version 2012. texas water resources institute, tr-439, 650. retrieved from http://swat.tamu.edu/media/69296/swatio-documentation-2012.pdf asdak, c. (1995). hydrology and watershed management. yogyakarta: gadjah mada university press. banuwa, i. s. (2013). erosi. kencana prenada media group. jakarta. bsn. (2015). sni 6728.1:2015-penyusunan neraca spasial sumber daya alam. jakarta. retrieved from http://sisni.bsn.go.id/index.php/sni_main/sni/detail_sni/22945 butt, a., et al. (2015). land use change mapping and analysis using remote sensing and gis: a case study of simly watershed, islamabad, pakistan. the egyptian journal of remote sensing and space science, 18(2), 251–259. [crossref] chen, y.-r., & yu, b. (2015). impact assessment of climatic and land-use changes on flood runoff in southeast queensland. hydrological sciences journal, 60(10), 1759–1769. [crossref] csiro. 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[crossref] http://dx.doi.org/10.14710/geoplanning.4.1.97-108 http://repository.ipb.ac.id/handle/123456789/40577 https://doi.org/10.1002/hyp.9620 https://doi.org/10.1016/j.jhydrol.2011.07.012 https://doi.org/10.1016/j.jhydrol.2015.01.007 https://doi.org/10.1111/j.1752-1688.2006.tb03839.x https://doi.org/10.1016/j.ejrh.2014.09.003 https://doi.org/10.1002/hyp.9865 https://doi.org/10.3390/w8090401 | 122 geoplanning vol 6, no 2, 2019, 122-138 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.2.122-138 modelling 3d topography by comparing airborne lidar data with unmanned aerial system (uas) photogrammetry under multiple imaging conditions o. g. ajayia,b* , m. palmerb a department of surveying and geoinformatics, federal university of technology, minna, nigeria b faculty of environment and technology university of the west of england, bristol, united kingdom abstract: this study presents the effect of image data sources on the topographic modeling of part of the national trust site located at weston-super-mare, bristol, united kingdom, covering an approximate area of 1.82 hectares. the accuracy of the dem generated from 1m resolution and 2m resolution lidar data together with the accuracy of the dem generated from the uav images acquired at different altitudes, are analyzed using the 1 m lidar dem as reference for the accuracy assessment. using the nssda methodology, the dems' horizontal and vertical accuracy generated from each of the four sources was computed. simultaneously, the paired sample t-test was conducted to ascertain the existence of a statistically significant difference between the means of the x, y, and z coordinates of the checkpoints. the result obtained shows that with an rmse of -0.0101499 and horizontal accuracy of -0.175674686m, the planimetric coordinates extracted from 2 m lidar dem were more accurate than the planimetric coordinates extracted from the uav based dems. in contrast, the uav based dems proved to be more accurate than the 2m lidar dem in terms of altimetric coordinates. however, the dem generated from uav images acquired at 50 m altitude gave the most accurate result when compared with the vertical accuracy obtained from the dem generated from uav images acquired at 30 m and 70 m flight heights. these findings are also consistent with the result of the statistical analysis at a 95% confidence interval. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): ajayi, o. g., & palmer, m. (2020). modelling 3d topography by comparing airborne lidar data with unmanned aerial system (uas) photogrammetry under multiple imaging conditions. geoplanning: journal of geomatics and planning, 6(2), 122-138. doi: 10.14710/geoplanning.6.2.122-138 1. introduction the accuracy of the distances obtained by the electronic distance measurement (edm) technique is a critical factor to some applications as geodetic network monitoring or geodetic control of structures (ussisoo, 1969; brunner, 1984). the accuracy of edm depends on two factors; the first, known as internal, is related to each instrument's manufacturing characteristics. the second case found the external factors that are more complex since they depend mainly on the environmental conditions of the medium in which the electromagnetic wave propagates (rüeger, 1990). according to brunner (1984), rüeger (1990), torge & müller (2012), and ogundare & adekoya (2015), the principal effect that generates the medium is the variation of the propagation velocity of the wave due to mainly density changes in their composition. this situation directly affects the distance compute and, therefore, their accuracy. the demands for accurate 3d terrain or elevation models within the last decade has drastically increased due to its multi-faceted applications which cut across many disciplines such as civil and hydrological engineering (li, fu, shen, huang, & zhang 2017), agriculture (tijskens, ramon, & de baerdemaeker, 2003), urban planning, mapping, geological studies (yang, meng, & zhang, 2011; ricchetti, 2001), disaster applications and environmental monitoring and/or analysis (demirkesen et al., 2007; tsai et al., 2010), article info: received: 18 october 2019 in revised form: 18 november 2019 accepted: 15 dec 2019 available online: 30 dec 2019 keywords: unmanned aerial vehicle; lidar technology; digital elevation model; terrain modelling *corresponding author: o. g. ajayi department of surveying and geoinformatics, federal university of technology, minna, nigeria email: ogbajayi@gmail.com, gbenga.ajayi@futminna.edu.ng open access http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.6.2.122-138 mailto:ogbajayi@gmail.com mailto:gbenga.ajayi@futminna.edu.ng https://orcid.org/0000-0002-9467-3569 ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 123 | security, etc. this surge in demands has propelled the development of different technologies to produce 3d topographic models that accurately depict the terrain configuration of the earth, and more specifically, the area of interest (aoi). the most widely used data structure employed to store and analyze information about topography in a geographic information system (gis) environment is a raster digital elevation model (dem) (peralvo & maidment, 2004) because of the simplicity of its data structure (olivera et al., 2002). digital elevation models can be described as an array of square cells or picture elements (pixels) with each of the pixels having a unique elevation value which defines the height of the point it represents on the earth surface (peralvo & maidment, 2004; arun, 2013; rishikeshan, katiyar, & mahesh, 2014). dems can also be represented in the form of contour maps and triangulated irregular network (tin) (li et al., 2017). early techniques of the generation of dems involved direct interpolation of contour lines produced from topographic surveying using conventional ground surveying techniques or from irregularly spaced threedimensional points collected from field surveys. apart from the conventional ground surveying techniques' expensive nature, it cannot be deployed in inaccessible and highly risky terrains. advanced and more technologically sophisticated approaches have seen the introduction of synthetic aperture radar (sar) interferometry, aerial photogrammetric techniques, laser altimetry, and others (peralvo & maidment, 2004; ardiansyah & yokoyama, 2002; yue et al., 2015). generation of dems from sar consists of two main methods which are the interferometry sar (insar) that uses the phase information from two sar images of the same scene and the stereo sar (huang et al., 2004). the insar technique utilizes the phase information of the backscatter signal to measure the distance between the radar sensor and the illuminated target (chu & lindenschmidt, 2017). each sar interferogram is generated from precise co-registration of two complex sar images, cross-multiplying the first sar image with the complex conjugate of the other (hanssen, 2001). the processing of stereo sar images makes use of three popular models such as (1) the model of the range and doppler equations (yuan, 2003), (2) the parallax and elevation relation model, which uses the relation between parallax and elevation to calculate elevation difference and the plane coordinates, (3) the equivalent line central projection model based on photogrammetric theory (huang et al., 2004). though a relatively fair accuracy of between 1m-10m can be obtained from insar dems (gelautz et al., 2003; gruber et al., 2012) especially from single-pass systems like srtm and tandem-x (chu & lindenschmidt, 2017), models that are generated in this way are often compromised by the gaps that occur due to radar shadow and layover. also, accuracy at this level is generally not good enough for planning purposes that require high-resolution base models. this is especially peculiar with extreme conditions such as high mountain terrains, where there is a high tendency that no information will be extracted within the gapped areas (hoja, reinartz, & schroeder, 2007). repeat-pass interferometry (e.g., ers-envisat, radarsat, and alos-palsar) also suffers from temporal decorrelation that seriously affects the accuracy of the estimated elevations, particularly for dynamic land surfaces, such as vegetation and snow-ice covered areas (rott, 2009). these major demerits of dems generated from sars made it imperative to investigate other possible alternatives and for this study, lidar and uav photogrammetry are investigated. light detection and ranging (lidar) data have been recognized as a valuable data source for mapping and 3d modeling of the earth surface (moussa & el-sheimy, 2010). lidar is a tool that provides both spatial and spectral segmentation, providing high resolution horizontal and vertical spatial point cloud data. it is increasingly being used in a number of applications and disciplines, which have concentrated on the exploit and manipulation of the 3d data it provides (antonarakis, richards, & brasington, 2008). lidar data allows for the generation of a set of crown structural variables based on both the ranges and intensities of individual pulse returns or characterization of the full waveform (alonzo, bookhagen, & roberts, 2014). the lidar sensor rapidly transmits pulses of laser, which travels to the surface, and the signals are returned (reflected) back to the sensor when they make contact with the surface. the return pulses are converted from photons to electrical impulses and collected by a high-speed data recorder. the transmission time intervals are derived and then converted to distance based on positional information obtained from ground/aircraft gps receivers and the onboard inertial measurement unit (imu). there are two main types of lidar, terrestrial laser scanning (tls) conducted from the ground, and airborne laser scanning (als), which records laser pulses from a scanner mounted on an aircraft. als topographic elevation is determined by measuring the round travel time the laser pulse takes from being released from the aircraft, being reflected off a surface, and returning back to the scanner (olsen, young, & ashford, 2012, doyle & woodroffe, 2018). with the aid ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 | 124 of direct geo-referencing technique, the laser scanning equipment installed on the aircraft (manned or unmanned) collects a cloud of laser range measurements for calculating the 3d coordinates (xyz) of the survey area. in contrast to the 2d planimetric remote sensing data, the explicit lidar data point cloud describes the 3d topographic profile of the earth's surface (yan, shaker, & el-ashmawy, 2015). lidar has been applied in numerous applications such as the power industry patrolling electrical power line, electricity distribution, and asset management (xu et al., 2008; you et al., 2013; ussyshkin et al., 2011), estimation of herbage removals in pasture quadrats (radtke, boland, & scaglia, 2010) measurement of tree stem diameter (wieser et al., 2017), coastal change mapping, forestry, etc. lidar can measure surface topography (to within a few centimeters), and it enables difficult-to-access landscapes to be surveyed quickly and accurately (reddy et al., 2015). however, the costs of lidar surveys are prohibitively high (simpson et al., 2016, long et al., 2016) and as such, research effort is being invested in the discovery of more costeffective, yet accurate methods and techniques for the extraction 3d topographic modeling. recently, attention has shifted to the application of uav photogrammetry in the generation of dems using structure from motion technology due to some advantages such as: (1) a high level of automation of photographic survey; (2) a very low operating cost; (3) a high repeatability of the survey; (4) the possibility to obtain aerial photography with centimetric resolution (gonçalves & henriques, 2015), etc. unmanned aerial vehicle (uav) platforms provide various types of cost-effective remote sensing data, including true color, multispectral, hyperspectral, microwave, and thermal data, at very high spatial resolutions and at flexible acquisition periods (bhardwaj et al., 2016; hardin & jensen, 2011; knoth et al., 2013; linchant et al., 2015; shahbazi, théau, & ménard, 2014; whitehead et al., 2014; wallace et al., 2012). it is more flexible and controllable than traditional satellite remote sensing in terms of flight height, viewing angles, and forward and side overlap (anderson & gaston, 2013; candiago et al., 2015; everaerts, 2008; turner et al., 2014). this research seeks to assess and analyze the accuracy of 3d topographic models generated from both airborne lidar data and uav photogrammetry under multiple imaging conditions. 2. data and methods 2.1. study area the study area is located north of weston-super-mare, north somerset, in the united kingdom, located 2.91 km north-west of wick st lawrence and 3.5 km north-east of kewstoke, uk (figure 1). it covers about 1.82 hectares and lies approximately between latitudes 51º 23′ 53.09″ n to 51º 23′ 46.28″ n and longitudes 2º 56′ 15.11″ w to 2º 56′ 20.02″ w. the nature of the terrain configuration is quite rugged, which makes it a suitable choice for this research. the site is owned by the national trust and permissions were obtained from both the national trust and the former leaseholders (qinetiq) before conducting the uav flight missions. 2.2. data acquisition dtm lidar data of both 1 m and 2 m resolution covering the study area were downloaded from the edina digimap services (https://digimap.edina.ac.uk/). edina is a world-class center for digital expertise, based at the university of edinburgh, uk. lidar digimap is a collection of open data from various national and government agencies, including the environment agency, scottish government, sepa, scottish water and natural resources wales. the data was captured by firing very rapid laser pulses (thousands of times per second) at the ground surface. by examining the laser energy reflected back from the ground, the surface was captured as a dense cloud of 3d points. these points are then converted into highly detailed terrain models of the surface of the earth. uav flight missions were conducted over the same study site using a dji marvic pro quadcopter (https://www.dji.com/mavic/info#specs) equipped with a sony camera sensor, the imx377 of 12 mp resolution, to acquire 2d overlapping nadir images at different flight heights (see figure 2a-c). the study area is fenced off and the flight missions were launched outside of the fenced boundary. in essence, the study area was not accessed during the fieldwork and no ground control points (gcps) were established. this is to examine the accuracy of the models without gcps and when the area is inaccessible. three different flight missions were conducted and the images were acquired at 30 m, 50 m, and 70 m flight heights for the first, https://digimap.edina.ac.uk/ ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 125 | second, and third missions, respectively. apart from the varied flight heights, other details and specifications were the same for all the flight missions. the sidelap of 65%, front lap of 75%, flight direction of -900, and maximum flight speed of 15 m/s were all specified when the drone deploys app, which was used for the automatic piloting of the drone, was configured at the flight planning stage of each of the flight missions. one hundred seventy-seven overlapping images were acquired at 30m altitude, 68 images were acquired at 50m altitude, and 44 overlapping images were acquired at 70m altitude. both the lidar data and the uav images were acquired around the same time (february 2018) to ensure that seasonal variation will have minimal effect on the comparative analysis outcome. figure 1. the study area (adapted from www.mapsofworld.com and google earth) (a) (b) (c) figure 2. (a) dji marvic pro uav in folded form, (b) the uav with its rotors/propellers fixed, (c) uav’s remote controller (https://www.dji.com/mavic) http://www.mapsofworld.com/ ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 | 126 2.3. data processing and generation of 3d topographic models the lidar dtm of 1 m and 2 m resolution of the study area, which comes in four (4) tiles, were imported into arcgis 10.4, where they were mosaicked to get a single lidar dtm of the study area at both 1 m and 2 m spatial resolutions respectively. the mosaics were then used to generate a digital elevation model (dem) of the study area and used to extract the xyz coordinates of each of the cloud points depicting the landscape of the study area. figures 3a and b present a simplified workflow of the processing stages for generating the dems from the lidar data. (a) (b) figure 3. (a) process of generating dem from lidar dtm and (b) process of extracting xyz data from the lidar dtm all the nadir images acquired during the uav flight missions were processed using agisoft photoscan digital photogrammetric software installed on a laptop computer with 8gb ram, intel core i5 vpro specifications. the image processing involves relative orientation, interior orientation, absolute orientation, and the generation of 3d models from the 2d acquired image sequences using structure from motion (sfm) photogrammetric range imaging technique (westoby et al., 2012). sfm aims to recover camera parameters, pose estimates, and sparse 3d scene geometry from 2d image sequences (hartley & zisserman, 2003). it is a photogrammetric method for creating 3d models of a feature or topography from overlapping 2d photographs taken from many locations and orientations to reconstruct the photographed scene. the steps involved in the generation of the orthomosaics and the dems from the 2d nadir images acquired during each of the three (3) flight missions are presented in figure 4. the orthomosaics are the mosaics of the study area obtained after successfully registering the overlapping image pairs, with their heights and tilt distortions totally removed to ensure geometric correctness. the generated dems were finally imported into arcgis 10.4 software environment for the representation of the dem in shades of tone, which depicts the height values of each point on the study area in different colors. 2.4. model precision assessment in order to assess the precision of the generated 3d models, and since the study area was not accessed, which afforded no opportunity for the establishment gcps, the model generated from the lidar data of 1 m resolution was used as the benchmark for the accuracy evaluation and the models generated from the other four (4) sources were referenced to it. eight (8) points designated as checkpoints (cps) were marked on the 1 m lidar generated dtm and their coordinates were extracted. the number of the cps were restricted to ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 127 | eight since a high number of cps does not significantly affect the altimetric accuracy of the produced 3d models (james et al., 2017), with insignificant effects noticed on the models’ accuracy when more than six (6) cps were used (oniga, breaban, & statescu, 2018). the coordinates of these cps were also extracted from the 2 m lidar dtm, and the models produced from the image pairs acquired from the uav at 30 m, 50 m, and 70 m flight heights. these coordinates were used to ascertain the accuracy of the generated models using the computed root mean square errors (rmse), horizontal and vertical accuracy using the methodology from geospatial positioning accuracy, part 3 of the national standard for spatial data accuracy (nssda), and statistically using paired samples t-test analysis. figure 4. workflow of the photogrammetric image processing in agisoft photoscan (ajayi et al., 2017) the horizontal and vertical accuracy was computed by applying a 95% confidence level to the result obtained using the nssda method as presented in equations (1) and (2) respectively (ajayi et al., 2018): horizontal accuracy = 1.7308 × rmser (1) vertical accuracy = 1.96 × rmsez (2) where rmser and rmsez depict the root mean square errors of the horizontal and vertical discrepancy respectively computed using equation (3). 𝑅𝑀𝑆𝐸 = √ ∑(𝑁𝑖−𝑁𝑗)2 𝑛 (3) ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 | 128 where 𝑁𝑖 is the observed values of the coordinates extracted from each of the four 3d models (2 m lidar dem, dems generated from images acquired when the uav was flown at 30 m, 50 m, and 70 m flight heights), 𝑁𝑗 is the reference values of the coordinates extracted from the 1 m lidar dtm and 𝑛 is the number of cps (ajayi et al., 2017). paired samples t-test is used when testing the statistical difference between two points, between two conditions, two measurements, or between two matched pairs. for this test, the condition for when the population variance is unknown and is unequal was used. the standard deviations (𝜎) computed from the coordinates extracted from each of the models generated from the different data sources were used as the input parameters for the paired sample t-test, which was conducted using the statistical package for social sciences (spss) v23. the standard deviations were derived from the mathematical manipulations on the deviations of the computed linear distances of each coordinate extracted from the dems generated from each of the methods when compared to the reference standard or benchmark (1 m lidar dem accuracy). equation (4) presents the mathematic expression used for the computation of the standard deviations. 𝜎 = √{ ∑𝑣2 (𝑛−1) (4) where v = residual (deviation of each linear distances from the set benchmark) and 𝑛 = considered number of linear parameters. for the 2-tailed test, the following hypotheses were postulated and tested for: i. 𝐻0: 𝜇1 − 𝜇2 = 0 (the difference between the paired population equal to zero) ii. 𝐻1: 𝜇1 − 𝜇2 ≠ 0 (the difference between the paired population means is not equal to zero). where: 𝜇1 = the mean of the components of the coordinates extracted from the dem generated from the 1 m lidar data 𝜇2 = the mean of the components of the coordinates of the other dems the formula for paired sample t-test for the difference of two means when the population variance is unknown and unequal is given in equation (5) 𝑡 = �̅�𝑑𝑖𝑓𝑓−0 𝑠𝑥 (5) where: 𝑠𝑥 = 𝑠𝑑𝑖𝑓𝑓 √𝑛 (6) and the degree of freedom expressed as 𝑑𝑓 = 𝑛 − 1 �̅�𝑑𝑖𝑓𝑓 = sample mean of the differences 𝑛 = number of observations (sample size) 𝑠𝑑𝑖𝑓𝑓 = sample standard deviation of the differences 𝑠𝑥 = estimated standard error of the mean ( 𝑠 𝑠𝑞𝑟𝑡(𝑛) ) the t value is compared to the critical t value (table t value) with 𝑑𝑓 = 𝑛 − 1 from the t distribution table for a chosen confidence interval. if the obtained t value falls outside the boundary of the confidence interval, it will be concluded that there is a significant difference between the means (and as such, the null hypothesis would be rejected), otherwise, there exists no significant difference between the means and the null hypothesis would be accepted. the test was carried out at a 95% confidence interval (p-value of 0.05). ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 129 | 3. results and discussion figures 5a and 5b present the dem generated from the lidar data of 1 m and 2 m resolutions, respectively. the orthomosaic and dem produced from the images acquired when the drone was deployed at 30 m flight height is presented in figures 6a and 6b, respectively. in comparison, figures 7a and 7b present the orthomosaic and dem produced from the flight mission at 50 m, respectively. the orthomosaic and dem generated from the nadir images acquired by the uav at 70 m flight height are presented in figures 8a and 8b, respectively. 3.1. results of model precision assessment for the generated topographic models the coordinate differences obtained when the coordinates of the cps extracted from the 2 m lidar dem, and the models generated from the uav images acquired at 30 m, 50 m, and 70 m flight heights were compared with the coordinates of the cps extracted from the 1 m lidar dem are shown in table 1. the table also presents the summation of the computed discrepancies and the horizontal and vertical accuracy for each of the generated dems. the summary of the computed horizontal and vertical accuracies for each of the generated dems is presented in table 2. from tables 1 and 2, δe(m), δn(m), and δz(m) represent the difference in easting coordinates, northing coordinates, and the height values, respectively. the paired samples t-test results for each of the dem obtained from the four sources are presented in tables 3-6, showing the computed mean values, standard deviation, standard error mean, tvalue, significance (2 tailed) values, etc. table 3 presents the t-test result obtained when the means of cp coordinates from the 2 m lidar dem were compared with the means of the cp coordinates of the reference value (coordinates of the cps extracted 1 m lidar dem) while the result obtained when the t-test was conducted using the cp coordinates extracted from the dem generated from the uav acquired images at 30 m flight height was compared with the cp coordinates extracted from 1 m lidar dem is presented in table 4. the t-test results obtained when the cp coordinates extracted from the dems generated from the uav images acquired at 50 m and 70 m were independently compared with the cp coordinates extracted from the 1 m lidar dem are presented in tables 5 and 6 respectively. though the visual differences observed in the generated 3d models from each experimented four dem sources were quite trivial, as shown in figures 5-8, the computed rmse and horizontal and vertical accuracy proved considerable differences in the accuracy of each of the models. the horizontal accuracy of the 3d model generated from the 2 m lidar data proved to be better than the horizontal accuracy obtained from the dem produced from the three uav flight heights. the model produced from the lidar data of 2 m resolution gave a horizontal accuracy of -0.100255 and -0.072679 along with the x and y directions, respectively. it was also observed that the planimetric coordinates obtained from the 3d model produced from the uav flight mission at 50 m flight height proved to be more accurate than the coordinates extracted from the 3d models produced from the uav flight missions conducted at 30 m and 70 m altitudes. meanwhile, the vertical accuracy obtained from the 3d models produced from the uav flight missions is each better than the vertical accuracy obtained from the 3d model produced from the lidar data of 2 m resolution. this implies that uav-based dem is more accurate in terms of elevation modeling than lidar at 2 m resolution irrespective of the uav's flight height during image data acquisition. further observation of the obtained accuracy revealed that the dem generated from the uav images acquired at 50 m flight height is more accurate (with a vertical accuracy of -0.0000735000000011343) when compared with the dem generated from the uav images acquired at 30 m and 70 m flight height. ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 | 130 (a) (b) figure 5. (a) 1 m lidar dem and (b) 2 m lidar dem (a) (b) figure 6. (a) orthomosaic at 30 m altitude and (b) dem at 30 m altitude ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 131 | (a) (b) figure 7. (a) orthomosaic at 50 m altitude and (b) dem at 50 m altitude (a) (b) figure 8. (a) orthomosaic at 70 m altitude and (b) dem at 70 m altitude ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 | 132 table 1. computed coordinate differences from the four dem sources cp id lidar dtm+2m points 30m 50m 70m ∆x (m) ∆y (m) ∆z (m) ∆x (m) ∆y (m) ∆z (m) ∆x (m) ∆y (m) ∆z (m) ∆x (m) ∆y (m) ∆z (m) cp1 -0.4727 1.54992 0.32 0.75252 -2.60328 -1.3439 -0.9675 -2.8774 -0.4751 -0.9675 -2.8774 -1.0862 cp2 -0.6155 0.36019 0.138 2.6085 -2.86502 -0.9909 -0.8692 -2.9884 0.5741 0.43987 -3.5416 -0.7467 cp3 0.0462 -0.374 -0.055 -0.11558 1.55582 0.2552 -0.0043 2.65787 1.3949 0.12459 2.53592 0.0095 cp4 -0.7628 -0.0884 1.383 -0.08862 0.589759 0.3357 -0.2719 1.69419 3.1644 0.25546 1.93511 0.0025 cp5 0.15328 -0.5104 -0.36 1.59009 0.353003 1.2442 0.66794 1.07196 -2.7173 0.95027 1.75189 1.2125 cp6 1.15781 -1.0314 -2.782 2.75028 -2.85233 0.5708 -0.8289 -2.6291 -4.2152 -0.9313 -2.7621 0.6371 cp7 -0.0022 0.53012 -0.453 1.66644 -2.59356 1.0878 7.48331 2.60879 4.0263 -1.13 -3.1327 1.5603 cp8 0.02518 -0.7773 -0.333 -0.49749 1.267905 -1.1593 -0.2499 1.64342 -1.7524 -0.015 1.58136 -1.5894 sum -0.47074 -0.34126 -2.142 8.66615 -7.1477 -0.0004 4.95945 1.18138 -0.0003 -1.27361 -4.50957 -0.0004 rmse -0.05884 -0.04266 -0.26775 1.08327 -0.89346 -5e-05 0.61993 0.14767 -3.75e-05 -0.1592 -0.5637 -5e-05 accuracy -0.10026 -0.07268 -0.52479 1.84567 -1.52228 -9.8e-05 1.05624 0.2516 -7.35e-05 -0.27125 -0.96043 -9.8e-05 table 2. the accuracy obtained from each of the four dem sources dem source horizontal accuracy vertical accuracy 2m lidar dtm -0.175674686 -0.5247900000000000000 uav dem @ 30m altitude 0.328515143 -0.0000980000000002068 uav dem @ 50m altitude 1.328569003 -0.0000735000000011343 uav dem @ 70m altitude -1.251190993 -0.0000979999999997716 table 3. paired sample t-test of lidar data 1m resolution and lidar data 2 m resolution paired differences t df sig. (2tailed) mean std. deviation std. error mean 95% confidence interval of the difference lower upper pair 1 lidar data-1mx (me) lidar data-2mx (me) .059000000 .600550224 .212326568 -.443072552 .561072552 .278 7 .789 pair 2 lidar data-1my (mn) lidar data-2my (mn) .042625000 .834517643 .295046542 -.655049209 .740299209 .144 7 .889 pair 3 lidar data-1mz (heightsm) lidar data-2mz (heightsm) .267750000 1.174056430 .415091631 -.713785738 1.249285738 .645 7 .539 ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 133 | table 4. paired sample t-test of lidar data 1 m resolution and dem generated from uav images acquired at 30 m flight height paired differences t df sig. (2tailed) mean std. deviation std. error mean 95% confidence interval of the difference lower upper pair 1 lidar data1mx (me) uav-30m x -1.083625000 1.260254842 .445567373 -2.137224415 -.030025585 -2.432 7 .045 pair 2 lidar data1my (mn) uav-30m y .893250000 1.998567040 .706600153 -.777593859 2.564093859 1.264 7 .247 pair 3 lidar data1mz (heights m) uav30m z .000000000 .917598418 .324420032 -.767131475 .767131475 .000 7 1.000 table 5. paired sample t-test of lidar data 1 m resolution and dem generated from uav images acquired at 50 m flight height paired differences t df sig. (2tailed) mean std. deviation std. error mean 95% confidence interval of the difference lower upper pair 1 lidar data-1mx (me) uav-50m x -.620125000 2.825751806 .999054132 -2.982512629 1.742262629 -.621 7 .554 pair 2 lidar data-1my (mn) uav-50m y -.147750000 2.522517209 .891844512 -2.256627162 1.961127162 -.166 7 .873 pair 3 lidar data-1mz (heightsm) uav50m z .000125000 .728798510 .257669184 -.609165802 .609415802 .000 7 1.000 furthermore, the result of the paired sample t-test in table 3 also revealed that there is no statistically significant difference between the means of the x and y coordinates extracted from the 1 m lidar dem and 2 m lidar dem based on their p-value (which is close to 1) at 95% confidence interval. though the difference in means of their z coordinates is statistically insignificant, the obtained p-value is not close to 1, which implies that the altimetric coordinates obtained from the 2 m lidar dem are not as reliable planimetric coordinates. from the p-values presented in table 4, it can be observed that the x coordinates have a p-value of 0.045, which is less than 0.05. this implies that there is a statistically significant difference between the means of the x coordinates extracted from the 1 m lidar dem and that of the dem generated from the uav acquired images at 30 m flight height. it was also observed that the means of the y and z coordinates extracted from the uav acquired images at 30 m flight height is not statistically different from that of the 1m lidar dem, though the height is more reliable because it gave a perfect p-value of 1.000. as shown in table 5, the p-values obtained for each of x, y, z coordinates (0.554, 0.873, and 1.000 respectively) are greater than 0.05, which implies that there is no statistically significant difference between the means of cp coordinates extracted from the 1 m lidar dem and the dem generated from the uav images acquired at 50 m flight height at 95% confidence interval. ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 | 134 table 6. paired sample t-test of lidar data 1 m resolution and dem generated from uav images acquired at 70 m flight height paired differences t df sig. (2tailed) mean std. deviation std. error mean 95% confidence interval of the difference lower upper pair 1 lidar data-1mx (me) uav-70m x .159000000 .761013234 .269058809 -.477222986 .795222986 .591 7 .573 pair 2 lidar data-1my (mn) uav-70m y .563500000 2.711574450 .958686341 -1.703432971 2.830432971 .588 7 .575 pair 3 lidar data-1mz (heightsm) uav-70m z 0.000000000 .724421543 .256121693 -.605631566 .605631566 0.000 7 1.000 this observation was also valid for the results obtained when the 1m lidar dem was compared with the dem generated from the uav images acquired at 70 m flight height at a 95% confidence interval (see table 6). the uav based dem produced when the uav was flown at 50 m flight height also yielded an optimal mix or combination of planimetric and altimetric accuracy in this research, which agrees with the findings of agüera-vega, carvajal-ramirez, & martinez-carricondo (2017). these results further affirm the findings of the horizontal and vertical accuracies, which affirms that the 2 m lidar dem is more planimetrically accurate when compared to the uav based dem but the uav based dems are more accurate than the 2 m lidar dem in terms of vertical or altimetric coordinates which implies that uav based dem is more reliable than lidar data of 2 m resolution for 3d topographic modeling. also, while the uav flight height has a significant effect on the horizontal accuracy (planimetric coordinates) of the produced dems, it has no significant influence on the vertical accuracy (altimetric coordinates), which is consistent with the findings of oniga et al. (2018). 4. conclusion in this work, we have analyzed and compared the accuracy of dems generated from lidar data of 2 m resolution, uav images acquired at 30 m, 50 m, and 70 m flight heights using lidar data of 1m resolution as a reference benchmark. the uav acquired images were processed for dem generation using agisoft photoscan digital photogrammetric software. from the findings of the research, the following conclusions were drawn: 1. the planimetric coordinates extracted from the 2 m resolution lidar data are in better agreement with the planimetric coordinates extracted from the 1 m resolution lidar data. 2. the altimetric coordinates extracted from the uav based dem (irrespective of the flight height of the uav during image acquisition) agrees better with the altimetric coordinates of the 1 m resolution lidar data when compared with the 2 m resolution lidar data, which shows that the altimetric accuracy obtainable from uav based dem is better than the altimetric accuracy obtainable from lidar technology at 2 m resolution (this is valid as long as the flight height of the uav does not exceed 70 m as experimented in this research). 3. the difference in the uav's flight height does not have any significant effect on the obtainable accuracy of the altimetric coordinates. ajayi & palmer / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 122-138 doi: 10.14710/geoplanning.6.2.122-138 135 | 4. the most reliable accuracy of both planimetric and altimetric coordinates was obtained from the dem produced from the uav images acquired at 50 m flight altitude. it conveniently competes with lidar technology even at 1 m resolution and can be a robust alternative to the very expensive lidar technology in topographic modeling, especially in inaccessible areas. 5. acknowledgments the visiting academic scholarship granted to the first author by the faculty of environment and technology, university of the west of england, bristol, uk is gratefully acknowledged. this research was conducted in the course of the visit. 6. references agüera-vega, f., carvajal-ramirez, f., & martinez-carricondo, p. 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[crossref] https://doi.org/10.1080/13658816.2015.1063639 | 43 geoplanning vol 6, no 1, 2019, 43-54 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.1.43-54 mapping the alternative locations of street vendor stabilization in surakarta, indonesia rahayu a, c , i. buchori b , r. widjajanti b, r. a. putri a,c a doctoral program in architecture and urbanism, universitas diponegoro, semarang, indonesia b department of urban and regional planning, universitas diponegoro, semarang, indonesia c. department of urban and regional planning, universitas sebelas maret, surakarta, indonesia abstract: the arrangement of street vendors is continuously undertaken by the government of surakarta city for the sake of attaining the city order and public welfare. the inclination of stabilization success level in achieving the goal of street vendor arrangement strategy indicates that the location characteristics conforming to the street vendors’ preferences become one of the determinations in terms of the arrangement success. this article aims at mapping the alternative locations of street vendor stabilization in surakarta by applying the spatial analysis resting upon geographic information system (gis) by means of two stages. they encompass: 1) identifying the conditions of the existing street vendor stabilization locations, and 2) formulating the alternative locations of street vendor stabilization based on the criteria which entail the proximate main activities, the crowds of environment, and the availability of stateowned land. the result of spatial analysis indicates that there are 19 alternative locations in surakarta which are aligned with the criteria and can be utilized for new street vendor stabilization locations. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license how to cite (apa 6th style): rahayu, m. j., et al. (2019). mapping the alternative locations of street vendor stabilization in surakarta, indonesia. geoplanning: journal of geomatics and planning, 6(1), 43-54. doi: 10.14710/geoplanning.6.1.43-54 1. introduction the growth of street vendors in several cities in the world continuously occurs. as a sub-sector, the number of street vendors is the most dominant, and they occupy public spaces in the city without necessity to pay rent as the formal-sector traders do (cross & karides, 2007). the cheap prices of commodities become the main attraction for the customers of street vendors. street vendors play an important role because they provide appropriate consumption for the poor and having contribution for local economic growth especially in developing countries (bhowmik, 2007). the supply and demand concept of street vendors’ activities is one of the factors that support this subsector to flourish in surakarta as a medium-sized city in indonesia. surakarta has not managed to achieve zero growth of street vendors although the efforts of arrangement in the form of relocation and stabilization have been carried out for 18 years (rahayu, et al., 2013). relocation is an effort of arrangement by means of moving street vendors to traditional markets (mcgee & yeung, 1977). the relocation as such is also called formalization. formalization, besides locating street vendors in traditional markets, also provides them with trading places in the existing malls or modern markets through a system of rental (sarjono, 2005). nonetheless, as the case encountered in the usa, when the government attempted to encourage street vendors to enter the market areas, with the aim of avoiding their presence around the streets and sidewalks, this effort even had an impact on the destruction of their profits (cross & article info; received: 7 june 2017 in revised form: 10 oct 2018 accepted: 30 april 2019 available online: 30 august 2019 keywords: locations, street vendor, stabilization corresponding author: murtanti jani rahayu department of urban and regional planning, universitas sebelas maret, surakarta, indonesia email: mjanirahayu@gmail.com open access https://doi.org/10.14710/geoplanning.6.1.43-54 mailto:mjanirahayu@gmail.com rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 54 | karides, 2007). likewise in uganda, street vendors preferred to go back to streets after being arranged to trade in markets (lince, 2011). the arrangement in the form of stabilization locates street vendors in public spaces such as in the parts of sidewalks, parks, and fields (adedeji, et al., 2014; mcgee & yeung, 1977; rahayu et al., 2013; rukmana, 2016). the form of stabilization is in fact favored by street vendors if compared to that of relocation (cross & karides, 2007; lince, 2011; rahayu, et al., 2016). in surakarta, the designation of public spaces for street vendors is determined by the government (surakarta regional regulation, 2008) stabilization can make an urban area become more organized, not chaotic and beautiful, and street vendors become more interesting to be visited (henderson, 2012; werdiningtyas, et al., 2012). in addition, stabilization can make street vendors more secured while doing their businesses because they are not worried about being evicted at any time (rahayu, 2016). the placement of street vendors in public spaces indicates that stabilization requires the availability of state-owned land as the public spaces which are prepared to be used by street vendors (blackburn, 2011). besides, the public space locations given to street vendors should not be far from their previous locations before being arranged because those locations are also close to where they live (rahayu, et al., 2018) . this is aligned with the point elucidated by (mcgee & yeung, 1977; werdiningtyas et al., 2012). in principle, although street vendors occupy legal locations, their activities are still categorized into informal ones (kettles, 2007; puspitasari, 2010; tualeka, 2013). in 2018, surakarta has had 25 locations of stabilization (rahayu et al., 2018). the locations of street vendor stabilization are mostly proximate to productive activities in the crowded areas of the city. this is in line with the viewpoint given by several previous researcher (bromley, 1978; chandrakirna & sadoko, 1994; de soto, 1991; haryanti, 2008; kadir, 2010; mcgee & yeung, 1977; rahayu et al., 2016; sari, 2003; werdiningtyas et al., 2012; widjajanti, 2009). besides being in the most profitable areas in the city center, while trading, street vendors also choose the places which are easily accessible or those such as roadsides (hanifah & mussadun, 2014; novelia & sardjito, 2015; werdiningtyas et al., 2012), and those which are strategic in terms of being nearby and seen by consumers (rahayu et al., 2016; werdiningtyas et al., 2012). the criteria of places that are reachable, nearby, and visible conform to most people’s needs, wherein street vendors manage to overcome the conditions of society movements that are often in a hurry from home to work for the sake of fulfilling their needs or getting tired on their way home as well as getting entertainment (cross & karides, 2007; kettles, 2007). for a few shop owners doing businesses around street vendors, the presence of street vendors is regarded as providing informal security, reducing the number of other shops in the sense of reducing competition, and minimizing common crimes that often occur on the streets (cross & karides, 2007). the aforementioned criteria become the characteristics of street vendors’ locations that must be taken into account when choosing the targeted locations of stabilization. the efforts to stabilize street vendors are expected to capably decrease the street vendors’ rejections in light of the arrangement that does not conform to their chosen locations (rahayu et al., 2018). stabilization is also believed to be able to control the increasing number of street vendors. furthermore, stabilization is capable of reducing litter previously caused by the existence of street vendors before being arranged, and it is also able to avoid congestion because the stabilization executed in public spaces only uses a part of the sidewalks so that pedestrians still get their rights pertinent to the sidewalk use (kettles, 2007). appertaining to coping with waste disposal and fulfilling other basic needs, through stabilization the government will provide basic facilities including rubbish bins, parking area, water, and electricity (chandrakirna & sadoko, 1994; kettles, 2007; novelia & sardjito, 2015; werdiningtyas et al., 2012). the street vendors’ locating characters become the typical of their ways in choosing trading locations which can be viewed from the conditions of either location or non-location including the proximity to the center of productive activities, crowded locations, the proximity to their houses, the ease of accessibility, grouping, security, comfort, the needs of infrastructure such as (clean water, lighting, rubbish bins, and parking area), and cleanliness (bromley, 1978; chandrakirna & sadoko, 1994; de soto, 1991; haryanti, 2008; mcgee & yeung, 1977; novelia & sardjito, 2015; rahayu et al., 2016; sari, 2003; werdiningtyas et al., 2012; widjajanti, 2009). in this study, the effort to map the alternative locations of street vendor stabilization is made by considering the spatial criteria which entail the proximity to main activities / land use, the degree of crowds and road activities, and the availability of state-own land as prerequisites for stabilization. https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 | 45 considering the growth of street vendors in surakarta that continuously occurs; the condition in which not all of the illegal street vendors can be organized; the stabilization resting upon the street vendor preferences; and the need to maintain public order, the authors believe that it is important to map the alternative locations for the new street vendor stabilization in surakarta based on the potency of stabilization locations which have been well-established. the prior studies addressing this issue are still very few. hence, the results of this study are expected to be a sort of input for surakarta government, especially in directing the selection of street vendors’ stabilization locations which are in accordance with the location characteristics and their activities. directing the selection of arrangement locations for street vendor stabilization as such has not also been widely carried out in other cities though those cities are very friendly to street vendors. 2. data and method 2.1 data the government of surakarta has executed the strategy for handling street vendors by means of stabilization at 25 street vendors’ locations. the aforementioned 25 locations encompass: (1) pasar notohardjo shelter, (2) silir notoharjo shelter, (3) galabo malam shelter, (4) mojosongo shelter, (5) komplang shelter, (6) timur pdam shelter, (7) kolang kaling shelter, (8) sekartaji shelter, (9) pedaringan shelter, (10) timur jurug shelter, (11) galabo pucang sawit shelter, (12) pasar pucang sawit shelter, (13) urban forest shelter, (14) solo square shelter, (15) buah purwosari shelter, (16) supomo shelter, (17) sriwedari shelter, (18) timur telkom shelter, (19) galabo siang shelter, (20) ks tubun shelter, (21) menteri supeno shelter, (22) sd kristen manahan shelter, (23 ) hasanudin shelter, (24) arcade at kotta barat, and (25) wahidin shelter (see figure 1). 2.2 method this article aims at mapping the alternative locations of street vendor stabilization in surakarta which is attained through two stages: a. identifying the condition of the existing street vendor stabilization locations in surakarta the identification is undertaken at 25 locations of the existing street vendor stabilization in surakarta, and it is executed by using spatial criteria which entail the proximate main activities, traffic crowds, and the availability of the state-owned land (blackburn, 2011; ray bromley, 1991; chandrakirna & sadoko, 1994; de soto, 1991; haryanti, 2008; mcgee & yeung, 1977; novelia & sardjito, 2015; rahayu et al., 2016; sari, 2003; werdiningtyas et al., 2012; widjajanti, 2009). thus, what is meant by the characteristics of street vendor locations in the present study is the potency of street vendor arrangement locations resting upon the above criteria. the conformity between the location characteristics of street vendor stabilization and the street vendors’ needs in carrying out their trading activities has an impact on the success of the street vendor arrangement in the form of stabilization in surakarta, where in it is indicated by their consistency in continuously trading at the stabilization locations (rahayu et al., 2016). this point leads the stabilization areas to be harmonious and livelier so that street vendors’ incomes after stabilization increase (rahayu et al., 2016). therefore, the location characteristics of the existing street vendor stabilization are used to identify the alternative potential locations for further stabilization as a way to provide the government with a solution with respect to the arrangement of locations for street vendors who have not yet been stabilized. b. identifying the alternative locations for street vendor stabilization the alternative locations for street vendor stabilization are formulated based on the potency of the existing stabilization locations encompassing the proximate main activities, the traffic crowds, road activities, and the availability of the state-owned land. the technique of analysis which is applied refers to the superimpose analysis of gis-based maps for each criterion. those criteria are assumed to have been aligned with the needs of the locations that accommodate the street vendors’ trading activities. thus, the replica of success can be realized. https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 54 | 3. results and discussion the results and discussion are split into two parts, namely the location conditions of the existing street vendor stabilization in surakarta, and the alternative potential locations for street vendor stabilization. 3.1 the location conditions of the existing street vendor stabilization in surakarta the strategy of street vendor arrangement by means of stabilization has a higher success rate compared to relocation (rahayu et al., 2016). this is because stabilization establishes trade facilities and locating street vendors at their previous locations or at the places close to their previous ones. stabilization minimizes the changes of street vendors’ locating characteristics so that they do not need to adapt while doing their trading activities at the post-arrangement locations. with similar characteristics to the prior locations but with better conditions, the increase in street vendors’ welfare as the goal of arrangement can more easily be attained (rahayu et al., 2016). according to (rahayu et al., 2016), the street vendors’ locating characteristics have a large influence on the success of the street vendor arrangement strategy applied in surakarta. it is indicated by the harmony established in the stabilization areas, some street vendors that do not move to other places while carrying out trading activities, and most of the street vendors’ incomes that increase. therefore, identifying the potency / conditions of street vendor stabilization locations are essential to be done. furthermore, seeking new locations resting upon the identified potency of locations can give the bases for mapping the alternative stabilization locations especially for the street vendors who have not yet been arranged. the following map displays the distribution of street vendor stabilization locations in surakarta shown in figure 1. figure 1. the map of the distribution of street vendor stabilization locations in surakarta (source: observation results, 2017) anchored in the results of identifying the characteristics of the existing street vendor stabilization locations in surakarta, it is known that the stabilization locations tend to approach consumers with settled services. the approach to consumers is done by taking the locations which are proximate to the main productive activities such as trade and services, settlements, offices, education, and recreation. this is aligned with the opinion conveyed by (r bromley, 1978; chandrakirna & sadoko, 1994; de soto, 1991; haryanti, 2008; kadir, 2010; sari, 2003; werdiningtyas et al., 2012; widjajanti, 2009, 2016), whereby the main characteristics of street vendors’ locations are close to the main productive activities in the crowded areas of the city center. the areas having productive activities will offer the presence of respective customers to street vendors, so they do not have to worry about the market segment of their commodities. https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 | 47 besides being located in the most profitable areas in the city center, while trading, street vendors will choose the places that are easily accessible or near the roads (hanifah & mussadun, 2014; novelia & sardjito, 2015; werdiningtyas et al., 2012), so that their positions are strategic, or in other words, they are nearby and seen by the consumers (werdiningtyas et al., 2012). the criteria that extend to being easily accessible, nearby, and visible conform to the people’s needs in that street vendors are able to cope with the conditions of people who are often in a hurry from home to work, to get entertainment, or to fulfill their needs (cross & karides, 2007; kettles, 2007). therefore, the strategic locations are those near the roads/sidewalks/pedestrian paths on the collector and local road corridors with the traffic conditions that are commonly crowded to very crowded. thus, crowded areas also become tha potential locations of the existing street vendor stabilization in surakarta. grounded in the 25 stabilization locations, the characteristics of the stabilization locations on the state-owned land can be shown as follows. • the locations that are proximate to trading activities and are on local roads with crowded up to very crowded traffic conditions can be seen at the locations of galabo malam shelter, galabo siang shelter and timur telkom shelter. the locations that are proximate to trading activities and are on the collector roads with crowded up to very crowded traffic conditions are those of komplang shelter, galabo pucang sawit shelter, pasar pucang sawit shelter, arcade of kotta barat, and wahidin shelter. the locations that are close to trading activities and are on neighborhood roads with quite crowded to crowded traffic conditions are those pasar notoharjo shelter, silir notoharjo shelter, solo square shelter, sekartaji shelter, buah purwosari shelter, and pedaringan shelter. • the location that is proximate to residential activities and is on the collector road with a very crowded traffic condition is that of mojosongo shelter. the locations that are close to residential activities and are on neighborhood roads with quite crowded up to crowded traffic conditions are those of timur pdam shelter, kolang kaling shelter, urban forest shelter, and sd kristen manahan. the locations that are proximate to residential activities and are on local roads with quite crowded up to crowded traffic conditions are those of supomo and hasanudin stabilization. • the location which is proximate to recreational activities and is on the neighborhood road is ks tubun shelter. the locations which are close to recreational activities and are on local roads are those of menteri supeno shelter and timur jurug shelter. the location that is close to recreational activities and is on the collector road with crowded traffic condition is sriwedari shelter. 3.2 alternative locations for street vendor stabilization in surakarta the placement of street vendors in the locations chosen by the government in an effort to execute the strategy of stabilization arrangement has to adjust to the environment and the characteristics (dimas & others, 2008; mcgee & yeung, 1977). the conformity between the stabilization locations and the street vendors’ locating characters has a major influence on the success of stabilization arrangement which has been carried out in surakarta. it is manifested by the stabilization areas which seem to have been harmonious, some of the street vendors who consistently stay in their stabilization areas, and the incomes of some street vendors that increase (rahayu et al., 2016). hence, the results of spatial identification as regards the location characteristics of the existing street vendor stabilization become the staple criteria for mapping the alternative stabilization locations which are potential in surakarta. this mapping can be utilized as a strategy of arranging street vendors who have not been managed. the aforementioned criteria entail the availability of the state-own land, the proximity to main activities, and crowds as well as road activities. 3.2.1. the proximity to productive main activities the proximity to productive main activities is really important for the presence of street vendors as to provide the supporting activities in an area. the presence of street vendors is capable of strengthening the function of a public space (shirvani, 1985). the aforementioned productive main activities are the commercial ones such as trade, offices, and recreation (widjajanti, 2009). other activities encompass settlements, education, sports and culture-related activities. the locations which are close to the main activities become the most interesting locations for street vendors, and this also refers to their preferences in selecting locations considering that productive activities encourage high movements (m. rahayu, putri, & andini, 2015). thus, it is it suitable for street vendors in that in selling their commodities they directly face the potential consumers who are passing (mcgee & yeung, 1977; widjajanti, 2012). https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 54 | the locations encouraging vast social movements as such are expected to be able to attract consumers to come around because their locations are proximate. the people’s arrival to the stabilization locations will indirectly be able to attract them to become the street vendors’ consumers, and the street vendors’ incomes can increase. people come to visit the stabilization locations at some point because they have particular interests in those locations, and also because they are near the main activities. however, some consumers come to visit the stabilization locations as the main destination because of particular needs, or the presence of street vendors with their attractiveness become the people’s tourism destination (henderson, 2012). for this reason, the criteria with regard to the existence of these main activities are important to identify, so the designation of new alternative stabilization locations results in good market potency. the following is a map displaying the distribution of land uses in surakarta. the distribution of land uses in surakarta manifests the existence of productive activities (see figure 2). the map displayed below depicts the land uses in laweyan, serengan and pasar kliwon sub-districts, which are dominated by settlements with the percentage of 90% and the rest referring to trade and services, education, health, open spaces, and offices. in jebres sub-district, land uses are dominated by settlements along with industry, trade and services, education, open spaces, warehousing, and health. in banjarsari subdistrict, the land uses are dominated by settlements, trade and services, open spaces, offices, and education. the dominance of successive land uses in surakarta extends to settlements, trade and services, education, health, offices, and open or recreational spaces. figure 2. the map of land uses in surakarta (source: development planning agency at sub-national level and observation, 2017) 3.2.2. the crowded traffic conditions and road activities street vendors trade their commodities by making use of the opportunities around them. for this reason, street vendors do their businesses close to the road and the crowded environment with activities, so street vendors have opportunities to get consumers (chandrakirna & sadoko, 1994; de soto, 1991; mcgee & yeung, 1977; novelia & sardjito, 2015; rahayu et al., 2016; widjajanti, 2009). the crowded arrangement locations are usually near the roads with heavy traffic but with medium speeds. the kinds of arterial and primary collector roads have a high speed of traffic so that the possibilities of the traffic users to stop incline to be small. the kinds of secondary collector, local, and neighborhood roads have a medium and low speed, so people’s possibilities to stop at the street vendor stabilization locations are greater (rahayu et al., 2018). the more consumers come, the greater their commodities are sold. thus, their opportunities to earn increasing incomes are also greater. resting upon this highlight, the potential locations for street vendor stabilization are those close to the high level of crowds and those having a medium traffic speed, such as the locations near the secondary collector, local, or neighborhood roads. https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 | 49 anchored in the map of crowds and road activities, it can be viewed that the highest level of crowds is found in banjarsari, jebres, and laweyan sub-districts, and followed by pasar kliwon and serengan subdistricts (see figure 3). specifially, among them entail tentara pelajar street, letjen suprapto street, ki mangun sarkoro street, sumpah pemuda street, kapten mulyadi street, brigjen sudiarto street, ronggowarsito street, dr. soepomo street, adi sucipto street, mt haryono street, dr. setiabudi street, colonel sugiyono street, kapten piere tendean street, ir. juanda street, brigjen katamso street, major ahmadi street, tangkuban perahu street, letjen sutoyo street, adi sucipto street, sri rejeki dalam 7 street, dr. moewardi street, kebangkitan nasional street, bhayangkara street, honggowongso street. the more crowded the conditions of environment and traffic, the greater the consumers’ possibilities to come around. figure 3. the map of the crowded level of traffic in surakarta (source: development planning agency at sub-national level and observation, 2017) 3.2.3. the availability of the land for public facilities or state-owned land the street vendors’ role that can strengthen the function of public spaces (shirvani, 1985) also make their position stronger to obtain their rights in the public spaces. in addition, the nature of public spaces is open and can be shared for a variety of activities (carr & lynch, 1981). this condition is supported by the street vendor arrangement in public spaces in the form of stabilization (mcgee & yeung, 1977), hence, the presence of public spaces owned by the government becomes a key requirement for this (blackburn, 2011; de soto, 1991). the distribution of land-owned government in surakarta becomes one of the criteria for identifying the alternative to new stabilization locations (see figure 4). usually, in these locations, there have already been the street vendors who trade their commodities without regulation. the existence of the public facility owned by the government / state-owned land is mostly found in banjarsari and laweyan sub-districts and followed by jebres, serengan, and pasar kliwon sub-districts. the following is the distribution of stateowned land as a public facility spread throughout the city of surakarta. https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 54 | figure 4. the map of the existence of state-owned land as a public facility (source: development planning agency at sub-national level and observation, 2017) resting on the result of superimposing the maps of 3 criteria for street vendor stabilization throughout the areas of surakarta, it is found 19 alternative locations which are potential to be used as street vendor stabilization (see figure 5). the first essential criterion for enabling the strategy of stabilization arrangement to be carried out is the availability of government or state-owned land, so that an additional effort to do land acquisition is not necessary. the formulated alternative locations for stabilization are those near the roads or on the sidewalks (which are permitted) and the park which is as a public facility. this point conveys the essence that the locations must be state-owned land. this is in line with the opinion given by (blackburn, 2011) stating that the placement of street vendors in public spaces (adedeji et al., 2014; mcgee & yeung, 1977; rahayu et al., 2013; rukmana, 2016) indicates that stabilization demands the availability of state-owned land the subsidy in the form of public spaces that will be used by street vendors. the availability of land becomes important to avoid conflict and chaos as well as becomes subsidized spaces from the government to street vendors. the alternative of potential locations also meets the criterion for being proximate to the main activities that have already existed such as offices, education, and trade. this point conforms to the opinion stated by (rachbini & hamid, 1994) in that anytime a new building is established; it is always followed by the emergence of street vendors, so that it does not change the basic characteristic of street vendors pertinent to the activities becoming the attracting factors. street vendors seek strategic places that have high population densities at the crossing points of public spaces, or the places adjacent to the economic activities which have already existed (mcgee & yeung, 1977). the same perspective is conveyed by (haryanti, 2008) who elucidates that there are a number of factors that influence the locations of street vendors' trading activities. they consist of the crowds of locations which mean to be proximate to consumers, and the high possibility of consumers in shopping. with being adjacent to productive activities or using locations having high possibilities of consumers, street vendors can get the benefit from the products supporting activities and consumers who need them (widjajanti, 2016). the criterion of location that is on the corridor of collector, local, or neighborhood roads that have a medium speed of transportation along with heavy traffic also becomes an important criterion. the spread of street vendor stabilization locations must be able to capture the consumers whose movements pass through the street vendors for instance the movements from home to the destination places (widjajanti, 2016). in addition, the fulfillment of criterion for the ease of accessibility signified by crowded road activities and busy traffic is also important to capture the consumers who make the street vendors' locations as their main goal (not just passing through). https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 | 51 based on the result of superimposing the maps as regards the 3 criteria, 19 alternative/potential locations scattered throughout the city of surakarta are found. the highest number of alternative locations is in laweyan sub-district with the percentage of 37% (7 locations), 32% of the locations (6 locations) are in banjarsari sub-district, 21% of the locations are in jebres sub-district, and the rest 5% of the locations are respectively in serengan and kliwon sub-districts. hence, with the fulfillment of all criteria of street vendors’ locating characteristics in the 19 alternative stabilization locations, it can be said that those locations have been able to fulfill the street vendors’ needs to operate their trading activities if discerned from the location-related point of view. the fulfillment of street vendors’ needs for the locations is expected to enhance the chances of success in respect of the strategy handled through stabilization, whereby street vendors will not move to other locations considered capable of fulfilling their needs. figure 5. the distribution map of street vendor stabilization alternative locations based on the result of superimposing the location criteria of the existing street vendor stabilization in surakarta (source: development planning agency at sub-national level and observation, 2017) the nineteen (19) locations were the east of sala view hotel (near trading activities on the local road), the east of kasih ibu hospital (close to residence and health on the neighborhood road), the north of tax office (proximate to offices and residential activities on the neighborhood road), the south of tax office (near offices and residential activities on the neighborhood road), the west of laweyan sub-district office (close to office activities on the neighborhood road), the south of lotte mart tipes (near trade and service activities on the local road), yosef high school (near settlement and education activities on the neighborhood road), adi sucipto fruit market (proximate to trade and service activities on the collector road), the front of bonoloyo public grave (near residential activities on the arterial road), the east of aub and utp mojosongo (close to trade and education activities on the local road), the east of moewardi hospital (proximate to health activities on the neighborhood road), the west of junior high school 4 (adjacent to educational and office activities on the collector road), the front of the education office (near office activities on the local road), the front of samsat (near office and residential activities on the collector road), the south of pedaringan warehouse (near trade and service activities on the local road) , the east of state land office (close to office and education activities on the neighborhood road), the west and north of senior high school 6 (adjacent to educational and residential activities on the collector and local roads), the south of kartopuran field (proximate to recreational and settlement activities on the neighborhood road), and the front of surakarta hospital (close to health and residential activities on the neighborhood road). https://doi.org/10.14710/geoplanning.6.1.43-54 rahayu, et al./ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 43-54 doi: 10.14710/geoplanning.6.1.43-54 54 | 4. conclusion the effectiveness of street vendor arrangement is greatly affected by the efficacy of locations to meet the street vendors’ basic needs in carrying out their trading activities. the inconformity of arrangement location characters will only be a temporary arrangement strategy because street vendors tend to move to other locations that possibly meet their needs. therefore, the street vendor stabilization strategy inclines to have a higher success rate compared to relocation. this is because street vendors do not need to adapt to their new location characteristics, so the trading activities will continuously run as they were before, along with the added value of the physical arrangement of the areas. the obtained alternative locations based on the 3 criteria gained from the characteristics of the existing stabilization locations entail: (1) the proximate main activities such as trade and services, settlements, offices, education, and recreation; (2) traffic conditions and the proximity to roads; and (3) the availability of state-owned land. by fulfilling the three location characteristics of street vendor stabilization, the 19 alternative stabilization locations which are mapped can prevent street vendors from space conflicts (the spaces refer to the state-owned land), are able to capture consumers around the nearby productive activities, have good accessibility to capture the needs of consumers that pass the street vendors or the consumers who indeed make the street vendors as their main goal, and enable consumers to have alternative choices and to obtain their complementary needs from the provided agglomerations because the street vendors’ locations are adjacent to the productive main activities. the street vendors’ alternative locations which have been formulated are based on the criteria of the existing street vendors’ locations, so that these findings are expected to capably be a sort of input and be considered by the government in making decisions for arranging street vendors by means of stabilization. in addition, the criteria utilized in formulating the alternative locations can also be applied as the bases of consideration to designate the alternative locations for street vendor relocation because those criteria have accommodated the street vendors’ locating characteristics as the subjects of arrangement. 5. acknowledgement we would like to show our gratitude to the education funding agency (lpdp), doctoral program of architecture and urbanism at universitas diponegoro, and sebelas maret university for the support in the study processes as well as the preparation of this article. we are also immensely grateful to rr. ratri werdiningtyas and to all parties for the contributions having been made during the processes of this study. 6. references adedeji, j. a., fadamiro, j. a., & adeoye, a. o. 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"predicting the impacts of land use/land cover change on land surface temperature using remote sensing approach in al kut, iraq", physics and chemistry of the earth, parts a/b/c, 2021 publication exclude quotes off exclude bibliography on exclude matches off | 201 geoplanning vol 4 , no. 2, 2017, 201-212 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.201-212 cellular automata modeling in the built-up areas within urban development at pontianak e. nurhidayati a,b, i. buchori c , m. mussadun c a department of architecture and planning, pontianak state polytechnic, indonesia b department of architecture and urbanism, diponegoro university, indonesia c department of urban and regional planning, diponegoro university, indonesia abstract: this research integrated the gis-cellular automata model with the regression model to predict urban development in pontianak within the built up area change phenomena approach. the research aimed to understand built-up land use development in pontianak during 1990-2015 and to predict its regional development in 2033. the employed method were satellite the image interpretation approach, hybrid interpretation, and built up land development prediction using transition rules like driving factors and inhibiting factors of urban development. the driving ones are accessibility related to distances to cbd, to main roads, and to the existing built regional areas while the inhibiting ones are peatland and the protected areas. the result showed that the hybrid interpretation, between visual and digital interpretations from the landsat images, can be used to map the built up lands with 94.8% of sampling point’s precision. the non-built up areas in pontianak during 1990-2015 were 83.52 ha/year, and the modelling result predicts that non-built regional areas in pontianak during 2015-2033 will be 80.51 ha/year heading toward northern and central areas of pontianak. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to site (apa 6th style): nurhidayati, e., buchori, i., mussadun, m (2017). cellular automata modeling in the built-up areas within urban development at pontianak. geoplanning: journal of geomatics and planning, 4(2), 201-212. doi:10.14710/geoplanning.4.2.201-212. 1. introduction the dynamics of land use change always happens in urban and regional planning, so it needs good skills in predicting and making spatial simulations (wu, 1998). using gis technology, cellular automata is one of the best methods available today in performing spatial simulations such as land use (wu, 1998). ca modeling formulates the urbanization process employing scientific algorithms and raster-based tools that are effective for urban modeling and land use change (wu, 1998). ca simulation methods are generally applied to assess changes and predict future land use (aburas et al., 2016; guan et al., 2011; wu, 1998). the ca model is appropriate to model the urbanization process underlying the influence of environmental effects (zhou et al., 2012). ca models are helpful in forming patterns and processes in rules and relationships among spatial elements (silva et al., 2008). ca models are often used in the study of land use dynamics based on environmental interactions, such as urbanization, residential growth and new housing development (pan et al., 2010; fuglsang et al., 2013; guan & rowe, 2017). spatial modeling with ca method aims to examine the relationship between obstacles and existing land conditions, transportation networks and urban growth (he et al., 2006). moreover, the ca land use transition is used in urban modeling and development within the scope of time, space and particular conditions (moghadam & helbich, 2013). ca is a method often integrated in dynamic models because it has a spatial pattern on different temporal and spatial scales (garcía et al., 2012). furthermore, ca-based gis models are widely used to predict land use change and development, using a simple set of transition rules used to control changes in spatial patterns in future planning policy rules (garcía et al., 2012; gonzález et al., 2015). the ca modeling aims to simulate urban spatial dynamics, urban growth and land use based on parameters related to the consequences of urban spatial change (aljoufie et al., 2013; deep, 2014; guan et al., 2011; kim et al., 2017; maria et al., 2014; votsis, 2017) article info: received: 13 january 2017 in revised form: 10 may 2017 accepted: 30 august 2017 available online: 30 oct 2017 keywords: cellular automata, hybird interpretation, built-up area, landsat imagery corresponding author: ely nurhidayati department of architecture and planning, pontianak state polytechnic, indonesia email: elmartptk@gmail.com open access https://doi.org/10.14710/geoplanning.4.2.201-212 https://doi.org/10.14710/geoplanning.4.2.201-212 mailto:elmartptk@gmail.com nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 202 | the ca-based gis method is usually employed to present vertical urban growth with a bottom-up approach ( he et al., 2015; lin et al., 2014). some of the issues of urban growth cover the issues of urban development and the dynamics of social space (garcía et al., 2012; aljoufie et al., 2013). in addition to the bottom-up approach, land use change can be collaborated with a set of driving factors with the integration of bottom-up and top-down approaches, like simulating complex relationships among land use changes, natural factors, and socio-economic factors (lin et al., 2014; he et al., 2015; xu et al., 2016). it also simulates land use changes with geophysical factors, socio-economic factors and risk factors as the development driving forces influencing changes in urban land use (liao et al., 2016). changes in spatial and temporal resolutions in land use can be used to map local phenomena such as urban surface dynamics and urban thermal environments that are affected by historical urban growth patterns (darlington et al., 2017). hence, the ca model can be used as an urban system such as land use patterns, work sites concentration, population changes, economy, demographics, transportation and the environment (mitsova et al., 2011). in addition the ca model is also used to test urban theories based on spatial interaction models within urban systems (mitsova et al., 2011). over the past 20 years, there are more researches using the ca approach to assess the impact of disaster risks due to urban growth (hien, 2015; berberoğlu et al, 2016). the ca model can present neighboring cell space information, matrix area transition and suitability maps that are used to simulate future land use (guan et al., 2011). the ca model is implemented to calculate spatial properties since it is cloesly related to geospatial elements and neighboring cells (lin et al., 2014; guan et al., 2011; rocha & ferreira, 2016). the modeling is used to understand the diversity of cities in determining development strategies and land-use policies (wang et al., 2012; pérez-molina et al., 2017). within the framework of geographic concepts, spatial analysis focuses more on investigating patterns and various attributes and regional images, and it uses modeling to improve comprehension and prediction (rustiadi, 2018). spatial distribution centers have been discussed more in spatial location theories like von thunen’s where various centers of activities have different effects on their land use patterns (rustiadi, 2018). therefore, location factors such as the distance of economic center, the distance of main roads and the distance of the existing lands potentially influence the development of non-constructed lands to be constructed ones due to the dynamics of the city in fulfilling the supply of urban system including development actors and government policies. the variables used in this study refer to the spatial and ecological approach (yunus, 2010). spatial approaches are related to changes in non-built up and built up land covers over a period of time. on the other hand, the ecological approach is related to changes in protected lands, cultivated lands and peatlands having impacts on the environment particularly in pontianak that has specific geological conditions (yunus, 2010). the law number 26/2007 has set up protected areas and cultivated areas that have national strategic values in forest resources utilization. protected forest area is a forest area that has the main function as a regulator of life support system to regulate water system, prevent flood, control sea water intrusion and maintain soil fertility. furthermore, the government regulation no. 71/2014 on peatland ecosystem protection and management policy stipulates that the water limit is 0.4 meters below the surface of peatlands. peatlands are very important as water reservoirs throughout the year, and they prevent floods and droughts. ca modeling uses a mathematical approach in which each cell is given various scores that can change over time according to transition rules (de almeida & gleriani, 2005; fuglsang et al., 2013). these cells may represent several factors existing in urban areas and then model the interactions between these factors (gonzález et al., 2015). the simulation is shown by spaces in the form of grids (rasters) in which the grid attributes calculate attributes of each cell around them (silva et al., 2008; van vliet et al., 2009). the cell attributes are basically the part of cell or space elements, limited cell sets, the cell environment, sets of simultaneous rules and time transition. (de almeida & gleriani, 2005; van vliet et al., 2009; dabbaghian et al., 2010). from the above explanation, the purpose of this paper is to elaborate the ca transitional rules that were applied to determine the change of non-built land covers into built land used data in 2000-2007 as dependent variables. meanwhile driving factors in built land change consisting of the distance to the economic centers, the distance to the main road, the distance to the existing built up lands and the peatlands are the independent variables. https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 | 203 2. data and methods 2.1. research sites this research took place in pontianak city. pontianak city is the capital of west kalimantan province located at 002'24 "n 0001'37" s and 109016'25 "w 109023'04" w and cleaved by kapuas river and landak river. the geological conditions in pontianak are grouped into the category of peneplant and alluvial sediments that are physically classified as clay type while this kind of land is peat that was mud sediment of kapuas river. the peat areas have thickness between 1-6 meters, so it causes decrease of soil bearing capacity when constructing large buildings and agricultural lands. having these conditions, peat is very unstable and has low bearing capacity. the structure of peat is the layer that was mud sedimentation of kapuas river. the clay layer can be reached at a depth of 2.4 meters above sea level. pontianak has tropical climate with the highest temperature between 28-32° c. the data used in this study are: a. landsat 8 oli satellite image in 2015 b. landsat 5 tm satellite image in 1995 c. digital data of rupa bumi indonesia map on the scale of 1: 25.000 2.2. research methods the flow of the research framework started from the theory of land use structures and empirical data of constructed lands. from both concepts and theories were found the relationship of variables with the existing conditions and the concepts of land use. these variables were used as transition rules in the binary logistic model ca assessment. thus, the output was the equation of the ca transition rules that could be used as a reference for predicting the development of built up lands in certain years. more detail explanation can be seen in figure 1 and figure 2. figure 1. procedures in defining transition rules (analysis, 2016) figure 2. the framework of the ca binary logistic binary model (analysis, 2016) https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 204 | the interpretations of the map image used as a reference map of land developed were pre processing satellite images, hybrid interpretation, and predicted built-up land changes (danoedoro, 2012). the pre processing satellite images is image conditioning that has provided accurate information both geometrically and radiometrically (danoedoro, 2012). this process consists of geometry and radiometric correction. geometry correction was performed by image rectification to the corrected image (danoedoro, 2012). the process of correction was done by selecting the pair of coordinate points on the images and the corrected images (danoedoro, 2012). on the other hand, the radiometric correction was conducted by converting the pixel values to the spectral radian values and the reflectant with equation (1) for landsat 8 (usgs, 2015) and equations (2) and (3) for landsat 5 (chander & markham, 2003). ρλ = ……………………………………………………………………………………………(1) in which: ρλ = top-of-atmosphere planetary spectral reflectance mρ = reflectance multiplicative scaling factor for the band). aρ = reflectance additive scaling factor for the band qcal = the quantized calibrated pixel value in dn θ = solar elevation angle lλ = ((lmaxλ lminλ)/(qcalmax-qcalmin)) * (qcal-qcalmin) + lminλ ………………………(2) in which: lλ = spectral radiance at the sensor's aperture in watts/(meter squared * ster * μm) lminλ = the spectral radiance that is scaled to qcalmin in watts/(meter squared * ster * μm) lmaxλ = the spectral radiance that is scaled to qcalmax in watts/(meter squared * ster * μm) qcalmin = the minimum quantized calibrated pixel value qcalmax = the maximum quantized calibrated pixel value qcal = the quantized calibrated pixel value in dn in which: ρλ = unitless planetary reflectance lλ = spectral radiance at the sensor's aperture d = earth-sun distance in astronomical units esun = mean solar exoatmospheric irradiances θ = solar zenith angle in degrees the hybrid interpretation is the extraction of built land information was performed by hybrid interpretation (danoedoro, 2012). hybrid interpretation is a combination of visual interpretation and digital interpretation (danoedoro, 2012). there were several stages performed on the hybrid interpretation process in this study such as visual interpretation to delineate built land areas and digital interpretation ………………………………………………………………………………… (3) https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 | 205 with classification supervised using the maximum likehood method in the delineated area as the built land area (danoedoro, 2012). next the results of the constructed land interpretation conducted by hybrids were tested for accuracy (danoedoro, 2012). there were two statistical accuracy test methods, first, relying on the sample data taken as the reference source of the accuracy assessment and second, relying on data sources that were independent and were never used in sampling (danoedoro, 2012). the accuracy test in this research was done by referring to second accuracy test technique. the use of independent data was as the reference sources like orthophoto area data of pontianak city. the test was done on the hybrid interpretation map. this accuracy test method was performed by using points representing each pixel of the hybrid interpretation results, and then they were displayed with the orthophoto data. the predicted model of built up land changes is the built land prediction model in this study calculated both driving factors and inhibiting factors of the development of built up lands. driving factors were the distance to the activity centers, accessibility, and distance to existing built up lands while the inhibiting factor was the peat depth. the parameters were analyzed using a binary logistic regression model that would produce probability values related to changes of non-built up lands to the built-up lands. the integration of the ca-binary logistic regression model predicted the number of built up lands in 2033 using built land maps from hybrid interpretation results in 2015 and in 1995. in this study, the prediction results of land expansion results were limited based on the need for built up lands. built up lands were classified into the population number in the area of study and the non-built up land covers except rivers and protected areas like urban forests, municipal parks, and protected areas in peatlands. 3. results and discussion 3.1. development of built land at pontianak city in 1990-2015 the results showed that hybrid interpretation between visual interpretation and digital interpretation of landsat satellite images can be used for built up land mapping with accuracy of 94.8%. the built up lands at pontianak city in 2015 were 4,250.36 ha in width while the built up lands in 1990 were 2,162.35 in width. within 25 years, the built up lands in pontianak will have doubled. the development of built up lands in pontianak from 1990-2015 was 83.52 ha / year. more details can be seen in figure 3. based on the analysis using spatial interpolation polynomial order 3 to the location of built land expansion 1990-2015 at pontianak city showed the development center of built up lands was in the middle and the south of the city, precisely in pontianak kota and pontianak selatan subdistricts. based on the data and field observation, these areas before were forests and mixed plantations before converted into built up lands. the northern parts of pontianak selatan and pontianak kota subdistricts are strategic areas for economic sectors. these areas cover the trade area on tanjung pura and jalan gajah mada road. in addition, there is also port seng hie, a strategic area for distribution of goods, services and for people movement through the cross-city/district river transportation. on the other hand, in the middle part between the two districts are offices and government areas. pontianak selatan is located around ahmad yani and sutoyo street while in kota pontianak subdistrict on jalan rahadi usman, jalan alianyang and jalan sutan syahrir. https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 206 | figure 3. the map of land use development in pontianak (analysis, 2016) 3.2. the prediction of land use development at pontianak before performing the process of automaton in the prediction of built up land at pontianak city, binary logistic regression analysis was firstly conducted to know the driving factors of built up lands development in pontianak. analyzes on equations (4) and (5) were performed to predict the width of built up areas based on the population growth. more details can be seen in table 1. 2000 2010 1989 1995 https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 | 207 table 1. the population growth and built up land development in pontianak (analysis, 2016) no. year population built up lands (ha) 1. 1990 396,658 2,162.35 2. 1995 447,632 2,267.36 3. 2000 464,532 2,593.31 4. 2010 554,764 3,536.33 5. 2015 590,606 4,250.36 binary logistic regression analysis was used to find out the changes from non-built up land covers to built up lands in 2000-2007 as dependent variable while the driving factors of the change of built up lands consisted of the distance to the economic centers, the distance to the main roads, the distance of existing built up lands and the depth of the peatland as an independent variables. the binary logistic regression analysis equation is described by equation (4) below. y = 0.1443 – (0.000637*x1) – (0.000652*x2) (0.067940*x3) – (0.120945*x4) ....................................(4) y: logit changes from non-built up lands to built up lands x1: the distance to the main roads x2: the distance to the economic centers x3: the distance to existing built up lands x4: the depth of the peatland equation (4) showed that the biggest regression coefficient was found in the peatland variable (-0.12). it indicated that the peatland depth variables had the most significant effect on the conversion of non-built up lands into built up lands in 1990-2015. the aforementioned equation has a negative coefficient value. it means that the peatland has a low depth. on the other hand, the non peatland is likely to change from non-built up lands to built up lands. some driving factors of built up lands will change over time. the number of main roads in 2007 was different from the number of main roads in 2015, because in 2015 there was an addition and construction. binary logistic regression analysis aims to predict the built up lands in 2033. variables used are still the same as the ones used before, and the difference is only at the current time 2015. binary logistic regression analysis between the changes of non-built up land cover to built-up lands in 1995-2015 was used as the dependent variable. the driving factors of the land conversion included the distance to the economic centers, the distance to the main roads, the distance to existing built up lands and the depth of the peatland as independent variables. the binary logistic regression analysis equation is described by the following equation (5). y = 1.0233 – (0.000121*x1) – (0.000060*x2) + (0.029974*x3) – (0.068942*x4)...................................(5) y: logit changes from non-built up lands to built up lands x1: the distance to the activity centers x2: the distance to the main roads x3: the depth of the peatland x4: the distance to existing built up lands equation (5) showed that the biggest regression coefficient was in the distance of existing built up land variable (-0.000060). this indicated that the smaller the distance variables (the closer a site to the existing built up lands), the bigger the possibility of non-built up land covers to be built up lands. this research used ca markov method to predict built up lands widith in 2033. to limit the number of pixels that changed during the automaton process, a transition area matrix was made. the transition area matrix was based on a simple equation between the population increase and the expansion of built up lands. changes in the size of built up lands were obtained from the process of hybrid interpretation while the population numbers https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 208 | were obtained from the central bureau of statistics in pontianak (table 1). more details can be seen in figure 4. figure 4. the linear graph of land use development and the population numbers in pontianak (analysis, 2016) the population of pontianak city in 2033 was predicted as much as 739.913 inhabitants. figure 4 shows that the equation y = 0.011x – 2,439.5. the prediction of the size of built up lands in 2033 is 5,699.54 ha. therefore, it can be seen that the development of built up lands in pontianak 2015-2033 slightly declined from the previous period of 80.51 ha / year. prior to the modeling of built up lands in pontianak in 2033, the model of built up land conditions in pontianak in 2015 was firstly made referring to the pattern of built up land use development in 2000 and 2007. the results then could be used to test the accuracy. in predicting process of built lands at pontianak in 2015, the number of pixels were limited based on the equation y = 0.011x – 2,439,5 which was 45,089 pixels or 4,058 ha. the results of the accuracy test showed that cellular automata integration and binary logistic regression in the prediction of land change in pontianak produced overall accuracy as much as 79.70%, and the highest kappa index was 0.65. the prediction result showed that the built up land in pontianak in 2033 was 5,699.54 ha. the result of spatial interpolation of polynomial order 3 to the location of land up expansion was built in 2015-2033 in pontianak city indicates that the center of built up land development will be in pontianak selatan and pontianak city in the north; since these area are strategic economic center areas, such as the trade area on jalan tanjung pura road, jalan gajah mada and port seng hie. pontianak timur and the southern of pontianak utara are the center of the built up land use development because pontianak timur develops as the economic zone, the service area and the tourist destination (keraton kadariah and jami mousque). on the other hand, pontianak utara with its inter cities/regencies roads develops as the economic zone, the industrial area (factories), the tourist destination (the equator monument and batu layang cemetery), and regencies. https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 | 209 figure 5. land use development trend map at pontianak (analysis, 2016) land use development trend at pontianak can be described into two periods in 1995-2015 and 20152033. the trend of the development in 1995-2015 was toward the pontianak kota and pontianak subdistricts. both areas are the government and trade centers, and there are also public facilities such as hospitals, schools, campuses, trade and service areas and offices. since the built up land was already densely populated, the trend of the built up land development center in 1995-2015 was in white color gradations to brown color. where white is the center of the development. that the development centers from 1989 to 2015 were in pontianak kota and pontianak selatan subdistricts was because these areas were the government and trade centers, the government center is at jalan sultan abdurahman (the new city area), and the economic center is at jalan tanjungpura and jalan gajah mada. more details can be seen in figure 5. the trend of development in 2015-2033 was towards pontianak selatan, some part of pontianak kota, pontianak timur and pontianak utara. both regions develop in economic, trading, industrial and service sectors. in pontianak timur 2 markets were built including pasar belimbing and pasar anggrek while in pontianak utara subdistrict; there is an agribusiness terminal (budi utomo road). there is a plan to develop new formal market in this area like the one in pontianak timur subdistrict facilitated by the municipality because of the density of street vendors and unorganized activities in this area. in addition, that there is a traffic growth towards tayan (trans kalimantan) dividing the kapuas river will eventually cause the development trend to head toward pontianak timur area. more details can be seen in figure 5. some ca modeling studies aim to simulate urban growth in the changjiang delta region based on scenarios to quantify city growth predictions (guan & rowe, 2016). ca modeling is used to prove the hypothesis that the ca-markov model can serve as an alternative tool for regional assessment and simulation of land use management (zhou et al., 2012). ca modeling is used to develop an integrated planning strategy in the metropolitan areas through application of sleuth and cvca (silva et al., 2008). ca modeling of sleuth and cvca methods aims to derive values and compare the resulting of urban growth (silva et al., 2008). ca modeling is used to simulate land-use change, urban growth patterns and future policy development (aburas et al., 2016). in the other studies, ca modeling was used to determine the impact of variations in the scale of land use change, so that small cell and environmental-size combinations can resulted in improper land use transitions (pan et al., 2010). ca modeling is used to explore the impact of coastal flood risk management strategies on urbanization parameters and property prices, zoning planning and adaptation strategies (votsis, 2017) ca modeling is used to measure the urban growth through analysis of land use change and to predict the sustainable urban scenarios (deep, 2014). ca modeling is used to identify and to evaluate the 2015 2033 https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 210 | drivers and inhibiting factors of land-use change that are integrated with economic, political, environmental, biophysical, institutional and cultural factors (basse et al., 2014). in relation to environmental ecosystems, ca modeling research is used to predict lucc and ecosystem changes using swat methods that aim to support south korea's water catchment laws (kim et al., 2017) and to project city growth through prediction changes in land use and green areas, thus its contributing to the city green infrastructure planning (mitsova et al., 2011). whereas in relation to building structures, ca modeling is used to visualize the application of strategies and to visualize the building structures using the lucia model, thus indicating that the suitability between homogeneous building structures with the speed of work and energy (fuglsang et al., 2013). in relation to the urban development approach, ca modeling was used to track the past developments and to predict the future expansion plans, the results show a new urban expansion scenario model in a bottom-up and top-down approaches based on ca modeling (he et al., 2006). ca modeling is used to develop gis-ca models in exploring vertical growth in urban areas, the results show the compact development trends in high-rise buildings, phase transitions from mono centers to bi-centers, building growth balance of low, medium and high (lin et al., 2014). in relation to climate change, ca modeling is used to determine the impact of urban growth on microclimate, to predict land use distribution and to analyze soil surface temperatures, so that the thermal temperature and comfort temperature can be used as the influence factors in urban growth (darlington et al., 2017). the authors found a weakness in obtaining the data used in this study. in predicting the development of built up land in pontianak, the weakness is the limited map image data used in ca modeling. however, these weaknesses are corrected through steps in the accuracy process such as interpretation of landsat image maps (danoedoro, 2012) and comparison of population growth. the result of accuracy in predicting the land use development in this research is 94.8%. this results have good accuracy to prove that the method used in this study is feasible to predict the development of built up land. particularly the parameter of peatland depth can be used as an alternative in inhibiting factor of urban growth and urban development. 4. conclusion gis-ca modeling research with binary logistic regression algorithm approach has novelty in preparing predictions of urban development emphasizing built up land use aspects. this ca modeling is able to give contribution in determining the direction of urban planning and development policies. there are driving factors and inhibiting factors as parameters used as transitional rules such as the land use map, the distance to economic centers, the distance to main roads, the existing built up lands and the peat depth. variables like transportation network, land conditions, and existing built up lands have proven able to influence the development of land use in pontianak. these variables can be used as a reference in planning the direction of urban development and growth in pontianak. variables like peat depth and the distance to existing built up lands have greater influence than variables like the distance to economic centers and the distance to main road toward the change and development of land use in pontianak. the result of this research is the hybrid interpretation in mapping of built up land use that has accuracy 94.8%. the built up land areas at pontianak city in 2015 was 4,250.36 ha while in 1990, it was 2,162.35 ha. within 25 years, the area of built up land in pontianak has doubled while the development of buit up lands in pontianak in 1990-2015 was 83.52 ha / year. the prediction result of binary logistic ca model showed that built up land expansion in 2015 2033 was took place in the central area of built up land development, such as in pontianak selatan, pontianak kota, pontianak timur and pontianak utara. the trend of built up land use development in 1995-2015 headed toward pontianak kota and pontianak selatan. meanwhile the development trend of built up land use in 2015-2033 headed toward the areas of pontianak selatan, part of pontianak kota, pontianak timur and pontianak utara. both periods of the development trends in built up land use above indicate that these developments took place in centers of activities that have been very surfeited and crowded, and such condition is due to the development of urban centers from various sectors especially the built up lands for housing construction, economic centers, trade and service centers, and education centers. the prediction of built up land use development will take place in the suburbs of city/subdistrict close to the trans kalimantan https://doi.org/10.14710/geoplanning.4.2.201-212 nurhidayati, et al/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 201-212 doi: 10.14710/geoplanning.4.2.201-212 | 211 roads, and it will bring a positive impact on the urban development in the province of west kalimantan generally. 5. acknowledgments the authors thank to the academicians in the department of architecture and planning, pontianak state polytechnic and department of architecture and urbanism, diponegoro university for providing opportunities to conduct the research in urban planning study. the authors also thank to mr. trida ridho fariz in geography program of semarang state university for his inputs in this research. 6. references aburas, m. m., 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between aviation-related noise, demographics, and tree canopy mayra i. rodriguez-gonzález1,2*, kevin g. torres-garrido3 1. hartford county extension center, university of connecticut, farmington, connecticut, usa 2. research on resilient cities, racism and equity, university of connecticut in hartford, hartford, connecticut, usa 3. independent researcher, former fellow of the secretaría de educación superior, ciencia, tecnología e innovación, quito, ecuador doi: 10.14710/geoplanning.10.2.179-184 abstract the intricate relationship between aviation-related noise pollution, demographic factors, and tree canopy cover can hold significant implications for targeted interventions promoting environmental equity, sonic justice, and sustainable urban development. this study offers a geospatial exploration of these interconnections within the continental united states by employing national transportation noise pollution data from the united states department of transportation alongside tree canopy cover from the united states geological survey’s national land cover database and demographic data from the american community survey in a correlation analysis. our analysis reveals stark disparities in noise exposure levels, notably underscoring that low-income and predominantly hispanic neighborhood shoulder a disproportionate burden of aviation-related noise. moreover, a correlation between aviation-related noise pollution and low tree canopy cover suggests a potential avenue for utilizing nature as a buffer against heightened noise levels. however, recognizing the delicate equilibrium between fostering a thriving tree canopy and ensuring aviation safety highlights a need for innovative urban planning solutions capable of simultaneously addressing sonic injustice and tree inequity. copyright © 2023 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction aviation-related noise pollution is a concerning issue. the sound of jet engines and of aircraft taking off and landing can cause elevated noise levels that disturb wildlife habitats and ecosystems (alquezar & macedo, 2019). however, the impacts of excessive aviation-related noise are not just felt by local ecosystems. nearby human communities can also suffer from detrimental health impacts due to heightened exposure (basner et al., 2017). the impacts of noise pollution on human health have been extensively documented. individuals living near airports can suffer from disturbed sleep patterns, heightened stress levels, impaired cognitive function, and increased risks of cardiovascular disease and elevated blood pressure (basner et al., 2017; kaltenbach et al., 2008). disadvantaged communities, such as those mostly comprised by racial and ethnic minority groups and lowincome residents, experience a disproportionate share of noise-exposure impacts, which can be exacerbated by challenges in healthcare access and housing mobility (collins et al., 2020; penman-aguilar, 2016). in the united states, health inequity is tied to race, ethnicity and income, among other variables. discriminatory policies and limited financial opportunity relate to high rates of chronic diseases and low life expectancy among historically marginalized populations (penman-aguilar, 2016). however, nature can play an important role in reducing these health issues, and, thus, increasing the quality of life of disadvantaged communities. e-issn: 2355-6544 received: 01 november 2023; accepted: 28 december 2023; published: 29 december 2023. keywords: noise pollution, sonic justice, tree equity, spatial correlation *corresponding author(s) email: rodriguezgmayrai@gmail.com https://doi.org/10.14710/geoplanning.10.2.179-184 mailto:rodriguezgmayrai@gmail.com rodriguez-gonzález and torres-garrido. / geoplanning: journal of geomatics and planning, vol 10, no 2, 2023, 179-184 doi: 10.14710/geoplanning.10.2.179-184 180 nature, with its numerous benefits, plays a vital role in promoting positive mental and physical health by acting as a buffer against noise (ow & ghosh, 2017). trees can play a crucial role in reducing noise pollution through their dense foliage, including leaves, branches and trunks, which act as natural barriers that absorb and deflect sound waves. however, the distribution of trees is often unequal, a phenomenon known as tree inequity, leading to disparities in tree canopy cover, especially in urban areas (riley & gardiner, 2020). this inequality disproportionately affects low-income neighborhoods and communities of color, exacerbating environmental disparities and potentially leaving residents in these areas more susceptible to the adverse effects of noise pollution (riley & gardiner, 2020). consequently, urban greening not only promotes overall urban well-being but also addresses sonic injustice (i.e., the disproportionate exposure of disadvantaged communities, such as lowincome residents and racial and ethnic minorities) to noise pollution (collins et al., 2020). while studies have established that socioeconomically vulnerable groups face heightened exposure to noise pollution, further research is necessary to comprehensively understand the specific contributions of various noise sources, such as aviation-related noise, to these disparities (collins et al., 2020; trudeau et al., 2023). additionally, if considering trees as a nature-based solution to combat noise, it is essential to grasp the interconnected dynamics between tree canopy cover, distinct noise sources, and demographic factors including income, race, and ethnicity. to our knowledge, no study has assessed the combined relationship between aviation-related noise, tree canopy cover, and population demographics. thus, this study aims to assess the spatial correlation between aviation-related noise pollution, tree canopy cover, income, race and ethnicity to gain a better understanding of the relationships between these variables altogether. through a spatial analysis, we answered the following question: is there a significant correlation between aviation-related noise pollution, tree canopy cover, and the income, race and ethnicity of residents living near airports? we hypothesized a positive significant relationship between high aviation-related noise pollution, low tree canopy cover, and high percentages of low-income residents and racial and ethnic minorities. 2. data and methods this study accounts for all areas within the continental united states that have documented aviationrelated noise pollution. using arcgis pro (esri, 2023), we performed zone-based summary statistics to estimate mean exposure to aviation-related noise pollution (in decibels) by census block group, the smallest census denomination for the united states. isolated values of aviation-related noise pollution were downloaded from the department of transportation’s national transportation noise pollution public database (united states department of transportation – bureau of transportation statistics, 2022) (refer to table 1 for data details). table 1. data inputs and sources. description data type year origin download source national transportation noise pollution (aviation-related noise only) raster 2020 department of transportation united states department of transportation – bureau of transportation statistics, 2022 national land cover database tree canopy cover raster 2020 united states forest service united states geological survey, 2023 demographic variables (median household income, population by race, and population with hispanic origin) tabular 20172021 american community survey 5-year data release manson et al., 2023a census block groups polygon (shapefile) 2020 united states census bureau manson et al., 2023b https://doi.org/10.14710/geoplanning.10.2.179-184 https://www.bts.gov/geospatial/national-transportation-noise-map https://www.bts.gov/geospatial/national-transportation-noise-map https://www.bts.gov/geospatial/national-transportation-noise-map https://www.bts.gov/geospatial/national-transportation-noise-map https://www.mrlc.gov/data/type/tree-canopy https://www.mrlc.gov/data/type/tree-canopy http://doi.org/10.18128/d050.v18.0 http://doi.org/10.18128/d050.v18.0 rodriguez-gonzález and torres-garrido. / geoplanning: journal of geomatics and planning, vol 10, no 2, 2023, 179-184 doi: 10.14710/geoplanning.10.2.179-184 181 a boundary layer for census block groups was downloaded from the open-data website ipums national historical geographic information system (manson et al., 2023a). in arcgis pro, we also performed zone-based summary statistics of tree canopy cover to determine percent per census block group (united states geological survey, 2023). mean aviation-related noise pollution and percent tree canopy were imported along with three demographic variables from the united states american community survey (2017-2021), median household income, percent of individuals not white, and percent of hispanic or latine individuals (manson et al., 2023b), into the r statistical language (r core team, 2021) to perform a pearson correlation test between all variables. demographic data was preprocessed using microsoft excel (microsoft corporation, 2023). a detailed workflow diagram of our methodology is provided in figure 1. data inputs are listed in table 1. source: adapted from esri, 2023 figure 1. processing and analytical workflow 3. result and discussion 3.1. sonic justice the analysis of aviation-related noise pollution in relation to the demographic variables (median household income, percent not white, and percent hispanic or latine) revealed that lower-income neighborhoods exhibit higher levels of exposure in noise-polluted areas (figure 3). furthermore, despite there being an apparent negative correlation between high levels of aviation-related noise pollution and high percentages of non-white residents, we observed that aviation-related noise pollution also correlates positively with the percent of hispanic residents (figure 3). the analysis of aviation-related noise pollution in relation to tree canopy revealed a significant negative correlation between the two (figure 2 and 3). areas with reduced tree canopy cover experience higher aviationrelated noise pollution levels. greater percentages of hispanics correlated with both lower income and less tree canopy access (figure 3). thus, lower-income hispanics are particularly exposed to greater levels of aviationrelated noise while simultaneously lacking access to tree canopy cover. https://doi.org/10.14710/geoplanning.10.2.179-184 http://doi.org/10.18128/d050.v18.0 rodriguez-gonzález and torres-garrido. / geoplanning: journal of geomatics and planning, vol 10, no 2, 2023, 179-184 doi: 10.14710/geoplanning.10.2.179-184 182 (a) (b) source: national atlas of the united states, 2014; united states department of transportation – bureau of transportation statistics, 2022; united states geological survey, 2023 figure 2. (a) aviation-related noise pollution, and (b) tree canopy cover in relation to airports (for reference purposes) figure 3. variable distributions (on the diagonal), bivariate plots with fitted lines (below the diagonal), and correlation values with significance levels represented by asterisks (above the diagonal) for percent tree canopy (“canopy”), mean aviation-related noise pollution (“noise”), median household income (“income”), percent of residents not white (“not white”), and percent of residents who identify as hispanic or latine (“hispanic”). 3.2. discussion our study provided valuable insights into the relationships between aviation-related noise pollution, demographic factors, and tree canopy cover across the continental united states at the census block-group level. the analysis revealed the existence of disparities, as block groups that are predominantly low-income and hispanic disproportionately bear the greatest burden of aviation-related noise pollution while exhibiting low https://doi.org/10.14710/geoplanning.10.2.179-184 https://purl.stanford.edu/hh676zz3630 https://www.bts.gov/geospatial/national-transportation-noise-map rodriguez-gonzález and torres-garrido. / geoplanning: journal of geomatics and planning, vol 10, no 2, 2023, 179-184 doi: 10.14710/geoplanning.10.2.179-184 183 tree canopy access. this is consistent with other studies, which have documented the association of disadvantaged communities with greater noise exposure and with low tree canopy access, separately (collins et al., 2020; riley & gardiner, 2020; trudeau et al., 2023). although confounding variables are expected, our findings underscore the critical need to understand how intersecting marginalized identities experience multiple environmental challenges simultaneously and unevenly (in this case, being of hispanic origin and having a lower income level and experiencing both high exposure to aviation-related noise pollution and low tree canopy access. the analysis of aviation-related noise pollution in relation to tree canopy revealed a significant negative correlation between the two, suggesting that tree canopy cover could be used as a mitigation strategy. other studies have extensively documented the role that trees play in reducing noise pollution (ow & ghosh, 2017), even demonstrating how nature can reduce airport noise while promoting many ecosystem services (korol et al., 2018). despite the contributions of nature in buffering against noise, the presence of a tall and dense tree canopy also poses a concern to aviation safety due to reduced visibility for pilots and increased bird habitat that could lead to a greater chance of bird strikes (metz et al., 2020; mobini & sabzehparvar, 2022). this juxtaposition highlights a need for establishing best practices that promote vegetation growth without compromising aviation safety. knowing best practices for aviation safety standards in this context could support the use of nature-based solutions in mitigating aviation-related noise pollution. our findings suggest that there is a potential for urban forestry interventions to address both noise pollution and tree canopy disparities simultaneously through strategic tree plantings. however, the delicate balance between promoting tree canopy cover and ensuring aviation safety cannot be overlooked. while tall trees contribute positively to noise reduction and overall environmental well-being (korol et al., 2018; ow & ghosh, 2017), they introduce risks for pilots during takeoff and landing (metz et al., 2020; mobini & sabzehparvar, 2022). this translates into a challenge for urban planning that requires further research to explore the application of strategic tree placement and species selection near airports to find a balance between an abundant and equitable tree canopy and aviation safety. 4. conclusion the goal of our study was to provide key insights into the intricate connections between aviation-related noise pollution, demographic factors, and tree canopy cover to guide interventions for environmental equity, sonic justice, and sustainable urban development. our analysis highlighted a disproportionate impact on socioeconomically vulnerable populations, particularly those of hispanic origin and with lower income levels. it emphasized the need for nature-centric mitigation strategies, especially in block groups with low income or predominantly hispanic populations experiencing excessive noise pollution. however, the correlation between higher noise exposure and lower tree canopy also indicated the potential role of nature as a mitigation strategy, prompting further research to identify the delicate balance between promoting sonic justice and ensuring aviation safety. based on the key insight of this study, we suggest exploring any of the following as means to better capturing the intricate connections between aviation-related noise pollution, demographic factors, and tree canopy cover: (1) identifying tree species and placement patterns ideal for optimizing tree canopy cover for noise reduction without increasing aviation hazards; (2) applying temporal analyses to observe the relationship between aviation-related noise pollution, demographic dynamics, and tree canopy cover over time; (3) assessing mental and physical health impacts from aviation-related noise exposure and tree canopy access for a more holistic understanding of tradeoffs; (4) assessing the perspectives of residents affected by aviation-related noise to inform community-driven solutions that promote sonic justice and tree equity simultaneously; (5) by exploring these avenues of research, future studies could lead to a comprehensive understanding of the intricate relationships between aviation-related noise, demographics, and tree canopy, guiding the development of effective strategies that balance environmental, social, and safety considerations for sustainable urban development. https://doi.org/10.14710/geoplanning.10.2.179-184 rodriguez-gonzález and torres-garrido. / geoplanning: journal of geomatics and planning, vol 10, no 2, 2023, 179-184 doi: 10.14710/geoplanning.10.2.179-184 184 5. acknowledgment all data sources and software used, with exception of arcgis pro and microsoft excel, are open source and publicly accessible. access to arcgis pro and microsoft excel was obtained through institutional affiliation of the authors. 6. references alquezar, r. d., & macedo, r. h. (2019). airport noise and wildlife conservation: what are we missing?. perspectives in ecology and conservation, 17(4), 163-171.[crossref] basner, m., clark, c., hansell, a., hileman, j. i., janssen, s., shepherd, k., & sparrow, v. (2017). aviation noise impacts: state of the science. noise & health, 19(87), 41. [crossref] collins, t. w., nadybal, s., & grineski, s. e. (2020). sonic injustice: disparate residential exposures to transport noise from road and aviation sources in the continental united states. journal of transport geography, 82, 102604. [crossref] esri. (2011). arcgis pro 3.1.0. redlands, ca: environmental systems research institute. https://pro.arcgis.com esri. (2023). how zonal statistics tools work. redlands, ca: environmental systems research institute. https://doc. arcgis.com/en/arcgis-online/analyze/how-zonal-statistics-works.htm kaltenbach, m., maschke, c., & klinke, r. (2008). health consequences of aircraft noise. deutsches ärzteblatt international, 105(31-32), 548. [crossref] korol, e., shushunova, n., feoktistova, o., shushunova, t., & rubtsov, o. (2018). technical and economical factors in green roof using to reduce the aircraft noise. in matec web of conferences 170 (01081). edp sciences. [crossref] manson, s., schroeder, j., van riper, d., knowles, k., kugler, t., roberts, f., & ruggles, s. (2023a). ipums national historical geographic information system: version 18.0 [2020 census block groups – shapefile]. minneapolis, mn: ipums. [crossref] manson, s., schroeder, j., van riper, d., knowles, k., kugler, t., roberts, f., & ruggles, s. (2023b). ipums national historical geographic information system: version 18.0 [2017-2021 american community survey – tabular]. minneapolis, mn: ipums. [crossref] metz, i. c., ellerbroek, j., mühlhausen, t., kügler, d., & hoekstra, j. m. (2020). the bird strike challenge. aerospace, 7(3), 26. 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(2021). r: a language and environment for statistical computing. r foundation for statistical computing, vienna, austria. https://www.r-project.org riley, c. b., & gardiner, m. m. (2020). examining the distributional equity of urban tree canopy cover and ecosystem services across united states cities. plos one, 15(2), e0228499. [crossref] trudeau, c., king, n., & guastavino, c. (2023). investigating sonic injustice: a review of published research. social science & medicine, 115919. [crossref] united states department of transportation – bureau of transportation statistics. (2022). national transportation noise map. https://www.bts.gov/geospatial/national-transportation-noise-map united states geological survey. (2023). nlcd conus 2020 tree canopy cover. v2021-4. https://www.mrlc.gov https://doi.org/10.14710/geoplanning.10.2.179-184 https://doi.org/10.1016/j.pecon.2019.08.003 https://doi.org/10.4103%2fnah.nah_104_16 https://doi.org/10.1016/j.jtrangeo.2019.102604 https://pro.arcgis.com/ https://doc.arcgis.com/en/arcgis-online/analyze/how-zonal-statistics-works.htm https://doc.arcgis.com/en/arcgis-online/analyze/how-zonal-statistics-works.htm https://doi.org/10.3238%2farztebl.2008.0548 https://doi.org/10.1051/matecconf/201817001081 http://doi.org/10.18128/d050.v18.0 http://doi.org/10.18128/d050.v18.0 https://doi.org/10.3390/aerospace7030026 https://office.microsoft.com/excel https://doi.org/10.1155/2022/4320101 https://purl.stanford.edu/hh676zz3630 https://doi.org/10.1016/j.apacoust.2017.01.007 https://doi.org/10.1097%2fphh.0000000000000373 https://www.r-project.org/ https://doi.org/10.1371/journal.pone.0228499 https://doi.org/10.1016/j.socscimed.2023.115919 https://www.bts.gov/geospatial/national-transportation-noise-map https://www.mrlc.gov/ 25 geoplanning journal of geomatics and planning vol. 9, no. 1, 2022 original research changes in the coverage of essential services along the rural provincial border as a result of informal collaboration (case: d.i. yogyakarta province rural provincial border) isti andini1*, achmad djunaedi1, deva f. swasto1 1. department of architecture and planning, faculty of engineering, universitas gadjah mada doi: 10.14710/geoplanning.9.1.25-36 abstract the sustainable development goals prioritize universal essential public services as the second most important development goal after human basic needs in a global perspective. indonesia implements a public service provision standard with a territorial approach and a set of minimum population requirement that lead to urban bias, resulting in border areas failing to meet the requirements for the provision of public services. daerah istimewa yogyakarta province is one of indonesia's provinces with more than 70% of its border areas being rural, and more than 40% of border villages having limited essential public services. because of the territorial delivery system for essential public services, formal cross-border services require a significant number of resources. using quantitative approach by indexing essential public services availability, this paper examines changes of essential public services coverage when cross-border services are provided informally. the case of pustu panggang informal cross border service delivery provides lessons on how informal collaboration works. although it involves misdeeds and omissions, the application of informal collaboration in cross-border services increases essential public service coverage by 57 percent in daerah istimewa yogyakarta province's rural border areas. as a result, informal collaboration should be viewed as a low-cost coping strategy in indonesia's efforts to provide universal public service coverage. copyright © 2022 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction over the last two decades, indonesia has undergone a transitional state with highly dynamic institutional arrangements (antlöv, 2019; hidayat et al., 2018). changes in development management were brought about by the dynamics of global values in terms of achieving humanity's progress. good governance, which has emerged since the 1990s, necessitates fundamental changes such as decentralization and the incorporation of transparency and accountability values into public decision-making process (wulandari et al., 2019). globally, good governance is regarded as a strategic tool for improving government performance in public services due to the accountability and transparency principles (thapa et al., 2019). principles of good governance are considered to be the key factor in ensuring a better public resources management through specific procedures. by the principle of accountability, good governance requires well-documented development process. indonesia, on the other hand, has long followed an informal culture of public management (anderson, 1972; fahmi et al., 2016; muur, 2018; rukmana, 2015) and planning is no exception (hudalah & woltjer, 2007; zhu & simarmata, 2015). informal and personal contacts between stakeholders took various forms and ultimately aided the ongoing formal processes. e-issn: 2355-6544 received: 06 november 2021; accepted: 24 november 2022; published: 29 november 2022. keywords: rural borderland, essential public services, informal collaboration *corresponding author(s) email: isti.andini@mail.ugm.ac.id https://doi.org/10.14710/geoplanning.9.1.25-36 mailto:isti.andini@mail.ugm.ac.id andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 26 in indonesia, the implementation of good governance principles, particularly in development administration and decision making, can be seen as an attempt to shift the paradigm from government to governance. the concept of good governance is essentially a response to the conditions of public administration in order to ensure a sustainable development path (dhaoui, 2019). in developing countries such as indonesia, good governance has evolved into a set of solutions to numerous underachievement’s and problems in public administration (mahendradhata et al., 2017; wiseman et al., 2018; yusriadi, 2019). from a functional standpoint, good governance means that the government has functioned effectively and efficiently in achieving goals through the practice of public administration by enforcing policies in accordance with the law (jubaedah et al., 2008). the term “in accordance with the law” ensures the formality of decision-making process and the public services management in general. daerah istimewa yogyakarta province, located in the most populous island of java in indonesia, has problems providing essential public services in its rural provincial border areas amidst high level of centrality of essential public service provision in urban areas (putri et al., 2016). using territorial approach and pooling technique, referral system used in the provision of public health services divides population into rigid service areas in order to manage the efficiency of health-care resources. because of the low population density in rural border, providing facilities has high per capita costs. together with the perception of border as a backward area, higher cost per capita in essential public services makes borderland unappealing for development (eny et al., 2018; mangels & riethmüller, 2018). meanwhile, the borderline appeared to be flexible and permeable (cappellano & makkonen, 2020; martins, 2020; varol & soylemez, 2018). the flexibility of the border, particularly in rural provincial borders, makes cross border transfer extremely fluid. daily cross border movements are common, particularly in rural borderlands where kinship and mutual values are deeply ingrained in daily life (ahmad, 2019). among rural communities, there is an obligation to help other members of the kin, and the border only serves as imaginary line and do not work as a barrier in cross-border communal activities. health and education services, as public goods managed by the state, must then adhere to the formal arrangement. transferring patient services across service areas necessitates changes to the patient data record, which must be approved by the authority based on the level of transfer (government regulation no. 28 of 2018 concerning inter-regional cooperation, 2018). for resource transfers that occur at the provincial boundary, authorization at the provincial level is required, with coordination at the state level. formalization of cross border services takes time to ensure that public administration adheres to good governance principles (harsanto et al., 2015; subianto et al., 2020), resulting in inefficiencies in public services and a delay in the delivery of health care. as an impromptu response to these administrative issues, informal collaboration emerged. informal collaboration is defined by the failure to meet the legal requirements for interregional collaboration outlined in pp 28 of 2018 concerning inter-regional collaboration in indonesia. the regulation mentions eight elements of collaboration that must be recorded in a written agreement between two regions that provide cross-border services. this specific procedure ensures good governance in cross-border transfers, but implementing it takes a significant amount of time and resources from both sides. when the number of potential transfers is compared to the total service counted in the administrative area, the less populated rural areas become less significant. the issue of cost-effectiveness arises in the evaluation of the significance of formal collaboration. informality in essential public services is not a new phenomenon. essential public services are always characterized by a communicative life and social contact, especially in developing nations where pure public service resources are limited. recent research on informality in public services indicates that informal services occur in part due to the necessity for reallocation negotiations (demmke, 2017) and extra access in decisionmaking (hunnicutt & gbaintor-johnson, 2020). informal acts take the form of personal contact in an informal setting (waring et al., 2018) and constitute a different rule of the game (jiménez-martínez, 2018). informal services are more likely to include and benefit intermediaries (ledeneva, 2018; müller, 2019). however, it is impossible to deny that informal acts provide such a significant advantage. https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 27 studies that evaluate the benefits of informal services on the side of service customers, such as rye et al. (2018), which identify cost efficiency in the transportation sector as a result of informal interactions, and warai (2021), who identified the time for service users performing informal acts, provides proof of the benefits of incorporating informal acts in public services. however, the advantages of informal interaction in the distribution of public services in border rural communities have remained elusive. by recognizing the importance of crossborder services as one of the coping strategies for acquiring public services, it is necessary to clarify how vast the advantage a region can gain by advocating informal services. although there is evidence that informal cooperation is a type of interaction that does not deliver mutual advantages to the parties involved, the ability of informal collaboration to provide public services more swiftly cannot be denied. however, some people believe that benefits for service users arise as well. mizes & cirolia (2018), for example, discover that the option to informally shorten the process results in increasing gains in terms of financing public services. furthermore, weiss-gal (2018) discovered that actors' involvement in informal interactions increases their commitment to service. with these studies, informal cooperation can also be considered as a lifesaver in the provision of public services during the time of limited resources . in light of these contradictions, this paper question how much change in the scope of essential public services will occur if informal cooperation implemented in rural provincial border of daerah istimewa yogyakarta province? this paper applies lesson learned from the case of pustu panggang in the practice of informal cross-border service. although misconducts were identified during the informal collaboration process, pustu panggang successfully contribute to the achievements of sdgs. the idea is to assume that informal collaboration as in pustu panggang were implemented in rural provincial border of diy province. this paper examines the changes in the coverage of essential public services to 46 other villages along the provincial borderline. the significance of the change in essential public service coverage demonstrates that informality can be used as a coping strategy for the provision of universal basic services, especially when resources are limited. 2. data and methods 2.1. case of informal collaboration in pustu panggang due to uneven terrain, the panggang-glagaharjo border area is a rural hamlet that is integrated on both sides but still has a non-built enclave. the panggang sub-primary health center (pustu panggang), located in panggang village, kemalang district, klaten regency, central java province, is the sole primary health care facility in this border area. the glagaharjo village region in cangkringan district, sleman regency, diy province, which shares a border with panggang, is formally served by the cangkringan health center, which is 7 kilometers away and separated from it by a natural barrier in the form of hills. formally, patients from glagaharjo can only be served at pustu panggang in an emergency and then sent to the cangkringan health center for further care. the location and the illustration of informal collaboration in pustu panggang are shown in figure 1. the findings in this case demonstrate that cross-border patients receive care, either emergency or nonemergency, without discrimination. cross-border patients obtain the same services as panggang residents without any cost, but also without any record being made. this case demonstrates a lack of service records even at the operational level. so, only operational officials were aware of informal cooperation. because informal cooperation information was not communicated at the tactical-strategic level, the influence of informal collaboration in the pustu panggang case became limited exclusively at the operational level. cross-border patients initiated the collaboration by personally visiting the pustu and obtaining acceptance from the pustu officers. contacts established by community leaders extending from request being made to the approval of service requests. although no explicit negotiations took place, agreement on service offers was evident by the absence of termination of the health services provided. https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 28 (a) source: analysis, 2021 (b) figure 1. (a) panggang-glagaharjo provincial border settlement. (b) informal cross border primary health care illustration. 2.2. essential public service availability along d.i. yogyakarta provincial border the border rural area in diy province consists of 47 villages in 18 sub-districts in 3 regencies. using the sdgs indicator, this paper assesses two types of basic public services, education and health services using data from the publication of the central bureau of statistics of diy province (2020). for education services, the essential public service availability index is calculated from the availability of elementary and junior high schools within a 5 km radius of the settlements in each village. meanwhile, the health service availability index is calculated from the availability of puskesmas or pustu as well as general practitioner practices at a radius of 5 km from the settlements of each village. as for the index of potential public services, the data of adjacent villages in central java province was derived from central bureau of statistics of central java province year 2020. the same variables were used to build both indexes, but the data of adjacent villages were filtered using criteria identified from lessons learned in pustu panggang case. 2.3. methods this study takes a quantitative approach, comparing the availability of essential public services in border areas in two conditions, namely without and with informal collaboration, using the model from the pustu panggang case. the value index function was then used to analyze data from the four variables. the availability of public services within the region is an index of available services, whereas the availability of public services within a 5 km radius (including cross-border) is an index of service potential. the two indexes are calculated as follows. 𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠 𝐼𝑛𝑑𝑒𝑥 = ( σ 𝑝𝑠 + 2(σ 𝑠𝑐) + (σ 𝑠𝑝ℎ + 2 (σ 𝑝ℎ) 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑟𝑎𝑛𝑔𝑒 𝑜𝑓 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 the first variable (ps) stands for primary school, and the second variable (sc) stands for secondary school. meanwhile, sph is an abbreviation for sub-community health centers, the lowest level of health-care provision. the last variable (ph) denotes a primary healthcare facility, which includes community health centers and general practitioners. secondary facilities were assigned a weight of two because the presence of secondary services indicates greater capacity in essential services. the index is then divided into three categories: low (0-0.35), https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 29 medium (0.36 – 0.8), and high (0.8 – 1.2). the two indexes used in the analysis employ the same mathematical function and serve the same purpose on two different conditions. the research took benefit from the geoprocessing analysis in arcgis 10.8, by implementing weighted overlay tool. both indexes (available and potential) were pictured spatially using the same technique. for the base map, this research uses topographic map from the ina-geoportal big (tanahair.indonesia.go.id/map). each variable was assigned different weight based on the equation, using village as the spatial unit for analysis. meanwhile, the pustu panggang case provides an informal collaboration framework that explains how expanded coverage can be achieved. several informal acts were identified as critical in the implementation of cross-border informal services. there are criteria on essential public services from cross border villages that are added to the calculation of the potential public service index, namely high spatial peripherality from nearest local urban centers and differences in public services available within a 5 km radius of rural provincial border settlements. the inclusion of public services in the two criteria distinguishes the index of potential public services. finally, the difference is calculated by comparing the available service index (only facilities within rural administrative boundaries) with the potential service index (adding informal collaboration to access adjacent public facilities). 3. result and discussion results should be clear and concise. the results should summarize (scientific) findings rather than providing data in great detail. please highlight differences between your results or findings and the previous publications by other researchers. for tables, they are sequentially numbered with the table title and number above the table. tables should be centered in the column and fi. the research findings are divided into three sections that explain the research process, namely the identification essential public service index, lessons from pustu panggang, and potential essential services index. following the identification of the research results at these three points, the discussion will look at how changes will occur if informal cooperation, such as what happened in pustu panggang, is applied to 46 other villages in the rural provincial border village od daerah istimewa yogyakarta province. the pustu panggang case set the criteria that the potential for additional services is limited to services that are not available in the 46 provincial border village and are located within a 5 km radius of border settlements. 3.1. essential service in rural provincial borderland of diy province the essential public service index is dominated by a low value index in 47 villages along the provincial border, indicating that 0-1 services are available within a 5 km radius of border settlements. a total of 41 villages are classified as low, 5 as medium, and 1 as high in terms of essential public services. this demonstrates that providing essential public services is a major issue in provincial border areas. the distribution of the essential service index by district is shown in table 1. the distribution of the index of available essential services along the provincial border are pictured in figure 2 for each regency. the three diy border regencies illustrate the same characteristics in terms of providing essential public services, with more than 70% of border villages having low services and only one type of essential service available within a 5 km radius. elementary schools provide the vast majority of essential services. elementary schools in border areas are the result of a national school construction program (sd inpres program) that began in the 1970s and has proven to be an essential service program that promotes equitable distribution of universal services and has a long-term systemic impact (akresh et al., 2018; brinkman et al., 2017). meanwhile, the distribution of health facilities demonstrates that the presence of primary health care facilities is linked to the presence of educational facilities. with a total of 25 villages, all villages that have health services also have education services (53 percent). this highlights the problem of urban bias in basic health services in indonesia, as discovered wulandari et al. (2019). https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 30 table 1. available public services index in rural borderland of diy province regency number of borderland village low available service index average available service index high available service index gunungkidul 18 17 1 0 sleman 14 12 1 1 kulonprogo 15 12 3 0 total 47 41 5 1 source: analysis, 2021 (a) source: analysis, 2021 (b) (c) figure 2. spatial disribution of available service index in each regency. (a) gunungkidul regency. (b) sleman regency. (c) kulonprogo regency. 3.2. lesson from pustu panggang informal collaboration the borderland of panggang-glagaharjo consists of two hamlets in panggang village and one hamlet in glagaharjo village. the terrain in this area is hilly, with a maximum elevation difference of 30 meters. this border area is bounded on all sides by hills and cliffs, making it difficult to access services outside the border settlements. there are hills on the side of sleman regency that prevent glagaharjo residents from accessing health services at cangkringan, its nearest local urban center approximately 7 km away. cross-border activities are intense, with a wide range of interests, such as shopping at panggang market, attending glagaharjo elementary school, or working on small-scale mining on both sides. from the perspective of the users, this collaboration was initiated because the nearest healthcare facility requires higher cost of accessing the service in a formal manner. since pustu panggang in kemalang, central java province, is only 200 meters from glagaharjo, crossing the border is a viable option compare to accessing services in cangkringan. from the perspective of the service provider, it was simply humanity and the health officials' code of conduct. furthermore, the frequency of visits from kemalang remains below the maximum service capacity. this ensures that the cross-border patient does not disrupt pustu panggang's performance. this collaboration occurred only at the operational level for cross-border services such as examining general diseases and disease prevention in the elderly (checking blood pressure, blood sugar and cholesterol). puskesmas kemalang's tactical level was aware of informal collaboration and services provided, but the information was never clearly reported. as a result, no action was taken in the area of cross-border services. the klaten health office's strategic level was unaware of this collaboration, as were health officials on the user side. there were no officials involved in the service user side, both formally and informally. this case is classified as informal collaboration based on the component because it does not meet the conditions for formal collaboration as stated in pp 28/2018. this collaboration consists of only three of eight elements and has never been formally recorded. collaboration was based on an agreement on the subject (pustu panggang and cross-border users), the object (primary healthcare services), and the termination (when the officials transferred to another pustu). pustu panggang officials modified cross-border service delivery https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 31 procedures by not recording patient data in the medical record. as a result, the informality of cross-border collaboration becomes a catalyst for critical misconducts. table 2 lists the informal acts and motives of the acts shaped the collaboration in pustu panggang. table 2. informal actions in delivering cross-border healthcare formal procedure level informal actions motives puskesmas cangkringan receives patient requests user to operational staffs make a direct request to the service provider closer distance application by puskesmas cangkringan for the transfer of primary health facilities to the district service tactical level not done no data from operational staffs request for cross-border service cooperation from the sleman regency health office to the yogyakarta provincial health office strategic level not done no request to be followed up initiation of collaboration from yogyakarta provincial health office to central java provincial health office for cross-border services provincial governments not done no request to be followed up agreements on collaboration and negotiations provincial governments not done no initiation recording on the state sheet state not done no formal collaboration detailed discussion of district-level collaboration strategic level not done no formal collaboration collaboration implementation in puskesmas kemalang tactical level not done no formal collaboration cross-border services in pustu panggang are provided in accordance with agreements operational level to users done based on informal agreement no medical record code of conduct small scale of resources source: analysis from interviews, 2021 the majority of settlement agglomeration in borderland takes the form of enclaves located near a worksite or a water source. along with the small number of service users, the mountainous terrain complicates healthcare delivery. in this case, the spm assessment revealed that the population of the border area was relatively served by primary health care facilities, but not within walking distance. the assessment of service availability ignored the pattern of agglomeration in rural border areas. according to spm, both sides of the border in the panggang-glagaharjo case are statistically served by primary health care facilities. as a result, there is no formal issue with service availability. however, accessibility is a concern because the shortest path for the glagaharjo community is approximately 7 kilometers away, with no public transportation and a mountainous topography. regardless of the administrative border and territorial primary health service, the accessibility for the panggang-glagaharjo community may be quite different. the furthest border settlement is only 2 kilometers away from pustu panggang. there is no physical barrier preventing the border community from visiting pustu panggang. the only issue with this accessibility is the administrative system of primary health care services. the service is within walking distance but is not formally accessible. the border variables and administrative system make primary health care facilities inaccessible although located within reachable distance. informal cooperation between regions in this case occurs in essential public service objects. the object of informal cooperation is a pure public service that does not increase the competitive advantage of the region confirms glinos et al. (2014) which states that user demand for public services will always be at the closest condition to the user to minimize costs and increase access. in border rural areas, special management is needed to ensure that the closest services can be provided. informal cooperation is a form of special management of https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 32 border rural areas to ensure that the demand for public services can be met. meanwhile, in the aspect of service provision, informal cooperation provides an alternative explanation of the weaknesses of public service provision based on a pooling system as in cattani & schmidt (2005). informal services provide additional demand for services to maintain the minimum demand prerequisites for public services to be provided. this case provides empirical evidence from the opinion of gostin & meier (2019) which states that the existence of professional ethics can justify informal services provided with resources from the formal system. it is important to separate informal services from a legal perspective which allows informal services to be seen as illegal. 3.3. potential public service in rural provincial borderland using informal collaboration scheme although the principle of good governance aims to ensure efficiency and fairness in development, the operationalization of these principles can technically prevent public services from being enjoyed non-excludable and non-rivalry. in the case of observations, good governance procedures actually create a long bureaucratic chain so that it is time for informal cooperation to be recognized as another path of development. the direction of thinking on border area planning management should start thinking about how to put informal cooperation in the legal framework of border area development in indonesia. assuming informal cooperation occurs in the border areas of diy province as happened at pustu panggang, the index of essential services in border villages will change. a total of 6 villages has a high essential service index and 17 villages have a medium essential service index. there are only 14 villages that are in the essential services index. table 3 shows the potential for essential services in border villages when informal cooperation occurs. figure 3 shows the spatial distribution of potential service index for each regency. table 3. index of potential service due to informal collaboration regency number of borderland village low potential service index average potential service index high potential service index gunungkidul 18 3 7 8 sleman 14 5 4 5 kulonprogo 15 6 6 3 total 47 14 17 16 source: analysis, 2021 (a) source: analysis, 2021 (b) (c) figure 3. spatial disribution of potential service index in each regency. (a) gunungkidul regency. (b) sleman regency. (c) kulonprogo regency. by applying informal cooperation to border areas, the three districts in the diy border area show an essential service index which is at an average level of 2 with 60% of villages having a medium and high service index in providing essential public services available within a 5 km radius. although not all border areas have come out of the low category, the majority of border communities have received essential public services in accordance with the universal public service coverage objective. using the informal collaboration model of the pustu panggang case, there is an increase in the coverage of essential public services without the cost of https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 33 providing additional facilities. based on the two previously identified indices, table 4 shows the changes in the essential service index that occur when informal cooperation is implemented. the biggest change occurred in the high classification with 15 villages ultimately achieving high availability of essential public services. this change can be seen spatially in figure 4 which presents the service index map, 4 (a) shows the essential service index without informal cooperation, while 4 (b) shows the essential service index with informal cooperation. significant changes can be seen in the number of villages with a high essential service index that utilize the existence of cross-border facilities. in general, there are no fees charged either to patients who use the service or to the government as a public service provider. using arcgis, the distribution of essential public services index is being mapped. to build the spatial distribution of empirical status of essential public services index, a set of variables being put into the equation using geoprocessing by applying weighted overlay tool. the weights are applied to the variables according to the equation in section 2.3. contrasting the red to the green, figure 4 compares the two indices and shows that changes in essential public services occur in almost all villages along the rural provincial border. the production of these two maps is essential to the discussion of the research since it shows spatially the changes of the coverage. villages with the same types of facilities as their adjacent villages do not receive additional benefits from the assumption of implementing informal collaboration. it can be seen that the red area is reduced significantly. when the map is examined in greater detail, it is revealed that the villages with the service index that does not change are villages with hilly landscape contours. this implies the possibility of a more dispersed population and, as a result, greater difficulty meeting the minimum requirements for providing basic services. table 4. changes in essential public services regency number of borderland village low potential service index average potential service index high potential service index gunungkidul 18 -14 +6 +8 sleman 14 -7 +3 +4 kulonprogo 15 -6 +3 +3 source: analysis, 2021 (a) source: analysis, 2021 (b) figure 4. (a) map of available public service index, (b) map of potential service index the result shows that there is a significant change in essential public services coverage when the informal collaboration is put into the development equation. this is supporting the hypothesis in this paper that informal action, interaction and collaboration do have advantages to the regional development in spatial perspective. confirmations on the advantages are seen through the sharp increase in coverage, without having any financial cost to provide extra facilities. as decentralization rolls in indonesia, the urgency of having regional collaboration rose. informal collaboration may be seen as an opportunity to build stronger regional cohesion as stated in pazos-vidal (2019). it is also important to highlight that rural borderland benefited largely from informal collaboration as stated in, for example, dabson & kumar (2021), ofem et al. (2018), and also mcfarland & dabson (2021). however, the study also shows contradiction to the findings of ledeneva (2018) on the role of https://doi.org/10.14710/geoplanning.9.1.25-36 andini et al. / geoplanning: journal of geomatics and planning, vol 9, no 1, year, 25-36 doi: 10.14710/geoplanning.9.1.25-36 34 intermediary agents to initiate the informal collaboration. in this study, no external agents were identified. the informal collaboration and informal provision of essential public services were done by the internal stakeholders. it is also revealed that no personal advantage is involved in this informal case. the reason of the absence of intermediary agents is because of the scale of the informal services that is too small to attract intermediary agents. 4. conclusion informal public service collaborations in rural provincial border of daerah istimewa yogyakarta province have been proved in increasing the coverage of essential public services in border rural areas by more than 60% without any additional facilities or costs from both service users and service providers. this demonstrates the potential of informal cooperation as a tool for achieving the sdgs, particularly in the third and fifth goals, which are related to health and education services. this informal collaboration would be a unique expression of essential public service governance for rural provincial borders with low populations that are isolated from their local activity centers. the ability to access cross border essential public services at minimum cost and resources open an opportunity to universal public service towards welfare-state in indonesia. related to the characteristics of rural border areas, further studies on the scale of border villages for informal collaboration effectiveness are also needed. because resources for internal services are limited, villages with large populations may not meet the criteria for the availability of cross-border service quotas. furthermore, in better peripheral conditions, the 5 km radius criteria can be reconsidered in light of the distance traveled related to emergency response, particularly in primary health care. however, implementing informal collaboration at the basic service level necessitates some changes to basic services governance. one particular adjustment is associated with service logging, which must be integrated across borders and multiple facilities. in urban areas, the administrative paper works can be integrated using digital computing systems that allow access from multiple locations during the same period of time. the rural provincial border lacks the privileged of technology to support this type of integration. as a result, flexibility in the accountability criteria is required so that informal acts in services can still be distinguished from the classification of misconducts. furthermore, an operational framework is needed to ensure standardize responds of the public service officials towards cross border requests. studies on detailed procedures are needed in various types of rural provincial borders to be able to grasps important points of informal collaborations. 5. acknowledgments first author would like to acknowledge lembaga pengelola dana pendidikan (indonesia endowment fund for education) for financial support during early phase of the research. 6. references akresh, r., halim, d., & kleemans, m. 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(2015). formal land rights versus informal land rights: governance for sustainable urbanization in the jakarta metropolitan region, indonesia. land use policy, 43, 63–73. https://doi.org/10.14710/geoplanning.9.1.25-36 https://doi.org/10.1111/ijsw.12363 127 geoplanning journal of geomatics and planning vol. 8, no. 2, 2021 original research the spatial model of paddy productivity based on environmental vulnerability in each phase of paddy planting rahmatia susanti 1,2, supriatna 1, rokhmatuloh 1, masita d. m. mannesa 1*, aris poniman 1, yoniar h. ramadhani 2 1. department of geography, faculty of mathematics and natural sciences, indonesia university, indonesia 2. geospatial information agency, indonesia doi: 10.14710/geoplanning.8.2.127-136 abstract the national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in indonesia. paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and gis technology approaches. this spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. the spatial model of paddy planting phase obtained from the analysis of vegetation index from sentinel-2a imagery using the random forest classification model. the variables for building the spatial model of the paddy planting phase are a combination of ndvi vegetation index, evi, savi, ndwi, and time variables. the overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. the paddy productivity model obtained from environmental vulnerability analysis with gis using the linear regression method. the variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35. copyright © 2021 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction paddy is food crops that are the staple food for most people in indonesia, so the amount of paddy production will significantly affect food needs in indonesia. physical characteristics of land will have an influence on paddy productivity in a region (widiatmaka et al., 2016). declining land physical factors can detected from environmental vulnerabilities that can cause changes in paddy productivity. environmental vulnerability according to regulation of the head of the national disaster management agency number 2 of 2012 compiled based on natural disaster hazard class or disaster-prone class factors with land use parameters. compiled based on natural disaster hazard class or disaster-prone class factors with land use parameters. bogor regency is one area in indonesia that has environmental vulnerability to natural disasters and a high paddy-producing. bogor regency is known as one of the largest paddy producers in west java province but based on bps data it was detected a decrease in paddy productivity by 4 tons/hectare during 2016-2017 period. the decline in paddy productivity in bogor regency influenced by the location of paddy fields in areas prone to natural e-issn: 2355-6544 received: 14 january 2021; accepted: 1 september 2021; published: 30 december 2021. keywords: environmental vulnerability, paddy productivity, paddy planting phase, random forest classification model, regression model, sentinel-2a *corresponding author(s) email: manessa@ui.ac.id https://doi.org/10.14710/geoplanning.8.2.127-136 mailto:manessa@ui.ac.id susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 128 disasters, like floods, droughts, and landslides. this study focuses on aspects of environmental vulnerability based on natural hazard-prone conditions that can affect paddy productivity in each phase of paddy planting in bogor regency, west java. remote sensing technology and geographic information systems can detect the paddy planting phase quickly, accurately and analyze environmental vulnerabilities to natural disasters. the use of remote sensing technology is widely use in the analysis of agricultural land, especially for continuous monitoring of agricultural land. remote sensing has great potential in monitoring paddy phenology for management and prediction of paddy production (he et al., 2018). utilization of sentinel-2a imagery is appropriate for mapping large-scale paddy fields because it has high spatial and temporal resolution than other remote sensing images such as landsat 8 which has a spatial resolution of 30 meters with 16 daily temporal resolution (dong et al., 2016). another advantage of this satellite image is that 3 (three) bands ‘red edge’ are suitable for identification of vegetation (liu et al., 2018). the use of gis technology can be used for spatial modeling in monitoring the paddy planting phase and estimation of paddy productivity based on environmental vulnerability factors. spatial modeling with gis can produce an algorithm that can be used to estimate paddy productivity in each area of paddy fields (muslim et al., 2015). the purpose of this study was the preparation of a spatial model estimation of paddy productivity based on the influence of environmental vulnerability in each phase of paddy planting. this spatial model is expected to be input in estimating the decline in paddy productivity due to environmental vulnerability in a region in each phase of paddy planting so that prevention or mitigation steps can be more quickly carried out. 2. data and methods this research divided into several stages of work. in the first stage is the preparation stage to determine theme and choose the title of the research to be conducted, after getting the title of research then proceed with the study of literature by looking for a literature review related to this research. the next stage is to collect secondary data, as well as download satellite sentinel-2a imagery. the stages of initial data processing divided into two, namely spatial data processing which processes data on environmental vulnerability variables and processing vegetation indices using sentinel-2a imagery. the initial data processing stage is to process environmental vulnerability variable data, which is analyzing secondary data consisting of drought-prone maps, flood hazard maps, and landslide-prone maps, and conducting spatial analysis of average monthly rainfall to obtain a monthly rainfall map. the next stage is the rasterization and classification of flood-prone maps, prone to drought, landslide-prone, and average monthly rainfall, according to the provisions of each theme. classification of the average monthly rainfall maps using the oldeman category. classification of flood-prone based sni 8197: 2015, classification prone to landslides based on sni 8291: 2016, and classification of drought-prone uses regulation of the head of the national disaster management agency number 2 of 2012. the next initial data processing stage is processing remote sensing data in the form of an analysis of the vegetation index. the vegetation index used is ndvi, savi, evi, and ndwi. ndvi analysis can highlight aspects of vegetation density and use near infrared (nir) and red bands (tucker, 1986 in danoedoro, 2012), as follows https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 129 the evi index is sensitive to changes in biomass and is resistant to canopy effects. the evi index can reduce atmospheric influence because it uses the blue band in calculations that can correct aerosol interference in the red and blue band (huete et al., 2002), the equation is as follows this index emphasizes the background of the soil by minimizing the effect of soil brightness using soil correction factors (huete, 1988). this index uses near-infrared (nir) and shortwave infrared (swir) bands which can detect moisture content which is useful for vegetation studies (gao, 1996). the results of the vegetation index analysis are used to estimate the paddy planting phase which idivided into four classifications, namely the initial planting phase (water dominance), vegetative phase, generative phase and fallow phase (lapan, 2015). the field survey phase was conducted to obtain data related to the paddy planting phase and productivity at each paddy planting period. this field sample was used as a training sample and a sample validation of the spatial model of paddy planting phase and spatial model the influence of environmental vulnerability on paddy productivity. besides, a field survey was conducted to check the physical environmental conditions in terms of areas that have flood-prone areas, are prone to drought, and prone to landslides that will affect paddy productivity. the last stage is divided into two, namely making the spatial model of the paddy planting phase and spatial model the influence of environmental vulnerability on paddy productivity. making a spatial phase of paddy planting model using the random forest classification model. making a spatial model the influence of environmental vulnerability on paddy productivity using a linear regression model. the two results of the model will then be applied to sentinel-2a remote sensing data to see the distribution of estimated paddy productivity in jonggol, cariu, sukamakmur, and tanjungsari sub-districts. 3. result and discussion 3.1. spatial model of paddy planting phase the compilation of the paddy planting phase spatial model uses the random forest classification method. the random forest classification method approach can used for paddy classification by extending the data dimensions in the spectral and temporal domains that influence the characteristics of paddy that has different climate and environmental characteristics (park et al., 2018). the training features used in this study were 3649 sample points spread over 138 locations with 33 periods (march 2017 – may 2018). modeling uses 5 (five) variables, namely the ndvi vegetation index, evi, savi, and ndwi as well as the recording phase of the planting phase. the processing results of random forest classification obtained the percentage of interest from the variables used to build the spatial model. ndwi index has the highest percentage of interest of 26%, ndvi index of 23%, savi index of 23%, evi index of 22% and variable that has the smallest percentage of interest is planting time recording variable which is only 6%. https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 130 the application of random forest classification in mapping paddy fields based on the planting phase has outstanding accuracy of 0.96 using the ndvi vegetation index variable and time variable (onojeghuo et al., 2017). the overall accuracy results in this study were 0.92. the highest accuracy is found in the water phase of 1.00, because in this phase water-dominated paddy fields have index values that are different from other planting phases, both in the ndvi index, evi, savi, and ndwi. the vegetative phase has an accuracy of 0.90, the generative phase has an accuracy of 0.88, and the fallow phase has an accuracy of 0.91. figure 1. vegetation index graph in a paddy planting phase period (data processing, 2019) spatial modeling of paddy planting phase using four combinations of vegetation index and time variables can be simplified by using two combinations of vegetation indexes which have different vegetation index value patterns in the planting phase period (figure 1). the vegetation index pattern in the planting phase period can seen that the ndvi vegetation index, evi, and savi have almost the same pattern, while the ndwi vegetation index is different so that the combination that can be used is the ndwi vegetation index with other vegetation indices. the results of the comparison of accuracy in the paddy planting phase model using a combination of two vegetation indices are shown in table 1. table 1. comparison of accuracy of models with a combination of vegetation index (data processing, 2019) vegetation index combination accuracy initial phase vegetative phase generative phase fallow phase ndwi-ndvi-evi-savi 1,00 0,90 0,88 0,91 ndwi – ndvi 0,99 0,93 0,90 0,91 ndwi – evi 0,93 0,87 0,86 0,91 ndwi savi 1,00 0,92 0,90 0,94 3.2. paddy productivity model based on environmental vulnerability variables of environmental vulnerability are made based on natural hazard-prone conditions that often occur in the study area. dinas tanaman pangan dan holtikultura kabupaten bogor in 2017 noted that the prone to natural disasters that occur on paddy fields in bogor regency is prone to droughts (figure 2), floods (figure 3), and landslides (figure 4). 0 1 2 v eg et a ti o n in d ex v a lu e paddy planting phase period (day) vegetation index in a paddy planting phase period evi savi ndwi ndvi https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 131 figure 2. drought-prone map of jonggol, cariu, sukamakmur and tanjungsari subdistricts (national disaster management agency, 2017) figure 3. flood-prone map of jonggol, cariu, sukamakmur and tanjungsari subdistricts (geospatial information agency, 2017) https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 132 figure 4. landslide-prone map of jonggol, cariu, sukamakmur and tanjungsari subdistricts (ministry of energy and mineral resources, 2017) rainfall variables are also used in the preparation of the paddy productivity model because linear regression models are formed based on temporal data, while secondary data in the form of natural disaster-prone maps issued by data custodians only at maximum prone or only one map in one year. spatial analysis of rainfall is used to determine the distribution of rainfall distribution in each month so that it can determine the effect of rainfall with the planting phase time and paddy productivity. the growth of paddy plants is very dependent on water sources, one of which comes from rainfall that is used to meet the needs of plants for water and good water management is needed for irrigating paddy farms (thakur et al., 2013). rainfall analysis based on oldeman classification shows that jonggol, tanjungsari, cariu, and sukamakmur sub-districts only have dry months and humid months (figure 5). dry months are from may to october, at that time the condition of paddy fields in the condition of the final generative phase is at the time of cooking until the condition of the fallow phase or not during the paddy planting period. humid months are in the period from november to april, at that time is the initial phase of planting in november and march. in the humid months it can be produced two times the paddy planting period. figure 5. average monthly rainfall patterns in jonggol, cariu, sukamakmur and tanjungsari subdistricts (data processing, 2019) 0 50 100 150 a v er a g e m o n th ly r a in fa ll (m m ) month cariu jonggol sukamakmur tanjungsari https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 133 the method used in making a model of paddy productivity is a linear regression model with the ols method or the least-squares method. the ols method will produce an estimator that is unbiased, linear and has a minimum variance (best linear unbiased estimators blue) (frost, 2018). the results of the linear regression model in the form of an algorithm that can be used to estimate paddy productivity based on the conditions of the paddy planting phase. the results of the spatial model in the form of an effective algorithm, one of which is paddy rice planting index (prpi), which is used to map rice paddies nationally in china (song et al., 2018). the algorithm produced in this study is as follows. a. paddy productivity in the initial phase of planting = 66.40 – (1.033 * rainfall) – (0.204 * prone to landslides) – (0.430 * prone to drought) – (0.486 * prone to floods) b. paddy productivity in the vegetative phase = 69.46 – (1.59 * rainfall) – (0.35 * prone to landslides) – (1.33 * prone to drought) – (0.50 * prone to floods) c. paddy productivity in the generative phase = 68.51 + (0.73 * rainfall) – (0.69 * prone to landslides) – (2.15 * prone to drought) – (0.62 * prone to floods) the influence of environmental vulnerability and rainfall in the vegetative phase has an inverse relationship, which decreases the level of flood-prone, prone to landslides, prone to drought and rainfall will result in higher paddy productivity. the increase in minimum temperature, rainfall, and relative humidity has a positive impact on paddy productivity even though it is not significant (grover & upadhya, 2014). in the vegetative and generative phase, drought-prone variables have a large influence on the estimation of paddy productivity, so that if there is an increase in prone drought it will cause a decrease in paddy productivity. rainfall variables also have a large influence on productivity predictions, but in the vegetative phase there is no need for high rainfall or in the dry to moist months. the comparison of the relationship of environtmental vulnerability to paddy productivity in each paddy planting phase can be seen in table 2. table 2. comparison of the relationship of environmental vulnerability to paddy productivity in each paddy planting phase (data processing, 2019) no planting phase variable relationship to paddy productivity adjusted r2 1 initial planting phase rainfall negative 0.35 prone to flooding negative drought-prone negative prone to landslides negative 2 vegetative phase rainfall negative 0.63 prone to flooding negative drought-prone negative prone to landslides negative 3 generative phase rainfall negative 0.61 prone to flooding positive drought-prone negative prone to landslides negative the adjusted r2 results in the vegetative phase and the generative phase has a strong enough relationship to predict paddy productivity using the paddy planting phase classification model (mclean et al., 1980), while the initial phase of planting has a weak or cannot be used to predict paddy productivity. 3.3. spatial model of paddy productivity based on environmental vulnerability in each paddy planting phase the spatial model of paddy productivity based on environmental vulnerability in each paddy planting phase is a combination of paddy planting phase classification models and linear regression models of paddy productivity so that it can be used to estimate paddy productivity spatially. the application of the two models was carried out on sentinel 2a images with recording date 31 in 2017. the results of the paddy planting phase https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 134 prediction produced according to the planting calendar issued by upt jonggol and upt cariu. planting phase based on cropping calendar shows that paddy plants at the end of december 2017 are in the vegetative phase condition for jonggol, sukamakmur, cariu and tanjungsari sub-districts. the results of the spatial modeling of the paddy planting phase on december 31, 2017, were dominated by the vegetative phase in all sub-districts (figure 6). figure 6. planting phase distribution map december 31, 2017 (data processing, 2019) the prediction results of the distribution of the paddy planting phase based on the random forest classification model need to be validated again to determine the product accuracy of the paddy planting phase distribution map on december 31, 2017. validation is done by testing the accuracy using the confusion matrix method. the data used in the accuracy test is the planting phase data from the prediction of the random forest classification model which is used as primary data which then compared with the planting phase data resulting from field data processing. the results of the test using the confession matrix method are of the overall accuracy of 87.5% in sentinel-2a processing images on december 31, 2017. the distribution map of the paddy planting phase on december 31, 2017 is acceptable because the overall accuracy is greater than 85%, which according to the united states geological survey (usgs) that the level of accuracy of classification or minimum interpretation using remote sensing data is 85%. the results of the distribution of the paddy planting phase then calculated for estimating the paddy productivity algorithm. the highest estimated productivity of paddy or greater productivity of 6.17 tons/ha found in mekarwangi village, cariu district. the results of predictions of paddy productivity from both spatial models illustrate that paddy productivity in a region is very influential on the geographical and climate conditions of a region. the geographical conditions of research with varied topography provide different environmental vulnerabilities. this environmental vulnerability illustrates environmental degradation that will affect paddy productivity. appropriate use of paddy plantations and not in areas prone to natural disasters will provide higher paddy productivity. jonggol and cariu sub-districts which have relatively flat topography and have a lot of river flow, making the potential of irrigation in these two sub-districts easier. whereas in tanjungsari and sukamakmur subdistricts faced with limiting factors such as the relatively high topography conditions, the slope is relatively steep so that it has relatively high landslide vulnerability and poor agricultural irrigation factors which are highly https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 135 dependent on rainfall which causes lower paddy productivity. beside geographical conditions, climate conditions also widely affect paddy productivity, especially in paddy fields, whose primary source of irrigation comes from rainfall. this can be seen from the results of the paddy productivity model (figure 7) that produced that the variables prone to drought and rainfall are the variables that have the highest influence on the estimation of paddy productivity and have a direct impact on paddy crops. figure 7. map of estimated productivity of paddy in december 2017 (data processing, 2019) 4. conclusion the spatial model of the paddy planting phase with a beneficial random forest classification method is used to see the distribution of the paddy planting phase in an area. the factors used to estimate the planting phase, namely the combination of vegetation indexes consisting of ndvi, evi, savi, and ndwi are mutually temporal with overall accuracy of the model of 92 %. the spatial model of the influence of environmental vulnerability on paddy productivity which consists of variables prone to flooding, prone to drought, prone to landslides and rainfall in each phase of paddy planting can use as a tool to estimate paddy productivity. the difference in the influence of environmental vulnerability in productivity estimation occurs in each phase of paddy planting. estimation of paddy productivity by spatial modeling influence of environmental vulnerability can be detected in the vegetative and generative phases because it produces a relatively stable accuracy of 0.63 in the vegetative phase and 0.61 in the generative phase. whereas the initial phase of planting cannot use in estimating paddy productivity because it has a weak accuracy of 0.35. the highest environmental vulnerability variables that affect paddy productivity are prone to drought and rainfall. 5. acknowledgements this research was funded by universitas indonesia under research grant puti q3 2020 with grant contact number nkb-4492/un2.rst/hkp.05.00/2020 https://doi.org/10.14710/geoplanning.8.2.127-136 susanti et al. / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 127-136 doi: 10.14710/geoplanning.8.2.127-136 136 6. references danoedoro, p. 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[crossref] https://doi.org/10.14710/geoplanning.8.2.127-136 https://doi.org/10.1016/j.rse.2016.02.016 https://doi.org/10.1016/s0034-4257(96)00067-3 https://doi.org/10.3390/rs10020340 https://doi.org/10.1016/s0034-4257(02)00096-2 https://doi.org/10.1016/0034-4257(88)90106-x https://doi.org/10.1016/j.scitotenv.2018.04.415 https://doi.org/10.2307/1174363 https://doi.org/10.1007/s10661-015-4564-9 https://doi.org/10.1080/01431161.2017.1395969 https://doi.org/10.3390/rs10030447 https://doi.org/10.1016/j.isprsjprs.2018.08.015 https://doi.org/10.1007/s10333-013-0397-8 https://doi.org/10.1016/j.proenv.2016.03.061 41 geoplanning journal of geomatics and planning vol. 8, no. 1, 2021 original research modelling precipitable water vapour (pwv) over nigeria from ground-based gnss bawa swafiyudeen 1*, usman i. sa’i 1, adamu bala 1, aliyu z. abubakar 1, adamu a. musa 2, nura shehu 1 1. department of geomatics, ahmadu bello university, zaria, kaduna, nigeria 2. department of surveying and geoinformatics, nuhu bammalli poly, zaria, kaduna, nigeria doi: 10.14710/geoplanning.8.1.41-50 abstract global navigational satellite system (gnss) over the past and present time has shown a great potential in the retrieval of the distribution of water vapour in the atmosphere. taking the advantage of the effect of the atmosphere on gnss signal as they travel from the constellation of satellite to ground-based gnss receivers such that information (water vapour content) about the atmosphere (mostly from the troposphere) can be derived is referred to as gnss meteorology. this paper presents the spatiotemporal variability of precipitable water vapour (pwv) retrieved from ground–based global navigation satellite system (gnss) stations over nigeria for the years 2012 to 2013. in this paper, the gnss data were processed using gamit (ver. 10.70). the gnss pwv were grouped into daily and monthly averages; the variability of the daily and monthly gnss pwv were compared and validated with the daily and monthly pwv from national centre for environmental prediction (ncep) and monthly rainfall data for the study years respectively. the results revealed that the spatiotemporal variability of pwv across nigeria is a function of geographic location and seasons. the result shows that there is temporal correlation between gnss pwv, ncep pwv and rainfall events. the research also affirms that gnss pwv could be used to improve weather forecasting/monitoring as well as climate monitoring. copyright © 2021 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction global navigational satellite system (gnss) over the past and present time has shown a great potential in the retrieval of the distribution of water vapour in the atmosphere (see for example bevis et al., 1994, 1992; davis et al., 1985; gurbuz et al., 2015; isioye et al., 2016). the potential of gnss in meteorology was first proposed by bevis et al. (1992). ground-based gnss meteorology can be advantageous in numerical weather forecasting. regionally and globally, it can be adopted for climate monitoring and atmospheric research. taking the advantage of the effect of the atmosphere on gnss signal as they travel from the constellation of satellite to ground-based gnss receivers such that information (water vapour content) about the atmosphere (mostly from the troposphere) can be derived is referred to as gnss meteorology (xiaoming et al., 2010; uangaree et al., 2014). water vapour gradiometers, lidar, radio sounds and solar spectrometers are other techniques that can be used for water vapour retrieval, but one disadvantage is that they are expensive unlike their gnss counterpart. in this study, the gamit (herring et al., 2010; li, 2021) has been used to estimate precipitable water vapour over nigeria from ground-based gnss stations. the ionosphere and the troposphere are among the major cause of gnss signal delay as they travel down to the earth surface from constellation of satellite. the ionospheric delay can be mitigated using dual frequency gnss receivers and utilizing its dispersive characteristics (tregoning et al., 1998; wielgosz et al., 2019; perevalova et al., 2020). to the geodesist, the atmospheric errors are a cause for concern but for meteorological studies, the tropospheric error is useful for climate studies and other related applications. e-issn: 2355-6544 received: 1 january 2020; accepted: 1 january 2021; published: 30 july 2021. keywords: ncep, gnss, water vapour, nignet, rainfall *corresponding author(s) email: bswafiyudeen@gmail.com https://doi.org/10.14710/geoplanning.8.1.41-50 mailto:bswafiyudeen@gmail.com swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 42 the delay caused by the troposphere called zenith total delay (ztd) can be divided into two parts (liu et al., 2017). the non-hydrostatic (wet) part (zwd) which is water vapour and temperature dependent component and the hydrostatic (dry) part (zhd) which is surface pressure dependent (gurbuz et al., 2015; tsidu et al., 2015; suresh raju et al., 2007; boutiouta & lahcene, 2013) . the zhd amounts to about 90% of the ztd. 𝑍𝑊𝐷 + 𝑍𝐻𝐷 = 𝑍𝑇𝐷 (1) the path length taken by satellite signal to the receiver can be equally expressed as equation (2). 𝑍𝑇𝐷 = 10−5 ∫ 𝑁(𝑠)𝑑𝑠 (2) where 𝑁 = 106(𝑛 − 1) is the atmospheric refractivity, is refractive index. in practical application, the refractivity can't be computed with high precision. therefore, ztd is routinely obtained from ground-based gnss station by using high precision gnss software like gamit/globk as used in this study. the zhd can be computed using surface meteorological data given in equation (3) (davis et al., 1985; saastamoinen, 1972; chen & liu, 2015). 𝑍𝐻𝐷(𝜌𝑠, 𝜆, ℎ) = 𝜌𝑠(2.2779±0.0024) (1−0.00266 cos(2𝜆)−0.00028ℎ) (3) where 𝜌𝑠 is the surface pressure in mbar, 𝜆 is the latitude of antenna and ℎ is station altitude in kilometre above the ellipsoid. this is a function of latitude. however, zwd is mostly inaccurately calculated due to the dispersed and unpredictable water vapour content in the atmosphere. the errors budget can reach up to several cm at the zenith. it can be calculated by subtracting zhd from ztd, as given in equation (4). 𝑍𝑊𝐷 = 𝑍𝑇𝐷 − 𝑍𝐻𝐷 (4) once zwd is estimated, pwv can be computed. pwd is roughly proportional to zwd, given by equation (5) 𝑃𝑊𝑉 = ∏ − 𝑍𝑊𝐷 (5) ∏ is a proportional constant that is dimensionless. this is given by equation (6) ∏−1 = 10−6(𝑘3𝑇𝑚 −1 + 𝑘2 ′ )𝑅𝑣𝜌𝑣 (6) where 𝑘2 ′ and 𝑘3 are refractivity constants with values 22.1 ± 2.2(k/mb), and 373900 105± 0.012(k2/mb) respectively. rv is gas constant for water vapour with the value 461.524 (jk-1 kg-1) (bevis et al., 1992). the parameter tm in equation (6) is the weighted mean temperature depending on surface temperature ts given by (davis et al., 1985) 𝑇𝑚 = ∫(𝑒 𝑇⁄ )𝑑𝑧 ∫(𝑒 𝑇2)𝑑𝑧⁄ (7) where e is the partial pressure of water vapour and t is absolute temperature of the surface of interest. tm can be estimated from numerical weather model or surface temperature. the most commonly used model for the computation of tm is given by (bevis et al., 1994) 𝑇𝑚 = 𝑎 + 𝑏𝑇𝑠 (8) the coefficients a and b are season and region specific, thus, vary from season to region. isioye et al. (2016) provided tm for nigeria (equation (9)) and the west africa region (equation (10)). also, bevis et al. (1992) provided a tm model as expressed in equation (11). https://doi.org/10.14710/geoplanning.8.1.41-50 swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 43 𝑇𝑚 = 0.5245𝑇𝑠 + 132.12 (9) 𝑇𝑚 = 0.5745𝑇𝑠 + 116.60 (10) 𝑇𝑚 = 0.72𝑇𝑠 + 70.2 (11) 2. methodology 2.1. data, processing, and methods presently, fifteen (15) cors are available across the country as presented in figure 1 (bawa et al., 2017), with the station in kano excluded because of its short data span. the summary of datasets and sources adopted for the study is presented in table 2 (bawa et al., 2017). gamit/globk version 10.7 developed at massachusetts institute of technology (mit), the harvard-smithsonian centre for astrophysics (cfa), scripps institution of oceanography (sio), and australian national university (herring et al., 2010; tsai et al., 2015; kindu, 2017; godah et al., 2020) was used for processing the tracking stations rinex files. it can also be used for atmospheric delays estimation, station coordinates and velocities estimation, functional or stochastic representation of post-seismic deformation, earth orientation parameter and satellite orbits (herring et al., 2010). the processing parameters for accurate estimation are presented in table 1 (bawa et al., 2017). for a better and accurate construction of the numerical weather model (nwm) the vienna mapping function one (1) (vmf1) obtained from everest.mit.edu was used to interpolate hydrostatic and wet mapping function coefficients as a function of time and location. after estimation of daily zenith total delays (ztd) by gamit, pwv values are computed with sh_metutil of gamit using the bevis et al. (1994, 1992) tm model. figure 1. spatial distribution of cors stations in nigeria https://doi.org/10.14710/geoplanning.8.1.41-50 swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 44 table 1. summary of dataset and sources adopted for the study s/n dataset source(s) purpose(s) 1 rinex files associated with 14 selected nignet tracking stations from 1st january 2011 to 31st december 2015 (1826 days) www.nignet.net time series analysis, position and velocity estimate and strain computation 2 nine (9) international gnss services (igs) sites stations from 1st january 2011 to 31st december 2015 (1826 days) ftp://cddis.gsfc.nasa.gov position, velocity and frame realization 3 sp3 precise ephemeris orbits http://cddis.nasa.gov for gamit/globk processing 4 ocean tide loading model(fes2004) ftp://everest.mit.edu/pub/grids correction for ocean tide loading 5 dry and wet mapping function (vmf1) ftp://everest.mit.edu/pub/grids incorporate and estimate tropospheric delay for both dry and wet mapping function 6 atmospheric tidal loading(atl) and non-tidal atmospheric loading(atml) ftp://everest.mit.edu/pub/grids/ correction for tidal and non-tidal atmospheric loading table 2. basic processing parameters parameter description rinex data 30 seconds sampling rate orbital data igs final/precise orbit ocean tide loading fes2004 ionospheric model double difference ionospheric free(if) linear combination adjustment kalman filter tropospheric delay model saastamoinen model elevation cut-off 10o antenna model elev earth tide model iers03 choice of experiment baseline dry and wet mapping function new vienna mapping function (vmf1) atmospheric tidal loading(atl) and non-tidal atmospheric loading(atml) yes observations 30-seconds sampling interval satellite orbits/earth orientation parameters igs final orbits(sp3) and igs final eop products a priori meteorological observation source vmf1 3. results and discussions 3.1. gnss precipitable water vapour the results of the daily average precipitable water vapour for the years 2012 and 2013 as observed from thirteen (13) nignet tracking stations namely: abuz, bkfp, cggt, clbr, fpno, futa, futy, gemb, hukp, osgf, rust, ulag and unec is presented in figure 2 and figure 3. from figure 2, the minimum daily average value of pwv for the year 2012 are 12.41mm, 20.13mm, 28.11mm and 31.63mm for cggt, gemb, abuz and bkfp respectively. the maximum average values of pwv were 55.65mm, 54.46mm, 51.27mm and 49.23mm for fpno, rust, clbr and ulag respectively. furthermore, from figure 3, the minimum daily average value of pwv for the year 2013 is 20.36mm, 27.52mm, 30.24mm and 30.56mm for gemb, abuz, hukp and bkfp respectively, while the maximum daily average values were 56.16mm, 54.43mm, 53.42mm and 48.42mm for rust, clbr, fpno and ulag respectively. stations with low pwv (cggt, gemb, abuz, futy and bkfp) fall within the temperate region of the country. while stations with high pwv (fpno, rust, clbr, unec and ulag) fall within the tropical or coastal region of the country where rainfall is high. https://doi.org/10.14710/geoplanning.8.1.41-50 http://www.nignet.net/ ftp://cddis.gsfc.nasa.gov/ http://cddis.nasa.gov/ ftp://everest.mit.edu/pub/grids ftp://everest.mit.edu/pub/grids ftp://everest.mit.edu/pub/grids/ swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 45 figure 2. daily average pwv from gnss observations for year 2012 figure 3. daily average pwv from gnss observations for year 2013 https://doi.org/10.14710/geoplanning.8.1.41-50 swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 46 figure 4. average daily pwv for stations of interest from gnss and ncep for 2012-2013 3.2. temporal variability of ground based gnss pwv and ncep pwv the temporal pattern of estimated water vapour from gnss observation for the study period is compared with ncep. the temporal correlation of the stations of interest between gnss and ncep is presented in figure 4. zero values indicate no observation available for such day due to the inconsistencies of the tracking stations. though, it can be observed that for stations with minimal data gap, the temporal characteristics of the pwv estimated from ground-based gnss stations is the same for the ncep pwv. the coefficient of correlation between the gnss and ncep pwv for the thirteen stations namely: abuz, bkfp, cggt, clbr, fpno, futa, futy, gemb, hukp, osgf, rust, ulag and unec were 0.4541, 0.3312, 0.1728, 0.0076, 0.009, 0.0026, 0.1464, 0.0144, 0.1831, 0.2098, 0.0125, 0.015 and 0.0067 respectively. the results are summarized in table 3. possible reason for weak correlation between gnss pwv and ncep pwv as indicated by r2 values in table 3 is because of the relatively high spatial grid interval (2.50x2.50) of the ncep, the weighted mean temperature (tm) adopted and non-collocation of the gnss stations with meteorological stations. https://doi.org/10.14710/geoplanning.8.1.41-50 swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 47 table 3. summary of coefficient of correlation between gnss and ncep pwv s/no station id r2 percentage (%) 1 abuz 0.4541 45.41 2 bkfp 0.3312 33.12 3 cggt 0.1728 17.28 4 clbr 0.0076 0.76 5 fpno 0.009 0.9 6 futa 0.0026 0.26 7 futy 0.1464 14.64 8 gemb 0.0144 1.44 9 hukp 0.1831 18.31 10 osgf 0.2098 20.98 11 rust 0.0125 1.25 12 ulag 0.015 1.5 13 unec 0.0067 0.67 3.3. validation with rainfall events the gnss pwv for years 2012 and 2013 are compared with rainfall events obtained from (http://sdwebx.worldbank.org/climateportal/index.cfm?page=country_history_climate&thisccode=nga) for the years 2012 and 2013 and the results presented in figure 5 and 6. the monthly pwv increases with monthly rainfall events and vice-versa. this can be observed in figure 5 and 6. seasonally, for year 2012 the pwv is denser in the months of june july, august and september with 51.51mm, 51.56mm and 47.83mm and 50.61mm respectively which are all within the rainy season. on the other hand, the pwv was found to be less in january, february march and december with 21.03mm, 28.34mm, 28.64 and 24.41mm, respectively. this is presented in figure 5. similarly, for year 2013, the pwv is higher in may, june, july and august, with 46.36mm, 48.43mm, 48.24mm and 48.63mm respectively. on the other hand, the pwv was found to be less in, january, february november and december with 28.42mm, 37.72mm, 29.72mm and 20.98mm respectively. the pwv is higher during rainy season and less during dry season. this is presented in figure 6. in both figure 5 and 6 pwv and rainfall events were found to be high in the months of june, july, august and september and lesser in the months of january, february march and december. figure 5. gnss pwv in comparison with rainfall data for year 2012 https://doi.org/10.14710/geoplanning.8.1.41-50 http://sdwebx.worldbank.org/climateportal/index.cfm?page=country_history_climate&thisccode=nga swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 48 figure 6. gnss pwv in comparison with rainfall data for year 2013 3.4. spatial variation of pwv the figures 7 and 8 show the spatial variation of pwv across nigeria for years 2012 and 2013. the pwv for years 2012 and 2013 is high in south-west, south-east south-south part of the country as they are closer to the tropical or coastal region of the country and some parts of north-central (kwara, kogi, benue and tarabar) and less in north-east, north-central and north-west part of the country. the rainfall data is observed to be more in 2012 than 2013 which indicate the 2012 had more rainfall. this clearly justifies that ground-based gnss stations can be used in climate research and operational weather nowcasting. figures 7. mean spatial distribution of pwv over nigeria for year 2012 https://doi.org/10.14710/geoplanning.8.1.41-50 swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 49 figure 8. mean spatial distribution of pwv over nigeria for year 2013 4. conclusion the daily average precipitable water vapour for years 2012 and 2013 had been estimated from gnss observation and the results show that the pwv is more in southern part as the result of their closeness to the coast and attributed to lower elevation. whereas the pwv was found to be lesser in the northern part of the country which is attributed to higher elevation. with respect to seasonal variability, it was observed that the pwv is denser in rainy months (april to october) and less in dry season (november to march) due to the fact that the troposphere contains lower amount of water vapour in the winter than in summer. in comparison of gnss pwv with rainfall data and ncep pwv for 2012 and 2013, a good correlation was observed both spatially and temporally with respect to rainfall data. as for the ncep pwv, the coefficient of determination of r2 was observed to be more in northern part and less in southern part of the country which indicate weak correlation between the two. it is therefore concluded that gnss can be used in numerical weather assimilation and operational weather now casting. 5. acknowledgement the authors would like to express their profound gratitude to the office of the surveyor general of the federation (osgof) for the gnss data. massachusetts institute of technology (mit) is also appreciated for making gamit/globk a freeware. https://doi.org/10.14710/geoplanning.8.1.41-50 swafiyudeen et al. / geoplanning: journal of geomatics and planning, vol 8, no 1, 2021, 41-50 doi: 10.14710/geoplanning.8.1.41-50 50 6. references bawa, s., ojigi, l. m., & dodo, j. d. 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[crossref] https://doi.org/10.14710/geoplanning.8.1.41-50 https://doi.org/10.1175/1520-0450(1994)033%3c0379:gmmzwd%3e2.0.co;2 https://doi.org/10.1029/92jd01517 https://doi.org/10.1029/rs020i006p01593 https://doi.org/10.5772/60025 https://doi.org/10.1002/met.1584 https://doi.org/https:/doi.org/10.1016/j.eqrea.2021.100028 https://doi.org/https:/doi.org/10.1016/j.jastp.2020.105335 https://doi.org/10.1029/98jd02516 https://doi.org/10.5194/amt-8-3277-2015 https://doi.org/10.1109/iita-grs.2010.5603260 volume 1, no 1, 2014, 1-12 http://ejournal.undip.ac.id/index.php/geoplanning | 1 open access geoplanning e-issn: 2355-6544 pemanfaatan citra satelit untuk penentuan lahan kritis mangrove di kecamatan tugu, kota semarang d.a. ardiansyaha, i.buchorib a universitas diponegoro, indonesia, email: deniesyach@gmail.com b universitas diponegoro, indonesia, email: i.buchori@undip.ac.id abstract: this study aims to mapping the level of degraded land of mangrove forest area in tugu sub-district, semarang, by comparing the results between the landsat 7 etm + images of 2009 and alos avnir-2 in 2009. in determining the degradation of mangrove forest area, we used geographic information systems and remote sensing as a tool of analysis that is based on three (3) criteria; land use type, canopy density, and soil resilience from abrasion. from 2 satellite image data used, it will be supervised image classification using er mapper software to get the criteria type of land use and density of the canopy. for soil resilience from abrasion, we used soil types reclassification techniques, using arcgis software. based on landsat imagery, obtained results 92.22 % of mangrove forest area included in severely damaged condition and 7.78% is included in the category of moderate damage. meanwhile, based on the results of alos image, 77.73 % of mangrove areas in severely damaged condition and 22.27 % are included in the category of moderate damage. from this study, it can be concluded that alos and landsat imagery is good for the determination and identifying critical mangrove area and distribution of mangrove forests, but the degraded land of mangrove maps generated by landsat, less detailed than alos in classification and representation the conditions of critical mangrove area in tugu sub-district. abstrak: penelitian ini bertujuan memetakan tingkat kekritisan lahan hutan mangrove dengan membandingkan hasil antara citra landsat 7 etm+ tahun 2009 dan citra alos avnir2 tahun 2009. dalam penentuan lahan kritis hutan mangrove ini digunakan sistem informasi geografis dan penginderaan jauh sebagai alat bantu analisis yang didasarkan pada 3 (tiga) kriteria, antara lain, jenis penggunaan lahan, kerapatan tajuk, dan ketahanan tanah terhadap abrasi. dari 2 data citra satelit yang digunakan akan dilakukan klasifikasi citra terbimbing dengan menggunakan software er mapper untuk mendapatkan kriteria jenis penggunaan lahan dan kerapatan tajuk. untuk kriteria ketahanan tanah terhadap abrasi menggunakan teknik reklasifikasi peta jenis tanah dengan menggunakan software arcgis. dari penelitian yang telah dilakukan, berdasarkan citra landsat diperoleh hasil 92,22% kawasan hutan mangrove termasuk dalam kondisi rusak berat dan 7,78% termasuk dalam kategori rusak sedang. sedangkan bedasarkan hasil dari citra alos sebanyak 77,73% kawasan mangrove di kecamatan tugu termasuk dalam kondisi rusak berat dan 22,27% termasuk dalam kategori rusak sedang. dari penelitian ini dapat disimpulkan bahwa citra alos dan citra landsat sudah baik untuk penentuan lahan kritis mangrove khususnya dalam identifikasi luasan dan sebaran hutan mangrove di suatu kawasan, tetapi peta lahan kritis mangrove yang dihasilkan oleh citra landsat kurang mempresentasikan secara detail pengklasifikasian kondisi lahan kritis mangrove di kecamatan tugu. 1. pendahuluan hutan mangrove merupakan salah satu ekosistem pesisir tropis atau sub-tropis yang sangat dinamis serta mempunyai produktivitas, nilai ekonomis, dan nilai ekologis yang tinggi. faktor-faktor lingkungan juga berperan penting dalam menentukan keanekaragaman, distribusi, dan peranan secara ekologis dari faunafauna dalam ekosistem hutan mangrove. kadar garam, lama periode penggenangan, dan suhu pada permukaan hutan mangrove menjadi faktor pembatas utama bagi penyebaran fauna yang hidup di dalamnya (susetiono, 2005). secara umum dari hasil survei ekosistem mangrove di kawasan pesisir kota info artikel; diterima: 10 february 2014 hasil revisi : 1 maret 2014 disetujui: 10 maret 2014 publikasi on-line: 25 maret 2014 kata kunci: citra satelit, lahan kritis, mangrove, sig, penginderaan jauh. article info; received: 10 february 2014 in revised form: 1 march 2014 accepted: 10 march 2014 available online: 25 march 2014 keywords: satellite imagery, critical land, mangrove, gis, remote sensing mailto:deniesyach@gmail.com mailto:i.buchori@undip.ac.id geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 2 semarang sudah mengalami degradasi, kerusakan tersebut sejalan dengan perkembangan kota yakni pembukaan lahan di wilayah pesisir untuk budidaya tambak intensif, untuk kawasan permukiman, kawasan industri dan kawasan pelabuhan. berdasarkan kondisi tersebut disimpulkan secara umum kerusakan ekosistem mangrove di kawasan pesisir kota semarang sudah mencapai 90% dan dalam kategori rusak berat/tutupan lahan di bawah 25% (rtrwp kota semarang, 2009). perda kota semarang nomor 7 tahun 2010 tentang penataan ruang terbuka hijau (rth) pasal 27, menyebutkan bahwa luas rth kawasan pantai berhutan bakau wilayah kecamatan tugu ditetapkan sebesar ±225,000 ha. hal tersebut menunjukan bahwa terjadi kesenjangan antara peraturan yang ada dengan kondisi sebenarnya di lapangan. akibat dari degradasi hutan mangrove di kecamatan tugu antara lain adalah selama periode 1991-2010 garis pantai mundur mencapai 1,7 km dengan area genangan mencapai 1.211,2 ha (bappeda kota semarang, 2012). hal tersebut disebabkan oleh hilangnya fungsi mangrove sebagai pemecah gelombang laut penyebab bencana abrasi. tanaman mangrove memiliki salah satu ciri khas yaitu akar berongga sehingga dapat memantulkan gelombang dan menahan sedimen secara periodik sampai terbentuk lahan baru (arief, 2003). setiap citra digital yang dihasilkan oleh setiap sensor mempunyai sifat khas datanya. sifat khas data tersebut dipengaruhi oleh sifat orbit satelit, sifat dan kepekaan sensor penginderaan jauh terhadap panjang gelombang elektromagnetik, jalur transmisi yang digunakan, sifat sasaran (objek), dan sifat sumber tenaga radiasinya. sifat orbit satelit dan cara operasi sistem sensornya dapat mempengaruhi resolusi dan ukuran pixel datanya (purwadhi dan sri hadianti, 2001).dalam penentuan lahan kritis hutan mangrove diperlukan data citra satelit yang digunakan sebagai data. citra satelit tentunya memiliki karakteristik masing-masing bedasarkan berbagai kriteria yang terdapat di citra satelit tersebut. oleh karena itu, diperlukan data citra satelit yang tepat dalam penentuan lahan kritis hutan mangrove.wilayah yang akan di lakukan penelitian adalah7 kelurahan di kecamatan tugu diantaranya yaitu: jrakah, tugurejo, randugarut, karanganyar, mangkan kulon, mangunharjo, dan mangkan wetan. untuk lebih jelasnya dapat dilihat pembagian wilayah studi pada gambar 1 berikut. gambar 1.wilayah studi kecamatan tugu, kota semarang (bappeda kota semarang, 2010) 2. data dan metode pada mulanya, hutan mangrove hanya dikenal secara terbatas oleh kalangan ahli lingkungan, terutama lingkungan laut. mula-mulanya, kawasan hutan mangrove dikenal dengan istilah vloedbosh, kemudian dikenal dengan istilah payau, karena sifat habitatnya yang payau. bedasarkan dominasi jenis pohonnya, yaitu bakau, maka kawasan mangrove juga disebut sebagai hutan bakau. kata mangrove sendiri berasal dari bahasa portugis yaitu mangue yang berarti tumbuhan dan bahasa inggris grove yang berarti belukar atau hutan kecil (arief, 2003). hutan mangrove atau yang biasa disebut hutan bakau merupakan tipe hutan geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 3 yang khas dan tumbuh disepanjang pantai atau muara sungai yang dipengaruhi oleh pasang surut air laut (sk dirjen kehutanan, 1978). hutan mangrove mempunyai ciri-ciri umumnya tumbuh pada daerah intertidal yang jenis tanahnya berlumpur, berlempung dan berpasir; daerahnya tergenang air laut secara berkala, baik setiap hari maupun yang hanya tergenang pada saat pasang purnama. frekuensi genangan menentukan komposisi vegetasi hutan mangrove; menerima pasokan air tawar yang cukup dari darat (bengen, 2000). beberapa fungsi dari hutan mangrove secara fisik (arief, 2003) antara lain untuk menjaga garis pantai agar tetap stabil; melindungi pantai dan tebing sungai dari proses erosi atau abrasi, serta menahan atau menyerap tiupan angin kencang dari laut ke darat; menahan sedimen secara periodik sampai terbentuk lahan baru; sebagai kawasan penyangga proses intrusi atau rembesan air laut ke darat, atau sebagai filter air asin menjadi air tawar. bedasarkan pedoman inventarisasi dan identifikasi lahan kritis mangrove yang diterbitkan oleh departemen kehutanan (2005), suatu lahan mangrove dapat dikategorikan sebagai lahan kritis, apabila lahan tersebut sudah tidak dapat berfungsi lagi, baik sebagai fungsi produksi, fungsi perlindungan maupun fungsi pelestarian alam. kriteria-kriteria yang dapat digunakan untuk penentuan tingkat kekritisan lahan mangrove untuk masing-masing teknik penilaian adalah sebagai berikut (departemen kehutanan, 2005): a) jenis penggunaan lahan (jpl).dalam pedoman inventarisasi dan identifikasi lahan kritis mangrove tahun 2005, interpretasi penutupan lahan menggunakan metode ‘digitize on screen’. metode tersebut digunakan karena objek yang ditafsir berkorelasi kuat dengan objek air,sehingga pantulan air sangat mempengaruhi pantulan objek mangrove. pada kondis demikian, penafsiran secara visual akan lebih menguntungkan karena unsu rsubjektivitas penafsir akan dibantu dengan pemahaman kunci penafsiran. pada kriteria jenis penggunaan lahan ini, dapat diklasifikasikan menjadi tiga kategori (departemen kehutanan, 2005), yaitu: hutan (kawasan berhutan), tambak tumpang sari dan perkebunan, dan areal nonvegetasi hutan (pemukiman, industri, tambak non tumpang sari, sawah dan tanah kosong). selanjutnya, dalam penentuan kerapatan tajuk mangrove ini digunakan forrmula ndvi. prinsip kerja analisis ndvi adalah dengan mengukur tingkat intensitas kehijauan. intensitas kehijauan berkorelasi dengan tingkat kerapatan tajuk vegetasi dan untuk deteksi tingkat kehijauan pada citra landsat yang berkorelasi dengan kandungan klorofil daun, maka saluran yang baik digunakan adalah saluran infra merah dan merah. oleh sebab itu, dalam formula ndvi digunakan kedua saluran tersebut. formula yang digunakan pada ndvi adalah sebagai berikut (landgrebe, 2003): keterangan :  saluran 3 : nilai spektral saluran merah  saluran 4 : nilai spektral saluran inframerah dekat  ndvi : normalized defference vegetation index klasifikasi kerapatan tajuk mangrove ditentukan berdasarkan rentang nilai ndvi hasil perhitungan. jumlah klasifikasi kerapatan mengacu pada buku pedoman inventarisasi dan identifikasi mangrove yang diterbitkan oleh direktorat jenderal rehabilitasi lahan dan perhutanan sosial departemen kehutanan. pembagian klasifikasinya adalah sebagai berikut: 1. kerapatan tajuk lebat (0,43 ≤ ndvi ≤ 1,00) 2. kerapatan tajuk sedang (0,33 ≤ ndvi ≤ 0,42) 3. kerapatan tajuk jarang (-1,00 ≤ ndvi ≤ 0,32) dalam penentuan lahan kritis hutan mangrove ini, jenis-jenis tanah yang dapat diperoleh dari peta land system dan data gis lainnya serta dikategorikan menjadi tiga kategori (departemen kehutanan, 2005), yaitu; jenis tanah tidak peka erosi (tekstur lempung), jenis tanah pekaerosi (tekstur campuran), jenis tanah sangat peka erosi (tekstur pasir). sedangkan menurut kepekaannya terhadap erosi, tanah dapat dibagi ke dalam kelas-kelas sebagai berikut: geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 4 tabel 1. jenis tanah menurut kepekaan terhadap erosi (menteri pertanian, 1980) berdasarkan 3 kriteria penentu lahan kritis hutan mangrove di atas, total nilai skori (tns) dihitung dengan rumus sebagai berikut (departemen kehutanan, 2005): tns = (jpl x 45) + (kt x 35) + (kta x 20) keterangan; tns : total nilai skor jpl : skor jenis penggunaan lahan kt : skor kerapatan tajuk kta : skor ketahanan tanah terhadap abrasi dari total nilai skor (tns), selanjutnya dapat ditentukan tingkat kekritisan lahan mangrove sebagai berikut: • nilai 100 – 166 : rusak berat • nilai 167 – 233 : rusak • nilai 234 – 300 : tidak rusak setelah kriteria diperoleh, penentuan lahan kritis hutan mangrove dilakukan beberapa tahap klasifikasi dalam mendapatkan variabel yang telah ditentukan. dalam penentuan beberapa variabel seperti, jenis penggunaan lahan dan kerapatan mangrove, digunakan teknik klasifikasi terbimbing (supervised classification). teknik tersebut digunakan karena peneliti telah mengetahui kelas dari kriteria-kriteria untuk masing-masing variabel dalam penentuan lahan kritis hutan mangrove. dalam klasifikasi penentuan variabel jenis penggunaan lahan dan kerapatan mangrove diatas tentunya membutuhkan keahlian seorang peneliti dalam observasi visual. para peneliti membedakan menurut perbedaan antara karakteristik geometrik fitur permukaan jelas didasarkan pada pertumbuhan mangrove dan lingkungan sekitarnya. itu penggabungan antara survei dan foto udara dengan bantuan penginderaan jauh dari warna citra, cahaya, fitur geometris, dan lokasi geografis. kemudian melalui manusia-mesin interpretasi interaktif dan menetapkan berbagai fitur permukaan dan citra satelit sesuai dengan karakteristik yang relevan dari gambar digital menggunakan warna mata, penampilan dan penafsiran perbedaan. hal ini sering digunakan dalam klasifikasi mangrove penginderaan jauh (song xue fei et al, 2011).proses pengklasifikasian citra dilakukan 2 kali karena menggunakan 2 data citra yaitu citra landsat 7 etm+ tahun 2009 dan citra alos avnir-2 tahun 2009. dalam penentuan variabel jenis penggunaan lahan, citra landsat dan alos menggunakan kombinasi band 321. kombinasi band tersebut merupakan kombinasi paling umum dalam pengunaan citra karena menampilkan warna citra yang mendekati sebenarnya (true colour). sedangkan dalam penentuan kerapatan tajuk, citra landsat mengunakan komposit antara band 3, 4 dan 5 (murray et al, 2002), dan susunan kombinasi yang dirasa cocok dalam interpretasi habitat mangrove oleh peneliti adalah 453. kombinasi band tersebut merupakan kombinasi band yang umum dalam penggunaannya untuk penelitian vegetasi. dalam kombinasi band tersebut vegetasi dapat dibedakan dengan warnanya yang jingga, dan habitat mangrove sendiri ditandai warna jingga yang lebih gelap. untuk citra alos, kombinasi yang digunakan adalah 432. kombinasi band tersebut digunakan bedasarkan percobaan beberapa kombinasi oleh peneliti, dan kombinasi tersebut yang dirasa cukup baik dalam interpretasi sebaran vegetasi khususnya mangrove dengan ditandai warna merah. kemudian hasil dari pengklasifikasian tersebut digunakan sistem informasi geografis dalam proses overlay hasil dari masing-masing variable yang telah kelas tanah jenis tanah keterangan 1 aluvial, tanah glei planosol hidromorf kelabu, literita air tanah tidak peka 2 latosol agak peka 3 brown forest soil, non calcis brown, mediteran kurang peka 4 andosol, laterit, grumosol, podsol, podsolik peka 5 regosol, litosol, organosol, renzina sangat peka geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 5 diketahui, dari hasil overlay tersebut diketahui peta tingkat kekritisan lahan hutan mangrove di kecamatan tugu, kota semarang. 3. hasil dan pembahasan jenis penggunaan lahan, variabel ini diperoleh dari hasil klasifikasi terbimbing melalui software ermapper untuk setiap data citra yang digunakan yaitu citra landsat dan citra alos dengan menggunakan komposisi band 321. dari data citra tersebut jenis penggunaan lahan dapat dklasifikasikan menjadi 7 kelas atau region, diantaranya adalah hutan, tambak, perkebunan, pemukiman, industri, sawah, dan tanah kosong. dari ketujuh kelas tersebut dapat diklasifikasikan lagi menjadi 3 kelas besar (departemen kehutanan, 2005), yaitu: 1) hutan (kawasan hutan); 2) tambak tumpang sari dan perkebunan; 3) areal non-vegetasi hutan (pemukiman, industri, tambak non-tumpangsari, sawah, dan tanah kosong). gambar 2. klasifikasi penggunaan lahan dengan landsat (analisis, 2014) gambar 3. klasifikasi penggunaan lahan dengan alos (analisis, 2014) geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 6 kerapatan tajuk, diperlukan beberapa tahap analisis dalam penentuan variabel kerapatan tajuk ini dengan bantuan software ermapper dan arcgis. tahap pertama adalah pengklasifikasian secara terbimbingsebaran hutan mangrove di kecamatan tugu. untuk klasifikasi sebaran hutan mangrove dengan menggunakan citra landsat, digunakan komposisi band 453, sedangkan pada klasifikasi sebaran mangrove dengan menggunakan citra alos, digunakan kombinasi band 432. gambar 4.klasifikasi sebaran mangrove dengan landsat (analisis, 2014) gambar 5.klasifikasi sebaran mangrove dengan alos (analisis, 2014) tahap berikutnya adalah proses klasifikasi ndvi untuk mendapatkan klasifikasi kerapatan tajuk di kecamatan tugu. hasil klasifikasi nilai ndvi dibagi menjadi 3 kelas (dephut, 2005), yaitu: 1) kerapatan tajuk lebat (0,43 ≤ ndvi ≤ 1,00); 2) kerapatan tajuk sedang (0,33 ≤ ndvi ≤ 0,42); dan 3) kerapatan tajuk jarang (1,00 ≤ ndvi ≤ 0,32). geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 7 gambar 6. klasifikasi ndvi kecamatan tugu dengan landsat (analisis, 2014) gambar 7. klasifikasi ndvi kecamatan tugu dengan alos (analisis, 2014) dari hasil klasifikasi ndvi menjadi 3 kelas untuk masing-masing citra yang digunakan di atas, kemudian dilakukan penggabungan dengan peta klasifikasi sebaran mangrove di kecamatan tugu untuk masingmasing citra untuk memisahkan anata vegetasi mangrove dan vegetasi nonmangrove. proses ini dilakukan pada program arcgis dengan perintah clip, sehingga didapatkan kerapatan tajuk yang hanya terdiri dai vegetasi mangrove saja. geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 8 gambar 8. analisis kerapatan tajuk mangrove dengan landsat (analisis, 2014) gambar 9. analisis kerapatan tajuk mangrove dengan alos (analisis, 2014) ketahanan tanah terhadap abrasi, untuk analisis ketahanan tanah terhadap abrasi di kecamatan tugu, diperlukan data berupa peta jenis tanah di kecamatan tugu. data peta jenis tanah yang digunakan pada penelitian ini adalah data yang bersumber dari bappeda tahun 2010. dari peta jenis tanah kecamatan tugu, kemudian diklasifikasikan menjadi 3 kelas (dephut, 2005), yaitu: 1) jenis tanah tidak peka erosi (tekstur lempung); 2) jenis tanah peka erosi (tekstur campuran); dan 3) jenis tanah sangat peka erosi (tekstur pasir). jika ditelaah bedasarkan sk menteri pertanian (1980), jenis tanah alluvial dapat dikategorikan menjadi jenis tanah yang tidak peka erosi. dari pernyataan tersebut maka dapat disimpulkan jenis tanah di seluruh wilayah kecamatan tugu dapat diklasifikasikan menjadi jenis tanah yang tidak peka erosi. geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 9 gambar 10. jenis tanah di kecamatan tugu (pemerintah kota semarang, 2014) gambar 11. kepekaan tanah terhadap erosi di kecamatan tugu (analisis, 2014) lahan kritis mangrove, setelah beberapa tahap dilakukan, langkah berikutnya adalah pembobotan atribut (skoring attribute) untuk menentukan lahan kritis mangrove di kecamatan tugu untuk masingmasing peta. pembobotan ini dilakukan pada program arcgis dengan bantuan program ms. excell sebagai alat bantu untuk menuliskan rumus atau formula. setelah dilakuan pembobotan untuk masing-masing peta, kemudian dilakukan penggabungan antara peta klasifikasi jenis penggunaan lahan, peta kerapatan tajuk mangrove, dan peta kepekaan tanah terhadap erosi. penggabungan ketiga peta ini dilakukan dengan menu intersect. geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 10 gambar 12. lahan kritis mangrove berdasarkan citra landsat (analisis, 2014) gambar 13. lahan kritis mangrove berdasarkan citra alos (analisis, 2014) 4. kesimpulan dari penelitian yang telah dilakukan, dapat disimpulkan bahwa kondisi mangrove di kecamatan tugu sangat memprihatinkan. jika dilihat dari hasil klasifikasi lahan kritis bedasarkan citra landsat lebih dari sekitar 92,22% kawasan hutan mangrove termasuk dalam kondisi rusak berat dan sisanya sekitar 7,78% termasuk dalam kategori rusak, sedangkan bedasarkan hasil dari citra alos sebanyak 77,73% kawasan mangrove di kecamatan tugu termasuk dalam kondisi rusak berat dan sisanya sebesar 22,27% termasuk dalam kategori rusak berat. tidak ada satu lokasi pun di kecamatan tugu yang memiliki klasifikasi hutan mangrove yang tergolong dalam kategori tidak rusak. terdapat perbedaan perbandingan antara kondisi lahan kritis dengan kategori rusak dan rusak berat untuk citra alos dan citra landsat. pada citra alos geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 11 perbandingan keduanya hanya sekitar 3:7, akan tetapi untuk citra landsat perbandingannya mencapai 1:9. citra landsat lebih banyak mengklasifikasikan lahan kritis menjadi kategori rusak, hal tersebut mengindikasikan bahwa citra landsat kurang dapat mengklasifikasikan kerapatan tajuk dengan baik. hal ini dapat disebabkan oleh berbagai faktor. salah satunya adalah resolusi spasial yang lebih rendah, sehingga kurang dapat menangkap nilai spektral yang sebenarnya dari klasifikasi ndvi, kemudian diklasifikasikan menjadi kerapatan tajuk yang rendah. jika ditelaah bedasarkan validasi di lapangan, peta lahan kritis mangrove yang dihasilkan oleh citra landsat memiliki nilai validitas sebesar 37,5% dari total titik sampel yang diambil, sedangkan peta lahan kritis mangrove yang dihasilkan bedasarkan citra alos memiliki nilai validitas sebesar 62,5%. maka dapat disimpulkan pula bahwa citra landsat kurang dapat mempresentasikan kondisi lapangan dengan baik karena hanya memiliki nilai validitas sebesar 37,5% dari titik sampel yang divalidasi. akan tetapi jika dilihat dari luasan dan sebaran mangrove, kedua citra yang digunakan sudah dapat mempresentasikan kondisi di lapangan dengan baik. dari beberapa hal yang telah dijabarkan diatas, dapat disimpulkan kembali bahwa kedua citra yang digunakan dalam penelitian ini yaitu citra alos dan citra landsat sebenarnya sudah baik dalam penggunaannya untuk penentuan lahan kritis mangrove dalam hal luasan dan sebaran hutan mangrove di suatu kawasan. akan tetapi peta lahan kritis mangrove yang dihasilkan oleh citra landsat kurang dapat mempresentasikan secara baik untuk detail pengklasifikasian kondisi lahan kritis mangrove di kecamatan tugu. dalam pemilihan atau penggunaan data citra dalam penelitian bedasarkan penginderaan jauh dan atau sistem informasi geografis khususnya dengan subjek penelitian mangrove. citra yang memiliki resolusi spasial lebih besar merupakan data citra yang lebih baik dalam pengklasifikasian kategori lahan kritis mangrove dibandingkan citra yang memiliki resolusi spasial lebih kecil. hal tersebut didasarkan oleh hasil penelitian diatas yang menyatakan bahwa citra alos lebih dapat mengklasifikasikan kerapatan tajuk dengan baik secara detail klasifikasi lahan kritis mangrove dibandingkan dengan citra landsat. pemilihan data citra dengan resolusi spasial yang lebih baik tentunya menjadi prioritas dalam penelitian lahan kritis mangrove baik untuk kalangan pemerintahan maupun akademisi. penggunaan citra landsat yang telah menjadi standar dalam pedoman inventarisasi dan identifikasi lahan kritis mangrove yang dikeluarkan oleh departemen kehutanan harus ditinjau ulang. karena semakin besar resolusi spasial dari data citra yang dipakai untuk penelitian, maka semakin baik dan semakin valid pula hasil yang diperoleh dari penelitian tersebut. jika dilihat dari hasil klasifikasi lahan kritis mangrove di kecamatan tugu dibandingkan dengan rencana tata ruang yang ada, rekomendasi yang dapat diberikan adalah pembangunan tanggul pantai untuk penanggulangan bencana rob dan banjir di kota semarang hanya pada lokasi lahan kritis dengan kondisi rusak berat saja. mengingat pembangunan pantai membutuhkan biaya yang tidak sedikit. sedangkan lokasi lahan kritis dengan kondisi rusak, dilakukan reboisasi atau rehabilitasi hutan mangrove mengingat tumbuhan mangrove membutuhkan waktu untuk tumbuh sehingga dapat menghalau gelombang laut dengan kuat. selain itu, perlu juga adanya penegakan hukum yang tegas untuk berbagai jenis pelanggaran yang dapat menyebabkan kerusakan habitat mangrove di kecamatan tugu pada khususnya dan pesisir pantai kota semarang pada umumnya, baik dalam gangguan fisik, kimia maupun biologis. karena tidak akan berarti perencanaan yang menyangkut tentang mangrove dibuat, akan tetapi dalam implementasi di lapangan masih tidak ada ketegasan. 5. daftar pustaka arief, a. 2003. hutan mangrove fungsi dan manfaatnya. yogyakarta: kanisius. bappeda kota semarang. 2012. lokakarya nasional integrasi adaptasi perubahan iklim dan pengurangan resiko bencana dalam kebijakan, pembangunan dan penganggaran keuangan daerah. badan perencanaan pembangunan daerah kota semarang. bengen, dietriech g. 2000. sinopsis ekosistem dan sumberdaya alam pesisir. bogor: pusat kajian sumberdaya pesisir dan lautan-ipb. departemen kehutanan. 2005. pedoman inventarisasi dan identifikasi lahan kritis mangrove. direktorat jenderal rehabilitasi lahan dan perhutanan sosial. geoplanning 2014,vol: 1, no: 1, 1-12 ardiansyah dan buchori | 12 landgrebe, d.a. 2003. signal theory methods in multispectral remote sensing. new jersey: john willey & sons inc. murray, m.r et al. 2003. “the mangroves of belize. part 1: distribution, composition and classification”. elsevier science b.v. forest ecology and management vol. 174: 265–279. peraturan daerah kota semarang nomor 7 tahun 2010. tentang penataan ruang terbuka hijau (rth). purwadhi dan sri hadiyanti. 2001. interpretasi citra digital. jakarta: grasindo. rtrwp kota semarang. 2009. rencana tata ruang pesisir kota semarang 2009. dinas kelautan dan perikanan kota semarang song xue fei et al. 2011. “remote sensing of mangrove wetlands identification”. elsevier ltd. procedia environmental sciences vol. 10: 2287 – 2293. sk dirjen kehutanan. 1978. surat keputusan direktorat jenderal kehutanan no.60/kpts/dj/1978. direktorat jenderal kehutanan. sk menteri pertanian. 1980. surat keputusan menteri pertanian no. 837/kpts/um/11/1980. kementrian pertanian. susetiono. 2005. krustasea dan molluska mangrove delta mahakam. jakarta: pusat penelitian oseanografi. lembaga ilmu pengetahuan indonesia. pemanfaatan citra satelit untuk penentuan lahan kritis mangrove di kecamatan tugu, kota semarang abstract: this study aims to mapping the level of degraded land of mangrove forest area in tugu sub-district, semarang, by comparing the results between the landsat 7 etm + images of 2009 and alos avnir-2 in 2009. in determining the degradation of man... abstrak: penelitian ini bertujuan memetakan tingkat kekritisan lahan hutan mangrove dengan membandingkan hasil antara citra landsat 7 etm+ tahun 2009 dan citra alos avnir-2 tahun 2009. dalam penentuan lahan kritis hutan mangrove ini digunakan sistem i... 1. pendahuluan hutan mangrove merupakan salah satu ekosistem pesisir tropis atau sub-tropis yang sangat dinamis serta mempunyai produktivitas, nilai ekonomis, dan nilai ekologis yang tinggi. faktor-faktor lingkungan juga berperan penting dalam menentukan keanekaraga... keywords: satellite imagery, critical land, mangrove, gis, remote sensing gambar 1.wilayah studi kecamatan tugu, kota semarang (bappeda kota semarang, 2010) 2. data dan metode pada mulanya, hutan mangrove hanya dikenal secara terbatas oleh kalangan ahli lingkungan, terutama lingkungan laut. mula-mulanya, kawasan hutan mangrove dikenal dengan istilah vloedbosh, kemudian dikenal dengan istilah payau, karena sifat habitatnya y... bedasarkan pedoman inventarisasi dan identifikasi lahan kritis mangrove yang diterbitkan oleh departemen kehutanan (2005), suatu lahan mangrove dapat dikategorikan sebagai lahan kritis, apabila lahan tersebut sudah tidak dapat berfungsi lagi, baik seb... a) jenis penggunaan lahan (jpl).dalam pedoman inventarisasi dan identifikasi lahan kritis mangrove tahun 2005, interpretasi penutupan lahan menggunakan metode ‘digitize on screen’. metode tersebut digunakan karena objek yang ditafsir berkorelasi kuat ... selanjutnya, dalam penentuan kerapatan tajuk mangrove ini digunakan forrmula ndvi. prinsip kerja analisis ndvi adalah dengan mengukur tingkat intensitas kehijauan. intensitas kehijauan berkorelasi dengan tingkat kerapatan tajuk vegetasi dan untuk dete... tabel 1. jenis tanah menurut kepekaan terhadap erosi (menteri pertanian, 1980) setelah kriteria diperoleh, penentuan lahan kritis hutan mangrove dilakukan beberapa tahap klasifikasi dalam mendapatkan variabel yang telah ditentukan. dalam penentuan beberapa variabel seperti, jenis penggunaan lahan dan kerapatan mangrove, digunaka... dalam penentuan variabel jenis penggunaan lahan, citra landsat dan alos menggunakan kombinasi band 321. kombinasi band tersebut merupakan kombinasi paling umum dalam pengunaan citra karena menampilkan warna citra yang mendekati sebenarnya (true colour... 3. hasil dan pembahasan jenis penggunaan lahan, variabel ini diperoleh dari hasil klasifikasi terbimbing melalui software ermapper untuk setiap data citra yang digunakan yaitu citra landsat dan citra alos dengan menggunakan komposisi band 321. dari data citra tersebut jenis ... gambar 2. klasifikasi penggunaan lahan dengan landsat (analisis, 2014) gambar 3. klasifikasi penggunaan lahan dengan alos (analisis, 2014) kerapatan tajuk, diperlukan beberapa tahap analisis dalam penentuan variabel kerapatan tajuk ini dengan bantuan software ermapper dan arcgis. tahap pertama adalah pengklasifikasian secara terbimbingsebaran hutan mangrove di kecamatan tugu. untuk klasi... gambar 4.klasifikasi sebaran mangrove dengan landsat (analisis, 2014) gambar 5.klasifikasi sebaran mangrove dengan alos (analisis, 2014) gambar 6. klasifikasi ndvi kecamatan tugu dengan landsat (analisis, 2014) gambar 7. klasifikasi ndvi kecamatan tugu dengan alos (analisis, 2014) ketahanan tanah terhadap abrasi, untuk analisis ketahanan tanah terhadap abrasi di kecamatan tugu, diperlukan data berupa peta jenis tanah di kecamatan tugu. data peta jenis tanah yang digunakan pada penelitian ini adalah data yang bersumber dari bapp... 4. kesimpulan 5. daftar pustaka 149 geoplanning: journal of geomatics and planning, vol. 11, no. 2, 2024, 149-164 original research traffic noise absorption and propagation in a three-dimensional spatial environment nevil v. wickramathilaka1, 2, uznir ujang1*, suhaibah azri1, tan liat choon1, attygalage r. rupasinghe2 1. 3d gis research lab, faculty of built environment and surveying, universiti teknologi malaysia, 81310, johor bahru, johor, malaysia 2. southern campus, general sir john kotelawala defence university, edison hill, nugegalayaya, sewanagala, sri lanka doi: 10.14710/geoplanning.11.2.149-164 abstract the impact of noise barriers on noise propagation is vital for traffic noise calculations and visualizations. noise barriers create a major noise reduction. green belts are the most common type of noise barrier to mitigate road traffic noise. the width, height, and surface area of leaves a green belt, as well as the noise absorption coefficient of leaves, are vital for noise absorption. this review aims to compare the characteristics and performance of green belts barriers built for traffic noise reduction. individual tree canopies play the main role in absorbing noise in green belts. therefore, identifying the canopy's properties is important. the side scan and nadir scan from the lidar survey were used to detect the tree canopy points cloud. the voxel-based, convex hull, and concave hull methods are used to visualize tree canopies in three-dimensional (3d). concave hull provides an extract fitting surface than convex hull visualization. however, these hull surfaces do not provide accurate estimation of surface area of leaves. further, voxel-based horizontal layers through the voxel-based profiling describes a significant method to calculate surface area of leaves in tree canopies. establishing green belts as barriers is more cost-effective, making the former better for developing countries. copyright © 2024 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction road traffic noise is a major contributor to overall noise pollution (subramani et al., 2012) and traffic noise creates 90% of urban noise pollution (gilani & mir, 2021; halim et al., 2018; islam et al., 2021; kurakula & kuffer, 2008). noise barriers are the major form of noise reduction in sound propagation areas (can et al., 2008; guarnaccia et al., 2012). traffic noise travels in all directions from the source point, so it affects a 360-degree range (almansi et al., 2024; huang et al., 2018; wickramathilaka et al., 2023). therefore, the three-dimensional visualization of noise barriers is vital for predicting the performance of noise barriers (dubey et al., 2022; pamanikabud & tansatcha, 2009). recently, traffic noise visualization scenarios have been implemented using noise-reduction barriers in urban areas (jamrah et al., 2006; murthy et al., 1970; peng et al., 2021; tobollik et al., 2019; yang et al., 2020). trees can be used as noise barriers along roads. especially tree leaves absorb road traffic noise (wickramathilaka et al., 2024). the structures of trees such as group of trees, isolated trees and tree belts act as different ways to block the noise (wickramathilaka et al., 2024). tree belts along roads have long been identified as having noise reduction potential than other tree structures (van renterghem, 2014; wickramathilaka et al., 2024). the width e-issn: 2355-6544 received: 19 december 2023; revised: 24 september 2024; accepted: 28 october 2024; available online: 30 november 2024; published: 04 december 2024. keywords: green belts, noise barriers, noise propagation, tree canopy detection, tree canopy three-dimensional visualization *corresponding author(s) email: mduznir@utm.my https://doi.org/10.14710/geoplanning.11.2.149-164 mailto:mduznir@utm.my wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 150 and height of a tree belts are impact to reduce noise (wickramathilaka et al., 2024). however, the performance of individual trees in tree belts is significant in terms of noise mitigation. in particular, tree leaves absorb road traffic noise (karbalaei et al., 2015; kowalska-koczwara et al., 2021; samara & tsitsoni, 2011; tang & ong, 1988), and the amount of noise absorption depends on the noise absorption coefficient of the leaves, their surface area of leaves, and the depth of tree (watanabe & yamada, 1996). the noise absorption coefficient of the leaves varies with the size, texture, and thickness of the leaves. in addition, as the leaves become wider and thicker, so does the amount of noise absorption (joshi et al., 2013). the amount of greenery in a leaf improves its capacity to absorb sound (jang, 2023). because they usually have dense foliage all year round, evergreen trees are better suited to serve as a continuous sound barrier than trees with dried leaves (jang, 2023; samara & tsitsoni, 2011). when the leaves dry out, some of their flexibility and density are lost. however, compared to evergreen foliage, dry leaves might not be as successful at reducing noise (jang, 2023). younger leaves often have higher moisture content, are softer, and are more flexible than older leaves. compared to older leaves, younger leaves are often softer, more flexible, and have higher moisture content. thus, the noise absorption coefficient depends on several factors of the leaves. therefore, several studies have suggested an experimental method of impedance tube to identify noise absorption coefficient of leaves. this review paper shows more information about identifying noise absorption coefficient of leaves. furthermore, the surface area of the leaves is vital to absorb noise. it means that the canopy of the tree is prominent. the road traffic noise absorption equation describes the depth of tree is vital to absorb noise (watanabe & yamada, 1996), identifying surface area of leaves is not an easy process. because leaves spread in a 3d space. thus, this study tries to convey information about finding the surface area of leaves to identify noise absorption. to identify the noise-mitigation performance of tree belts, it is vital to construct individual trees in a three-dimensional space using their actual dimensions. 3d visualization of a canopy is essential to identify surface area of leaves, and depth of trees accurately. therefore, this study demonstrates the visualization of the 3d tree and their accuracy comparison for traffic noise absorption. but the visualization of trees is still an issue. recently, 3d points clouds have been widely used for the visualization of tree canopies (itakura & hosoi, 2018; parmehr & amati, 2021), and a combination of terrestrial laser scanning and drone survey techniques are vital for the detection of tree canopies (shimizu et al., 2022). furthermore, developments of terrestrial scanning survey to identify the surface area of leaves are described in this review. the depth of the tree can be found directly from the 3d point clouds. however, there are several methods to calculate the surface area of leaves through the surface generation of the point clouds. furthermore, finding an exactly fitting surface with canopy point clouds enhances the accuracy of the canopy properties (suwardhi et al., 2022). recently, the convex hull, concave hull (kempf et al., 2021)and voxelbased methods have been widely used for surface fitting for point clouds (suwardhi et al., 2022). somehow, several studies have demonstrated the convex using the slice method to identify canopy properties. the visualization of a tree canopy using a convex hull and concave hull forms a surface mesh, and a cartographic generalization of the mesh demands that a tree canopy has a real three-dimensional appearance (li & nan, 2021). although several noise studies are available on mitigating traffic noise pollution in urban areas, the problem remains the same. green areas are vital for reducing traffic noise levels through their absorption properties. in particular, tree belts are more effective in terms of noise reduction than isolated trees. if green spaces are manipulated for noise mitigation, it is essential to extract the properties of trees into 3d space. further, the detection of tree canopies using modern survey techniques and their visualization in a three-dimensional environment are important to identify the noise reduction by trees. the main objectives of this review are to address the problems mentioned above and find applicable solutions by reviewing previous research. 2. methodology for this paper, two hundred (200) research papers were collected to review traffic noise absorption of trees and 3d tree visualization to identify traffic noise absorption. one hundred (100) of the best research papers were selected to review under the subtopics; traffic noise absorption and trees, noise absorption coefficient of trees, https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 151 tree canopy detection and visualization, tree canopy 3d visualization, concave hull and convex hull, 3d tree visualization developments, and 3d tree visualization object modeling. figure 1 shows the flow chart of the methodology used in this review paper. figure 1. flow chart of the methodology 3. results and discussion 3.1 traffic noise absorption and trees well-grown vegetation belts are an effective part of the mitigation of road traffic noise levels (maleki et al., 2010). therefore, the identification and recommendation of suitable plants for vegetation belts are vital (pathak et al., 2011). traffic noise is reduced by 50% when vegetation is improved from minimum to moderate vegetation density, and when vegetation barriers increase from moderate to dense, this can reduce noise by 9 db(a)-11 db(a). a 5 m deep vegetation belt was identified to be the general depth needed to mitigate of traffic noise (ow & ghosh, 2017). previous research has shown that trees can absorb 5 to 10 db(a) of road noise, which 10–24% of the pollution caused by traffic noise (li & xie, 2021). to determine the trees' ability to absorb traffic noise, comprehensive data about the trees is required (kalansuriya et al., 2009). furthermore, several studies have shown that road noise is reduced by tree belts. this study measured traffic noise levels at 5, 10, and 20 meters away from moving cars using three different planting schemes: minimal, medium, and dense. table 1 shows the research findings. the findings showed how much noise reduction along the roadsides with and without trees. at 5 m, the width of the tree belt, the noise reduction was 2, 3, and 2 db(a) in relation to the site. furthermore, at a tree belt width of 10 m, the reduction in noise was 1, 2, and 2 db(a) with respect to the site. furthermore, at 20 m tree belt width, the site noise reduction was 4, 8, and 6 db(a) (ow & ghosh, 2017) table 1 shows the noise absorption of trees. in this paper, the identification of tree-based noise absorption is discussed. however, determining noise absorption remains a challenge. noise levels were measured in this investigation using a sound level meter. but there is no proper formula for figuring out whether trees in tree belts absorb noise or not from these kinds of investigation. standard noise formulae should be used to measure noise levels from road traffic to ensure study accuracy. for leaves to absorb road noise, their noise absorption coefficient is essential. the sizes, thicknesses, https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 152 and textures of leaves are the main components that absorb noise (joshi et al., 2013; watanabe & yamada, 1996). in addition, the depth of a tree's canopy and the surface area of its leaves are ideal for absorbing noise. furthermore, compared to dry leaves, green leaves are more effective in absorbing noise (safikhani et al., 2014). surface area, depth, noise-absorption coefficient, and traffic noise frequency have an impact on the ability to absorb noise. an equation has been developed to determine the amount of noise absorption by trees. equation 1 shows the noise absorption of leaves. a = −10log (1 − ( 𝐺 × 𝐹 × 𝐿 × 𝑓0.5 8 ))……………. (eq.1) where: g – coefficient (the frequency-absorption factor of leaves), f – the surface area of leaves for unit volume, l – the depth of the tree, f – the frequency of the road-traffic noise table 1. noise absorption based on different sites site at source db(a) difference between source & at 5m 5 m from source db(a) difference between 5 and 10m 10m from source db(a) difference between i0 and 20 m 20 m from source db(a) total reduction db(a) minimal planting scheme 78 1 77 2 75 1 74 4 sparse to medium planting scheme 73 3 70 3 67 2 65 8 dense planting scheme 67 2 65 2 63 2 61 6 the size, thickness and texture of leaves are important factors in the absorption of noise; therefore, the noise absorption coefficient of the leaves depends on these attributes. the canopy of a tree affects noise absorption because the leaves cover a larger area than the bark and branches. tree belts are a more effective way to absorb noise than isolated trees. when measuring noise absorption, factors such as leaf surface area and tree depth accuracy have a significant impact. the findings indicate that a tree belt's depth, width, and tree-to-tree spacing of a tree belt are the main determinants of how much road noise it blocks (ow & ghosh, 2017; peng et al., 2014). furthermore, tree structures such as tree belts and groupings of trees reduce traffic noise pollution better than single, isolated trees (ow & ghosh, 2017; wickramathilaka et al., 2022). in addition to covering larger areas than tree bark and branches, leaves are essential to increase noise absorption; therefore, it is important to concentrate on leaves to absorb noise (dobson & ryan, 2000). this means that the canopy of a tree is vital for absorbing noise. furthermore, traffic noise attenuation through 10 m to 20 m width of tree belts was found to be 2 db to 3 db (with a tree spacing of less than 0.5 m), while it was up to 7 db through 120 m tree belts of eucalyptus vegetation (with a tree spacing greater than 0.5 m) (huddart, 1990; ow & ghosh, 2017; peng et al., 2014). moreover, researchers have found that a narrow belt of dense conifer vegetation reduced noise by 5 db through 3m (kragh, 1981; ow & ghosh, 2017). the height, width and density of a tree belt are the most important factors for noise reduction, rather than characteristics of leaf sizes and branches. a confirmed width (at least 30 m) has a positive impact on noise reduction, while height provides greater opportunities for noise reduction because high tree belts consist of a greater surface area (fang & ling, 2003). research by fang & ling (2003), found that shrubs offered greater noise reduction. therefore, both trees and shrubs should be taken advantage of in the context of noise mitigation. 3.2 noise absorption coefficient of trees the sound absorption coefficient refers to the acoustical effectiveness of a material and the incident sound energy absorption (bohatkiewicz, 2016). a sound absorption coefficient value closer to zero means poor noise absorption (joshi et al., 2013). thicker, denser, and heavier materials are the main factors for noise absorption (karlinasari et al., 2012; tudor et al., 2020). the leaves of trees are more effective for noise absorption than their https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 153 trunks. leaves have several acoustic properties like absorption, scattering and reflection, but noise absorption is a more effective acoustic property of leaves. the size, shape, thickness and texture of leaves are the factors affecting their noise absorption coefficient (watanabe & yamada, 1996). a study was carried out to estimate the noise absorption coefficient of leaves. various leaf thicknesses (1 cm, 1.75 cm and 2 cm) and sizes (0.5 × 0.5 cm2, 1.0 × 1.0 cm2 and 2.0 × 2.0 cm2) were used under different frequencies in the impedance tube (jung et al., 2020). as a result of this study, the noise absorption coefficient of twenty-two (22) leaves is shown in figure 2. moreover, the acoustic properties of leaves depend on the frequency of the noise, and the noise absorption coefficient varies with different noise frequencies (joshi et al., 2013). source: joshi et al., 2013 figure 2. noise absorption coefficient of leaves 3.3 tree canopy detection and visualization well-grown trees are vital for the mitigation of road traffic noise levels (margaritis et al., 2018). the canopy of a tree is an effective biomass for noise absorption because it consists of leaves (pathak et al., 2008). therefore, detecting the tree canopy (jichen et al., 2017) is very important for identifying the amount of noise absorption using the parameters of the surface area of leaves and tree depth (watanabe & yamada, 1996). lidar point clouds are widely used for decision-making processes in vegetation operations (wulder et al., 2008). the distribution of lidar points in the canopy provides reliable information, based on individual trees (wallace et al., 2012). mini-unmanned aerial vehicles (uav) are a platform to use when conducting high spatial resolution surveys for tree canopy height detection (wallace et al., 2012). in order to assess canopy height, a set of quantitative statistics was used for each point cloud (donoghue et al., 2007; lim & treitz, 2004). however, the effect of flight conditions on measuring vegetation is still being examined. it is recommended to use a lower flying height, a small survey area and high point densities for mini-uav surveying (disney et al., 2010; goodwin et al., 2006; lovell et al., 2005). these studies described using the beam divergence, point density, scan angle and internal properties of scanners for the detection of individual trees. lovell et al. (2005), found that the measurements of tree heights were formulated using a higher point density. furthermore, this study described how the use of a large scan angle reduced the number of lower canopy returns and helped with a large canopy cover (hao et al., 2021). the results obtained by wallace et al. (2012), showed the points cloud of a tree canopy generated at an average flying height of 48.3 m; the first (blue), second (green), and third (red) return signal points were shown in point clouds. the multi-rotor drone with an ibeo lux laser scanner was used for this study. the histograms of the above-ground level of the lidar return signals from the point clouds were captured at different flight heights (30 m, 50 m, 70 m and 90 m). the point cloud density for an individual tree was significantly different from different flying heights (wallace et al., 2012). figure 3 shows an example of the point clouds of a tree canopy. https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 154 source: wallace et al., 2012 figure 3. an example of the point clouds of a tree canopy the vegetation heights returned over a single plot for point clouds were captured at above-ground flying heights of a) 30 m, b) 50 m and c) 70 m. there were an obvious attenuation of the upper canopy returns due to the flight altitude. figure 4 shows the canopy point clouds at various flying heights. source: wallace et al., 2012 figure 4. an example of the point clouds of a tree canopy terrestrial laser scanning (tls) is used to obtain precise information about trees (zhong et al., 2016). it enables the extraction of tree information such as the crown size, tree height and crown base height more easily than a uav survey (hillman et al., 2021). tls systems can be adapted to measure the actual shape of a tree canopy accurately, and tls data can be used to delineate the boundaries of the canopy. typically, tls does not measure the horizontal top view of the canopy or the top view of the canopy due to its side scanning. therefore, embedding the nadir perspective and tls approaches are vital for capturing tree canopy details (paris et al., 2017). figure 5 shows the side scanning and nadir scanning of a tree canopy. source: paris et al., 2017 figure 5. side and nadir scanning of tree canopies https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 155 3.4 tree canopy 3d visualization the voxel-based method, the convex hull by slices and the 3d convex hull are the methods usually used for the 3d visualization of a tree canopy (yan et al., 2019). three-dimensional tree modelling is an indispensable part of real-world visualization (zhang et al., 2022). however, due to the irregular and intricate structure of trees, large-scale 3d modelling is impossible (xu et al., 2021). nevertheless, to identify the traffic noise absorption of trees, accurate visualization is essential. a mesh surface is often used for vegetation modelling and this leads to a time-consuming modelling process (tang et al., 2013). unlike the triangular mesh model, the voxel model consists of a voxel grid model. recently, voxel grid modelling has been used for individual tree modelling, and the voxel size is important for 3d modelling (li et al., 2017). the voxel size can be defined according to the density of the point clouds and the user requirements (hancock et al., 2017). however, researchers are searching for standardised modelling methods for voxels (chakraborty et al., 2019; zhang et al., 2022). the study by park et al. (2010) used k-dimensional tree (kd-tree) algorithms for the voxelisation of point clouds. traditionally, to estimate the canopy properties, the boundary box was used, which was a simpler method. gaps in the canopy structure are not considered in the boundary box model (smart & robinson, 1991). voxelization algorithms are usually based on classifying points into three-dimensional grid voxels (fernándezsarría et al., 2013), and all analysis vox functions are provided in the voxr package (béland et al., 2014). a simple voxelisation can be fully filled by rounding the point coordinates of the three-dimensional cartesian system: (coord (x, y and z) *res)/res, where res is the voxel resolution. the vox function output is discrete, and the centre of each voxel and the number of points is represented within each voxel (fernández-sarría et al., 2013; lecigne et al., 2018). figure 6 shows a voxel representation of tree canopies using voxel, resolutions of 0.1 m (a), 0.5 m (b), and 1 m (c). source: lecigne et al., 2018 figure 6. voxel representation and resolution of a tree canopy in the convex hull by slices method, the point clouds are divided into several irregular planes. all the point clouds are formulated in the direction of the z-axis, according to a certain interval. this interval depends on the density of the point clouds. if the interval is too large, the volume estimation is not accurate. however, if the interval is too small, the calculation may be too complicated. in processing, the interval is given as one to five times the point density. finally, the overall canopy volume can be estimated by summing the volume of each slice (li et al., 2016). figure 7 illustrates the use of the convex hull by slices method for the volume estimation of a tree canopy. the 3d convex hull method is a mesh method used to visualise the canopy of a tree in 3d, including the creation of minimal vertex and points enclosed by external planes. the convex hull volume is calculated using the boundaries of planes, which consist of many delaunay triangles (yan et al., 2019). figure 8 illustrates the volume estimation using the 3d convex hull method. https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 156 source: lecigne et al., 2018 figure 7. volume estimation from the 3d convex hull, the convex hull by slices figure 8. volume estimation from the 3d convex hull 3.5 concave hull and convex hull the estimation of tree canopy measurements depends on the raster and vector visualization (dunbar et al., 2004). the raster visualization of point clouds depends on the voxel, but the canopy parameters may vary with the voxel size. by contrast, tree canopy point clouds from vectors use networks of irregular triangles with a higher level of accuracy (soma et al., 2021). the well-known convex and concave hull methods, in particular, are used for tree canopy estimation (colaço et al., 2017; yan et al., 2019). parmehr & amati (2021), compared the convex hull and concave hull representations of tree canopy point clouds. the canopy visualization was projected onto a two-dimensional plane to calculate the maximum diameter and area of a tree canopy. figure 9(a) describes the surface fitting of a tree canopy convex hull in red and a concave hull in blue. whereas a convex hull provides an overestimated volume, a concave hull provides an extract-fitting surface to the point clouds while managing accuracy and reliability (parmehr & amati, 2021). figure 9(b) illustrates the 3d visualization of a tree canopy from a convex hull, and figure 9(c) illustrates the 3d visualization of a tree canopy from a concave hull. (a) (b) (c) source: parmehr & amati, 2021 figure 9. a) describes the surface fitting of a tree canopy convex hull in red and a concave hull in blue; b) illustrates a 3d visualization of a tree canopy from a convex hull; c) illustrates a 3d visualization of a tree canopy from a concave hull 3.6 3d tree visualization developments convex hull, concave hull, and voxel-based are the methods to create a surface for the tree canopy. however, these methods are not shown a clear identification of the total surface area of leaves in the tree canopy. therefore, tree visualization development is vital. several studies have developed mathematical methods to identify the leaf area density (lad) of a tree canopy. the surface area of leaves in unit volume is prime to calculate, traffic noise absorption from the tree canopies. the studies of hosoi & omasa (2006), kargar et al. (2019), and gu et al. (2022) and, have conducted studies to identify the lad of a tree canopy using lidar point https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 157 clouds. points cloud segmentation is required to select leaves' points cloud separately. normal of returns associate the surface structure in unorganized point clouds. figure 10 is illustrated the difference between normal distributions of leaves and branches. source: gu et al., 2022 figure 10. difference between the normal distribution of leaves and branches the normal changing difference is small in the leaf points cloud. according to equation (2), n (p) is the normal of the point cloud in pth point. r is the average distance between two leaves. the leaves points cloud can be extracted using the amount of δn. δn (𝑝, 𝑟) = ( 1 𝑁 ) ∑ (𝑛(𝑝) − 𝑛(𝑝𝑖))𝑁 𝑖=1 ………... (eq. 2) moreover, the points cloud is segmented into layers on the horizontal direction (x-axis), and lad is calculated using voxel-based canopy profiling (vcp). vcp describes voxel-based profiling (see figure 11) between two horizontal layers in a points cloud. source: gu et al, 2022 figure 11. horizontal voxel layer of leaf point clouds 𝐿𝐴𝐷(ℎ, δℎ) = 𝛼(𝜃) ( 1 δh ) ∑ 𝑛1(𝑘) 𝑛1(𝑘)+ 𝑛𝑝(𝑘)′ 𝑚ℎ+δh 𝑘=𝑚ℎ ………... (eq. 3) according to equation (3), where δh is the thickness of the layer, and mh and mh+δh indicate the voxel coordinates on the vertical direction (y-axis), θ is the zenith angle of the lidar beam. the heights h and h + δh, and n1(k) and np(k) represent a number of voxels consisting of leaf points and excluding. respectively, n1(k) + np(k) denotes the total number of incident laser beams in the k-th layer. the α(θ) is a correction factor of the leaf inclination angle. 3.7 3d tree visualization object modeling the concept of object modeling for a tree canopy is not widely used to estimate the volume of the canopy. object modeling does not represent an extract fitting surface to canopy points cloud. but it derives some https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 158 geometric shapes from tree canopies. therefore, volume estimation of tree canopies from object modeling is not an accurate method. but object modeling is vital for enhancing the visual quality (geometric shapes). computer programing methods are inserted to identify the area of the canopy. canopy volume calculation is vital to find noise absorption by trees. tree height, canopy diameter, and canopy height are the parameters for object modeling. biomass estimates from canopy volume (becvol), and arbour structure (arborstruq) are the methods of corresponding object modeling for tree canopies (melville et al., 1999). arborstruq is a more user-friendly method than becvol, and a complex algorithm is used in becvol. there are several geometric shapes have been derived in the arborstruq method (see figure 12), and corresponding equations of geometric shapes to find the volume is described in figure 13. where, x is the total tree height in centimeters, y is the height of the lowest leaves in centimeters, r is the greatest canopy diameter in centimeters, and h is the variable height. source: melville et al., 1999 figure 12. geometric shapes of the tree canopy figure 13. equation to find the volume of geometric shapes 3.8 discussion increasing green spaces is a cost-effective method of mitigateing noise pollution from road traffic noise. according to the recent study of gharibi & shayesteh (2024), noise reduction of green areas is done by two approaches. the study has found the variation of traffic noise with and without tree noise barriers. according to the result of this study, green sound barriers reduce noise by 0 to 4.5 db. on average, the mean noise reduction by green barriers is 0.1–6.4 db which is 9–11 db in the study of ow and ghosh. however, this study does not describe the impact of green spaces for noise reduction in a 3d space. in particular, moderate and high-thickness green belts along roads are more effective than isolated trees in introducing traffic noise. the study by sultan et al. (2024), has found the quantity of sound absorption by a vegetation barrier is related to the width and height of the vegetation barrier. although several studies have discussed the exact general depth (5 m) needed for minimizing traffic noise, the mitigation depends on the width and height of the tree belt and the type of tree. accurately determining the width and height of a tree belt to mitigate highway noise pollution is challenging in practical scenarios. however, a suitable height and width for the tree belt can be determined after visualizing the trees in a 3d environment. according to the study of wickramathilaka et al. (2024), a method has been demonstrated and developed to identify noise absorption by tree belts through a 3d visualization of trees. in addition, tree spacing and surface areas are vital for noise reduction. it is not possible to identify the impact of tree spacing for road traffic noise reductions, the study of variety of trees for sound attenuation, and it was succeeded in minimizing traffic noise pollution in urban cities (martínez-sala et al., 2006). the study of yofianti & usman (2021) has mentioned that not only trees but also shrubs offer greater noise reduction, so a combination of both trees and shrubs should be employed to achieve the maximum noise reduction through green areas. when considering individual tree properties, the size, thickness, and texture of the leaves are important for noise absorption. in particular, the noise absorption coefficient of the leaves directly relates to the noise absorption. a leaf will vibrate at a particular (resonant) frequency when the sound's wavelength is comparable to that of the leaf. sound energy is converted to heat during this process. this means that the sound energy is reduced (romanova et al., 2019). in conclusion, the optimal noise reduction effect would probably be achieved https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 159 by combining large, textured, and fairly thick leaves. for real-world uses, think about how these elements could be used into sound barriers or landscape architecture. furthermore, compared to dry leaves, wet leaves often have a higher sound absorption coefficient. the density of the leaf material can increase due to the dampness, increasing its capacity to absorb sound energy (ali et al., 2020). in general, the frequency of traffic noise is not constant and varies with the speed, amount, and composition of traffic. it indicates that in an urban setting, the frequency of road traffic noise is dynamic. the noise absorption coefficient of the leaves varies with the frequency of the noise. therefore, different noise frequencies are needed to identify the average noise absorption coefficient of leaves (adhika et al., 2023). therefore, identifying the noise absorption coefficient of leaves using an impedance tube is important to determine the noise absorption of trees. in general, varied leaf thicknesses )1 cm, 1.75 cm, and 2 cm) and sizes (0.5 × 0.5 cm2, 1.0 × 1.0 cm2, and 2.0 × 2.0 cm2) are used in impedance tube tests. however, to calculate the noise absorption of leaves, the frequency absorption factor of leaves is vital. therefore, the standard noise equation can be used to identify the frequency absorption factor of leaves through the noise absorption coefficient of leaves. to identify the performance of trees as noise barriers, detecting and visualizing the tree canopy are crucial because the tree canopy consists of leaves. the surface area of leaves is vital for noise absorption. when considering individual trees, the surface area of the leaves and the depth of the trees are vital to noise absorption. a combination of terrestrial laser scanning (tls) for side scanning and mini-unmanned aerial vehicles (uav) for nadir scanning provides a high spatial resolution survey for tree canopy detection. tls is better for detecting the point clouds of a tree. uavs (using first-and last-return signals) are better for detecting the depth of a tree. for that identification of the surface area of leaves is vital. the voxel-based, convex hull by slices and 3d convex hull methods are usually used to visualize a tree canopy in 3d. recently, voxel grid modelling has been used for individual tree modelling, and the voxel size is important for 3d modelling. however, gaps in the tree canopy structure are not visualised in this voxel-based method (ross et al., 2022). decreasing the voxel size enables accuracy of visualization. to avoid voxel complications, the convex hullslice method is one solution (dong et al., 2021). in this method, all the point clouds are formulated in the direction of the z-axis according to a certain interval. in processing, the interval is taken as one to five times the point density. due to the inaccurate volume estimation of the convex hull using the slices method, the minimal vertex visualization (3d convex hull) method is the solution required to eliminate this inaccurate volume estimation. not only the convex hull method, the concave hull method can also be used for tree canopy estimation. however, a convex hull provides an overestimated volume, while a concave hull provides an extract-fitting surface to the point clouds while managing accuracy and reliability. as a development of tree canopy visualization, a modern approach has been developed to find the surface area of leaves using horizontal voxel-based layers. here, the size of the voxel is the average surface area of a leaf. furthermore, this approach describes the surface area of the leaves instead of the total canopy area. however, simple geometric object modelling for tree canopies alone can be used to visualize tree canopies. geometric object modelling provides inaccurate calculation of the surface area of leaves. however, it is mentioned in this review paper, the 3d tree visualization developments provide higher accuracy to identify the surface area of leaves. the laser scanning proceedings of this method is prime to detect leaves of a canopy. 4. conclusion recently, road traffic noise pollution has become a major social issue in both developed and developing cities. although developed cities have addressed these problems robustly, developing countries are not focusing on noise pollution issues due to the economic pressure they are facing. the leaves of trees act as natural sound barriers, absorbing a significant amount of noise. tree belts, tree groupings, and lone trees are some of the tree structures that can be used to muffle traffic noise. tree belts are more effective to noise reduction than group of trees and isolated trees. establishing green belts as barriers is more cost-effective. moreover, a combination of grass areas and shrubs among trees inside tree belts improves the noise absorption ability of green belts and is vital for noise reduction. when detecting the noise absorption performance of green belts, the individual tree properties are crucial and the tree canopy in particular impacts noise reduction. https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 160 in addition to these benefits, the leaves on the trees are very effective in blocking out road noise. it is crucial to understand how well plants absorb noise for this reason. well-manicured vegetation belts are a useful tool for reducing noise pollution from moving vehicles. consequently, it is critical to identify and suggest appropriate pants for vegetation belts. important variables influencing noise absorption are tree depths, leaf surface areas (per unit volume), and leaf noise absorption coefficients. furthermore, the ability of a leaf to absorb noise is influenced by its size, thickness, age, moisture content, dryness, and greenness. but for effective noise reduction, the noise absorption coefficient of leaves must be calculated precisely. moreover, these circumstances modify the noise absorption coefficient of leaves. determine each leaf's noise absorption coefficient (if possible) rather than relying on only one figure. it is feasible to precisely distinguish which trees absorb traffic noise, and consequently, which trees are the greatest at doing so. apart from their surface area, the noise absorption coefficient is a crucial element that influences noise reduction. the noise-absorption coefficient of leaves is significantly influenced by the frequency of sounds. the noise-absorption coefficient of leaves under various frequencies in an impedance tube should be examined, and an average value should be taken, according to the noise absorption equation. alternatively, instead of choosing an average value, it has been recommended that studying the actual noise absorption coefficient of the leaves for each tree may improve the accuracy of the noise absorption. the visualization of 3d trees has become a viable method for precisely measuring leaf surface areas and tree depths in recent years. tree modelling programs utilizing terrestrial laser scanning (tls) have been shown to be successful in gathering comprehensive data on tree attributes, including tree depths, canopy areas, and canopy volumes. furthermore, 3d visualization is a crucial tool for figuring out leaf surface areas because it can improve the accuracy of the canopies' details. this work used the tls method to detect 3d trees; however, to accurately detect all of the information, it is more effective to combine the tls and als approaches for 3d tree detection. it is still difficult to estimate the true surface areas accurately. creating surfaces from point clouds can be accomplished in part by embedding lidar point clouds into a surface fitting technique (such a convex one). it has been demonstrated that this approach is more accurate than other surface fitting techniques such as voxel or the triangular irregular network (tin). however, more investigation is required to increase the accuracy of the leaf surface area computation. however, the 3d convex hull approach overestimates 3d tree canopies due to this surface fit with the outside points of point clouds. this removes a little point cloud gap from this. the 3d concave hull method can be advised as a precautionary measure. it is uncommon to estimate the volume of a tree canopy using the idea of object modelling. a canopy points cloud extract fitting surface is not represented by object modelling. however, it takes some geometric shapes from the canopies of trees. consequently, the estimation of the volume of tree canopies using object modelling is an imprecise technique. techniques to generate a surface for the tree canopy are voxel-based, convex, and concave hull. however, the entire leaf surface area within the tree canopy cannot be clearly identified using these methods. therefore, the development of tree visualization is essential. the leaf area density (lad) of a tree canopy can be found using mathematical techniques that have been developed in several studies. as future suggestions; planting trees along the roads is vital to reduce noise pollution. to identify the suitable height and width of the tree belts, the 3d visualization of tree is vital. due to the individual performance of a tree in a tree bel being vital, the noise absorption coefficient of leaves should be taken into account. this means that the type of tree is a considerable factor in the absorption of noise from road traffic. to identify the properties of the trees such surface area of the leaves and depth of the trees, the 3d tree detection and 3d tree visualization is vital accurately. 5. acknowledgments this work was supported by the ministry of higher education through the fundamental research grant scheme (frgs/1/2022/wab07/utm/02/3). https://doi.org/10.14710/geoplanning.11.2.149-164 wickramathilaka et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 149-164 doi: 10.14710/geoplanning.11.2.149-164 161 6. references adhika, d. r., prasetiyo, i., noeriman, a., hidayah, n., & widayani, s. 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urban growth components, together with the natural growth of birth and death, and migration from rural to urban areas. however, regarding the previous research, reclassification has demonstrated a shortcoming in studies because discourses on urban development have mostly focused on the strength of existing urbanized areas. historical data confirms that the economic growth in urban areas is due more to a residual increase from the change in migration and reclassification rather than natural growth. this paper contributes to the empirical context discussion of the reclassification of urban growth and its subsequent spatial changes in the rural area of temanggung regency, indonesia. the study utilizes the comparison analysis by examining the growth of industrial employment as an urban activity in rural areas and looking at this relationship with changes in the physical built-up area as an indication of the urbanization process. this study found that the reclassification in the temanggung regency has encouraged urbanization in rural areas by developing industrial activities based on local resources and labor and promoting economic growth in rural areas. the reclassification that occurs is predominantly due to the wood products manufacturing business that is supported by the local workforce and resources rather than the active role of government institutions. copyright © 2022 gjgp-undip this open-access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction rapid urban development, especially in developing countries, requires a better understanding of the component of urban population growth (mezgebo & porter, 2020). the urbanization process has been accompanied by adverse socio-economic impacts for some time. regarding the physical aspect, the expansion of urban areas undermines the demand for land (ravallion et al., 2007; simon et al., 2004). this situation enables us to understand that urban development needs to be improved to have a more beneficial impact on society and the environment (united nations, 2017). demography is an essential component of urban development that scholars are interested in (jones et al., 2020). the demographic part of urban growth is influenced by a combination of natural growth factors in urban areas, migration from rural to urban areas, and the reclassification of the population living in rural to urban areas (farrell, 2017; jiang & o’neill, 2018; un desa, 2019). the empirical experience in china confirms that most urban population growth comes from reclassification, which accounted for roughly 33.4% of the total urban population growth from 2010 to 2015 (li gan et al., 2016). unlike natural urban growth, migration and reclassification are residual factors related to economic growth (gross & ouyang, 2021). the urban transition in developing countries is different from developed countries. as in the case of developing countries in asia, e-issn: 2355-6544 received: 30 january 2022; accepted: 27 may 2022; published: 01 june 2022. keywords: reclassification, rural development, rural urbanization, urban growth *corresponding author(s) email: hwijaya@lecturer.undip.ac.id https://doi.org/10.14710/geoplanning.9.1.1-16 mailto:hwijaya@lecturer.undip.ac.id wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 2 many densely populated rural areas have the potential to develop into urban areas without the need for significant urbanization, which is an indication of an opportunity for reclassification (mcgee, 2009). unfortunately, thus far, research on the reclassification of rural and urban areas remains limited (farrell, 2017; jones et al., 2020). moreover, the conceptualization of reclassification as an independent research subject separate from demography, economy, and the urban transition has not been widely carried out, primarily due to limited supporting data (bocquier & costa, 2015), even the discussions were also less in historical accounts (farrell, 2017). most researchers tend to combine the effects of reclassification and migration when examining the contribution of reclassification to urban development (national research council, 2003; preston, 1979). as well, most of the studies have not considered the spatial component as a critical factor in the reclassification process (jones et al., 2020) in indonesia, there are administrative and statistical classifications of villages as an urban and rural areas. the administrative classification relates to the formal administration to governance autonomy of the villages (law of village, 2014), which is slow to follow the urban changes due to government bureaucratic mechanisms and the need for agreements of development stakeholders. the urban statistical classification was established by statistics indonesia as a national central agency on statistics in 2010 and updated in 2020 (bps-statistics indonesia, 2010, 2020) to better identify and monitor urban changes. the statical classification of the urban village is based on the three main variables, i.e., the population density, percentage of agricultural households, and the existence of urban facilities. although the urban statistics classification information is more up-to-date, the available data are still limited to the last ten years and have not explained the mechanism of urban growth. in the context of local government institutions in indonesia, urban and rural administrative areas can be distinguished at the level of local village administrative areas, namely desa (villages) and kelurahan (urban villages). kelurahan is the urban administrative unit generally found in the city region. however, it is also possible in the regency some kelurahan show urban function areas in the region as centers of activities and the collection and distribution of goods in the regional structure system. formally, the procedure of village administration to become a kelurahan requires agreement from stakeholders at the village level and local government policies (law of village, 2014) that take time in the process to change. temanggung regency has experienced the composition shift of the population's livelihood where agriculture sector in 2020 no longer dominates (41%). meanwhile, there is the growth of urban employment sectors, which are the industry as well as trade and services, 18% and 39%, respectively (bps statistics of temanggung regency, 2021), which was caused by the development of the wood products manufacturing industry in temanggung regency. these changes also impact built-up area expansion, especially in kranggan and pringsurat, which have become industrial designation areas. due to the industrial activity and local economic growth, the potential for reclassification in the rural areas of temanggung regency is supported by the availability of local workers and resources supports, which is not much based on government policy initiatives as has happened in some other countries (farrell, 2017; farrell & westlund, 2018; goldstein, 1990). this paper aims to demonstrate a context of the reclassification process on urban growth in rural areas based on local economic initiatives and agriculture resource-based industry. it examines the rural-urban reclassification regarding the shifts in rural employment structures, the development of the manufacturing industry in rural areas, and the changes in built-up areas. the discussion results provide an empirical context for the reclassification in urban growth and the impacts on spatial change in rural areas of the temanggung regency. the objective is achieved by examining the changes in the employment composition that compares the statistics data of temanggung regency and verifying the growth of the wood products manufacturing industry. the spatial examination was done with a gis analysis to identify the growth of the built-up area in time series change. https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 3 2. literature review reclassification is an expansion of urban area as a part of urban growth components, together with the natural increase in urban population and rural-urban migration. it is also part of urbanization as the reclassification adds the number share to the urban population. reclassification indicates the extension of urban boundaries, resulting in larger urban residential areas. this growth creates new urban areas that differ from rural regions and increases population, economic size, and density, contributing positively to urban growth (un desa, 2001, 2019). it may reflect the actual expansion of cities because of the socio-economic and technological changes and population growth, or simply as sudden changes in the definition of urban places. for instance, there were 6,211 designated towns in china in 1984, but these became 10,609 in 1984 or 71.81% in just four years. reclassification also includes the growth of major metropolitan cities, suburban outskirts expansion, and increased commuting resulting from economic restructuring (zhu, 2017). there are two types of reclassifications mechanism. the first occurs when a settlement exceeds the minimum size or density threshold, meeting the requirements of an urban environment. the second type occurs when governments change the definition of "urban" and administrative status, as the united states did in the 1950s and china in the 1980s (national research council, 2003; zhu, 2017). this phenomenon has reinforced that reclassification is a part administrative conversion process. meanwhile, regarding the spatial context of urban expansion, the reclassification can be separated into three types: expansion (or shrinkage) of existing city boundaries, annexation (or surrender) of adjacent settlements, addition (or reduction) of new settlements that grow beyond the specified threshold (dyson, 2011; farrell, 2017; national research council, 2003). as part of the process of urban and rural development in developing countries, regional growth changes fast as the result of economics and politics on urban growth (h. farrell & knight, 2003). in a demographic context, cities mostly grow due to a natural increase in population, births, and deaths. the economic factors consist of rural push and urban pull, which encourage rural to urban migration (harris & todaro, 1970; jedwab et al., 2014). village pressures are mostly related to conditions that promote villagers to migrate from their villages due to limited economic opportunities in rural areas. on the other hand, the attractiveness of cities is the situation in urban areas that offer better jobs and incomes. meanwhile, political factors correlated with the reclassification of rural areas into urban areas consisting of expansion of city boundaries, changes in the status of adjacent regions, and changes in the status of new areas outside the city's area of influence (farrell, 2017). based on research conducted by gross, urban economic development is mainly correlated with residual urban growth, namely the increase in urban population originating from internal migration and reclassification of rural to urban areas (gross & ouyang, 2021). reclassification is a process of the rural area changing into an urban area characterized by developing industrial activities and urban services (long et al., 2011). the urbanization process in developing countries is indicated by the growth in the number and size of urban areas, describing the development process and development challenges. in this process, there is a change in spatial, development institutions, society orientation, and social mobilization, leading to the interests and scale of the urban conditions (brenner, 2013). the reclassification growth type can be framed as a valuable tool for addressing regional inequalities (kulcsár & brown, 2011) and is considered a productive strategy to stimulate economic development (farrell, 2017). furthermore, in the perspective of local development, the reclassification also can promote the administrative status and improvement the quality of rural areas, i.e., better autonomy and political power, improve access to infrastructure, increase the investment opportunities, as well provide revenues from landbased financing (farrell & westlund, 2018). therefore, a proper reclassification policy may promote proper urban growth in rural areas to support improved quality of life in rural areas (un desa, 2021). https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 4 3. study area, data and method 3.1. study area the study covers the administrative area of the temanggung regency in jawa tengah (central java) province, indonesia (see figure 1). the selection of temanggung regency as the research study area is based on the observation of the reclassification process in the area in line with the growth of the wood products manufacturing industry in temanggung regency since the 1990s. the development of industrial areas affects changes in the region's employment composition. significant changes in the built-up area, especially around the industrial designation area, indicate possible reclassification. 3.2. data and methods the research seeks the relationship between the shift in the composition of the structural employment change, the development of the wood products manufacturing industry in rural areas, and the changes in builtup areas in the temanggung regency. the study uses secondary data and field verification results, including population and employment data, the number and location of manufacturing industries, and land cover data of landsat tm data bands 5,7, and 8. the shift in the employment compositions is conducted by comparing the sectoral labor numbers of temanggung regency from 2000 to 2020 from the secondary data of temanggung statistics. the employment data is represented by selected sectors, i.e., the agricultural sector, industrial sector, trade, and services, as the leading activity indicators in the urbanization process. the number of people working in the agricultural sector characterizes rural activities, while the industrial, trade and service sectors indicate urban activities. figure 1. map of the study area https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 5 understanding the growth of the wood product manufacturing industry in temanggung regency is obtained by analyzing the secondary data and field verification. secondary data depicts the industry's profile. meanwhile, field verification is conducted to validate the number of industries, especially those still at work. the analysis of spatial change was conducted in two stages, which identify the built-up area through supervised analysis on landsat tm bands 5, 7, and 8, followed by overlaying the identified built-up area in time series. first, supervised analysis of land classification was carried out by selecting pixels that represented a recognized pattern or which can be identified with the help of other sources of information (tewabe & fentahun, 2020) on the temanggung regency digital map. meanwhile, the overlay analysis to indicate the changes in a built-up area is conducted by comparing the results of the supervised analysis of built-up land cover on the maps of temanggung regency in 2000, 2010, and 2020 or the so-called post-classification comparison. this approach makes the possibility to identify differences between independent images classified each year, thereby enabling the creation and updating of gis databases as classes/categories are assigned. as a result, a quantitative value for each class can be determined (fichera et al., 2012). 4. results and discussions the following sections elaborate on and discuss the reclassification of rural areas in the process of employment dan spatial changes in temanggung regency, central java, indonesia. the analysis discussion follows some stages; the first, the discussion examines the level of urbanization and the rate of population growth in the temanggung regency. this process identifies the reclassification process that occurred in temanggung regency. the second analysis relates the reclassification process to the rural area's livelihood changes due to industrial development. the third analysis linkages the reclassification with the built-up area changes. the last discussion elaborates on the institutional and policy aspects of reclassification that exist in the temanggung regency as a part of the urban growth mechanism in developing countries. 4.1. population profile temanggung regency, as a research study area, is part of java island that the most densely populated area and a place for population concentration and urbanization in indonesia (firman, 2017). data from the population census in 2020 shows that as many as 151.59 million people, or 56.1% of the 270.2 million indonesian population, live on java island. however, the island area is only about 7% of the total area of indonesia. furthermore, identification of urban and rural village conditions in 2020 shows that as much as 65.15% of villages on java island are characterized as urban villages, which is the only island in indonesia with a ratio of the number of urban villages that is larger than the rural areas (bps-statistics indonesia, 2021). the current condition of the population phenomenon that occurs on the island of java is mega urbanization that occurs on a massive scale, which indicates the transformation of urban villages (firman, 2017; setyono et al., 2016). most of the regencies and cities around big cities experienced higher population growth than places in the city's center (firman, 2017). rural and urban development in central java cannot be separated from the corridor area that connects the urban system in java island and central java province. java island has the main urban activities center in jakarta metropolitan region in the western area, and surabaya metropolitan region in the eastern area as a second level. semarang city is in the middle of a northern corridor of jakarta and surabaya, which also connects to yogyakarta, an urban center in the southern area, by a north-south corridor of the central java region. however, these last two cities are lower level than jakarta and surabaya. the geographical position between the two main urban centers in central java has influenced the region to become more urbanized in the last two decades (setyono et al., 2016). temanggung regency, which is in the western area of the north-south corridor, has begun to develop into an urban area due to most of its region still being a rural area. the population profile in temanggung regency regarding the growth rate and population density from 2010 to 2020 indicates that several sub-districts have experienced an increase in the population growth rate (see https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 6 figure 1). part of the population growth with the highest rates is in the kranggan, pringsurat, kandangan, kaloran, and selopampang areas (see figure 2), where these areas are turning into urbanized areas. source: bps statistics of temanggung regency, 2010, 2020 figure 2. average annual population growth rate in temanggung regency the condition indicates the internal population growth in temanggung is not evenly. some sub-districts have higher growth rates than others, related to specific factors. but in general, the faster population growth in the regency does not occur at the current urban activities center in temanggung and parakan sub-districts. the higher change mainly happened in the area where new activities emerged in rural areas, i.e., the wood products manufacture industry at kranggan, pringsurat, kandangan, and ngadirejo; tourism and agribusiness in kledung and bejen; agribusiness in tembarak, selopampang, and kaloran. the rural push and urban pull forces (harris & todaro, 1970; jedwab et al., 2014) do not always promote the migration from rural areas to existing urban areas, but the forces can also promote the internal region movement from less to a faster developed rural area. source: bps statistics of temanggung regency, 1990, 2020 figure 3. grdp temanggung regency at current market price by industry year 1990 and 2020 the process also seems to be a potential factor in increasing economic growth, as dyson (2011) sees the population growth rate will benefit the region to improve its economy. conditions in temanggung regency for economic growth were identified from changes in the value of grdp from 1990-to 2020 (see figure 3). the 0.82 1.27 1.02 0.98 0.84 0.91 1.14 1.32 1.34 1.19 1.22 1.22 1.04 1.09 0.9 0.93 0.93 1.24 1.05 1.08 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 average annual population growth rate per sub-district in temanggung regency (%) 2010-2020 https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 7 differences in the economic sectors in the temanggung regency can be divided into two types. the first is the sector that consistently experienced a decline that was the agricultural sector, which from 1990 contributed 35% to the regional economy, reduced to 24% in 2020. secondly, the industry sector has experienced an increasing share, which in 1991 contributed 16% to 27% in 2020. changes in the leading economic sectors in temanggung regency, which shift to the industrial sector, show sub-districts with higher population growth rates. kranggan and pringsurat sub-districts have a higher population growth rate that has been designated as industrial areas. in temanggung regency, it is indicated that industry influences population growth. the increasing industry and trading sectors, and inversely the decreasing agriculture sector, show the growth of the urban sectors. in a spatial context, the shifting economic sectors form the urban area expansion or reclassification of rural to urban areas. 4.2. changes in employment structure temanggung regency is a hinterland region with high-value agriculture products, e.g., tobacco, coffee, and woods. in the three decades, the area has undergone significant changes marked by the growth of the industrial and urban sectors. statistical data shows population growth of 27% from 1990 to 2018, but the increasing number of workers in the industrial sector reached eighteen times from the original 4,544 workers in 1990 to 87,971 workers, and the urban service sector workers increased three times from the original 43,121 workers to 177,929 workers (the results of calculations by the temanggung regency bps 1990 and 2018). on the other hand, the presence of local workers supports the significant wood processing products industry. source: mapping of bps statistics of temanggung regency, 2000, 2020 figure 4. map of the employment composition change per sub-district from 2000 to 2020 as for the employment distribution per sub-district (see figure 4), most of the population aged ten years and more who have worked are laborers in the agricultural sector. there are eighteen of twenty sub-districts in https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 8 temanggung regency, with the majority population in the agricultural sector. this condition occurred from 2010 to 2020, although there were fluctuations in the percentage of each sector. there are two sub-districts in temanggung regency with the dominant population working in the non-agricultural sector, namely parakan and temanggung sub-districts. most of the people in these sub-districts work in the trade and service sector. the dominance of workers in the trade and service sectors in these two sub-districts occurred from 2010 to 2020. source: bps statistics of temanggung regency, 2000, 2020 figure 5. five sub-districts with the highest employment change in the industry sector in temanggung regency year 2000 – 2020 (%) besides agriculture, trade, and services, the industrial sector absorbs plenty of rural labor. based on the employment distribution, several sub-districts have significantly changed in industrial sector employment compared to other sub-districts, especially pringsurat, kranggan, and kandangan sub-districts (see figure 5). the research in temanggung regency found that the changes in population activities have the same characteristics as several developing countries in asia, which indicates the transformation of rural areas because of industrialization and urbanization. the agricultural sector’s productivity characterizes the transformation, labor productivity, technology changes, and improvements in rural infrastructures and socio-economic conditions (buchori et al., 2021; liu et al., 2017; long et al., 2011). 4.3. the development of industry and the spatial changes. the study analyses the built-up area changes of the temanggung regency in 1990, 2000, 2010, and 2020 (see figure 6). industrial growth has affected the urbanization of suburbs and the peri-urban regions (buchori et al., 2021). in contrast to the general urbanization understanding centered on current cities, the new form of urbanization in peri-urban areas tends to be in situ, as in many cases of urbanization in central java (setyono et al., 2016). handayani (2013) also argues that the shift from rural to urban areas in central java is due industrialization process that occurs not only in larger urban centers but also in smaller urban areas. (handayani, 2013). land cover changes in temanggung regency mainly exist along the main road corridor. the built-up area changes in regency between 2000 and 2020 were mostly higher in the sub-districts of kranggan, pringsurat, bansari, and bulu. the major socio-economic forces that mainly drive the rural land-use change are industrialization, urbanization, structural adjustment of agriculture, and housing construction in the rural area 6.01% 5.62% 3.60% 0.98% 0.87% -4.00% -3.00% -2.00% -1.00% 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% pringsurat kandangan kranggan tembarak gemawang c h an ge s in e m p lo ym en t se ct o rs sub districts agriculture industry service & trading https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 9 (li et al., 2010). this region changes because of the increasing extent of economic activity and the need for rural land, impacting the growth of industrial activity in temanggung regency. the industrial activities shift the employment in the regency from rural and agricultural activities to become workers in manufacturing activities that encourage the development of built-up areas. these growths also indicate the shift of rural population to urban due to the change from rural to urban employment sectors. source: gis analysis on landsat tm data years 2000, 2010, and 2020. figure 6. map of land built-up area changes in 2000-2020 the tendency to become the built-up area per sub-district has increased (see figure 7). the increase of built-up areas is influenced by the development of community needs and socio-economic conditions. the most significant land-use change occurred in the pringsurat sub-district, especially from 1990 to 2000 (440.07%) and from 2010 to 2020 (249.19%). in addition, the kranggan sub-district is the second-highest built-up area change, namely from 2010 to 2020 (152.56%). the significant difference in built-up land in the pringsurat sub-district and kranggan sub-district is caused by the development of the main road to magelang regency and yogjakarta province. in addition, the result of policies for the kranggan and pringsurat sub-districts as industrial estates also encourage the development of built-up areas in the temanggung regency. https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 10 source: gis calculation results, 2021 figure 7. percentage of built-up area change in sub-districts, temanggung regency 2000–2020 this study indicates that the development of the wood industry is related to spatial changes, especially in the phenomena of built-up area growth. therefore, identifying the spatial difference by comparing the built-up area in 2000, 2010, and 2020. source: bps-statistics indonesia, 2010; bps, 2021, and field survey in 2020. figure 8. changes in the urban and rural characteristics of villages and manufacturing company locations. 0.00% 50.00% 100.00% 150.00% 200.00% 250.00% p er ce n ta ge o f b u ilt -u p a re a c h an ge sub district https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 11 furthermore, the distribution statistics reclassification of the urban area is compared with the distribution of wood products manufactures agglomeration (see figure 8 and table 1). the calculation result shows the changes in the built-up area emerge in the surrounding wood products manufacturing agglomeration, which is primarily residential. the spatial impact of industrial development in temanggung shows about 500 ha of builtup area was formed within a 3-kilometer radius of the agglomeration, and about 1500 ha of the built-up area grew within a 5-kilometer radius. table 1. changes in industry and urban-rural areas in the sub-districts, temanggung regency 2000–2020 no sub-districts number of industries 2010 number of urban villages 2010 number of industries 2020 number of urban villages 2020 industrial change percentage 2010-2020 percentage of spatial change in urban village 2010-2020 1 kandangan 0 3 2 8 200% 167% 2 kedu 1 7 1 14 0% 100% 3 kranggan 7 0 12 7 71% 700% 4 ngadirejo 1 5 1 10 0% 100% 5 pringsurat 9 0 12 9 33% 900% 6 temanggung 1 18 1 24 0% 33% source: gis calculation and statistics data 2000, 2020 industrialization and urbanization change shifts the agricultural activities and housing development in rural areas, which are the main factors to drive spatial change in rural areas (li et al., 2010). rural land development is growing along with urbanization and industrialization, as indicated by the expansion of residential areas (yang & li, 2020). in addition, industrialization also accelerates economic growth and the process of transforming rural-urban relations (liu, 2018). the industrialization has significantly impacted rural land use transition (liu, 2018; yang & li, 2020; yang et al., 2021). similar conditions also occur in temanggung regency. the availability of local resources has attracted wood product manufacturing industries to operate in several villages, especially in kranggan and pringsurat. the development of industry, especially the wood product manufacturing industry, impacts spatial changes in rural areas. changes in the built-up land occurred significantly in the countryside around the industrial location. these changes occurred massively from 2010 to 2020. 4.4. spatial changes the industrial built-up area experienced rapid growth in the period 1990 2000, while the increased number of workers occurred more rapidly in 2000-2010, which shows that optimal production and absorption of workers in the wood processing industry occurred after the establishment of the factories. the increase in industrial workers is in line with the growth of urban employment shown by the urbanization process in this area. the growth of industrial land also increases the development of the surrounding area of the industry, which was initially dominated by rural areas. the increasing number of industrial workers is also part of the growth of urban activities, rural industrialization in the temanggung regency has become an activity that promotes urbanization in the region. it specifies that the bottom-up urbanization pattern was not initially a government development strategy but rather a private business initiative. in its later development, the government then allocated the area in the southern part of the regency passed by regional roads as an industrial zone. however, it remains problematic because the factories built at the early time were partly outside of the industrial zones that were later defined in the regency spatial plan. urban changes in temanggung regency are not driven by the spillover of the existing main cities. based on data, the industrial development in the region promotes the characteristics changes of the area that was initially classified as a rural into an urban area. the phenomenon exists mainly in the kranggan and pringsurat sub-districts, which had massive industrial area growth in 2010-2020 with 71% and 114%, respectively. industrial development has influenced the villages in the surrounding area to become more intense built-up https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 12 areas. this shift is due to the industrial development transforming the structure of people's livelihood from agriculture to the industrial base activity. the change in the employment structure was followed by the other employment opportunities that support the activities of the wood processing industry, both in the forward and backward production chains. the backward activities of the wood processing industry encourage sawmills and wood collection from farmers and the local timber trade. the forward activities of the wood processing industry rise to the marketing, distribution, and transportation of products and other supporting activities. these improvements are included regional developments and residential activities for workers, financial service institutions, and other service sectors. the activities of the wood processing industry in temanggung regency also absorb agricultural wood products from the regencies surrounding temanggung and the province of west java. the fact shows that the development of the wood processing industry has also promoted the area that was initially rural into centers for collecting and producing wood product processing at the regional level. source: gis calculation and statistics data 2000, 2020 figure 9. map of built-up area and structural employment changes 2000-2010 in temanggung regency industrialization and urbanization that traditionally generate migration do not fully occur in the urban transition process in the rural areas of temanggung regency. the average annual population growth rate in temanggung regency is around 0.96%/year, only slightly above the population growth rate of jawa tengah province of 0.82%, but still below the national population growth rate of 1.4%/year in the 2010-2014 period (bps-statistics of jawa tengah province, 2015). temanggung regency experiences a shift in the activities and employment structure of the local population from agriculture to industrial activities and urban production activities, which due to the regency population has been already dense and meets the needs of industrial and urban activities. therefore, migration from outside the sub-district is more to providing skilled labor that cannot be fully provided internally, and the production activities in the border area that supported by the commuter from outside workers. https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 13 regarding the employment and spatial change, there is a relationship between the dynamic of the workforce composition and the growth of the built-up area in temanggung regency (see figure 9). the employment structure change to industry and urban activities mostly occurs in pringsurat, kranggan, and kandangan sub-districts. the existence of a wood product manufacturing industry in the sub-district has attracted local labor, which then impacts built-up area change that indicates the reclassification existence in the area. the livelihoods shifting then have an impact on the spatial changes. the kranggan and pringsurat subdistricts area has been changed mainly in the 2010-2020 period as the location of industrial development. the changes in the employment structure and built-up area coverage of temanggung regency indicate the reclassification of urban growth in the rural area of temanggung regency. this reclassification may reflect the urban expansion as a series of changing socio-economic conditions (jiang & o’neill, 2018). the rural transformation in the temanggung regency has statistically reclassified the villages into urban areas, which occurred functionally due to the manufacturing influence and economic growth in rural areas supported by the local resources and workforces. the growth of wood processing industrialization in the rural areas exists due to the availability of resources, workforce, and supporting infrastructure, which is also supported by marketing connectivity abroad and domestically. the change in the village employment system, which was initially based on agriculture and village businesses to become an industrial worker community, has initiated the initial stage of reclassification of villages as part of urban growth and urbanization. as farrell (2017) suggests about the rapid urban growth triad, the reclassification may become an important option to balance the rural and urban development due to the potential of the new urban area of reclassification to take part in a strategy to hinder the rural-urban migration. this result also confirms that the reclassification can be occurred not only driven by the government initiative (national research council, 2003; zhu, 2004) but also due to the occurrence of the manufacturing industry and the dynamic of the local economy in the rural area. as the reclassification is a political administration process (farrell, 2017; goldstein, 1990), there is a contrast in the facts for the dual administrative and statistical classification of villages in indonesia. from 2010 to 2020, there was no change in the administrative classification of the villages in temanggung regency, while in the statistical classification in this period, there were 104 villages experiencing reclassification changes (bpsstatistics indonesia, 2010, 2020). as the national statistics variables classification, this fact means villages have changed in urban components of population density, decreasing agriculture households, and urban facilities access. in this condition, the urban villages in temanggung regency are still managed with the rural administration system, which becomes a challenge in the development process, due to the absence of appropriate institution capacity and regulations dealing with socio-economy and physical urban issues. 5. conclusion the case of the temanggung regency demonstrates an initial functional reclassification of urban growth due to the shift of rural employment from the agricultural to become industrial and urban sectors. the reclassification in the case study is due mainly to the development of the wood products manufacturing industry that is supported by the availability of local workforce and resources, rather than the government institution driven. urbanization occurs in the existing dense village areas as part of the mega-urban regions of java island, where reclassification expands the urban areas in the temanggung regency because of a series of changes in social and economic conditions in rural areas. the growth of manufacturing factories that make wood products has transformed the rural and agricultural community and changed the livelihood and economic structure in temanggung regency, notably for rural areas. the reclassification process in the rural area as part of industrialization then promoted spatial change. more built-up areas identified in the surrounding factories’ agglomeration indicate the transformation of people’s capacities and perspectives to develop the area. therefore, reclassification as part of urban residual has more https://doi.org/10.14710/geoplanning.9.1.1-16 wijaya and buchori / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 1-16 doi: 10.14710/geoplanning.9.1.1-16 14 impact on economic growth in people and rural areas. this urban growth process becomes part of urbanization, as reclassification adds the urban population component to the total population. there is a dual reclassification process in which political administration is slower than the statistics process, which becomes the challenge for the urbanized rural area development due to the absence of appropriate institution capacity and regulation to deal with the socio-economic and physical growth urban issues. the reclassification discourses are expected to become an essential part of the research agenda to deal with the current sustainable development to put the rural and urban in supportive linkages as a balance of complementary activity and spatial function. 6. acknowledgments the paper is based on the research study supported by the drpm universitas diponegoro by scheme international publication research of universitas diponegoro (rpi undip 2020). 7. references bocquier, p., & costa, r. 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[crossref] https://doi.org/10.14710/geoplanning.9.1.1-16 https://www.taylorfrancis.com/chapters/edit/10.4324/9781315248073-11/changing-urbanization-processes-situ-rural-urban-transformation-reflections-china-settlement-definitions-yu-zhu https://doi.org/10.1007/bf03500917 43 geoplanning journal of geomatics and planning vol. 11, no. 1, 2024 original research mapping landslide vulnerability using machine learning approach along the taba penanjungkepahiang road, bengkulu province camelia b. abrar1, ashar m. lubis2*, darmawan i. fadli2, arya j. akbar1, rida samdara1 1. physics study program, university of bengkulu, indonesia 2. geophysics study program, university of bengkulu, indonesia doi: 10.14710/geoplanning.11.1.43-56 abstract landslides occur when masses of rock, debris or soil move due to various factors and processes that cause land movement. the taba penanjung-kepahiang route is one of the areas in bengkulu province that is highly prone to landslides. this causeway is the only fastest land route connecting the bengkulu-kepahiang area. in recent years, the road area has often been cut off due to landslides and fallen trees, which have caused road access to be cut off and obstructed and claimed lives. this study uses a machine learning (ml) and gis approach with variable frequency ratio using 16 independent factors obtained from the spatial database and dem, which correlate with landslide events. this research aims to gain an in-depth understanding of the factors that cause landslides. in addition, the research focus is the development of a disaster mitigation model to design and implement effective strategies to reduce the risk and impact of landslide disasters through in-depth analysis the dependent factor is the location of the landslide from the historical landslide area for the last five years, with a distribution of 70/30%. furthermore, frequency ratio is used to analyze the correlation between conditioning factors and historical landslides. then, the independent and dependent factors were normalized to create a landslide susceptibility map. frequency ratio (fr) indicates the likelihood of an event occurring, with drainage density (fr= 0.69), shear wave velocity (vs30) (fr= 0.66), slope (fr= 0.60), and rainfall (fr= 0.55). the output of the processed data is in the table below. copyright © 2024 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction landslides are one of the geological disasters that often occur in indonesia. landslides occur when a mass of rock or soil moves due to controlling factors and triggering processes. a landslide is a movement of the ground with a slope direction and moves it on an avalanche (fadilah et al., 2019). landslide is a process of mass wastage that occurs on slopes formed naturally or engineered by the movement of rock masses, debris, or soil down the slope, which is influenced by gravity (cruden & vandine, 2013). landslides occur continuously from year to year and make landslide disasters the center of attention and become a severe problem in almost all parts of the world because they cause economic or social losses to private and public property (rotaru et al., 2007). one of the areas in bengkulu with a high level of vulnerability to landslides is the bengkulu-kepahiang route; this is because the bengkulu-kepahiang route is an area with diverse geomorphological conditions. this area is the only fastest connecting land road that connects the bengkulu-kepahiang area, but the area is a forest area. in recent years, fallen trees and landslides have been obstructed and have caused road access to be cut off and obstructed, claiming lives. previous cases show that landslides occurred at two or more points on the same day, causing several vehicles to get stuck between the 2 points. landslides can occur with coverage of more than one slope, triggered by the same phenomenon (froude & petley, 2018). e-issn: 2355-6544 received: 01 april 2023; accepted: 07 february 2024; published: 08 march 2024. keywords: landslide, machine learning, frequency ratio *corresponding author(s) email: asharml@unib.ac.id https://doi.org/10.14710/geoplanning.11.1.43-56 mailto:asharml@unib.ac.id abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 44 the area has no alternative roads that allow motorists to turn around until the landslide, trees, or other materials from the road can be cleared. therefore, research related to analyzing and determining landslide hazards on the bengkulu-kepahiang route must be carried out as a non-structural mitigation effort. this effort helps identify relatively safer areas from landslides so that material and non-material losses can be minimized. this research aims to identify landslide-prone areas on the bengkulu-kepahiang route, with the primary objective of taking mitigation steps to enhance the safety of people around the area. according to alcántaraayala & sassa, (2023) by mapping landslide-prone areas, we can focus on risk management and prevention. additionally, this research offers insight into the vulnerability of landslides on the bengkulu-kepahiang route, the region's primary land route. with a better understanding of the potential for landslides, we can take action to optimize transportation access and minimize disruption due to landslides, which can harm local communities and the economy. several previous studies regarding landslides on the bengkulu-kepahiang cross route, such as suhendra & sugianto, 2018; hadi et al., 2021; hadi & siswanto, 2016; and sugianto, 2021. in general, studies on landslides that previous researchers have carried out show that the area on the bengkulu-kepahiang cross route has the potential to experience ground movement. however, the previous study was still carried out deterministically, and the parameters used were still very few, so it is necessary to map landslide-prone areas on the bengkulukepahiang cross road. to overcome this issue, achine learning (ml) method is one solution that can solve problemsthe ml model is considered essential in disaster mitigation and an ideal landslide management plan in landslide modeling for disaster mitigation and disaster management as a mitigation effort (pourghasemi et al., 2018). making a vulnerability map requires data with high accuracy. the more input parameters, the more accuracy and sensitivity analysis in mapping landslide vulnerability will be more accurate (ghorbanzadeh et al., 2019). the ml landslide detection study uses different classifications (roodposhti et al., 2019) to increase the efficiency of the outputs. mapping of landslide-prone areas will be calculated using frequency ratio (fr) and parameters used such as elevation (topography), geological conditions, slope aspect (slope aspect), slope (slope), rainfall, plan curvature, distance from faults/faults (kavzoglu et al., 2019) as well as other supporting factors. the interconnection of these factors makes a holistic approach important in dealing with and preventing landslides. the factors mitigate the adverse impacts of landslides, and it is essential to implement a comprehensive strategy that includes prevention and preparedness. sustainable land use planning, reforestation, early warning systems, and advanced technology integrating vulnerable areas are essential to an effective landslide mitigation plan. developing a disaster mitigation model in the jalan taba penanjung-kepahiang area aims to design and implement an effective strategy to reduce the risk and impact of landslides through an ml approach. additionally, we hope that information from this research can be used to increase awareness of community preparedness, help design risk mitigation programs, and facilitate faster responses in emergencies. this is expected to reduce the impacts materially and non-materially so that the road can be traversed safely. 2. data and methods 2.1. study area and geological setting. the taba penanjung-kepahiang route (fig. 1) is an area that connects central bengkulu regency and kepahiang regency, which are in bengkulu province, with an area of 1124.44 ha of the research area. this area is where landslides frequently occur in bengkulu province, and narrow access roads do not allow motorists to turn around and cause long traffic jams. based on dem data, the topography on the bengkulu-kepahiang cross route is at an elevation of 96 to 880 masl (suhendra & sugianto, 2018) with high rainfall with an average of 235280 mm/year, so that it will increase the potential for ground movement or landslides (natasya et al., 2022). landslides occur due to factors arising from the internal geology of the slope as well as the external environment. the internal factors, which include geomorphology, stratum lithology, and topography, will control the occurrence of landslides. the main external factors in landslides are environmental factors, hydrogeology, and https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 45 human engineering activities (xiao et al., 2019). these factors will be input and extracted as the final input on ml and soil susceptibility index (lsi) in the form of fr values (zhu et al., 2021). figure 1. (i). jalan lintas taba penanjung – kepahiang is using google satellite. (ii), (iii), (iv). landslides on the taba penanjung route kepahiang in 2021-2022 the hulusimpang formation (tomh) (fig.2), with an orange symbol flanked by andesitic basalt volcanic rocks, is green. tomh is around the late mid-miocene oligocene, with the most extensive zone mainly along the ketahun-musikeruh fault zone and several places in the western part of lambar. source: analysis, 2022 figure 2. geological conditions and study area of the taba penanjung-kepahiang route 2.2. frequency ratio (fr) calculation fr is a well-known method for mapping landslides (ozdemir & altural, 2013). fr measures the degree of correlation between landslide locations according to their independent factors (solaimani et al., 2013). fr is a method for calculating the effect of subclasses of conditioning factors on landslides (he et al., 2012). from fr for each class from all data layers, it will be combined with the landslide inventory map independent factor map using the equation (eq.1): fr = 𝑁𝑝𝑖𝑥(𝑆𝑖)/𝑁𝑝𝑖𝑥(𝑁𝑖) ∑ 𝑁𝑝𝑖𝑥(𝑆𝑖)/∑ 𝑁𝑃𝑖𝑥(𝑁𝑖)𝑖𝑖 ……………... [eq.1] npix (si) is the number of pixels of landslides, and npix (ni) is the number of pixels of a class. the landslide susceptibility index (lsi) is calculated by the sum of each factor ratio value using the equation (eq.2): lsi = fr1 + fr2 + fr3 + frn……………..eq.2] https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 46 lsi will be obtained by adding up each fr value for each conditioning factor correlated with landslide history, where fr is the level of each type of factor. landslide vulnerability maps are obtained from the correlation between the factors overlaid using spatial weight overlay analysis (okoli et al., 2023). 2.3. landslide inventory and casual factor data processing uses the qgis application and software r. processing data on software r uses a grid base with factor attribute values from ml that differ from specific grid sizes (wei et al., 2022). input data is in the form of local characterization of the avalanche geometry and internal structure, which is used to further describe slope stability in modeling (dou et al., 2020). for vulnerability analysis, a correlation is depicted between predisposing causes and triggering factors using a numerical model (van asch et al., 2007). data processing uses 16 independent factors (table 1.), continuous and categorical scale factors, and dependent factors obtained from landslide inventory maps: location data, places, dates, and other information regarding landslides in an area (guzzetti et al., 2012). in this study, 16 conditioning factors were used, which were classified using different methods, specifically manual, equal interval, and natural breaks (arabameri et al., 2017). the slope aspect parameter (fig. 3.a) correlates closely with weather conditions (bednarik et al., 2010). it determines its exposure to wind and sunlight, with vulnerability affecting soil moisture and vegetation. while in general, the curvature (fig. 3.b) is the number of surface defects in an area. the greater the surface defects, the greater the degree of curvature. curvature can map stratigraphic features using structural deformation models to predict natural fractures and paleo stress (lisle, 1994; roberts, 2001; chopra & marfurt, 2007). source: analysis, 2022 (a.) (b.) figure 3. (a.) slope aspect and (b.) curvature of research area one of the critical environmental factors in landslide mapping is the elevation (fig.4.a) (marjanović et al., 2011). figure 4. a shows elevations ranging from 112-918 m. in the research area, musi and manna segments (fig. 4.b) exist in the taba penanjung and kepahiang regency areas. source: analysis, 2022 (a.) (b.) figure 4. (a.) elevation. of research area in m and (b.) distance from fault and fault location on the research area in km in the horizontal direction, plan curvature (fig. 5.a) reflects ridges and valleys on a surface, affecting flow dispersion and convergence. in the standing order, the profile curvature (fig. 5.b) can reflect the degree of slope transformation involving the flow's acceleration and deceleration (lee et al., 2018). https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 47 source: analysis, 2022 (a.) (b.) figure 5. (a.) plan curvature and (b.) profile curvature of research area the slope of the slope (fig. 6.a) is a factor that significantly determines the occurrence of landslides in an area and affects the level of soil slides (fan et al., 2022). the greater the degree of slope, the higher the level of vulnerability in the area. however, steep slopes naturally formed due to bedrock outcrops are not prone to landslides (mohammady et al., 2012). shear wave velocity vs30 (fig. 6.b) is the average shear wave velocity with a depth of up to 30 m from the ground surface (hadi et al., 2018). source: analysis, 2022 (a.) (b.) figure 6. (a.) slope and (b.) shear wave velocity (vs30) of research area normalized difference vegetation index (ndvi) refers to the active vegetation biomass or forest cover (fig. 7.a). landslides usually occur on bare land and grasslands (wang et al., 2020). road construction is one of the factors controlling slope stability, with the hypothesis that landslides occur more frequently along the road. this is due to cutting drainage and cutting slopes from making roads that are not suitable (dahal et al., 2008). this research focuses only on the taba penanjung-kepahiang route. the relationship between distance from roads (fig. 7.b) and landslide risk can be influenced by several factors, including an area's geological and topographic characteristics. source: analysis, 2022 (a.) (b.) figure 7. (a.) normalized difference vegetation index (ndvi) and (b.) distance from road at research area other topographical factors such as topographic wetness index (twi) (fig. 8.a) and sediment phosphorous index (spi) (fig. 8.b) use processed dem data in fill dem, flow direction, slope (o), and flow accumulation using the jenks natural breaks method (ciurleo et al., 2016) (equation 3 and 4): twi = loge( 𝐴 𝑡𝑎𝑛𝛽 ) ……………... [eq.3] https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 48 spi = a ∙ tan 𝛽……………... [eq.4] a is the flow accumulation value in meters squared, and β is the slope(o). twi accurately describes how topographic changes impact land runoff, while spi reflects the ability of a water system to erode the soil surface (moore & grayson, 1991; xiao et al., 2019). source: analysis, 2022 (a.) (b.) figure 8. (a.) topographic wetness index (twi) and (b.) sediment phosphorous index (spi) of research area geological factors in the form of lithology, where there are only two classifications of geological conditions, hulusimpang formations, and andesit-basalt volcanic rocksstructures, are described in (fig. 9.a). the supporting factor used is the average rainfall data in the last ten years obtained from the data center for river region vii (bwsvii) bengkulu city (fig. 9.b). the rainfall data is allocated and applied in analyzing recurring periods (koutsoyiannis, 2004; wallis et al., 2007; shou & lin, 2020). source: analysis, 2022 (a.) (b.) figure 9. (a.) lithology at research area; (b.) rainfall at research area in mm/year the slope control factor to the ratio of the total length of the river basin is called the drainage density (fig. 10.b). in general, the higher the density of infiltration drainage, the lower it will be, and the movement of the soil surface will be faster. drainage density indicates the degree of saturation with the flow, which can adversely affect slope saturation (pachauri et al., 1998; nagarajan et al., 2000; çevik & topal, 2003). the distance between drainage systems (fig. 10.a) and landslide risk is also essential in planning infrastructure and minimizing landslide risk. good drainage can help reduce groundwater levels around slope areas. source: analysis, 2022 (a.) (b.) figure 10. (a.) distance from drainage and (b.) drainage density at research area https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 49 this research began with a literature study, carried out by studying previous studies related to this proposed research. the research data collection carried out in this research was a secondary collection in the form of demnas data, geological maps, and administrative maps. next, data processing uses the fr algorithm. the next stage is an analysis of the results of existing ml algorithms. analysis was carried out using the roc curve to evaluate and validate the data obtained to obtain the best landslide susceptibility model. the higher the accuracy value of the roc curve, the better the model produced, and vice versa. the analysis results will be depicted as a landslide susceptibility map on the bengkulu-kepahiang route. in general, the research stages are as shown in the flow diagram below (fig. 11) figure 11. research diagram table 1. conditioning factors and their classification factors classes data scale techniques aspect (a) f (–1); n (0–22.5; 337.5–360); ne (22.5– 67.5); e (67.5–112.5); se (112.5–157.5); s (157.5–202.5); sw (202.5–247.5); w (247.5–292.5); nw (292.5–337.5) https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem curvature (c) -319-0; 0; 0-268; https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem elevation (e) 112-150; 250-450; 450-650; 650-850; 850918 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem distance from fault (df) 0-100; 100-250; 250-350; 350450; >450 https://geologi.esdm.go.id/geomap indonesia catalogue service for geographic information buffering plan curvature (plc) (-13)-(-10); (-10)-0; 0-12 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud continue 8 x 8 m dem profile curvature (prc) (-15)-(-10); (-10)-0; 0-5; 5-10; 10-20 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem slope (o) (s) 4o-8o; 8o-16o; 16o-35o; 35o-55o; >55o https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem spi (-10)-(-6); (-6)-(-2); (-2)-2; 2-6; 6-7 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem ndvi 1300-1500; 1500-1700; 1700-1900; 19002100; 2100-2685 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud extract by mask drainage density (drd) 0-0.5; 0.5-1; 1-1.5; 1.5-2; 2-2.91 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem twi 1.7-8; 8-16; 16-20 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud 8 x 8 m dem vs30 360-760; 760-1500 https://earthexplorer.usgs.gov/ (28 december 2022) geospatial data cloud extract by mask lithology (lit) 1;2 https://www.indonesia-geospasial.com/ national catalogue service for geographic information digitization process rainfall (r) 2500-2800; 2800-3165 data curah hujan 10 tahun balai wilayah sungai vii kota bengkulu. kriging interpolation method distance from road (dr) 0-100; 100-250; 250-350; 350-450; >450 https://tanahair.indonesia.go.id/portal-web peta aoi buffering distance from drainage (dd) 0-1000; 1000-2500; 2500-3500; 3500-4500; >4500 https://earthexplorer.usgs.gov/ (22 december 2022) geospatial data cloud buffering source: analysis, 2022 https://doi.org/10.14710/geoplanning.11.1.43-56 https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://geologi.esdm.go.id/geomap https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://earthexplorer.usgs.gov/ https://www.indonesia-geospasial.com/ https://tanahair.indonesia.go.id/portal-web https://earthexplorer.usgs.gov/ abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 50 3. result and discussion 3.1 correlation analysis between landslide and independent factor the ratio between the slide classification, a percentage of the overall failure, and the class area as a percentage of the entire map is called fr (nourani et al., 2014). fr is generated from each conditioning factor with weights in each sub-class (umar et al., 2014). the correlation of each element with landslide history determines the fr value (shahabi et al., 2014). the conditioning factor sub-class is used in the input variables of the data-based model, with the classification of parameters used in determining the value of the distribution of attribute intervals related to the sub-class (huang et al., 2020). the fr results are a probability comparison value between safe and landslide-prone areas, normalized to frn with a range of 0-1 to see the correlation between conditioning factors and landslide history (chen & chen, 2021). to show the correlation between conditioning factors, pairwise value analysis is used to ensure each index factor's independence level between each conditioning factor. table 2. pairwise comparison matrix of conditioning factors factors s e df spi a c prc plc r dr twi dd ndvi drd vs30 lit s 1.00 e 0.51 1.00 df 0.68 1.34 1.00 spi 0.45 0.89 0.66 1.00 a 0.37 0.72 0.54 0.81 1.00 c 0.21 0.41 0.30 0.46 0.56 1.00 prc 0.35 0.69 0.51 0.78 0.95 1.69 1.00 plc 0.04 0.09 0.07 0.10 0.12 0.22 0.13 1.00 r 0.16 0.31 0.23 0.35 0.43 0.76 0.45 3.48 1.00 dr 0.61 1.21 0.90 1.36 1.67 2.97 1.76 13.69 3.93 1.00 twi 0.64 1.26 0.94 1.42 1.74 3.10 1.83 14.27 4.10 1.04 1.00 dd 0.45 0.89 0.66 1.00 1.22 2.17 1.28 10.01 2.88 0.73 0.70 1.00 ndvi 0.48 0.95 0.71 1.07 1.32 2.34 1.38 10.79 3.10 0.79 0.76 1.08 1.00 drd 1.11 2.18 1.62 2.46 3.02 5.37 3.17 24.70 7.10 1.80 1.73 2.47 2.29 1.00 vs30 0.54 1.07 0.80 1.20 1.48 2.63 1.55 12.10 3.48 0.88 0.85 1.21 1.12 0.49 1.00 lit 0.02 0.04 0.03 0.05 0.06 0.10 0.06 0.48 0.14 0.04 0.03 0.05 0.04 0.02 0.04 1.00 source: analysis 2022 the relationship between conditioning factors is shown in table 2. the vertical table shows the plan curvature as the indicator with the highest correlation among other factors. while horizontally, the drainage density indicator has the highest value among other factors. the highest correlation is owned by drainage density and plan curvature, with a correlation of 24.70. previous case studies, by hadi et al. (2018) and sugianto (2021) regarding landslide-prone mapping using different factors. sugainto's 2020 study revealed the structure of the shear wave velocity (vs) or subsurface structures along the bengkulu kepahiang causeway based on measurements and inversion of microtremor data. this study showed a correlation between the rate of the vs3o shear waves on the taba penanjung kepahiang cross road, the results of which were associated with the potential for landslides. whereas hadi et al.'s research, applied the hvsr and saw methods related to the potential for landslides in the kepahiang district. both of these studies still use deterministic methods using only a few parameters. this research uses 16 parameters from processed dem data extracts and processed catalog map data. the area of this study is only 1124.44 (ha), with the minimal classification of conditioning factors due to the small space. these conditioning factors greatly influence the fr results, where a few combinations will result in a low fr. classifying many conditioning factors with a large area is necessary to increase the fr output. fr analysis shows (table 3.) that in aspect indications, the highest probability is in the north west class, while the lowest possibility is in the east class. this is related to the vegetation index (ndvi) in the northwest direction, which is dominated by a high vegetation index. following lee & min (2001), there is a correlation between the vegetation index and the slope. as for the elevation indicator, the highest probability is at an altitude of 450-650 with fr=0.39 and a massive difference with the lowest fr in the 112-250 altitude class with fr=0.08. the https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 51 relationship between the fault and the landslide shows that class 800-1200 has the highest probability value with fr=0.49, while the fault distance with the lowest fr is in class >1200 with fr=0.02. in the curvature indication, the highest probability is in the concave and convex class with fr=0.38. the plan curvature indication has the highest probability value in 2 categories, specifically (-10)-0 and 0-12 with fr=0.34, and the lowest fr is (-13) -(-10) with fr=0.32. as for the profile curvature indicator, the highest probability is in class 10-20 with fr = 0.32, while the lowest is in class 0-5 with fr = 0.11. these three indicators are extracted from the same input data. with spatial analysis, the extraction of the three indicators is produced simultaneously. in the slope indicator, the highest probability is in the class with the highest degree of slope, specifically >55 with fr=0.60, while the lowest fr is in the slope with the lowest degree, specifically 4-8 degrees with fr=0.003. this is consistent with a general aspect, the shear stress slope material will increase according to the increase in slope degrees, and landslides are likely to occur on the steepest slopes (yilmaz, 2009). the highest probability of spi is in class 6-7, where fr=0.35, while the lowest chance is in class (-2)-2, with fr=0.08. as for ndvi, the highest probability is in the vegetation class 1300-1500 with fr = 0.29 and in class 2100-2658 showing no likelihood of landslides with that class, where the results of the data allocation in that class show fr = 0. at drainage density, the highest probability is at a density of 0.5-1 with fr=0.69. at a density of 1.5-2.2-2.91, it shows that there is no probability in that class with fr=0. for the twi indicator, the probability of landslides is in class 8-16 with fr=0.46, followed by class 1.7-8 with fr=0.45, and the lowest probability is in class 16-20 with fr=0.08. in the vs30 indicator, there are only two classes. the highest probability is found at the shear wave speed with a value of 360-760 fr=0.66, and the lowest is at 760-1500 with fr=0.34. lithological indicators in this area only have two types of rocks: quaternary volcanoes and andesite. the highest probability is in andesitic rocks with fr=0.51, while in quaternary volcanoes, the probability differs significantly from andesitic rocks with fr=0.49. the rainfall indicator is allocated from rainfall data for the previous ten years, so 2-factor classifications are obtained. the highest probability is in class 2500-2800 with fr=0.55, not different from the probability in class 2800-3165 with fr=0.45. according to regmi et al. (2014), landslides usually occur along cut roads and road construction processes that damage the natural conditions of the slopes. in this indicator, the distance of the research area from the station causes the minimum classification it can obtain. on the distance from the road indicator, the highest probability is the shortest distance, specifically 0-100 with fr=0.43, while the lowest probability is at distances of 340-450 and >450 with fr=0.06 and 0.08. distance from drainage indicator, the highest probability is at a distance of 1000-2500 with fr = 0.35, while the lowest probability is at a distance > 4500 with fr = 0.08, which indicates that the frequency of landslides decreases with increasing distance to the drainage canal and can be attributed to the fact that during rainstorms the groundwater level will rise and the initiation of landslides is affected by the modified terrain conditions by ditch erosion (dai & lee, 2001). the landslide vulnerability map on the taba penanjung – kepahiang cross road is divided into five areas landslide areas with low, medium, high, and very high vulnerability. the results show that a very low classification has an area of 8% of the total area, low and high with an area of 25%, medium with 28%, and very high with 14%. as shown in figure 13 the final map of the landslide vulnerability mapping shows that the area symbol in red is for an area very prone to landslides. in contrast, the area with a blue sign indicates that the area has a very low landslide vulnerability. this research aims to identify landslide vulnerability in road areas through correlation analysis between images and diagrams. image diagram in figure 12, displays a comprehensive presentation of the area in figure 13 where the red area indicates a high level of vulnerability to landslides, supported by the significant frequency of landslides in that area. meanwhile, the blue area is considered safe against landslides, even though it has experienced such events because the factors that have been identified indicate a low level of vulnerability. https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 52 table 3. fr value calculation on factor classification factor classification classified area/km2 proportion of classified area/% number of landslide points/pts proportion of the number of landslide points/% density of landslide points/(pts/km2) frequency ratio (fr) aspect (-1) (-1)-22.5 22.5-67.5 67.5-112.5 112.5-157.5 157.5-202.5 202.5-247.5 247.5-292.5 292.5-337.5 7070 11162 13282 22373 30960 28036 25426 25063 9923 4.07 6.44 7.66 12.91 17.86 16.17 14.67 14.46 5.72 960 2496 1088 960 1856 3776 3648 3840 3264 4.38 11.40 4.97 4.38 8.47 17.25 16.66 17.54 14.91 1.08 1.77 0.65 0.34 0.47 1.07 1.14 1.21 2.60 0.10 0.17 0.06 0.03 0.05 0.10 0.11 0.12 0.25 curvature -319-0 0 0-268 31293 97435 46971 17.81 55.45 26.73 4928 10368 7040 22.06 46.41 31.51 1.24 0.84 1.18 0.38 0.26 0.36 elevation 112-150 250-450 450-650 650-850 850-918 40348 28087 23581 41270 42413 22.96 15.98 13.42 23.48 24.13 2432 4864 6528 5440 3072 10.88 21.77 29.22 24.35 13.75 0.47 1.36 2.18 1.04 0.57 0.08 0.24 0.39 0.18 0.10 distance from fault <400 400-800 800-1200 >1200 63850 57157 39505 15172 36.34 32.53 22.48 8.63 5760 6464 9472 448 26.01 29.19 42.77 2.02 0.72 0.80 1.18 0.06 0.26 0.29 0.43 0.02 plan curvature (-13) -(-10) (10)-0 0-12 33273 96843 45511 18.94 55.14 25.91 3968 12480 5888 17.76 55.87 26.36 0.94 1.01 1.02 0.32 0.34 0.34 profile curvature (-15) -(-10) (-10)-0 0-5 5-10 10-20 8724 44124 81519 36651 4681 4.96 25.11 46.39 20.86 2.66 2112 5568 8128 5184 1344 9.45 24.92 36.38 23.20 6.01 1.90 0.99 0.78 1.11 2.26 0.27 0.14 0.11 0.16 0.32 slope (o) 4o-8o 8o-16o 16o-35o 35o-55o >55o 15990 45271 99830 12120 84 9.22 26.12 57.60 6.99 0.04 64 3328 15104 3328 64 0.29 15.20 69.00 15.20 0.29 0.03 0.58 1.20 2.17 6.03 0.003 0.06 0.12 0.22 0.60 spi (-10) -(-6) (-6) -(-2) (-2)-2 2-6 6-7 6261 18724 60076 71244 19394 3.56 10.65 34.19 40.54 11.03 960 2432 3712 10112 5120 4.29 10.88 16.61 45.27 22.92 1.21 1.02 0.49 1.12 2.08 0.20 0.17 0.08 0.19 0.35 ndvi 1300-1500 1500-1700 1700-1900 1900-2100 2100-2685 27087 37659 37766 44657 28515 15.41 21.43 21.49 25.41 16.23 4864 6080 6400 4928 0 21.83 27.29 28.73 22.12 0 1.42 1.27 1.34 0.87 0 0.29 0.26 0.27 0.18 0 drainage density 0-0.5 0.5-1 1-1.5 1.5-2 2-2.91 142290 11577 9443 9817 2567 80.98 6.58 5.37 5.58 1.46 18432 3712 128 0 0 82.75 16.66 0.57 0 0 1.02 2.53 0.11 0 0 0.28 0.69 0.03 0 0 twi 1.7-8 8-16 16-20 105265 59795 10639 59.91 34.03 6.05 13952 8128 256 62.46 36.38 1.14 1.04 1.07 0.19 0.45 0.46 0.08 vs30 360-760 760-1500 17861 157865 10.16 89.83 4032 18176 18.15 81.84 1.79 0.91 0.66 0.34 lithology 1 2 162323 13348 92.37 7.59 20480 1728 92.21 7.78 1.00 1.02 0.49 0.51 rainfall 2500-2800 2800-3165 119300 56391 67.90 32.09 16000 6272 71.83 28.16 1.06 0.88 0.55 0.45 distance from road 0 100 100 250 250 350 350 450 >450 45297 37072 32586 31378 29156 25.81 21.12 18.56 17.88 16.61 11136 3328 5184 1088 1344 50.43 15.07 23.47 4.92 6.08 1.95 0.71 1.26 0.28 0.37 0.43 0.16 0.28 0.06 0.08 distance from drainage 0 1000 1000 2500 2500 3500 3500 4500 >4500 57274 32863 27695 25365 32292 32.63 18.72 15.78 14.45 18.40 6400 7360 2176 4480 1664 28.98 33.33 9.85 20.28 7.53 0.89 1.78 0.62 1.40 0.41 0.17 0.35 0.12 0.27 0.08 https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 53 source: analysis, 2022 figure 12. diagram of comparison of landslide susceptibility area in (%) in this research, focus is given to a relatively small research area that has a high probability of landslides. this aims to provide a more specific analysis compared to previous research conducted at the sub-district or district level. by utilizing ml method, this research is one of the first to apply this approach to a highway area that frequently experiences landslides, including the 12 most frequently landslide areas, according to the regional disaster management agency (bpbd). source: analysis, 2022 figure 13. landslide susceptibility mapping in this research we have been successfully implementing the ml approach to access and to map landslide vulnerability along taba penanjung kepahiang road. previously the ml has been widely used in landslide mapping in several regions in indonesia, such as aldiansyah & wardani (2024), darminto et al. (2021) and irawan et al., (2021). previous researches used ml with different algorithms, with almost the same input parameters. the broad field of study makes it easier to process data with good output, which is different from this research. this research only focuses on the road area, so it only maximizes the fr algorithm, but the results of this research can be used in the landslide mitigation process. finally, our results can also become a reference for the government and stakeholders in regional development, planning, and disaster management. it is also hoped that the resulting mapping can become an effective pre-disaster tool for carrying out specific and optimal mitigation in the area so that losses due to landslides can be minimized. it is hoped that this research can significantly contribute to efforts to prevent and manage disasters in highway areas that are vulnerable to landslides. 4. conclusion landslides are described as rock or soil movements influenced by various factors, causing economic and social losses. the bengkulu-kepahiang route has been identified as vulnerable due to diverse geomorphological conditions and landslides that disrupt road access and cause casualties the lack of alternative ways to advertise the impact of landslides on transportation has prompted the need for research to analyze and determine the dangers of landslides. this research aims to identify areas prone to landslides using ml as an effective disaster mitigation tool. ml models are essential for landslide modeling (purwanto, 2021), because they provide accurate vulnerability maps by combining several input parameters. the study area of the taba penanjung-kepahiang road has varying levels of vulnerability to landslides caused by several factors such as altitude, geological conditions, slope aspect, rainfall, curvature of the land, and distance from the fault. this research uses the fr https://doi.org/10.14710/geoplanning.11.1.43-56 abrar et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 43-56 doi: 10.14710/geoplanning.11.1.43-56 54 method to map landslide-prone areas, emphasizing the need for a holistic approach to handling and preventing landslides. vulnerability analysis integrates various factors, including topography, lithology, vegetation, distance from roads, and drainage characteristics. the landslide susceptibility index (lsi) is calculated by adding up the fr values for each factor thereby contributing to developing a comprehensive disaster mitigation model using ml. mapping landslide-prone areas can use a ml method approach with variable fr to help find the spatial relationship between landslide events and conditioning factors extracted using the weights of each class of each conditioning factor with an area. data processing uses 16 independent and dependent factors as historical points in the occurrence of landslides in the last five years. data processing uses pixel or grid methods with a specific grid size for each factor attribute value of the ml model. modeling-based representation of slope stability requires several indicators involving local characterization of landslide geometry and internal structure. the data processing results are a landslide hazard map with five classifications: very low, low, medium, high, and very high (very vulnerable). this research is still dominated by medium areas, precisely 28%. for future research, adding more parameters with more detailed classification and higher correlation can make the output results more accurate. this study emphasizes the importance of multidimensional landslide mitigation strategies, including sustainable land use planning, reforestation, early warning systems, and advanced technologies. the research aims to reduce the risk and impact of landslides, optimize transportation access, and increase community awareness and preparedness this research contributes valuable information to designing risk mitigation programs, facilitating rapid response to emergencies, ensuring safer road traffic, and minimizing material and non-material impacts. 5. acknowledgments the authors would like to thank the geohazards and climate change (gcc) laboratory, department of physics, bengkulu university, for technical assistance; the geospatial information agency (big); the bengkulu province regional disaster management agency (bpbd); and the sumatra vii river basin agency, for providing data in the study area for analysis; and the ministry of education, culture, research and technology for support in the form of student creativity program funds funded through the pkm-re scheme. most of the figures were generated by qgis (qgis development team, 2022). 6. references alcántara-ayala, i., & sassa, k. 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4 department of geography, malang state university, malang, indonesia 5 department of geomatic, itera, lampung, indonesia 6 faculty of geo-information science and earth observation (itc), university of twente, drienerlolaan 5, 7522 nb enschede, the netherland 7 department of geography, national university of singapore, 1 arts link, block as2, singapore 117570 8 faculty of arts & society, education & enabling, charles darwin university, ellengowan drive, casuarina nt 0810, australia 9 university of california, davis, usa doi: 10.14710/geoplanning.11.2.189-204 abstract indonesia is experiencing a rise in natural disasters due to its geographical position within a tropical region, with the upper solo river watershed exhibiting a heightened risk of flooding. this region has already suffered numerous floods due to excessive precipitation and insufficient drainage. susceptibility, hazard, and risk studies have been conducted to investigate this phenomenon but have been limited to specific regions within the catchment area. this study aims to construct a gisbased flood risk model using open-access spatial data (oasd) based on diverse physical characteristics, urbanization levels, and population. we used several oasd, including srtm, sentinel 2 msi, gpm v6, nasa-usda enhanced smap global soil moisture data, ghs-smod r2023a global human settlement layers, and ghsl: global population surfaces 1975-2030 (p2023a). the model integrates the risk parameters to identify flood risk using a weighted overlay in arcgis. the results demonstrate spatial heterogeneity in flood risk throughout the watershed. the result also reveals that surakarta city, with a high proportion of its area in the 'high' (57.3%) and 'very high' (29.54%) risk categories, is at the highest risk of flooding within the watershed. the study enhances understanding of this topic by comprehensively evaluating flood hazards, vulnerabilities, and risks. it highlights the significance of utilizing low-cost oasd to improve flood preparedness and response strategies. copyright © 2024 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction the frequency of natural catastrophes seems to rise due to natural phenomena and human activities, leading to significant human casualties, property damage, and material losses. human activities, such as deforestation, land clearance on mountain slopes, and cultivation of steeply sloping lands, have the potential to give rise to natural disasters. due to its geographical positioning within a region characterized by dynamic e-issn: 2355-6544 received: 03 september 2024; revised: 29 october 2024; accepted: 29 november 2024; available online: 30 november 2024; published: 04 december 2024. keywords: natural hazard, flood, risk, gis, solo river watershed *corresponding author(s) email: jumadi@ums.ac.id https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 189-204 doi: 10.14710/geoplanning.11.2.189-204 190 tectonic activity and volcanic activity resulting from the convergence of three tectonic plates, namely the indianaustralian plate, the pacific plate, and the eurasia plate, indonesia is prone to disasters (jumadi et al., 2016). floods in the country are significant natural disasters that can have disastrous consequences (susetyo, 2008), with indonesia having the highest incidence of flood disasters in southeast asian countries from 1980 to 2018 (samphantharak, 2019). the origin of flood events, a phenomenon experienced in the region for many years, can be traced back. heavy rains combined with depreciative drainage are one of the main causes of flooding in the area. the upper solo river watershed's previous flood histories are represented by historical statistics that show many major floods. especially in 1966, 2007, 2009, and 2010, the region suffered from severe flooding because of heavy rain (damayanti, 2011; fathimah & dahroni, 2014; gunawan, 2009), with these particular events being the most serious in recorded history. heavy rains, therefore, can devastate infrastructure, such as roads and bridges, and lead to the loss of life. the region has undergone periodic flooding, mainly due to heavy precipitation and inadequate drainage, since the 2007 flood, which was by far the worst in fifty years and submerged 11,500 homes (zein, 2010). subsequently, the municipal authorities have begun this preparation coupled with upgrades to the drainage infrastructure within the area, with the intention of reducing flooding. however, despite such continuing attempts, the upper solo river watershed remains susceptible to flooding, especially during the rainy season. in recent years, intense and extreme weather conditions have increased; as a result, the area has been significantly affected in the form of a higher frequency of flooding. for this reason, it is vital to manage and develop disaster-fighting measures to keep the impact of the floods on the residents in the region to a minimum. floods are natural calamities that must be prevented entirely. complex hydrological, geological, and geomorphological circumstances, combined with deforestation and urbanization, contribute to floods, resulting in substantial social, economic, and environmental consequences (amin et al., 2020; curebal et al., 2016; komolafe et al., 2020; mukherjee & singh, 2020; mustikaningrum et al., 2023; nada et al., 2023; sejati et al., 2023; purwitaningsih et al., 2020; saputra et al., 2022; skilodimou et al., 2019). factors like deforestation and urbanization disrupt soil absorption, resulting in unsuitable surfaces and causing further damage. as a consequence, rainwater collected from impervious surfaces accelerates the velocity and height of the intense flows that exacerbate flood events. urbanization and land use alterations can modify the built environment, impacting hydrological systems and altering river flow rates, hence heightening the risk of catastrophic flooding in specific sensitive areas (chagas et al., 2022). flooding constitutes a disaster as it adversely affects society, the economy, and the environment (komolafe et al., 2020; mukherjee & singh, 2020; skilodimou et al., 2019). floods result in fatalities, population displacement, infrastructure damage, agricultural and livestock losses, disease transmission, and water supply contamination (rincón et al., 2018). consequently, it is essential to provide floodrelated information to mitigate risks. a multitude of studies has been undertaken concerning flood analysis utilizing geographic information systems (gis) and remote sensing technologies (elkhrachy, 2015; greene & cruise, 1995; islam & sado, 2000; jumadi et al., 2024; ozkan & tarhan, 2016; paudyal, 1996; tehrany et al., 2017).various methodologies have been employed, including the spatial multi-criteria method (chen et al., 2012; zhou et al., 2021), cellular automata (ca) (ghimire et al., 2013), a one-dimensional hydraulic drainage network model (jamali et al., 2018), and analytical hierarchical processes (ahp) (negese et al., 2022; sarmah et al., 2020). nonetheless, research employing comprehensive open access spatial data for all variables is still limited. recent research has persistently utilized non-open access data sources for particular variables such as soil type (diriba et al., 2024; osman & das, 2023); rainfall (rana et al., 2024); flood depth (ayenew & kebede, 2023); flood-prone areas (li et al., 2024); and geology (osman & das, 2023). geographic information systems (gis) and remote sensing offer effective frameworks for assessing flood hazards; nonetheless, the use of proprietary or restricted data sets may hinder accessibility and repeatability in this domain. in contrast, unrestricted open-access geographical data may serve as a more economical and transparent alternative to democratize flood risk analysis in lowand middleincome nations. https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 184-204 doi: 10.14710/geoplanning.11.2.189-204 191 nevertheless, the degree to which open-access data can be included into flood risk studies necessitates further investigation in this underutilized domain; hence, traditional data sources should be employed, since they have been previously validated in other contexts. consequently, the integration of gis and remote sensing (rs) data with additional databases, facilitated by contemporary technical improvements, enables the identification, monitoring, and assessment of flood disasters (biswajeet & mardiana, 2009; haq et al., 2012; pradhan et al., 2009). the initial stage in comprehensively comprehending all aspects leading to flooding may entail the integration of diverse datasets into a broader framework for formulating flood control plans. various strategies have been utilized to tackle the flooding issue, including a flood hazard mapping tool that identifies regions at elevated risk of flooding. this study aims to construct a gis-based flood risk model using open-access spatial data (oasd) based on diverse physical characteristics, urbanization levels, and population. this study utilized physical characteristics and urbanization variables as stakeholders aim to produce a flood risk model for the upper reaches of the solo river by integrating gis and rs data in its construction process. in order to achieve the objective, the subsequent sections of this work are structured as follows. the following section presents a comprehensive overview of the methodologies, encompassing data collection techniques, descriptions of the parameters utilized for risk analysis, and the study framework employed. the subsequent section expounds on the outcomes and analysis, discussing the perils associated with flooding; vulnerability influenced by the degree of urban development, the associated risks; and the comprehensive findings. the concluding section presents a summary of the findings. 2. data and methods 2.1. study area the research was conducted in the upper part of the bengawan solo watershed (figure 1), located between 110º13'7.16" -110º26 '57.10" east and 7º26 '33.15" -8º6 '13.81" south. the watershed is the largest catchment area on java island, indonesia, covering a total area of 16,100 km2, and it plays a significant role in providing water for the daily needs and agriculture of those living in the area. it comprises three sub-watersheds: the upper bengawan solo, kali madiun, and the lower bengawan solo. the upper bengawan solo area covers approximately 6,000 km2. the topographic condition of the study area is varied; it is dominantly flat but with relatively undulating terrain in the northeast and northwest parts of the watershed close to the mountains. upper bengawan solo provides water from mount merapi and mount merbabu in the western part of the bengawan solo river and from mount lawu in the eastern region. figure 1. the study area https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 189-204 doi: 10.14710/geoplanning.11.2.189-204 192 2.2. datasets the study used several spatial datasets to develop the risk level model. first, shuttle radar topographic mission (srtm) data with 30m resolution was acquired as the digital elevation model. land cover and land use information was then interpreted from remote sensing image sentinel-2 (10 m spatial resolution). global precipitation measurement (gpm) data were utilized to obtain rainfall information, while soil moisture active passive (smap) data were used to establish the soil moisture condition of the study area. ghsl and ghssmod r2023a datasets are also used in this study. these datasets aid in analyzing human settlement patterns, density, and spatial changes, enhancing the demographic characteristics of the study area. all dataset characteristics and their sources are provided in table 1. table 1. flood risk parameter data sources no data description source derived data 1. dem shuttle radar topography mission (srtm). usgs (earth resources observation and science (eros) center, 2017) el, sl, fa, dr, cu, dd, twi 2. images sentinel 2 multispectral instrument esa (esa, 2023) lulc, ndvi 3. rainfall data global precipitation measurement (gpm) v6. nasa (nasa, 2019) rf 4. soil moisture data nasa-usda enhanced smap global soil moisture data. nasa (entekhabi et al., 2010) sm 5. level of urbanization ghs-smod r2023a global human settlement layers. european commission, joint research centre (jrc) (earth engine data catalog, 2023; pesaresi & politis, 2023; santillan & heipke, 2023; schiavina et al., 2023) dou 6. population ghsl: global population surfaces 1975-2030 (p2023a) earth engine data catalog (google earth engine data catalog, 2023) pop note: el – elevation, sl – slope, fa – flow accumulation, dr – distance to rivers, cu – curvature, dd – drainage density, twi – topographic wetness index, lulc – land use land cover, ndvi – normalized difference vegetation index, rf – rainfall, sm – soil moisture, ursd – level of urbanization, pop population. 2.3. research framework the study area flood-risk levels were determined using the general risk function (equation 1) (sar et al., 2015; unisdr, 2004). the study was separated into three main sections: flood hazard, vulnerability and risk analysis (figure 2). flood hazard was determined by weighted overlay operation of certain physical parameters related to flood occurrences (table 2). similarly, vulnerability was indicated by using weighted overlay by parameters related to population and level of urbanization (table 3). finally, risk was determined by multiplying hazard by vulnerability (samarasinghea et al., 2010). 𝑅 = 𝐻 𝑥 𝑉……………. (equation. 1) various parameters are used to define flood hazards, such as el, sl, fa, dr, cu, dd, twi, lulc, ndvi, rf, and sm are modified from negese et al. (2022) and purwanto et al. (2023). low-lying locations exhibit significant water accumulation, heightening flooding threats due to markedly reduced flow velocity in the flat portions of the terrain. the proximity of a river increases the likelihood of floods. areas at lower elevations near rivers are more susceptible to flooding due to elevated discharge rates and reduced water velocity. flood events are precipitated by flow accumulation and drainage density; elevated levels of flow accumulation and drainage density augment the probability of flooding. consequently, land use and land cover (lulc) are critical to assessing flood risk, as areas with high vegetation density demonstrate reduced susceptibility due to delayed water movement and enhanced infiltration rates. consequently, soil qualities are crucial; fine soils are recognized for accelerating surface runoff while diminishing permeability, thereby heightening the likelihood of inundation. https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 184-204 doi: 10.14710/geoplanning.11.2.189-204 193 table 2. flood hazard indicators no factor classification degree of flood hazard score weight (%) 1. slope (sl) (degree) > 45 very low 1 15 25-45 low 2 15-25 moderate 3 8-15 high 4 0-8 very high 5 2. rainfall (rf) (mm) 1,696-1,728 very low 1 11 1,728-1,761 low 2 1,761-1,793 moderate 3 1,793-1,825 high 4 > 1,825 very high 5 3. drainage density (dd) (km/km2) 0-0.372 very low 1 8 0.372-0.754 low 2 0.754-1.106 moderate 3 1.106-1.519 high 4 >1.519 very high 5 4. soil moisture (sm)(m3/m3) 15.4-16.14 very low 2 4 16.14-16.68 low 16.68-17.42 moderate 3 17.42-18.49 high 4 18.49-20.53 very high 5 5. land use/ land cover (lulc) dense vegetation very low 1 7 bare land low 2 open mining moderate 3 cropland high 4 built-up area very high 5 6. elevation (el) (m amsl) 558.8 697 very low 1 18 420.6 558.8 low 2 282.4 420.6 moderate 3 144.2 282.4 high 4 6 144.2 very high 5 7. distance to river (dr) (m) 0.372 1.2976 very high 1 11 1.2976 2.2232 high 2 2.2232 3.1488 moderate 3 3.1488 4.0744 low 4 4.0744 5 very low 5 8. ndvi – 0.16–0.29 very low 1 3 0.29–0.38 low 2 0.38–0.45 moderate 3 0.45–0.51 high 4 0.51–0.59 very high 5 9. curvature (ct) convex (positive) moderate 1 2 concave (negative) high 2 flat very high 3 10. flow accumulation (fa) < 250 very low 1 15 250–2195 low 2 2195–3415 high 4 3415–15,125 very high 5 11. topographic wetness index (twi) 2.48–5.91 very low 1 6 5.91–8.09 low 2 8.09–10.18 moderate 3 10.18–12.63 high 4 12.63–22.77 very high 5 additional factors encompass ndvi and curvature, which further assessing an area's vulnerability to flooding events. regions with greater vegetation cover exhibit prolonged precipitation runoff, whereas flat plain areas are especially susceptible to flooding. finally, precipitation and twi indices were utilized, revealing that significant rainfall provided substantial water, which, when combined with twi, indicated regions likely to possess saturated soils prone to flooding. curvature is a fundamental characteristic of the earth's surface, articulated by geomorphometric techniques. in this instance, it may serve as an effective instrument for flood https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 189-204 doi: 10.14710/geoplanning.11.2.189-204 194 management because to its correlation with water distribution and concentration (faisal & hayakawa, 2023; sofia, 2020; xiong et al., 2022). gis-based flood hazard maps were generated using arcgis overlays of flood components assigned specific weights. flood formation occurs due to eleven primary parameters: land slope (sl), elevation (el), flow accumulation (fa), rainfall (rf), drainage density (dd), distance to rivers (dr), topographic wetness index (twi), normalized difference vegetation index (ndvi), land use land cover (lulc), soil type (st), and curvature of water surface (cu)—consequently, raster data formats generated spatially refined information regarding these 11 parameters. low-lying areas exhibit significant water accumulation, heightening the risk of floods due to markedly reduced flow velocity in the flat portions of the terrain. the proximity of rivers increases the likelihood of floods. areas at lower elevations near rivers are more susceptible to flooding due to elevated discharge rates and reduced water velocity. flood events are precipitated by flow buildup and drainage density; elevated levels of both factors augment the probability of flooding. consequently, land use and land cover (lulc) are critical to assessing flood risk, as areas with high vegetation density demonstrate reduced susceptibility due to delayed water movement and enhanced infiltration rates. consequently, soil properties emerge as a significant factor: fine soils are recognized for accelerating surface runoff while reducing permeability, heightening the likelihood of inundation. additional criteria encompass ndvi and curvature, further influencing an area's vulnerability to flooding events. regions with greater vegetation cover experience prolonged rainwater runoff, whereas flat land areas are especially susceptible to flooding. finally, precipitation and twi indices were utilized, as significant rainfall provided additional water, which, when combined with twi, indicated regions likely to possess saturated soils susceptible to flooding. curvature is a fundamental characteristic of the earth's surface, articulated by geomorphometric methods. in this context, it may serve as an effective instrument for flood management because to its correlation with water distribution and concentration (faisal & hayakawa, 2023; sofia, 2020; xiong et al., 2022). gis-based flood hazard maps were generated using arcgis overlays of flood components assigned specific weights. flood formation occurs due to eleven primary parameters: land slope (sl), elevation (el), flow accumulation (fa), rainfall (rf), drainage density (dd), distance to rivers (dr), topographic wetness index (twi), normalized difference vegetation index (ndvi), land use land cover (lulc), soil type (st), and curvature of water surface (cu). consequently, raster data formats were employed to generate spatially refined information regarding these 11 parameters. table 3. vulnerability indicators no. factor classification degree of vulnerability score weight (%) 1. population 0 12 very low 1 50 13 37 low 2 38 75 moderate 3 76 127 high 4 128 290 very high 5 2. degree of urbanization water unclassified 0 50 very low density rural very low 1 low density rural low 2 rural cluster low 2 suburban or peri-urban moderate 3 semi-dense urban cluster high 4 dense urban cluster high 4 urban center very high 5 in addition, we quantified vulnerability based on the density of inhabitants. the higher the density, the higher the vulnerability. the ghs-smod r2023a dataset was used to provide this data. this is a spatial dataset that delineates the distribution and evolution of human settlements across the globe, tracking changes from 1975 through to 2030 in five-year increments. the dataset is instrumental in evaluating the susceptibility of various settlement types, highlighting an increased vulnerability in more densely populated areas (melchiorri, 2022). the 2023a release of the global human settlement layer ghs-smod settlement segments were classified https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 184-204 doi: 10.14710/geoplanning.11.2.189-204 195 into eight distinct types (earth engine data catalog, 2023; european commission, 2023) (table 3). for each type, a specific vulnerability level was determined, alongside a score that progressed from "very low" (1) for areas with very low density rural to "high" (5) for urban centers. this segmentation indicated a direct correlation between the compactness of the population of a settlement and its vulnerability to risks. in addition, the ghslderived global population surfaces from 1975-2030 (p2023a) classify flood vulnerability levels into five categories: very low (0-12), low (13-37), moderate (38-75), high (76-127), and very high (128-290), based on population density. the impact of this analysis is significant, as it provides understanding of vulnerability that can inform more effective flood risk management. by leveraging this data, policymakers and emergency responders can prioritize resources and interventions in high-density areas, ultimately reducing the potential for loss of life and damage to property during flood events. figure 2. research framework 3. result and discussion 3.1 flood hazard model figure 3 shows all the parameters used to define flood hazard, while the distribution of flood indicator classes in determining the hazard can be seen in figure 4. figure 5 shows the distribution of environmental and geographical factors across different flood hazard levels, providing quantitative insight into their impact on flood risk. elevation is included in 57.64% of the "very high" flood risk areas, with slopes in 49.15%. distance to rivers is a critical factor in flood risk, with 42.24% of these areas near water bodies. flow accumulation is high https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 189-204 doi: 10.14710/geoplanning.11.2.189-204 196 in 99.81% of the "very low" risk areas, indicating efficient water dispersal mechanisms. the ndvi shows that vegetation cover decreases significantly in "very high" risk areas, indicating the protective role of vegetation against flooding. rainfall contributes differently across hazard levels, with a notable percentage of 28.27% in "moderate" risk areas. soil moisture is higher in lower-risk areas and decreases to 2.85% in "very high" areas, highlighting the influence of soil water content on flood susceptibility. the topographic wetness index (twi) indicates potential water accumulation based on topography, with high levels in "very low" risk areas, but lower levels in "very high" areas. land use land cover (lulc) falls from 34.97% in "very low" risk areas to 7.76% in "very high" ones, illustrating the impact of land cover and human land use on flood risk. figure 3. flood hazard parameters. note: (a) sl, (b) rf, (c) dd, (d) sm, (e) lulc, (f) el, (g) dr, (h) ndvi, (i) cu, (j) fa, (k) twi figure 4. the percentage of hazard classes based on the parameters https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 184-204 doi: 10.14710/geoplanning.11.2.189-204 197 figure 5. reclassified parameters. note: (a) sl, (b) rf, (c) dd, (d) sm, (e) lulc, (f) el, (g) dr, (h) ndvi, (i) cu, (j) fa, (k) twi figure 6. flood hazard map 3.2 flood vulnerability figures 7 and 8 show the vulnerability indicators and their classification, respectively. figure 7a shows the level of urbanization, which is classified into vulnerability levels from very low to very high in figure 8a. figure 7b shows population density, while figure 8b shows the classification from very low to very high. vulnerability distribution across the research area is shown in figure 8. based on weighted overlay, this map was produced as a composite from figures 7b and 7c. the figure shows that urban areas are moderate to very https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 189-204 doi: 10.14710/geoplanning.11.2.189-204 198 highly vulnerable. for example, surakarta, boyolali, karanganyar, klaten, and sukoharjo exhibit a high presence within the ‘very high’ classification and a significant one within the 'very high' and 'moderate' classifications. figure 7. vulnerability factors and their classification figure 8. vulnerability factors and their classification figure 9. vulnerability class 3.3 flood risk model table 4 indicates the flood risk in different cities and regencies within the watershed, whereas the spatial distribution is presented in figure 9. boyolali possesses a substantial proportion of land categorized as 'moderate' risk, indicating a comprehensive approach to risk management. at the same time, karanganyar exhibits diverse https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 184-204 doi: 10.14710/geoplanning.11.2.189-204 199 risk levels, displaying an apparent propensity towards the 'very low' and 'high' classifications. the majority of the kota surakarta area, 57.3%, is classified as 'high' risk, with 29.54% classified as 'very high,' highlighting the urgent need for comprehensive urban planning and measures to mitigate flood disasters. figure 10. flood risk map table 4. area risk classes for each region. regency/city area class total wb % vl % l % m % h % vh % boyolali 1.88 0.46 165.64 40.31 137.14 33.38 64.56 15.71 40.07 9.75 1.62 0.39 410.9 gunung kidul 0 0 34.28 91.35 3.23 8.6 0.02 0.05 0 0 0 0 37.52 karanganyar 1.3 0.24 261.23 47.74 170.08 31.09 63.66 11.64 48.5 8.86 2.36 0.43 547.14 klaten 1.88 0.29 191.88 29.75 346.11 53.66 79.84 12.38 25.27 3.92 0 0 644.99 surakarta 0.21 0.45 0 0 1.93 4.21 3.89 8.5 26.24 57.3 13.53 29.54 45.79 pacitan 0 0 66.71 97.21 1.91 2.79 0 0 0 0 0 0 68.62 semarang 0 0 2.47 16.27 9.43 62.07 2.91 19.15 0.29 1.9 0.09 0.62 15.19 sleman 0 0 4.13 99.17 0.03 0.83 0 0 0 0 0 0 4.17 sragen 0 0 0 0 0 0 0.01 100 0 0 0 0 0.01 sukoharjo 1.7 0.35 59.76 12.33 232.44 47.96 104.5 21.56 78.52 16.2 7.74 1.6 484.64 wonogiri 54.15 3.92 983.05 71.1 365.41 26.43 30.5 2.21 9.22 0.67 0 0 1382.57 note: wb – water body (sq km), vl – very low risk (sq km), l – low risk (sq km), m – moderate risk (sq km), h – high risk (sq km), vh – very high risk (sq km). 3.4 discussion the study has revealed varied flood vulnerabilities across the upper solo river watershed, highlighting regions of significant susceptibility and the necessity for customized flood mitigation strategies based on the data analysis. this analysis demonstrates a multifaceted and diverse environment in which flood risk differs considerably among the various cities/regencies across the watershed (anna, 2021).additionally, a large river passes through surakarta (absori et al., 2023) which makes the place prone to floods. a study conducted by https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 189-204 doi: 10.14710/geoplanning.11.2.189-204 200 hussain et al. (2021), pointed out that closeness to rivers, high rainfalls, elevation, and numerous socio-economic factors were the main determinants of the seriousness of flood risk. it should be known that increasing the level of awareness and understanding of flood risks by the local government (cisternas et al., 2024), such as in surakarta, is one way of increasing resilience while at the same time mitigating vulnerabilities (muryani et al., 2021). more work must be done to design safety measures against floods that consider flood vulnerability features outlined in this research. risk factors and vulnerabilities should be assessed when formulating relevant mitigation plans for flooding (orru et al., 2023). by integrating components of physical, economic, and societal vulnerability within an interconnected system, there is a more extensive scope through which disaster impacts can be analysed, providing the direction for further improvement of mitigation strategies (ward et al., 2012). additionally, using gis-based multi-criteria approaches can help determine flood risks among vulnerable areas to favour mitigation choices and resource distribution (chakraborty et al., 2023). using spatial data can benefit decision-makers in designing mitigation plans precisely (bakhtiari et al., 2024; rezvani et al., 2023). for targeted mitigation measures, creation purposes like geographical location or attributes may identify at-risk areas (liu & li, 2016; tellman et al., 2020). concerning social aspects, this study looks into urbanization and population density levels. this work has added new dimensions to our understanding of flood vulnerability dynamics, especially in the upper solo basin, which helps us think about what we can face here. therefore, it strengthens the use of gis and remote sensing data, especially oasd to analyse flood risk. determining where the danger lies encourages focused and efficacious efforts to address flooding problems (chakraborty et al., 2023). on the contrary, some limitations existed during the study. for example, long-term climatic change variables and fast-growing urbanization characteristics were not fully integrated in designing the risk model formulation. therefore, future flood estimates should include hydrological data and other factors to maintain accuracy. however, these results come with limitations. changes in climate over several decades or the rapid expansion of urban areas within the upper solo basin may not have been considered by this risk model (marhaento et al., 2021). information constraints or inadequate integration of non-hydrological variables could lead to less accurate forecasts of forthcoming flood risks. besides, there has been an argument that risk maps alone might not properly illustrate susceptible regions unless they undergo supplementary validation processes. in any given research area, the social vulnerability of local populations is a crucial consideration that would require identifying probable locations to inform mitigation strategies. consequently, a gis-based methodology is useful because it helps us create flood risk maps needed for targeted interventions. after all, they show certain areas where more resources should be directed. the high-risk results for surakarta substantiate the necessity for evidence-based mitigation actions to enhance the area’s resilience against future flooding. moreover, surakarta is situated next to a notable river; hence, it is at risk of flooding. the study by hussain et al. (2021) underscored the role of the proximity of rivers, high precipitation levels, relief levels, and many socio-economic factors in determining a location’s criticality. realizing that people’s knowledge about places like surakarta, prone to floods, and their understanding of river inundation risks are essential to increase the population resilience and reduce vulnerabilities, as muryani et al. (2021) indicate. it is further evident from research findings that more effort is needed to develop flood safety measures suited to the characteristics associated with flood vulnerability identified here. if we wish to protect our cities from going under, we must know what puts them there and what makes them vulnerable. the integration of physical, economic, and societal vulnerability components results in cohesive framework that enhances disaster impact analysis, thereby offering more than adequate guidance for mitigation strategies (ward et al., 2012). in addition, gis-enabled multicriteria approaches can quantify flood risks within exposed zones, thereby giving preference to appropriate mitigation over available resources (chakraborty et al., 2023). high-risk regions' geographical attributes are very important in designing custom-tailored mitigation plans. this work’s urban dimension hinged on levels of urbanization and population density. for example, in the upper solo watershed, this study provides a new perspective on the risk to floods among different populations thus guiding risk understanding. therefore, this brings out the importance of detailed regional spatial analysis https://doi.org/10.14710/geoplanning.11.2.189-204 jumadi et al. / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 184-204 doi: 10.14710/geoplanning.11.2.189-204 201 using gis data and remote sensing techniques. identification of potential danger spots is thus supportive of specific and efficient strategies to counteract flooding (chakraborty et al., 2023). additionally, the historical data available serves as an extra reassurance for these conclusions. 4. conclusion this study uses oasd to build a gis-based flood risk model based on population, urbanization levels, and various physical factors. flood risk assessment in the upper solo river watershed has been successfully achieved using remote sensing data and geographic information systems. the results suggest that specific areas, namely wonogiri, karanganyar, and boyolali, exhibit a lower risk of floods. conversely, sukoharjo, surakarta, and certain parts of karanganyar have an elevated risk, with surakarta being the highest. the research highlights the need to incorporate physical and rural-urban attributes when evaluating flood risk, offering significant perspectives for formulating efficient approaches to preventing catastrophic events. the integrated methodology, which combines gis technology with an analysis of physical and social factors, highlights the interconnectedness of natural and human elements in shaping the probability of floods. the research findings significantly contribute to understanding the specific geographical area and to the broader scholarly debate on mitigating catastrophic risks. the possible application of the research methodologies and results to comparable locations further increases the global importance and impact of the research. the research on flood risk models has several limitations, mainly limited field validation. the model may not be sensitive to future factors like climate change, accelerating extreme rainfall patterns, and rapid land development in urban areas. additionally, remote sensing data and gis may not reflect actual field conditions fully. future research should consider long-term climate change scenarios, updated high-resolution data, population growth, and land use change to produce more accurate flood risk projections. a collaborative approach involving field data and hydrological modelling based on climate data could improve the validity of the findings. additionally, risk mapping at the micro or neighbourhood level could help develop more targeted mitigation strategies for local-scale implementation. the study recommends flood mitigation strategies in the study area, including developing green infrastructure, sustainable drainage systems, and increased collaboration with the private sector. green infrastructure, such as urban forests and infiltration parks, can reduce flooding impact and increase groundwater absorption. sustainable drainage systems optimize water management and reduce surface water flow. flood risk zoning should be implemented throughout the watershed, dividing areas based on vulnerability and potential flooding. development and spatial planning regulations should be strengthened to align with flood mitigation efforts. local governments should tighten building permits in flood-prone areas and require flood-resistant designs for new construction in high-risk areas. community participation in flood risk management programs is crucial. utilizing remote sensing technology and gis can help predict potential flooding risks more accurately and support risk-based spatial planning. capacity building of local governments and institutions is essential for supporting flood mitigation strategies, including training technical staff, strengthening coordination, and increasing disaster mitigation 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[crossref] https://doi.org/10.14710/geoplanning.11.2.189-204 https://doi.org/10.1007/s40808-015-0039-9 https://doi.org/10.1016/j.ijdrr.2020.101659 https://doi.org/10.5937/gp27-40927 https://doi.org/10.1007/s12665-018-8003-4 https://doi.org/10.1016/j.geomorph.2020.107055 https://doi.org/10.1016/j.geomorph.2020.107055 https://doi.org/10.3390/su12156006 https://doi.org/10.1002/rhc3.11 https://doi.org/10.1016/j.earscirev.2022.104191 https://doi.org/10.3390/w13111483 | 263 geoplanning vol 4, no. 2, 2018, 263-272 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.263-272 model of soil and water conservation measures application based on district spatial planning in mamasa watershed, south sulawesi k. murtilaksonoa, s.m. yusufa, r.k. astutia, s. arifina a bogor agricultural institute, bogor, indonesia abstract: depletion of watershed carrying capacity cannot be omitted from mismanagement of the watershed. the integration between swat model and remote sensing data are able to identify, assess, and evaluate watershed problem as well as a tool to apply the mitigation of the problem. the aim of this study was to arrange the scenario of watershed management, and decide the best recommendation of sustainable watershed management of mamasa sub watershed. the best recommendation was decided by hydrology parameters, e.i. surface runoff, sediment, and runoff coefficient. hydrology characteristics of mamasa sub watershed was analyzed based on land use data of year 2012 and climate data for period of 2010-2012. the scenarios were application of bunch and mulch in slope 1-15%; bunch terrace (scenario 1), mulch and strip grass in slope 15-25% (scenario 2), alley cropping in slope 25-40% (scenario 3), and combination scenario 1, 2, 3 with agroforestry in slope > 40% (scenario4). surface runoff value of mamasa sub watershed is 581.35 mm, while lateral flow, groundwater flow, runoff coefficient, and sediment yield of 640.72 mm, 228.17 mm, 0.29, and 187.213 ton/ha respectively. based on the scenario’s simulation, the fourth scenario was able to reduce surface runoff and sediment yield of 33.441% and of 51.213%, while the runoff coefficient declined to 0.194. thereby, the fourth scenario is recommended to be applied in mamasa sub watershed so that the sustainability in the watershed can be achieved. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. murtilaksono, k., yusuf, s.m., astuti, r.k., & arifin, s. (2017). model of soil and water conservation measures application based on district spatial planning in mamasa watershed, south sulawesi. geoplanning: journal of geomatics and planning, 4(2), 263-372. doi: 10.14710/geoplanning.4.2.263-272 1. introduction mamasa watershed potentially degraded due to the topography of its area which was dominated by slopes >40% (department of forestry, 2010). cultivation activities, encroachment, illegal logging, overharvesting, tillage without conservation practices, and the other activities of the community in the forest area were also to be the factors that contributed to land degradation (department of forestry, 2010). as 66.17% (76,497 hectares) area of mamasa watershed is categorized as degraded land. previous research showed that amount of erosion in the mamasa watershed in 2010 was amounted to 19,561,011 tons/year. the amount of erosion indicated the amount of sediment that occurs in the mamasa watershed. the volume of water in reservoirs of bakaru hydroelectric power is decreasing in average of 301,707 m3/year. mamasa watershed management underscore the need for planning, monitoring and evaluation in order to ensure the preservation of water distribution throughout the year and to minimize the increase in surface runoff or sediment. the assessment can be done by using a model such as described in previous review studies (srinivasan et al., 2010; cibin et al., 2013; daniel, 2011; qiu et al., 2012; himanshu et al., 2016; shi et al., 2017). from the literatures, it is known that swat hydrologic models are simplification representations of actual soil, land use, topographic, climate, and other interactions that occur within natural hydrologic and environmental systems that can be used to analyze runoff, sediment, and water balance. the model also can be effectively and efficiently used for simulating a various of conditions that could not be possible to measure in such complex conditions. model also can be chosen base on watershed representation and spatial scale (daniel, 2011). open access article info: received: 13 january 2017 in revised form: 30 april 2017 accepted: 5 august 2017 available online: 30 oct 2017 keywords: swat, remote sensing data, soil and water conservation, sustainable watershed management. corresponding author: sri malahayati yusuf bogor agricultural institute, bogor, indonesia email: malahayati10@gmail.com https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.14710/geoplanning.4.2.263-272 mailto:malahayati10@gmail.com murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 263-272 doi: 10.14710/geoplanning.4.2.263-272 264 | one model that can be used is swat hydrological model (arnold et al., 2012; neitsch et al., 2011; suryavanshi et al., 2017) it is widely used by researchers, government agency and other users. swat was developed to predict the impact of land management practices on water, sediment, agricultural chemical yields that enter the river in a complex watershed with varying soil, land use and management conditions over a long time period. several studies (moriasi et al., 2011; sunandar & suhendang, 2014; yusuf et al., 2016) showed that swat was able to described the impacts of land management on hydrological characteristics of watershed and successfully used in scenario analysis of variety conditions of climatic and environmental worldwide (douglas-mankin et al., 2010; gassman et al., 2014; krysanova & white, 2015). the model also able to analyze and to create some scenarios including best management practices (soil and water conservation technique), climate change, and land use change in a watershed. applying scenario of best management practices such as nutrient management, constructed wetland, and filter strip in big ditch watershed, indicate that the average nitrate-n load reduce of 15.8%, 17.1%, 9.9%, respectively (getahun & keefer, 2016). application of conservation crop rotation and no-till is the best scenario in reducing sediment load in st. joseph river watershed, while the conservation crop rotation and cover crop reduced the big amount of nutrients (her et al., 2016). jang et al., (2017) research showed that application of bmp in haean highland agricultural catchment of south korea such as vegetation filter strip, fertilizer control, and rice straw mulching could reduce the sediment load around 16-34.8%, 4.9-16.4%, and 3-14.1%, respectively. the other research by liu et al., (2016) indicated that application of nutrient management, buffer strip, cover crop, and wetland restoration in grand river watershed, southern ontario, reduce the sediment at the watershed outlet ranges between 0 – 5.54%, total phosphorus from 6.28 up to 41.32%, and total nitrogen between 1.97 – 18.54%. the aim of this study was to (1) estimate the hydrological characteristics including surface flow, lateral flow, base flow, and sediment yield in ungauged watershed of mamasa using swat model, (2) arrange several scenarios for the best management practices in mamasa watershed. this research is important for the stakeholders in mamasa watershed because the result will be used to evaluate the condition of mamasa watershed. 2. data and methods mamasa watershed is one of multifunctional watershed on the island of sulawesi, indonesia. the watershed is source of irrigation water, raw water for the people who live around it and hydroelectric power (bakaru power plant) (department of forestry, 2010). geographically, mamasa watershed is located between 119o13’-120o21’ e and 2o43’-3o46’ s and administratively between tana toraja, pinrang, enrekang, polewali mandar, and mamasa regency, which is located at west sulawesi (upstream of the watershed) and south sulawesi province (downstream of the watershed), indonesia. the total area of mamasa watershed is 115,607 ha. the study location is presented in figure 1. 2.1. data materials and data required in this study are: a) dem (digital elevation model, 30 x 30 m) map, b) land use map in 2014, c) soil map, d) daily climate data (rainfall, solar radiation, relative humidity, wind speed and maximum and minimum air temperature). primary data of soil characteristics was obtained from the laboratory analysis of soil samples for chemical and physical parameters of the soil (soil texture, bulk density, available water content, permeability, soil erodibility value and soil organic matter content). field observations included the measurement of effective depth, thickness of the horizon, soil structure, the pattern of crop management, conservation techniques, and river characteristic (roughness of the river and channel cover). https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2018, 263-272 doi: 10.14710/geoplanning.4.2,63-272 | 265 figure 1. the study location 2.2. method analysis of the hydrological characteristics of mamasa watershed was performed by using swat model version 2012 (swat2012) with the arcgis interface (arcgis 10.1). there are four (4) steps in swat model: delineate watershed, sub watershed, and stream network using digital elevation model (dem) data (1), generate hydrology response unit/hru in watershed using the information both map and characteristics of soil, land use, and slope (2), linking the hru with climate data, and write the table input of model (3), and running the simulation for the certain year (4). the model analyzed the hydrology by using some equations consisted of scs curve number, kinematic storage model, and the steady-state response of groundwater flow to recharge equation. the formula of scs curve number is (equation 1): qsurf = 2 )( )( sirday irday a a +− − ………………………………………………………………………………………. (1) where qsurf is amount of surface runoff volume in day i (mm), rday is amount of precipitation in day i (mm), ia is initial loss of surface storage, interception, and infiltration (mm), and s is retention parameter (mm). the kinematic storage model to calculate the lateral flow is (equation 2): qlat = 0.024          hilld satexcessly l slpksw . ...2 , …………………………………………………………………………… (2) qlat is lateral volume that flow into the main channel in day i (mm), swly,excess is excessive water in soil profile (mm), ksat is saturated hydraulic conductivity (mm/hour), slp is slope (mm), φd is soil porosity (mm/mm), and lhill is length of slope (m). the steady-state response of groundwater flow to recharge equation is (equation 3): qgw = wtbl gw sat xh l k 2 .8000 ………………………………………………………………………………………………. (3) where qgw is ground water volume (mm), ksat is saturated hydraulic conductivity (mm/hour), l2gw is distance between sub watershed to the main channel (m) and hwtbl is height of water table (m). the contribution of total runoff (amount of qsurf, qlat, and qgw) and sediment to the sub watershed were used to identify the degraded sub watershed in mamasa watershed. and then, the scenarios of soil and water conservation techniques were applied in the sub watershed to improve its condition. the scenarios are presented in table 1. https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 263-272 doi: 10.14710/geoplanning.4.2.263-272 266 | table 1. scenario of land management and soil and water conservation technique no. land use type conservation technique scenario slope (%) technique 1 2 3 4 1 upland agriculture, plantation/unirrigated agricultural field mixed with bushes 0-15 bunch and mulch √ √ √ √ 2 upland agriculture, plantation/unirrigated agricultural field mixed with bushes 15-25 bunch terrace, mulch, and strip grass √ √ √ 3 upland agriculture, plantation/unirrigated agricultural field mixed with bushes 25-40 alley cropping and silt pilt √ √ 4 upland agriculture, plantation/unirrigated agricultural field mixed with bushes >40 agroforestry √ 3. results and discussion 3.1. stream network, sub watershed, and hru the results from the first step in running the swat model are stream network, sub watershed, and watershed boundary. there are 16 sub watersheds in mamasa watershed, namely sub watershed 1 until sub watershed 16. sub watershed 1 is located in upstream, and sub watershed 16 is located in the downstream. the establishment of sub watershed was depended on area of threshold that used to delineate the stream network. the threshold is 2,500 ha, which is generates only the main channel in mamasa watershed. river network and sub watershed map is presented in figure 2. figure 2. river network and sub watershed map the results from the second step are hrus. hrus are small analysis unit in swat model that is derived from overlay the soil, land use and slope map and its characteristics. so, each hru has uniquely information about combination of soil, land use, and slope. the total amount of hrus in a watershed is depends on threshold which is used for each map. the hrus in mamasa watershed were performed using threshold by percentage method (0%) so that generated 626 hrus in mamasa watershed. the spatial distribution of hrus is presented in figure 3. https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2018, 263-272 doi: 10.14710/geoplanning.4.2,63-272 | 267 figure 3. hrus distribution in mamasa watershed 3.2. effect of land use to hydrological characteristics in mamasa watershed hydrological characteristics of mamasa watershed is presented in table 2. the result showed that the highest surface runoff in mamasa watershed was occur in december (304.92 mm) as 38.16% from the rainfall in the same month, while the lower was occur in october as 4.47% from the rainfall. the highest contribution of the rainfall to lateral flow was occurred in october as 37.74% and the highest base flow occurred in august as 28.65%. flow discharge of mamasa watershed is presented in figure 4. even this model was not calibrated because mamasa watershed is the ungauged watershed, but the result can be used to analyze the condition of watershed due to the input detail field data of soil characteristics into the model and adjusted sensitivity parameter base on watershed characteristics so the uncertainty from model can be reduced. the same study in ungagged basin of central vietnam showed that surface runoff was occurred in october with approximately 764 mm and the highest sediment yield as 580 ton per hectare (emam et al., 2016). but this study still calibrated using the regionalization river discharge and showed the nash suttcliffe efficiency range between 0.67-0.73. from result in table 2, then flow regime and runoff coefficient were calculated for mamasa watershed. the flow regime coefficient was 40.26 which categorized as lower rank (regulation of ministry of forestry ri no. p.61/menhut-ii/2014), while the runoff coefficient was 0.29 (lower category). this value indicates that mamasa watershed is still in good condition. but, the dominant steep slope in this watershed remains as the cause of the damage in the watershed. tabel 2. hydrological characteristics in mamasa watershed month average rainfall surface flow lateral flow base flow sediment yield mm -------------------mm ----------------tons/ha january 112.33 26.48 36.85 4.1 15.19 february 233.51 71.28 84.26 17.51 35.18 march 181.54 53.76 63.9 39.34 28.8 april 315.35 111.04 99.92 35.72 72.71 may 216.71 73.86 61.77 41.66 14.13 june 118.28 22.21 37.04 25.35 2.14 july 186.36 59.74 44.18 15.8 9.02 augustus 17.42 0 4.69 4.99 0 september 45.18 2.89 13.09 0.74 1.13 https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 263-272 doi: 10.14710/geoplanning.4.2.263-272 268 | october 80.7 3.61 30.46 0.23 0.11 november 180.5 40.13 66.75 5.34 1.66 december 304.92 116.35 97.81 37.39 7.54 total 1,992.8 581.35 640.72 228.17 187.61 figure 4. daily discharge of mamasa watershed in 2012 3.3. degraded of sub watershed in mamasa degraded sub watershed was determined based on contribution of total runoff and total sediment to the sub watershed. the total runoff and total sediment in mamasa watershed is presented in table 3 and spatially presented in figure 5 and figure 6. based on the data, sub watershed 15 was the sub watershed that most degraded with the contribution of total runoff and total sediment to its sub watershed as 46% and 59.75%, respectively. the second degraded sub watershed was sub watershed 16, followed by sub watershed 6. if the mamasa watershed is not well-managed, it is possible that the degradation in mamasa watershed will increase. in order to decrease the degradation in mamasa watershed, the management planning especially related to soil and water conservation is arranged for this watershed. table 3. total runoff and sediment from swat model in sub watershed, mamasa watershed sub watershed area (ha) total runoff (mm) % total runoff to sub watershed total sediment (tons/ha) % sediment to sub watershed 1 2,842.50 2,233.84 4.24 2,110.90 0.66 2 3,085.40 2,229.12 4.59 1,690.02 0.58 3 3,082.00 2,245.47 4.62 1,776.87 0.61 4 3,790.00 2,208.13 5.58 1,732.80 0.73 5 3,858.10 2,215.19 5.70 1,881.14 0.80 6 9,798.50 2,231.12 14.59 1,812.57 1.96 7 7,379.00 220.79 1.09 55.88 0.05 8 4,035.00 233.12 0.63 47.72 0.02 9 10,568.00 163.30 1.15 94.56 0.11 10 6,985.80 195.57 0.91 173.69 0.13 11 4.09 42.28 0.00 4.59 0.00 12 5,524.00 236.64 0.87 142.25 0.09 13 3,842.70 212.61 0.55 119.41 0.05 14 1,373.50 153.91 0.14 247.079 0.04 15 38,940.00 1,770.17 46.00 13,880.14 59.76 16 10,935.00 1,280.19 9.34 28,462.72 34.41 total 116,043.59 17,871.45 100.00 54,232.34 100.00 https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2018, 263-272 doi: 10.14710/geoplanning.4.2,63-272 | 269 figure 5. total runoff map from sub watershed in mamasa waterhsed figure 6. sediment yield from sub watershed in mamasa watershed 3.4. simulation of land management and soil and water the arrangement of scenario of land use is done to achieve the best management practices that can minimize the runoff and sediment which occur in mamasa watershed. scenario of land management and soil and water conservation were applied based on land use 2014 and in agriculture land because the highest contribution from it to the erosion process in a watershed. the goal of conservation technique https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 263-272 doi: 10.14710/geoplanning.4.2.263-272 270 | application is to increase soil capability in infiltrate the rain water, thus it cannot contribute to surface runoff, immediately. the scenarios were also arranged to ensure the preservation of water distribution in wet and dry season. the scenarios are presented spatially in figure 7. figure 7. map of scenario application in mamasa watershed the results of scenarios simulation to characteristics hydrology in mamasa watershed is presented in table 4. the conservation technique application in swat simulation is able to decrease surface runoff in mamasa watershed and also decrease the runoff coefficient. decreasing surface runoff, the increasing lateral flow and base flow from the scenario. surface runoff was decrease approximately between 2.48 – 33.44%, while the lateral flow and base flow were increase as much as 0.19 – 20.38%, and 1.96 2.58%. the highest decreasing of surface runoff was generated from scenario 4, which was the combination of whole scenario so the effect of conservation techniques to be maximum. table 4. hydrological characteristics from scenario application scenario rainfall surface flow lateral flow base flow c value sediment yield --------------------mm------------------tons/ha existing 1992.8 581.35 640.72 228.17 0.292 332.96 scenario 1 1992.8 566.95 641.91 234.06 0.284 330.28 scenario 2 1992.8 550.55 654.75 233.4 0.276 321.77 scenario 3 1992.8 480.62 702.71 232.65 0.241 290.13 scenario 4 1992.8 386.94 771.33 227.45 0.194 212.97 runoff coefficient of existing condition in mamasa watershed as 0.29 was decrease about 2.74 – 33.56% after the watershed management scenario was applied. the highest decreasing of runoff coefficient was achieved by applying scenario 4. the combinations of whole scenario are able to increase the effectiveness of applied conservation technique. mulch can decrease the rainfall energy so that it cannot destroy the soil structures, decrease the speed and amount of surface runoff thus can reduce surface runoff energy. mulch also reduces the evaporation and keeps the water content in soil. https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.14710/geoplanning.4.2.263-272 murtilaksono et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2018, 263-272 doi: 10.14710/geoplanning.4.2,63-272 | 271 the function of strip grass application to preserve the river sustainability is achieved by holding and capturing soil eroded/mud as well as nutrient and chemicals including pesticide from agricultural land. the hedge plant on alley cropping system functioned to hold water, fertilize the soil, minimizing the erosion and landslide, and increasing the microorganism activity. silt pilt was constructed to catch the runoff and soil eroded so that the water can infiltrate to the deep soil and reduce erosion. the simulation results also indicate a decrease in sedimentation. simulation of scenario 4 was able to reduce sediment up to > 40%. decreasing in sediment will be higher if the agricultural land is mostly applied by the conservation techniques. thus, it can be said that the implementation of appropriate conservation techniques and appropriate to the circumstances in the field are effective in reducing runoff and sediment. this result is linier with other studies about best management practices. application of vegetation filter strip (vfs) in haean highland agricultural catchment of south korea is the best way to reduce the sediment load, the vfs was applied in 3 different ways are vegetative filter strip with 1m, 3m, and 5m. the vfs5m is the best technique in reducing the sediment load in watershed outlet approximately 34.8%, while the vfs1m and vfs3m reduce sediment as 16.0 and 22.1%, respectively. however, the techniques fertilizer control, and rice straw mulching only reduce the sediment load around 4.9-16.4%, and 3-14.1%, respectively (jang et al., 2016). the decreasing of sediment load came from the discharge load contribution in upland crop areas. another research by liu et al. (2016) indicated that application of buffer strip and cover crop in upland fields are the best techniques in reducing the sediment load approximately 5 – 13% compare to the initial condition, while the whole scenario including nutrient management, buffer strip, cover crop, and wetland restoration only reduce sediment as much as 0 – 5.54% in the watershed outlet. 4. conclusion the accuracy of swat model in estimating hydrological characteristics in ungagged mamasa watershed is based on detail soil data from field study and sensitivity parameters which developed by the watershed characteristics. the estimate indicates that mamasa watershed is in good condition. four soil and water conservation scenarios including bunch and mulch scenario, bunch terrace, mulch, and strip grass scenario, alley cropping and silt pilt scenario, and agroforestry scenario plus combination all scenario were developed and evaluated. the agroforestry scenario plus combination all scenario has the highest reduction of surface flow and sediment yield compared to other scenario. the alley cropping and silt pilt scenario have the second highest reduction of surface flow and sediment yield, followed by bunch terrace, mulch, and strip grass scenario, and bunch and mulch scenario. the combination scenario from all scenario showed that overall impacts is higher than individual scenario because each technique is interacted with other, so the accumulative result will be highest than individual scenario. 5. acknowledgments the authors would like to thank to watershed management agency of saddang, ministry of forestry for funding this activity. and also, to center for environmental research, bogor agricultural university for funding research publication. 6. references arnold, j. g., moriasi, d. n., gassman, p. w., abbaspour, k. c., white, m. j., srinivasan, r., jha, m. k. 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[crossref] https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.14710/geoplanning.4.2.263-272 https://doi.org/10.13031/2013.34915 https://doi.org/10.5194/hess-2016-44 https://doi.org/10.2134/jeq2013.11.0466 https://doi.org/10.1016/j.swaqe.2016.06.002 https://doi.org/10.2489/jswc.71.3.249 https://doi.org/10.1007/s12665-016-6316-8 https://doi.org/10.1016/j.agwat.2016.06.008 https://doi.org/10.1080/02626667.2015.1029482 https://doi.org/10.1016/j.jglr.2016.02.008 https://doi.org/10.2134/jeq2010.0066 https://doi.org/10.1016/s1001-6279(12)60030-4 https://doi.org/10.1016/j.agwat.2016.12.007 https://doi.org/10.13031/2013.34903 https://doi.org/10.1080/02626667.2016.1271420 https://doi.org/10.1016/j.proenv.2016.03.054 | 109 geoplanning vol 4, no. 2, 2017, 109-120 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.109-120 land use analysis using time series of vegetation index derived from satellite remote sensing in brantas river watershed, east java, indonesia k. yoshino a, y. setiawan b,d, e. shima c a faculty of engineering, information and systems, university of tsukuba, 1-1-1 tennoudai, tsukuba, ibaraki, 305-8573, japan b faculty of forestry, bogor agricultural university, kampus ipb darmaga, bogor 16680, indonesia c school of veterinary medicine, kitasato university,23-35-1 higashi, towada, aomori, 034-0005, japan d center for environmental research, bogor agricultural university, kampus ipb darmaga, bogor, 16680, indonesia abstract: in this study, time series datasets of modis evi (enhanced vegetation index) data from 2002 and 2011 in the brantas river watershed located in eastern java, indonesia were analyzed and classified to make ten land use maps for each year, in order to support watershed land use planning which takes into account local land use and trends in land use change. these land use maps with eight types of main land use categories were examined. during the 10 years period, forested area has expanded, while upland, paddy rice field, mixed garden and plantation have decreased. one of the reasons for this land use change is ascribed to tree planting under the joint forest management system by local people and the state forest corporation. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): yoshino, k., setiawan, y., & shima, e. (2017). land use analysis using time series of vegetation index derived from satellite remote sensing in brantas river watershed, east java, indonesia. geoplanning: journal of geomatics and planning, 4(2), 109-120. doi:10.14710/geoplanning.4.2.109120 1. introduction the sustainability of regional environment and society has become an important issue due to the effects of global warming being recognized throughout the world (parry et al., 2007). regional land use is easily affected not only by global warming but also socio-economic development and population growth (kaneko et al., 1998). on the other hands, land use itself affects global environment and regional-global climate (foley et al., 2005). examination of regional land use over a long period of time is useful to determine any changes in the natural environment and society and to understand the trends in these changes, causes and processes (himiyama & okamoto, 1992). in regions where large-scale irrigation development projects such as dams, irrigation canals and so on all over the world (jica, 2011) have been carried out, the projects have affected both regional society and the natural environment, since infrastructure development induces socio-economic development, then urbanization begins in the region. generally, urbanization affects both regional society and environment. monitoring regional land use over a long period is helpful to study the sustainability of natural environment, society and development projects (ramankutty & foley, 1999). water infrastructure in indonesia had been developed since the dutch colonization period, in order to avoid floods and to build irrigation system. many of the infrastructures for irrigation and drainage constructed by these projects have remained and are still in use and providing social services to the region (pasandaran, 2007). these large-scale developing projects under some urban policies on regional development could actually have affected regional environment (amato et al., 2016). indonesia is a suitable area to study sustainable agricultural development and future land use against global climate change. that article info: received: 29 december 2016 in revised form: 7 april 2017 accepted: 9 may 2017 available online: 30 october 2017 keywords: time series dataset, land use classification, modis vegetation index, brantas watershed corresponding author: kunihiko yoshino university of tsukuba, 1-1-1 tennoudai, tsukuba, ibaraki, 3058573, japan email: sky@sk.tsukuba.ac.jp open access https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 110 | is how they can modify their regional development plans in future, because they have a very long history of regional development for longer than one hundred years, especially in the areas or watersheds where regional socio-economies have been rapidly growing. these areas are thought to be dramatically changing due to the expansion of urban areas or agricultural land development by increasing population. in these areas, the newly developed lands have been expanding following the land use plans of the local government and also by illegal development. in order to control this illegal development, the characteristics of regional land use patterns and their changing trends should be understood in order to propose some applicable land use plans for watersheds and also to have better sustainability of the global ecosystems and human beings (foley et al., 2011). brantas watershed is the biggest watershed in east java province (bbws brantas, 2011). however, the local environmental agency indicated that vegetated lands in this watershed are under pressure, especially in upstream areas. many existing land use allocations are inconsistent with spatial land use planning, even though the government rules, namely the law no. 26/2007 on spatial planning and law no. 41/2000 on basic forestry law, are stating that at least 30% of the area has to be allocated to vegetated land, such as garden and forest (blh prov. jawa timur, 2009). based on these reports, it could be estimated that the agricultural lands or residential lands have disorderly invaded into the natural land use such as forest land. as a result, the forest environment has been degraded. the land use in this area could be intently changing to agricultural lands. the objectives of this research were, 1) to create annual land use maps of the brantas river watershed in eastern java, indonesia from 2002 to 2011 using time series modis evi data applying a wavelet de-noise filter, and 2) to study the characteristics of regional land use patterns and trends in land use change. this paper contains five chapters. after mentioning the backgrounds of this research, data and methods used in this research are described in the chapter 2. in the chapter 3, the results of land use mapping for 10 years in brantas watershed using time series modis evi data are given. in the chapter 3, we also discuss about land use patterns in this area and land use change during these 10 years. finally, in the chapter 4, we conclude our findings resulted in this research. 2. data and methods 2.1. study site brantas river watershed located in the eastern part of java, indonesia (figure 1) and has a tropical monsoon climate. the annual average air temperature from 1996-2000 and the annual average precipitation are 25.12 degrees celsius and 1,876 mm/year, respectively (widianto et al., 2010; wmo, 2013). its area comprises approximately 12,000 km2 and surabaya city is situated at the mouth of brantas river, the second largest city in indonesia with population of about 3 million. it is a famous agricultural area. most of the land use types are: forest, plantation, mixed garden, rice paddy field, and uplands. triple-rice cropping is undertaken in this watershed (haruyama, ooya, & mizuhara, 1992). recently, the socioeconomic situation in this watershed has rapidly risen along with an increasing population (bhat, ramu, & kemper, 2005). therefore, rational land use and sustainable environmental planning are necessary (jbic institute, 2008). the downstream area of brantas river has frequently suffered from flooding such as the large flood occurred in 2010. the flooding caused damage to crops, inability to cultivate land due to water logging of soils, disruption of settlement, transportation and loss of property. to counter these problems, many flood control projects have been carried out by the indonesian government since the 1950’s as well as other large scale flood control projects such as construction of big dams and irrigation-drainage projects under japanese-aid (jica, 2011). however, in spite of these projects and the forest management policy of indonesia’s government, floods have frequently occurred (hidayat, 2009). in addition, dynamics changes of forest cover are found in forested areas (setiawan, yoshino, & prasetyo, 2014), and sedimentation on the river floor has become severe (adi, jänen, & jennerjahn, 2013; widianto et al., 2010). it is therefore assumed that there are other reasons for frequent floods. the characteristics of land use patterns and the trends in land use change in this watershed should be understood in detail to facilitate rational watershed management and sustainable agricultural development. https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 | 111 figure 1. outline map of brantas river watershed 2.2. remotely sensed imagery in this study monitoring land use precisely changes in large regions over a long period of time and to carry it out in detail is generally difficult. recently, satellite optical remotely sensed images are used to study land use and land cover both on a global scale and in regional scale (di palma et al., 2016). however, there are some shortcomings in applying this method to tropical regions where the cloud coverage rate is high (setiawan, yoshino, & philpot, 2013). time series modis (moderate resolution imaging spectroradiometer) data recorded by two earth observation satellites, terra and aqua, are valid to obtain land use and land cover change over a global scale or regional scale (friedl et al., 2002; sakamoto et al., 2006). these two satellites observe the whole surface of the earth twice a day. secondary modis datasets that were processed from observed data for specific purposes are open to the public and can be downloaded via the internet (lpdaac, 2013a). these datasets provide information on the condition and dynamics of the earth surface such as land use/land cover in short time intervals. the time series modis evi (enhanced vegetation index) produced from the observed data were used in this study. the datasets are referenced as h29v9 of modis sinusoidal tiling system (lpdaac, 2013b) and cover the study site. the 230 scenes of modis evi datasets, myd13q1 (lpdaac, 2013d) observed from july 2002 to june 2012, a period of 10 years. the 16-days composite data with the spatial resolution of 250m were downloaded from the nasa’s web-site (nasa, 2013). these datasets comprising point data of modis original scenes for 16 days as the probability for cloudless observation of the ground is considered optimal during this time period. https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 112 | evi is one of the vegetation indices developed by huete et al. (2002). it was computed using band data from the visible blue band, visible red band and infra-red band. it reduced the effects of variation in the sun irradiance, thin cloud or cloud shadow, topographic effects, sun elevation and view-angle effects. moreover, atmospheric effects such as scattering and absorption are removed as it uses visible blue band data. however, additional data processing is necessary, since datasets from tropical regions are affected by cloud noise (solano, didan, & jacobson, 2013). 2.3. procedures for data processing the 230 scenes of modis evi 16-days composite datasets for the 10 years were aggregated and analyzed as follows. every processing algorithm is standard and popular in order to analyze satellite remote sensing data. [1] reprojection of each image using mrt (lpdaac, 2013c): an application was used for reprojection of datasets from the modis original projection system to wgs-84 geographical projection system. for computation of area of land use, images were reprojected to utm coordinates system zone 49s and resampled at every 250m. at the same time, the qa (quality assurance) of each dataset was validated. [2] de-noising of the aggregated dataset: in order to remove the spike-like noises per pixel from the time series data in the aggregated dataset, a wavelet transformation filter was applied to the time sequential evi of every pixel in the aggregated dataset using matlab wavelet tool (mathworks, 2009), then a smooth time sequential evi dataset was obtained. the wavelet model was the coiflet model, the same model used by sakamoto et al. (2006). the order 2 for the wavelet was applied in order to retain the original characteristics of the data. this de-noised time sequential data was divided into 10 datasets corresponding to each year. [3] supervised classification for land use mapping: the de-noised image of the base year was classified with a supervised classification algorithm using reference training data. [4] computation of correlation coefficients (morita, 1985) between the base year dataset and the target year dataset: a pixel of each one-year dataset is a 23 dimensional vector. the correlation coefficients between pixels of the base year dataset and those of other one-year datasets were computed. the dataset from 2007 was retained as the base year dataset. later, pixels which had higher correlation coefficients than 0.8 were chosen as training samples for supervised classification. [5] statistics of signature of each land use category were computed using the training samples chosen in previous step [4]. [6] supervised classification for land use mapping: the algorithm of mlh (maximum likelihood) supervised classification was applied to map land use, and a land use map of each year was drawn. [7] characterization of trends: using a land use map of each year, the characteristics of land use and the change in this watershed were analyzed and discussed. 3. results and discussion 3.1. land use categories for classification and land use mapping of the base year nineteen land use categories were determined (table 1) by the previous work (setiawan, yoshino, & philpot, 2013). these 19 land use categories were those that could be selected as training data in this watershed. there were four categories of rice paddy fields, four of upland fields, one mixed garden, five of forest lands, three of plantations, one urban area, and one water surface. they are appropriate land use categories to study land use and land use change in this watershed. totally 13,723 pixels were selected as the training samples. the de-noised image of the base year, a dataset from 2007, was classified with a supervised classification algorithm using these training data. this land use map in 2007 is used as a base or reference land use map for analysis of datasets of other years. the reason of 2007’s dataset selection for creating a base land use map in this study is that a reliable land use map was drawn using satellite images taken in this year (department of forestry, 2008). https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 | 113 table 1. reclassification of land use categories in this study (analysis, 2015) land use category (setiawan et al., 2013) re-classed land use category 1. triple irrigated paddy field 1. triple crop paddy 2. double irrigated paddy field i 2. double crop paddy 3. double irrigated paddy field ii 4. double irrigated paddy field iii 5. upland 3. upland 6. upland with intensive agricultural land 7. upland with mixed agricultural land 8. irrigated fields 9. mixed garden with bush 4. mixed garden 10. forest mixed with bush 5. forest 11. bush mixed grass 12. dryland forest 13. heterogeneous mangrove forest 14. mangrove 15. rubber plantation 6. plantation 16. oil palm plantation 17. timber forest plantation 18. builtup 7. urban area 19. pond 8. water surface in order to provide a consistent definition for pixel based analysis, we need to resolve the differences among the nineteen land use categories in the dataset. the process of reassigning land use categories based on the knowledge of the each land use characteristic is shown in table 1. nineteen land use categories were re-classed into eight categories: triple cropping rice paddy field, double cropping rice paddy field, upland field, mixed garden, forest, plantation, urban area, and water surface. when tabulating an error matrix for evaluation of classification (richards, 2006), it was found the overall classification accuracy was 71 % for these 8 categories. in consideration of the spatial resolution (250 m) used on this dataset, this land use map is assumed to show a true land use pattern in this watershed around 2007. 3.2. sampling training data based on the correlation coefficients in general, strictly selected reference data should be used for supervised classification. usually, samples are selected by designating several pixels which are thought to be proper training areas of each land use category with several ways such as ground survey, referring reliable land use maps in order to obtain sufficient number of training samples (oobayasi & kojima, 2002). however, it is important to collect fine training data for every image to detect land use change in short periods from the results of land use classification in every year. in this study, training samples were collected by selecting pixels which have higher correlation coefficients than 0.8 between the base year dataset of 2007. the other year datasets where those pixels have the same land use categories as the pixels in the base-year dataset were equally used, because only in 2007, we have a reliable land use map authorized by the ministry of forestry of indonesia. for accessing the similarity of two spectral signatures which are recognized as two vectors of multiple variables, the angle between two vectors is observed as the similarity of two vectors. the smaller angle means that they have high similarity. the value of cosin of that angle is equal to the correlation coefficients in terms of the fundamental relationship between geometry and statistics. so, in this research, the correlation coefficients are used to measure the similarity of two vectors of the same pixels of two years. figure 2 shows the accumulated frequency in percent of the correlation coefficients between each year and the base year dataset of 2007. although the correlation coefficients of 2010 looks different from those of the other years, over 30 to 45% https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 114 | of pixels in the other year datasets have correlation coefficients higher than 0.8. thus, at least over 100 pixels were selected as training samples for each land use category in each year. figure 2. accumulated histogram of correlation coefficients (the first 2 digit number in the legend shows the year of interest) 3.3. supervised classification result supervised classification of land use for each year dataset except the dataset of 2007 was conducted with mlh (maximum likelihood method) using training samples selected in the former section. as example, the land use classification maps for 2002 and 2011 are shown in figure 3. in these two land use maps, it is clear that land use in this area has been changed in many places. in general, rice paddy fields widely spread downstream from kediri and the mountains are covered with forest. most lands except rice paddy fields and forest are upland field, mixed garden and plantation. in the coastal area of the eastern part of surabaya, large fish ponds are expanding. however, they were classified into water surface. 3.4. land use change from 2002 to 2011 the acreages of each land use category in each year from 2002 to 2011 are shown in km2, to study the trends of land use change in brantas river watershed (figure 4). variations of acreages in each year are rather large for many land use categories. one of the causes of these large variations of estimated acreages of land use classes is assumed to be attributed to the intrinsic drawback of modis composite products. a pixel value is affected by the viewing geometry of modis sensor of observation dates which changes the dimension of the pixel on the ground, so that a pixel consists of spectral signals from ground cover conditions or land use of surrounding pixels. this intrinsic drawback leads a fairly large number of misclassification of modis composite data (tan et al., 2006). as this drawback cannot be compensated, we will ignore the uncertainty of classification results in this paper. moreover, the previous study in east java (muhammad et al., 2016) also mentioned that the mixed pixel issue was an important consideration of land use classification using modis data since the classification result of specific land use classes revealed the overall accuracy to be 57.7 %. https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 | 115 apparently, the forest increased during the past 10 years, while other land use categories such as double cropping rice paddy, upland, mixed garden, and plantation seem to decrease. water surface, triple cropping rice paddy field and urban area maintained their acreages. the five years average acreage for each land use category from 2002 to 2006 was compared to those from 2007 to 2011 to summarize the trends of land use changes during the ten years period. as for forest, upland field and single cropping rice paddy field, they have increased to 718, 48 and 5 km2 in acreage, respectively. by contrast, double cropping rice paddy field, triple cropping paddy field, mixed garden, plantation and urban area have decreased to 345, 130, 124, 112, and 59 km2, respectively. land use categories such as forest, upland field and double cropping rice paddy field showed a rapid change in their acreage on a scale of several tens thousands hectares since 2009 compared to other years. forest area has increased, on the other hand, upland field and double cropping rice paddy field have decreased. the total annual precipitation of 2,483 mm and 2,274 mm during the period from july, 2009 to june, 2010 and during the period from july 2010 to june, 2011 in surabaya area, respectively, was the reason of this change. these heavy rainfalls were recorded at the surabaya meteorological observation station in this period. they were calculated based on precipitation records. these precipitation exceeded the 30 year average annual precipitation of 1,876 mm (wmo, 2013). due to the natural physiological response of tropical plants against sufficient rainfall, vegetation in this area consequently grew further between 2009 and 2010 than in other periods, so that the annual evi patterns in this period are understood to show different patterns from those of other periods. figure 3. eight categories land use maps in 2002 (left) and 2011 (right) https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 116 | figure 4. land use change in brantas river watershed 2002-2011 3.5. characteristics of land use change in the watershed 3.5.1. land use change matrix table 2 tabulated the land use change matrix between 2002 and 2011. the rows are the acreage for each land use in 2002, while the columns are those from 2011 in km2. the values in the diagonal items are unchanged acreages of the same land use categories both in 2002 and 2011. the other items indicate the land use changes from 2002 to 2011. in this table, the conversion of land use from 2002 to 2011 focused on changes more than one hundred km2; 110 km2 of triple cropping rice paddy field changed to forest, 490 km2 of double cropping rice paddy field changed to upland, 457 km2 to forest, 151 km2 to plantation and 107 km2 to urban area. about 498 km2 of upland have changed to forest, 321 km2 to plantation, 236 km2 to double cropping rice paddy, and 132 km2 to urban area. meanwhile, 189 km2 of mixed garden have been converted into forest, 142 km2 into plantation, 306 km2 into upland, respectively. then, 478 km2 of forest have been converted into upland, 311 km2 into plantation, 306 km2 into double cropping rice paddy field, 577 km2 of plantation into forest, 391 km2 into upland, 124 km2 into mixed garden, 94 km2 into double cropping rice paddy field, 85 km2 into forest. moreover, 17 km2 of water surface have changed to upland, 10 km2 to double cropping rice paddy field, 12 km2 to urban area. 3.5.2. long term land use change in order to assess the long term stability of land use from 2002 to 2011, the unchanged land use of every land use category was examined by computing the acreages that did not change between 2002 and 2011 for each land use category (see the last line of table 2). the total acreage of unchanged lands in 8 land use categories was about 872 km2. other lands have temporally or almost permanently changed to other land use. about 219 km2 of rice paddy field, 477 km2 of forest, 54 km2 of mixed garden, 84 km2 of water surface, 9 km2 of upland field, 13.1 km2 of plantation and 15.8 km2 of urban area respectively have not changed. https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 | 117 forest distributed on the hillside of mt. kelud was unchanged. unchanged rice paddy fields are seen in flat plain downstream of the brantas river. unchanged mixed garden is expanding in the suburban area near kediri town. unchanged water surface remains in the tidal area in the river mouth of the brantas river. unchanged triple cropping rice paddy fields are recognized upstream of the brantas river. the unchanged urban area and upland fields are scattered. they are not spatially accumulated. table 2. land use change matrix between 2002 and 2011 land use in 2002 land use in 2011 triple crop paddy double crop paddy upland field mixed garden forest plantation urban area water surface total triple crop paddy 139.7 62.1 40.2 21.3 110.7 15.3 21.6 0.1 411.0 double crop paddy 46.9 1153.4 490.8 96.1 456.6 150.9 106.5 6.4 2507.6 upland field 21.1 235.8 721.6 69.0 498.1 320.5 131.9 91.4 2089.3 mixed garden 14.3 61.3 101.7 417.0 188.6 142.1 10.9 0.1 936.0 forest 57.6 306.3 477.6 88.9 2017.3 311.1 53.4 20.4 3332.6 plantation 9.1 107.1 391.2 123.8 577.1 601.4 26.9 0.3 1836.9 urban area 7.2 93.3 116.8 4.2 85.3 19.4 251.3 37.8 615.3 water surface 0.1 9.8 16.7 0.0 6.0 0.2 12.0 184.9 229.6 total 296.1 2028.9 2356.4 820.3 3939.6 1561.0 614.6 341.4 11958.3 unchanged 30.8 188.0 9.1 54.3 477.1 13.1 15.8 83.8 871.9 (unit km2) 3.6. driving forces of land use change according to the results of land use classification in this study, forest has increased its acreage between the first 5 years and the last 5 years. furthermore, rice paddy field, upland field, mixed garden and plantation have decreased their acreages 100 km2 to several 100s km2. these results are contrary to expectation which was described in the introduction. as for the increase of forest, it is assumed that afforestation projects conducted by community based forest management system (cbfm) under the forest management system promoted by perhutani indonesia are working well to conserve the forest environment (asia forest network, 2004). the fact that forest in this area has been steadily increasing indicates that the regional environmental policies in this watershed have begun to show their effects to regulate land use pattern and to mitigate the imprudent land development (amato et al., 2016). since the increase of forest in the hillside reduces the surface soil loss rates from the sparsely vegetated lands to the river, soil suspension of the river water could get lower, then, the sedimentation on the river floor in the river streams would decline (yoshino & ishioka, 2005). consequently, the frequency and the severalty of flooding of brantas river will decrease in the near future. additionally, contrary to our expectation that the forest lands have intently decreased, the urban area did not significantly expand during the 10 years. one possible reason for this is that the spatial resolution of modis evi, which is 250 m x 250 m, is too large to detect urban change. the small changes in urban area could be buried in this large spatial resolution of modis data. in order to detect area and location of such kinds of small scale land use changes in nearly real time, it is better to use the satellite images with higher spatial resolution. plus, land use classification accuracies in this study are problematic. the highest overall accuracy was about 71% in 2007, while in other years, they were at most 50%. setiawan, yoshino, & philpot (2013) reported that over 40% of pixels from modis evi data are mixed pixels that contain several land uses. the low spatial resolution of modis evi dataset and temporal composite dataset of modis data https://doi.org/10.14710/geoplanning.4.2.109-120 yoshino, setiawan, shima / geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 109-120 doi: 10.14710/geoplanning.4.2.109-120 118 | products mainly resulted in the low land use classification accuracy (campagnolo et al., 2016; tan et al., 2006), although the satellites mounting modis sensors daily and observing the terrestrial surface and the remote sensed images are supposed to be useful to monitor temporal environmental changes of the earth. for detecting tiny land use changes over a wide extent of over several 100’s km, datasets of high spatial and low temporal resolution are more suitable for analysis, although some troublesome problems remain to be solved. actually, the answers to these problems depend on the research objectives. 4. conclusion comprehensive land use planning based on long term monitoring of watershed is necessary in developing countries. this research selected the brantas river watershed in eastern java, indonesia as a study area and analyzed land use change from 2002 to 2011 using a time sequential modis evi dataset. the results of this study were as follows: 1) during the 10 year period, forest had a tendency to increase acreage, while other land use categories, especially arable lands have decreased, 2) only 872 km2 of land remained unchanged in this watershed during these ten years, 3) examination of a land use change matrix between 2002 and 2011 clarified many types of land conversion, 4) afforestation projects conducted by local people promoted under the policy of perhutani indonesia could be one of the causes of land use changes in this watershed. lastly, the overall accuracies of classification were not highly adequate to deal with land use change in detail. congalton (1991) reported that over 85% to 90% of overall accuracy of land use classification was necessary to study land use change. a much higher classification accuracy to detect tiny land use changes was indispensable. the following future works are essential to obtain more detailed results on land use and land use changes in this area: a) the determination of timing of land use changes, locations and driving forces for these changes, b) analysis of the temporally trajectories of land use change and cycles of land use which seen in rice paddy fields or in traditional shifting cultivation, c) research on optimal watershed management policy for the sustainable watershed ecosystem. 5. acknowledgments this research was financially supported by research grand-aid of jsps (overseas research project 22402033) from 2010 to 2012. we very much appreciate anonymous reviewers for their very helpful comments and suggestions to improve our manuscript. 6. references adi, s., jänen, i., & jennerjahn, t. c. 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(2005). guidelines for soil conservation towards integrated basin management for sustainable development: a new approach based on the assessment of soil loss risk using remote sensing and gis. paddy and water environment, 3(4), 235–247. [crossref] https://doi.org/10.14710/geoplanning.4.2.109-120 http://www.worldweather.org/043%20/c00648.htm http://www.worldweather.org/043%20/c00648.htm https://doi.org/10.1007/s10333-005-0023-5 doi: 10.14710/geoplanning.4.2.109-120 how to cite (apa 6th style): yoshino, k., setiawan, y., & shima, e. (2017). land use analysis using time series of vegetation index derived from satellite remote sensing in brantas river watershed, east java, indonesia. geoplanning: journal of geomatics and planning, 4(2), 109-12... 1. introduction the sustainability of regional environment and society has become an important issue due to the effects of global warming being recognized throughout the world (parry et al., 2007). regional land use is easily affected not only by global warming but a... in regions where large-scale irrigation development projects such as dams, irrigation canals and so on all over the world (jica, 2011) have been carried out, the projects have affected both regional society and the natural environment, since infrastru... water infrastructure in indonesia had been developed since the dutch colonization period, in order to avoid floods and to build irrigation system. many of the infrastructures for irrigation and drainage constructed by these projects have remained and ... keywords: time series dataset, land use classification, modis vegetation index, brantas watershed corresponding author: kunihiko yoshino university of tsukuba, 1-1-1 tennoudai, tsukuba, ibaraki, 305-8573, japan email: sky@sk.tsukuba.ac.jp brantas watershed is the biggest watershed in east java province (bbws brantas, 2011). however, the local environmental agency indicated that vegetated lands in this watershed are under pressure, especially in upstream areas. many existing land use al... the objectives of this research were, 1) to create annual land use maps of the brantas river watershed in eastern java, indonesia from 2002 to 2011 using time series modis evi data applying a wavelet de-noise filter, and 2) to study the characteristic... 2. data and methods 3. results and discussion 4. conclusion 5. acknowledgments 6. references | 81 geoplanning vol. 6, no. 2, 2019, 81-88 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.2.81-88 the integration of transportation route of industrial area and lurik tourism for production and sales optimization g. yudana a, i. aliyah a *, r. sugiarti b a. department of urban and regional planning-faculty of engineering-sebelas maret university, indonesia b. research and development centre of tourism and culture, research and community service institute, sebelas maret university, indonesia abstract: the development of industrial and tourism areas brings the consequences of the arrangement of transportation routes. as an industrial area as well as lurik tourism, klaten district becomes the research location to know the mapping of access and integration of industrial transportation and lurik tourism route to optimize production and sales. the method employed in this research was digital mapping through gis, and superimposed analysis of mapping results to get integrated integration. the results reveal that transportation routes naturally form a pattern of industrial circulation with the orientation of efficiency and independence of distribution and sales of lurik products. meanwhile, the pattern of tourism circulation is formed with the direction of interconnection between industry and tourism support facilities. both patterns can be integrated to achieve the effectiveness of lurik industrial tourism development routes through a lurik industrial tourism concept based integration of transportation access. integration constructed can be optimized through the implementation of thematic tourism concept by unifying smoothness, security and easy access point between lurik industry and tourism. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): yudana, g, et al. (2019). the integration of transportation route of industrial area and lurik tourism for production and sales optimization. geoplanning: journal of geomatics and planning, 6(2), 81-88. doi: 10.14710/geoplanning.6.2.81-88 1. introduction the trend of foreign tourist arrivals in indonesia grew higher than three other major countries in southeast asia, based on foreign tourist data who visit singapore, malaysia, thailand and indonesia until june 24th, 2017. business times recorded foreign tourist arrivals in singapore in the first four months in 2017 reached 5.79 million tourists. this amount got increased by 4.4 per cent year on year (yoy) compared to the previous years. meanwhile, regarding the xinhua page on june 8th, 2017, the trend of foreign tourist arrivals in malaysia got decreased by 0.5 per cent year on year (yoy). however, foreign tourist arrivals in the land of white elephants, thailand, in the first four months in 2017 reached 12.02 million. this amount improved by 2.91 per cent compared to the same period in 2016 as much as 11.68 million tourists (jati, 2017). some efforts to develop tourism in asean countries continue to carry out. tourism collaboration models among asean countries have given a significant contribution. one model that displays the mechanism of asean tourism collaboration points out the environment where the partnership has taken place, the interactivity of various components, institutional arrangements, and feedback mechanisms between collaborative processes and collaboration preconditions (wong et al., 2011). besides, asean tourism forum (atf) is a regional effort to promote asean areas as a tourism destination. this annual event involves all tourism industry sectors from 10 asean countries including brunei darussalam, cambodia, indonesia, laos, malaysia, myanmar, philippines, singapore, thailand and vietnam as well as three asian countries including china, india and korea (prodjo, 2017). most asean countries are developing countries. they are indicated by some characteristics including 1) high population growth rates; 2) high unemployment rates; 3) dependence on the agricultural or primary sector; 4) ineffectual markets and information; 5) low per capita income, 6) inadequate job opportunities; 7) limited business capital. some countries in asean have some areas with the uniqueness becoming a cultural article info: received: 15 august 2019 in revised form: 10 dec 2019 accepted: 12 dec 2019 available online: 7 april 2020 keywords: transport mapping, gis, integration of transportation route, industrial area and lurik tourism, production and sales *corresponding author: istijabatul aliyah department of urban and regional planning-faculty of engineeringsebelas maret university, indonesia email: istijabatul@ft.uns.ac.id open access https://doi.org/10.14710/geoplanning.6.2.81-88 mailto:istijabatul@ft.uns.ac.id yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 82 | heritage and holding a high selling power in tourism. the tourism industry is one of the sectors occupying a vital role in the development and competitiveness of various areas (todaro & smith, 2012). likewise, alberti & giusti (2012) argue that the relationship between cultural heritage and regional competitiveness can be developed into cluster formation and development through tourism synergy and cultural heritage. while current research of grodach et al. (2017) is carried out in urban areas, the result shows that cultural policy of an area can be directed to cultural industry, small manufacturing and craft-based production. in their study, it also explores the relationship among the cultural industry as a small producer, cultural productionoriented development, and the existence of manufacturing in an area. douglass (2016) suggests that the goal of economic growth in asia is the emergence of a creative community in an environment based cultural and social relationship. additionally, richards (2011) claims that tourism development has creatively been conducted through the change of tourism orientation from cultural tourism in terms of traditional performance into tourism with daily life object in tourism destination area. the development of art-based tourism and rural community business has a potential to strengthen interactional networks and to encourage community's involvement, ownership, entrepreneurship and creative transformation (balfour et al., 2018). it seems to reclaim statement that tourists' innovative experiences can occur due to tourists' interaction with environments, business actors, products, services, and joint activities (tan et al., 2013). the transition of tourism interest on rural tourism is a social representation resulted from non-tourism aspects including culturalization, creative, experience, humanization, leisure, refinement, life-orientation and characteristics (lai et al., 2017; wang et al., 2015). rural tourism got admitted as a fundamental approach for rural development and poverty alleviation. traditional village revitalization model and the ideas for sustainable livelihoods are integrated to reveal three useful aspects in revitalizing village in terms of material, social, and spiritual (gao & wu, 2017). many tourist attractions are recognized in rural areas having the power to bring tourists both domestic and international. a potential of rural areas in indonesia is rural tourism with lurik product potential located in klaten district, centre java province, indonesia. this area has become an exciting tourists' destination. besides, various community's activities as lurik craftsperson’s and lurik production process become attractions for tourists to enjoy the beauty of lurik products, to shop lurik products as well as to see lurik craftsperson’s' lives directly. thus, the rural area manufacturing lurik products in klaten district becomes not only an industrial area but also a lurik tourism area. the ease of access to the tourism area is part of a journey to construct and to maintain the image of tourism destination over tourists. it emphasizes the study conducted by chen et al. (2013) that there is a significant relationship between journey restriction and the construction of tourism destination image. consequently, to develop rural tourism needs a study which can reveal the integration of transportation routes in industrial areas and lurik tourism to optimize production and sales of lurik products. the purpose of this study is to show the integration transportation route and a pattern of tourism circulation as a contribution to rural tourism development based on craft-industry society. 2. data and methods 2.1. study area this study took place at klaten district in centre java province is one of 35 districts/cities holding a strategic value and an important role in the development process of many areas in centre java. klaten district area is located in a very strategic route because it is directly adjacent to the special region of yogyakarta, acknowledged as one of tourism destination areas (dtw). klaten district is one of the areas having developed lurik in which the offices spread in three sub-districts including bayat, pedan and cawas. a village is officially proclaimed as tourism village-based lurik products in telingsing village, cawas sub-district, klaten district. the existence of cawas sub-district among other sub-districts of lurik producers in klaten district, as shown in figure 1. https://doi.org/10.14710/geoplanning.6.2.81-88 yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 | 83 figure 1. the existence of cawas sub-district toward bayat and pedan sub-districts in klaten district 2.2. lurik industrial area lurik products in telingsing village are products of traditional looms (atbm). the craftspersons have to innovate and compete in manufacturing various atbm lurik products with good quality and affordable price for all community levels. this is one of the unique levels by maintaining the good quality product with the economical price. the attraction of the beauty from the lines in an integrated colour arrangement with various patterns is not separated from life philosophy which is rich of meaning. lurik is a masterpiece worthed to preserve. the development of superior lurik products is not separated from lurik potential and characteristics in each area. the distribution of lurik industry in telingsing village is represented in figure 2. figure 2. map of lurik industry in telingsing village, cawas sub-district, klaten district lurik is one of the local cultures possibly developed as an attraction of tourism areas in klaten district. lurik products are one of the superior products in that area. besides, traditional lurik manufactured in telingsing village has become a local culture icon and local wisdom product. this is realised on the beauty of the colourful lines arranged and combined with full of harmony. lurik is a collection of lines with cultural meaning and philosophical values which cannot be separated from tradition. lurik is produced to wear in rituals related to belief in society. in telingsing village, klaten district, there are many small industrial businesses manufacturing traditional lurik manually with using traditional looms (atbm) (figure 3). the manufacturing of lurik products is mostly carried out by craftswomen in klaten district because traditional lurik production needs patience and carefulness, and women generally have both characters. job opportunities in traditional lurik production can give a contribution for strengthening family’s economy and encouraging local economy development. https://doi.org/10.14710/geoplanning.6.2.81-88 yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 84 | figure 3. lurik as a result of women’s handicrafts by using traditional looms (atbm) in telingsing village 2.3 lurik tourism region tourism area is a tourism destination area containing various tourism attraction. according to law of the republic of indonesia, number 10 year 2009 about tourism, tourism destination area is called as a tourism destination, referring to a geographical area located in one or more administrative areas containing tourism attraction, public facilities, tourism facilities, accessibility, and community which is interrelated and complement in maintaining tourism. tourism attraction is a driving factor for tourists to travel. these attractions can be natural resources in terms of rivers, lakes, caves, beaches, seas and forests, and can also be cultural elements including crafts, art performances, and community customs. meanwhile, accessibility deals with the reachability of a location or tourism attraction in accordance with physical and non-physical contexts. physically reachability refers to physical things such as the availability of roads, bridges, local airports, local transportations, and signposts. besides, non-physical reachability refers to non-physical things in terms of community customs in tourism location, local community's mindsets, local community's experiences in welcoming tourists arrivals before, which in turn can lead to if local community can welcome tourists' presence in their place. in a certain case, there is a phenomenon that a location or tourism attraction can physically be accessed, but it is unable to access non-physically because the local community in tourism destination area do not allow tourists arrivals. the constellation of lurik tourism area can be figured out in figure 4. figure 4. transportation route of lurik tourism area in telingsing village, cawas sub-district and its surroundings in klaten district 2.4. the transportation route of production and sales the development of transportation technology and the low cost of transportation stimulates the ease to travel to both domestic and international tourism. tourists’ movement and considerably shopping activities https://doi.org/10.14710/geoplanning.6.2.81-88 yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 | 85 have opened opportunities for each country in the world to organize each potential of tourism attraction object (odtw), so it becomes a primary destination for tourists. lurik tourism area in telingsing village, klaten district is a tourist destination which can be accessed physically and non-physically, especially transportation routes used by tourists. transportation route of production and sales is shown in figure 5. 2.5. methods klaten became the research location in order to know the mapping of access and integration of industrial transportation system and lurik tourism to optimize production and sales. the method employed in this research was digital mapping with gis, and superimpose analysis of mapping results to get integrated integration. the mapping of industrial transportation access and lurik tourism applied gis method by tracking the location of industry and tourism, which was then assembled in a series of linkages in function and characteristics of each location and access used. the series of mapping results of industrial transportation access and lurik tourism were analyzed by superimpose analysis technique to obtain the pattern of industrial and tourism transportation system. based on the results of the analysis, a further study of the result of integration of transportation system happening at industrial area and lurik tourism in klaten can be conducted. this research employed purposive sampling and snowball sampling. purposive sampling was conducted by taking samples since the key people selected from society and government stakeholder, particularly parties involving in the tourism village development, truly comprehend the problems related to this research. on the other hand, snowball technique was used to determine informants by contacting the first key person from society and government stakeholder who were chosen and finding the next key person from the information derived from the first key person and the next, until the data needed was fulfilled. figure 5. transportation system of lurik industrial tourism in bayat, pedan and cawas sub-districts, klaten district 3. result and discussion 3.1. the pattern of industrial transportation route based on the analysis of transportation routes in bayart, pedan and cawas sub-districts, klaten district on figure 6, it is suggested that the pattern of industrial transportation route naturally forms business network pattern in terms of lurik products distribution and sales from one lurik industry into other industries. this is in line with the results of research suggested by alberti & giusti (2012) that the relationship between cultural heritage and regional competitiveness can be developed by the establishment and development of https://doi.org/10.14710/geoplanning.6.2.81-88 yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 86 | clusters through tourism synergy with cultural heritage to enhance regional competitiveness. therefore, it is necessary to have a cultural policy in accordance with the region’s character, so that in its development it can be directed toward the culture industry, small manufacturing, and craft-based production. just as grodach et al. (2017) explores two aspects, namely the relationship between the culture industry and the small producers, and the cultural and manufacturing-oriented development within a region. the pattern of lurik industrial transportation route in klaten district area, forms a pattern with the orientation of efficiency and independence of lurik products distribution and sales in one lurik industry to or from other lurik industries, it can be recognized in figure 6. figure 6. pattern of lurik industrial transportation route with the orientation of efficiency and independence 3.2. the pattern of tourism transportation route the study of mapping result of lurik tourism transportation route in bayat, pedan, and cawas sub-district of klaten district shows some overlapping parts with transportation route of lurik industries. in some sections, it shows the linkage between one lurik industry and other lurik industries, as depicted in figure 7, because it has a relationship between semi-finished and finished goods such as clothing, bags, wallets, scarves and other products made of lurik materials. as suggested by douglass (2016) that the target of economic growth in asia is pointed out by the emergence of creative communities. moreover, richards (2011) proposes that tourism development has creatively occurred through the change of tourism orientation from performance tourism into community’s everyday activities in terms of lurik production. this is underpinned by the results of research conducted by tan et al. (2013) that tourists’ creative experiences can occur due to tourists’ interactions with environments, business actors, products, services and joint activities. consequently, this becomes tourism attraction to move from one lurik industry facilities into other lurik industries, until pattern of tourism with inter-industry orientation is formed. thus, it can be said that the pattern of lurik tourism network in bayat, pedan, and cawas sub-districts is naturally formed with inter-industry orientation and supporting tourism facilities. in other words, it is in accordance with the results of research suggested by bruce balfour, michael wp fortunato, theodore r. alter, (2016) that the development of art-based tourism and rural community efforts, has the potential to strengthen interactional networks and encourage community involvement, sense of belonging, entrepreneurship, and creative transformation (balfour et al., 2018). the existing pattern of tourism transportation routes can strengthen the development of tourism by involving various aspects as proposed by tan et al. (2013) namely the interaction of tourists with the environment, business actors, products, services, and joint activities. https://doi.org/10.14710/geoplanning.6.2.81-88 yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 | 87 figure 7. pattern of lurik tourism transportation route with inter-industry orientation 3.3. integration of industrial area and lurik tourism transportation the pattern of lurik industrial transportation routes in klaten district area forms a pattern with the orientation of efficiency and independence of lurik products distribution and sales in one lurik industry to or from other lurik industries. meanwhile, the mapping of lurik tourism transportation route shows some overlapping parts with lurik industry transportation route. the pattern of lurik tourism transportation route in lurik industrial areas in bayat, pedan, and cawas sub-districts, klaten district forms a pattern of tourism network with inter-industry orientation. some parts show interrelationship among lurik industries due to the relationship of semi-finished and finished goods such as clothing, bags, wallets, scarves, and other products to support the development of lurik tourism and industry. hence, the established integration can be optimized through the implementation of thematic tourism concept by integrating smoothness, security, and easy access point between industry and lurik tourism. this concept can support the research results of alberti & giusti (2012) namely the formation and development of clusters through tourism synergy with cultural heritage to enhance regional competitiveness. given the smoothness, security, and ease of industry access and lurik tourism, it is undeniable that the distribution of production and sales of lurik products can be increased so that optimization can be achieved towards the wider tourism development. this is in line with the expressions of wang et al. (2015) that tourism is closely related to globalization and modernity. 4. conclusion based on the discussion above, it can be concluded that the pattern of lurik industrial transportation route forms a pattern with the orientation of efficiency and independence of lurik products distribution and sales in one lurik industry to or from other lurik industries. this pattern will finally form the cluster of a lurik industry with interrelationship among industries in accordance to the result of research claimed by alberti et al. (2012) about tourism synergy and cultural heritage in increasing local competitiveness. hence, cultural policy adjusted to the characteristics of a certain area is needed. it is similar to the study of grodach et al. (2017). tourists’ interaction with environments, business actors, products, services, and joint activities become an attraction to move from one lurik industry facilities to others, so the pattern of tourism network with inter-industry orientation is formed. the result of overlying the pattern of lurik industrial transportation and lurik tourism transportation on some parts indicates the overlapping transportation routes between lurik industry and lurik tourism. it implies that part of routes integrated between industrial route with the https://doi.org/10.14710/geoplanning.6.2.81-88 yudana et al. / geoplanning: journal of geomatics and planning, vol 6, no 2, 2019, 81-88 doi: 10.14710/geoplanning.6.2.81-88 88 | orientation of efficiency and independence and tourism route with inter-industry orientation can produce the effectiveness of lurik industrial tourism development routes through a lurik industrial tourism conceptbased integration of transportation access. integration constructed can be optimized through the implementation of thematic tourism concept by combining accessibility, safety and ease of access between lurik industry and tourism. this concept is a scientific enrichment related to the formation and development of industrial clusters as a tourism area. along with accessibility, safety and ease of access to lurik industry and tourism, a wider development of tourism can be reached. 5. acknowledgments the researcher team expresses gratitude to the urban and regional planning department of engineering faculty, sebelas maret university and the research and development center of tourism and culture of research and community service institute of sebelas maret university who have facilitated the achievement of research funding through the competition of college prime research program of directorate general of higher education. in addition, the researchers thank all of the parties who have contributed towards the research, particularly the regional planning and development agency of klaten district. 6. references alberti, f. g., & giusti, j. d. (2012). cultural heritage, tourism and regional competitiveness: the motor valley cluster. city, culture and society, 3(4), 261–273. [crossref] balfour, b., fortunato, m. w. p., & alter, t. r. (2018). the creative fire: an interactional framework for rural arts-based development. journal of rural studies, 63, 229–239. chen, h.-j., chen, p.-j., & okumus, f. (2013). the relationship between travel constraints and destination image: a case study of brunei. tourism management, 35, 198–208. [crossref] douglass, m. (2016). creative communities and the cultural economy-insadong, chaebol urbanism and the local state in seoul. cities, 56, 148–155. [crossref] gao, j., & wu, b. (2017). revitalizing traditional villages through rural tourism: a case study of yuanjia village, shaanxi province, china. tourism management, 63, 223–233. [crossref] grodach, c., o’connor, j., & gibson, c. (2017). manufacturing and cultural production: towards a progressive policy agenda for the cultural economy. city, culture and society, 10, 17–25. jati, g. p. (2017, july 4). pariwisata indonesia melesat paling tinggi se-asia tenggara. cnn indonesia. lai, p.-h., morrison-saunders, a., & grimstad, s. (2017). operating small tourism firms in rural destinations: a social representations approach to examining how small tourism firms cope with non-tourism induced changes. tourism management, 58, 164–174. [crossref] prodjo, w. a. (2017, july 1). apa pentingnya asean tourism forum bagi pariwisata indonesia? kompas. richards, g. (2011). creativity and tourism. annals of tourism research, 38(4), 1225–1253. [crossref] tan, s.-k., kung, s.-f., & luh, d.-b. (2013). a model of creative experience in creative tourism. annals of tourism research, 41, 153–174. [crossref] todaro, m. p., & smith, s. c. (2012). economic development 11th edition. wang, d., niu, y., lu, l., & qian, j. (2015). tourism spatial organization of historical streets--a postmodern perspective: the examples of pingjiang road and shantang street, suzhou, china. tourism management, 48, 370–385. wong, e. p. y., mistilis, n., & dwyer, l. (2011). a model of asean collaboration in tourism. annals of tourism research, 38(3), 882–899. [crossref] https://doi.org/10.14710/geoplanning.6.2.81-88 https://doi.org/10.1016/j.ccs.2012.11.003 https://doi.org/10.1016/j.tourman.2012.07.004 https://doi.org/10.1016/j.cities.2015.09.007 https://doi.org/10.1016/j.tourman.2017.04.003 https://doi.org/10.1016/j.tourman.2016.10.017 https://doi.org/10.1016/j.annals.2011.07.008 https://doi.org/10.1016/j.annals.2012.12.002 https://doi.org/10.1016/j.annals.2010.12.008 | 205 geoplanning vol 5, no. 2, 2018, 205-214 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.2.205-214 potential of big data for pro-active participatory land use planning w.t. de vriesa a department of civil, geo and environmental engineering, technical university of munich, germany abstract: the presence of (spatial) big data presumes that citizens can more actively collect and analyse data for their own land use goals. this article evaluates that claim. given that land use planning heavily depends on participation and citizens own contributions the core question is whether and how (spatial) big data can enhance and or complement current land use planning endeavours. the article starts by defining and conceptualising the various phases and objectives of land use planning. this is needed to verify where citizen participation can play a crucial role and where bottom-up influence can aactually emerge. the article is fundamentally explorative. it relies on evaluating existing websites and documentation which conceptualise (spatial) big data and smart application, with a particular emphasis on ‘smart people’. a number of specific cases are explored in order to verify how and in which type of land use planning activity citizens are actively. the evaluation indicates that many the smart application making use of big data are still largely driven by conventional hierarchical governance structures. the choice of data and associated analytics are still largely confined and opportunities whereby the designs of the new and alternative land use option by citizens are accepted or adopted is still limited. the take-home message is that adoption of big data for the purpose of empowering citizens is still limited. there probably needs to be more exemplary projects and various forms of capacity development and exploratory pilots before the full potential of (spatial) big data can be employed for bottom-up land use planning. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. de vries, w.t. (2018). potential of big data for pro-active participatory land use planning. geoplanning: journal of geomatics and planning, 5(2), 205214. doi: 10.14710/geoplanning.5.2.205-214 1. introduction globally the impacts of land use, land occupation and allocations of land rights are changing. emerging effects include increasing land scarcity (gerber, hartmann, & hengstermann, 2018), rapid urbanization and growing hazards whereby ever larger numbers of people are at risk. this situation calls on the one hand for information sources which are available instantly and which have better quality, reliability and actuality, and on the other hand for planning processes which rely on more active participation and co-creation of citizens and enhanced informed decision making mechanisms. the rise of big, open, linked and voluntary data is claimed to (partly) address the former, whereas the renewed interests in concept of the ‘right to the city’ (brenner et al., 2012; mayer, 2012), the experiments of co-creation of spatial design and spatial governance (franz, tausz, & thiel, 2015; rooij & frank, 2016), and the occurrence of neo-cadastres (de vries, bennett, & zevenbergen, 2015) amongst others may address the latter. what are these developments however, and to which extent are they truly changing the landscape and the practice of spatial planning? the main research question of this article is does the presence of spatial big data-(1) increase the number of citizen-driven land use planning contributions; (2) improve the quality with which citizens can actively collect and analyse data to pursue their own land use goals; and (3) make citizens smarter. open access article info: received: 30 june 2018 in revised form: 15 sept 2018 accepted: 15 october 2018 available online: 25 october 2018 keywords: big data, land use planning, smart cities, land management, participation. corresponding author: walter timo de vries technical university of munich, germany email: wt.de-vries@tum.de https://orcid.org/0000-0002-1942-4714 https://doi.org/10.14710/geoplanning.5.2.205-214 https://doi.org/10.14710/geoplanning.5.2.205-214 mailto:wt.de-vries@tum.de de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 206 | this article discusses first qualifications and appraisals of big data, with a particular focus on geospatial or geotagged/georeferenced big data. this discussion also highlights a number of current concerns, i.e. dangers for privacy, unequal access, digital divides, etc. after this review on spatial big data, it discusses the variations in spatial and land use planning. this discussion is necessary to understand where and how big data can play a role in which parts or which phases land use planning. this includes a review of goals, tools and instruments in the different types of spatial planning, including the role of geospatial tools and instruments in spatial (land use) planning. part of this discourse is a specific focus on smart cities and smart (rural) regions. it is therefore crucial to understand how big data are influencing the ‘smart’ land use planning. this article will focus specifically on the element of ‘smart people’ and a classification of how and where smart people play a role in different actions and phases of land use planning. with this classification different examples in germany of smart people applications in spatial planning are discussed and reviewed in order to answer the research questions more specifically. the conclusion section then discusses how the research questions can be answered and what sort of further research is required to obtain a better understanding of the role and potential of big data in spatial land use planning. 2. data and methods 2.1. qualification and appraisals of (spatial) big data big data have gained a significant place in the discourse of multiple domains. however, in these discourses one can also observe a number of developments and variations in understanding and defining big data. batty (2013) characterizes big data as ‘any data that cannot fit into an excel spreadsheet’. however, size is not the only characteristic of big data. schintler & chen (2017) and doornik & hendry (2014) classify further that big data can be ’fat’ or ‘tall’. fat data has many attributes ‘m’ but lesser number of observations ‘n’ , while tall data has few attributes but many observations (m<n). french, barchers, & zhang (2015) compare the structure of big data to traditional data indicating big data infrastructures are far more ‘unstructured’: many of these records are tagged with geolocation or a time stamp, and sometimes both, time or location can often be used to join otherwise unrelated data sets. in addition to this traditional structured data, we now have vast amounts of unstructured data (e.g. drone videos, tweets, facebook posts, youtube videos, foursquare check-ins, surveillance videos and much more). the euclid project (http://euclid-project.eu/modules/chapter6.html) refer to big data as having three key aspects, referred to as the 3vs of big data: variety, volume and velocity (laney, 2001; schintler & fischer, 2018) add a fourth dimension: veracity. this has a lot to do with trueness and uncertainty of the sources and validity of the data. discussions on fake news facebook and those of cambridge analytica have also given rise to look critically at who owns which data and how third parties use data. regardless of these, table 1 summarizes the characteristics of these 4vs. recent discourses even add a 5thv, namely value. however, this specific v does not yet occur in many other literature sources, despite its obvious relevance. another distinction related to big data is the difference between ‘big’ and ‘small’ data. kitchin & lauriault (2015) list the following differences (table 2). table 1. thee 4vs of big data variety volume velocity veracity data characteristic structured, semistructured and unstructured large volumes of data streams, sensors, near real-time data, iot data may come from unknown or everchanging sources challenge data integration reasoning and querying reasoning & querying quality of data is uncertain and from unverified sources solution semantic technologies are a good fit distributed storage & processing, parallel processing stream reasoning & querying avoiding noise and abnormalities in the data https://doi.org/10.14710/geoplanning.5.2.205-214 http://euclid-project.eu/modules/chapter6.html de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 | 207 table 2. differences between big and small data small data big data volume limited to large very large exhaustivity samples entire populations resolution and identification course & weak to tight & strong tight & strong relationality weak to strong strong velocity slow, freeze-framed/bundled fast, continuous variety limited to wide wide flexible and scalable low to middling high the manner in which big data can be constructed or sourced depends largely on the manner in which these data are generated. on the one hand, one can distinct active to passive sensors or technical and human sensors, on the other one can make a distinction between user-generated, transaction-generated and sensorgenerated big data. the crucial differences in the latter qualification are given in table 3. table 3. data sources of big data (senatsverwaltung für stadtentwicklung un wohnen berlin, 2017) user generated data transaction-generated data sensor-generated data photo-platforms mobility platforms navigation systems rating-platforms real estate portals mobile phone data map portals hotels platforms surveillance data social media tenders building data gps tracking exchange / sharing platforms biometric data search portals job platforms wikis online business dating portals mobility data business networks constructing big data can be done in multiple ways. the comprehensive document by the senatsverwaltung für stadtentwicklung un wohnen berlin (2017) lists these possibilities: (1) via application programming interfaces (apis) – e.g. twitter, flickr, openstreetmap; (2) via webscraping – extracting of data via websites – clicking on links, completion of forms, scrolling; (3) via making data sources available for commercial purposes; (4) data brokers – e.g. airdna (for airbnb), gnip (twitter and diverse data sources of user-generated data ) and quintly; (5) via communities – e.g. data journalists, open communities. this list shows indeed the large variety in platforms, content, shapes and formats. what makes big data ‘spatial big data’ is the specific georeference. schintler & chen (2017) indicate how such georeference can be added to the data being constructed through the above listed possibilities and thus create spatial big data: (1) geo-tagged photos; (2) weather data (hourly, daily); (3) gps trajectories; (4) sns check-in records (twitter, facebook, etc.); (5) earth observation imagery; (6) public transportation card transactions; (7) spatial events, e.g., crimes, accidents; and (8) climate model simulations. research based on spatial big data requires however spatial analytics. the analytics needs to capture for example certain spatial patterns which cannot be seen by simple visual observation, and needs to be able to make predictions based on such patterns. spatial analytics, in other words, the capability to automatically derive predictions of spatially distributed features and phenomena, patterns of spatial collocation of factors or indicators which were previously not considered connected, finding hotspots of certain manifestations of phenomena, and needs to find changes and outliers in spatial patterns. https://doi.org/10.14710/geoplanning.5.2.205-214 de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 208 | 2.2. changes and variations in land use planning it is however not only the manner in which data are being constructed which is changing, also the way of land use planning is conducted changes. although literature is fairly consistent on what land use planning entails and what its goals are, at the same time there are variations on a similar theme. these variations have to do with different emphases in the professional and/or institutional set-up of land use planning systems in different countries and in the degree and type of experience gained with land use planning in different environments. a commonly used classification of land use planning is that from giz (wehrmann, 2012), which refer to land use planning as an iterative process of six phases (figure 1): definition of objective and approach; analysis; plan formulation; approval; implementation; monitoring. each phase has distinct characteristics and requires specific kinds of (spatial) data and associated (spatial) and social analytical tools. lagopoulos (2018) uses a different kind of description, referring to eight interconnected stages or actions (figure 2): decision to intervene; survey of spatial system; policy making (alternative scenarios); forecasting; model of spatial system; alternative spatial scenarios; evaluation and selection; implementation. metternicht (2018) list different variants of land use planning, which either reflect different traditions or different foci. table 4 list a number of such variants. figure 1. giz phases of land use planning (wehrmann, 2012) figure 2. land use planning stages or actions (lagopoulos, 2018) https://doi.org/10.14710/geoplanning.5.2.205-214 de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 | 209 table 4. examples of land use planning variants (metternicht, 2018) variants of land use planning description land use planning systematic assessment of land and water potential, alternatives for land use and economic conditions, in order to select and adopt the best land use options spatial land use planning interdisciplinary and comprehensive approach directed towards balanced regional development, and the physical organization of space according to an overall strategy integrated land use planning assessment and assignment of use of resources, taking into account different users, including all agricultural sectorspastoral, crop and forests – as well as industry and interested parties participatory land use planning planning of communal or common property land, important in many communities where lands are degraded, and where conflicts over land use rights exist regional land use planning process of territorial development designed to facilitate the elaboration of a general spatial concept and land use priorities 2.3. smart cities and planning once the spatial data are connected and/or integrated to the various forms, phases and spheres of land use planning, one can start to speak about smart land use planning. being or acting smart assumes however a number of things. literature on smart cities makes a specific set of characteristics whereby ‘smartness’ can be evaluated: smart economy, smart mobility, smart governance, smart environment, smart living and smart people. the benchmarking ranking model by the smart cities projects (http://www.smart-cities.eu) employs these six characteristics to rank cities in europe on the degree of smartness. crucial for land use planning is hereby the connection between technologies which may be employed for each of the phases on land use planning but also the degree to which people can actively contribute by creating their own data. the latter is evaluated in the ranking of smart people, which is ranked specifically as well (see figure 3). what is remarkable in this project with respect to this specific article is that the german cities relatively rank low on this list regarding the smart people characteristic. this is not to say that german cities are not smart, but that the degree, to which people are actively contributing to the smartness of the cities, including the usage of big data, seems relatively low. figure 3. excerpt from the european smart cities project (http://www.smart-cities.eu) https://doi.org/10.14710/geoplanning.5.2.205-214 http://www.smart-cities.eu/ http://www.smart-cities.eu/ de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 210 | 3. results and discussion 3.1. german cases of use of big (spatial) data and smart planning in order to evaluate the usage of spatial big data more specifically in germany cases, examples were extracted from the following sources: (a) big data und crowd data für die berliner stadtentwicklungsplanung 1; (b) european list smart cities tu wien 2; (c) smart cities in deutschland: so digital sind unsere städte wirklich3; (d) digitale stadt4; and (e) land atlas germany5. this list is by far complete, but it provides a good first insight in where and how data can be used specifically for land use planning. the study ‘big data und crowd data für die berliner stadtentwicklungsplanung’ (2017) qualifies the usage of spatial big data by ‘smart people’ in land use planning based on two dimensions, there are the degree to which the targeted land use interventions are either specific or general, and the degree to which the targeted interventions are initiated and generated from the citizens (bottom-up) or by the government (top-down). with these two dimensions the examples can be qualified as in figure 4. table 5 lists examples, which were evaluated fitting these classifications of figure 4. figure 4. qualification of smart people in land use planning 1 https://www.stadtentwicklung.berlin.de/planen/basisdaten_stadtentwicklung/big-data/downloads/big-data_crowddata_berlin.pdf. 2 http://www.smart-cities.eu/ 3 https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility 4 https://www.bbsr.bund.de/bbsr/de/stadtentwicklung/stadtentwicklungdeutschland/digitale-stadt/digitale-stadtnode.html 5 https://www.landatlas.de https://doi.org/10.14710/geoplanning.5.2.205-214 https://www.stadtentwicklung.berlin.de/planen/basisdaten_stadtentwicklung/big-data/downloads/big-data_crowd-data_berlin.pdf https://www.stadtentwicklung.berlin.de/planen/basisdaten_stadtentwicklung/big-data/downloads/big-data_crowd-data_berlin.pdf http://www.smart-cities.eu/ https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility https://www.wired.de/collection/life/smart-city-digitale-agenda-digital-smart-mobility-smart-carsharing-e-mobility https:// https:// https://www.bbsr.bund.de/bbsr/de/stadtentwicklung/stadtentwicklungdeutschland/digitale-stadt/digitale-stadt-node.html https://www.landatlas.de/ de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 | 211 table 5. examples of land use planning with spatial big data specific general top down ludwigshafen diskutiert wasserstadt dialog hannover dialog luft und lärm leipzig radständer nürnberg münchen mitdenken potsdam weiterdenken place2help rhein-main bürgerhaushalt berlinlichtenberg sag's doch friedrichshafen flashpoll die urbanauten münchen leerstandsmelder critical mass dresden bauleitplanung lingen planportal hamburg digitale dörfer nebenan bottom-up recht auf stadt hamburg viva viktoria bonn nordstadtblogger dortmund urbanophil berlin openberlin frankfurt gestalten hackaton freiburg code for berlin a number of these examples are discussed in more detail. recht auf stadt hamburg (‘right to the city hamburg), this specific case is part of a larger network http://www.rechtaufstadt.net/ . this website is a specific case of a bottom-up protest facility, providing an internet platform for citizens to share information on land use and property issues which are considered unfair, and on upcoming actions and events which require the mobilisation of people (such as protests, online petitions or demonstrations). the online facility currently focuses primarily on the high rents in cities and on the increasing difficulty for ordinary people to live affordably in the city. currently there are however not so many maps or spatial big data used, although there would certainly be a potential for this. examples such as https://www.antievictionmap.com/ in california/usa, showing maps, which display the location and degree of gentrification and locations of evictions for example, show that it is possible to go one-step further than simply informing and signalling problems. instead one can advocate and using blame and shame techniques with such maps. currently, however the recht auf stadt facility remains in the problem-framing sphere, instead of scaling it up to full collaborative problem solution tool. the leerstandsmelder (‘reporting vacant land and vacant buildings’) is an example of an awareness raising facility developed by both local governments and citizens. the site uses a map facility to collect and display information about the location and type of vacant / fallow land and/or buildings. as such it can provide both governments, private parties and citizens an idea of where unused or underutilized land exists, and where potentially active land use planning measures could be taken in order to revitalize the land or building. as such it offers a good insight in potential development problem areas and it could help to mobilize resources to act upon this information. it actively makes use of webgis technology to manage, locate and display the information. what is missing in this facility is, however, the possibility to interact with the information. citizens are not able to actively engage with the facility with suggestions or requests, or to link the information to other types of (spatial big) data to see possible reasons or trends. theoretically, such spatial analytics would be feasible. mapreduce models and hadoop open source software (https://hadoop.apache.org/) could potentially play a significant role here. collaboration and engagement are enhanced with this tool in land use planning, but scenario building and seeking bottom-up based land use planning solutions is not yet possible. code for berlin https://codefor.de/berlin/ is an example of an open data project. originating from a series of hackatons, whereby voluntary programmers design code to generate software solutions for a given problem, it now functions as a platform whereby all types of algorithms and software can be shared. the openness makes it accessible for any citizen, so engagement is unlimited in theory. however, programming remains a complicated activity for many ordinary citizens and therefore remains a rather exclusive activity. hence, the theory does not always translate practice. the scenarios and solutions may thus be limited to those are capable of handling the technology and as a result the technology is not value-free. at the same https://doi.org/10.14710/geoplanning.5.2.205-214 https://www.viva-viktoria.de/ https://www.antievictionmap.com/ https://hadoop.apache.org/ https://codefor.de/berlin/ de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 212 | time, there are also a number of intermediaries which can play an active role in making the technology more accessible. initiatives such as 52 north (https://52north.org/) and runder tisch gis (https://rundertischgis.de/) aim at enhancing the utilization of open source spatial technologies in the field of spatial land use planning (figure 5). yet, as said, there is still a certain professional threshold to make this a fully citizen-based technology. the project landatlas (https://www.landatlas.de) is a very independent project, but worthwhile mentioning here as it focuses specifically on rural areas. combining different publicly available datasets (such as the ones published by the national planning and statistical agencies) it aims at deriving a rich picture of the current situation in rural areas. it uses thereby an integrated indicator on ‘rurality’, comprising of 5 other integrated indicators: built-up area density, proportion of agricultural and forest land, proportion of family houses, regional population potential and accessibility to major centres. the combination of all these indicators, which are all georeferenced or geotagged, leads to a product landatlas. through the landatlas it is possible to research interactively possible spatial correlations or associations at the lowest administrative scale. this type of information may be highly useful for citizens who aim to fact-check regional development indicators (e.g. is our region really shrinking or are only certain municipalities affected) and for those who may signal certain consistencies and inconsistencies (e.g. are public finances in line with gross local products or degree of public and private debts). figure 5. big data for land management in germany (https://rundertischgis.de/) 3.2. discussion from the examples presented above one can observe that indeed many examples exist in germany whereby citizens are actively or passively involved with spatial big data in different phases of land use planning. with reference to the typologies of lagopoulos (2018), metternicht (2018) and wehrmann (2012) one could observe that most of the applications appear in both the definition and the plan formulation phase (using wehrmann (2012)), the survey of social system and the policy making stage (using lagopoulos(2018)) and the traditional land use planning variant (using metternicht (2018)). this clearly leaves a number of potential incorporations, especially in the phases and stages of developing and comparing alternative scenarios, and in those critically evaluating the effectiveness of certain land use planning decisions, untouched. the examples reveal also that maps and other forms of spatial data are not yet crucial in many strategic decision processes, and if they are, they heavily rely on professional geospatial technology expertise. the ‘non-expert’ examples are currently employed to signal, describe, display and categorize problems and to https://doi.org/10.14710/geoplanning.5.2.205-214 https://52north.org/ https://rundertischgis.de/ https://www.landatlas.de/ https://rundertischgis.de/ de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 | 213 find and highlight issues. in addition there are examples whereby citizens advocate alternative forms of action and mobilise people, yet these are without any maps or spatial analytical functionalities. the examples given in table 5 clearly indicate these types of actions. on the other hand, there are just very few good examples found in germany whereby citizens with the use of big spatial data actively indicate or prepare land use zones, analyse the effectiveness or efficiency of public restrictions and sanctions, intervene in land use acquisition processes, demand alternative locations or blame and shame decisions, which were against public agreements. this seems to indicate that there are still spheres of land use planning where the usage of big spatial data is still limited. when we compare these spheres one could distinct four spheres in which citizens could potentially contribute in land policy formulation, land politics and organization of land and active contributions to land use planning decisions (table 6). table 6. four spheres of land use planning focus land policy formulation land politics (public) organization of land citizens as contributors to land use planning traditional land use planning policy process = problem framing and agenda setting, policy development and decision making, policy implementation, policy control representation, idea generation and consideration, decision making and deliberation in parliament, democratic supervision executives; strategic top; operating core; technostructure; support staff citizens as rulers (participants in land use policy processes) citizens as ruled (subject to authority, recipients of land use plan) smart big data driven land use planning policy implementation; framing; protest democratic supervision; signaling problems (awareness & information) operating core; open portals; presenting integrated information citizens as consumers of services 4. conclusion in the context of the growing availability of spatial big data and in the light of the assumption that citizens use these spatial big data effectively and responsibly, this article questions the extent to which this assumption is valid. based on an exploratory analysis of usage of big (spatial) data in germany in the field of spatial planning the provisional conclusions are that there is indeed evidence of citizen-driven land use planning contributions, and the number of sites and the combined use of big data increases. furthermore, one could see that the big data such as the ones assembled by landatlas start to derive new insights in local land use facts. such new insights might support new discourses in what types of actions are useful and relevant. at the same time, the quality with which citizens can actively collect and analyse data to pursue their own land use goals can still be improved. there are indeed some active contributions, but there are also still many ‘fake’ or ‘opportunistic’ contributions. hence, regarding the big data characteristic veracity there are still problems. regarding the overall question do big spatial data already make citizens smart(er)? – one can only state partly. despite the many cases of open issue developments and voluntary code development often citizens are still more consumers of the smart applications and not so often producers of complete and verifiable data. smartness, in other words, therefore still seems to be more focused on enhancing smart economy and smart mobility for example in germany and less on enhancing smart people. possibly citizens are already actively engaged through conventional means in land use planning and do not require smart big data to enhance or increase their involvement. another explanation could be that it remains still difficult to use https://doi.org/10.14710/geoplanning.5.2.205-214 de vries / geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 205-214 doi: 10.14710/geoplanning.5.2.205-214 214 | spatial big data technologies and analytics for many unskilled people. this would suggest the need for more capacity development and the development of a sufficiently sized critical mass in this field. in both cases, the connection between geomatics, spatial planning and citizen sciences need to be improved and further developed. 5. references batty, m. (2013). big data, smart cities and city planning. dialogues in human geography, 3(3), 274–279. [crossref] brenner, n., marcuse, p., mayer, m., & others. (2012). cities for people, not for profit: critical urban theory and the right to the city. routledge. de vries, w. t., bennett, r. m., & zevenbergen, j. a. (2015). neo-cadastres: innovative solution for land users without state based land rights, or just reflections of institutional isomorphism? survey review, 47(342), 220–229. [crossref] doornik, j. a., & hendry, d. f. (2014). statistical model selection with “big data.” university of oxford, department of economics. retrieved from https://books.google.co.id/books?id=whfovqeacaaj franz, y., tausz, k., & thiel, s.-k. (2015). contextuality and co-creation matter: a qualitative case study comparison of living lab concepts in urban research. technology innovation management review, 5(12). [crossref] french, s., barchers, c., & zhang, w. (2015). moving beyond operations: leveraging big data for urban planning decisions. in 56th annual conference of association of college schools of planning (acsp), portland. gerber, j.-d., hartmann, t., & hengstermann, a. (2018). instruments of land policy: dealing with scarcity of land. routledge. kitchin, r., & lauriault, t. p. (2015). small data in the era of big data. geojournal, 80(4), 463–475. [crossref] lagopoulos, a. (2018). clarifying theoretical and applied land-use planning concepts. urban science, 2(1), 17. [crossref] laney, d. (2001). 3d data management: controlling data volume, velocity and variety. meta group research note, 6(70), 1. mayer, m. (2012). the “right to the city” in urban social movements. in cities for people, not for profit (pp. 75–97). routledge. metternicht, g. (2018). land use and spatial planning: enabling sustainable management of land resources. springer. rooij, r., & frank, a. i. (2016). educating spatial planners for the age of co-creation: the need to risk community, science and practice involvement in planning programmes and curricula. taylor & francis. schintler, l. a., & chen, z. (2017). big data for regional science. taylor & francis. retrieved from https://books.google.co.id/books?id=txiwdwaaqbaj schintler, l. a., & fischer, m. m. (2018). big data and regional science: opportunities, challenges, and directions for future research. wu vienna university of economics and business. senatsverwaltung für stadtentwicklung un wohnen berlin. (2017). big data und crowd data für die berliner stadtentwicklungsplanung. wehrmann, b. (2012). land use planning: concept, tools and applications. deutsche gesellschaft für internationale zusammenarbeit (giz). https://doi.org/10.14710/geoplanning.5.2.205-214 https://doi.org/10.1177/2043820613513390 https://doi.org/10.1179/1752270614y.0000000103 https://doi.org/10.22215/timreview/952 https://doi.org/10.1007/s10708-014-9601-7 https://doi.org/10.3390/urbansci2010017 | 83 geoplanning vol 4, no. 1, 2017, 83-96 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.1.83-96 spatial transformation of surakarta’s peripheral rural villages under in-situ urbanization phenomenon: the case of gentan village l. s. purnamasari a, g. yudana a, e. f. rini a, b a department of urban and regional planning, sebelas maret university, indonesia b center of information and regional development (pipw), sebelas maret university, indonesia abstract: surakarta is one of the rapidly growing indonesian cities. the pressure towards its peripheral area results in ‘in-situ urbanization’ phenomenon of its rural village surroundings. gentan is one of surakarta adjacent rural villages that has been undergoing rapid spatial transformation from rural to urban settlement in the last 20 years (1995-2016). this research aims to clarify the spatial transformation in gentan village through examinations of its spatial elements on higher resolution level; (1) transformation of its street network connectivity, (2) land use pattern, (3) building density, and (4) public facilities and accessibility. secondary data from satellite imagery and government institution and primary data from field survey were used in this research as sources. from gentan’s spatial elements observations, this research concluded that this village was transforming into urban settlements by its spatial elements characteristics. furthermore, this research provides interesting findings by its analysis on the neighborhood level that while gentan was transforming into urban settlement, its internal connectivity was decreasing, residential uses dominated its land use, the village was dominated by the formal settlement, and the reach distance of its public facilities fluctuated across the time. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): purnamasari, l. s., yudana, g., & rini, e. f. (2017). spatial transformation of surakarta’s peripheral rural villages under in-situ urbanization phenomenon: the case of gentan village. geoplanning: journal of geomatics and planning, 4(1), 83-96. doi:10.14710/geoplanning.4.1.83-96 1. introduction cities with more than 100.000 in total population are predicted to grow by 170% in size at 2030 and will transform their surrounding rural areas frequently resulting in surrounding village reclassification into urban annexes (un habitat, 2015). this transformation phenomenon of rural villages and their populations into urban without any significant relocation/migration of their residents is often called as in situ urbanization (brookfield, hadi, & mahmud, 1991; zhu, 2002, 2004). this kind of urbanization could occur in many forms as village in-city, desakota, quasi-urban area, and encompassed villages (hareedy & deguchi, 2011). the transformation of the outskirt area further forms the in-place peri urban zone which is characterized by an area located near the city with rapid rural-to-urban transformation. in indonesia, the population influx from urban core into its rural surroundings is actually an indispensable element of population growth in big cities (mamas, jones, & sastrasuanda, 2001). firman, kombaitan, and pradono, (2007) stated that the raise of urban population occurred much greater in peripheral rural villages and became the largest contributor to the overall accumulation of urbanization phenomenon. this kind of urbanization is strongly caused by the trend of urban sprawl that “swallowed” their surrounding rural villages and transformed them into urban in characters (brookfield et al., 1991; kalabamu & bolaane, 2013). beside the urban sprawl, village transformation phenomenon could be an accumulated result of certain policies imposed in urban-rural development system and the natural growth of the rural villages themselves (mcgee, 2009; zhu, 2000). in situ urbanization, like the term of urbanization in general, occurs in multi-dimensional way including physical-spatial, economic, social dimensions. in spatial aspect, the transformation process of village to be considered urban in spatial characteristic could be best possibly open access article info: received: 18 october 2017 in revised form: 2 february 2017 accepted: 20 february 2017 available online: 30 march 2017 keywords: spatial transformation, in situ urbanization, peripheral rural villages, surakarta corresponding author : lia sparingga purnamasari department of urban and regional planning, sebelas maret university, indonesia email: liaspurnamasari@gmail.com http://dx.doi.org/10.14710/geoplanning.4.1.83-96 http://dx.doi.org/10.14710/geoplanning.4.1.83-96 mailto:liaspurnamasari@gmail.com purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 84 | defined through observing the character of its morphological elements (urban form) that consist of land use pattern, street network, and buildings (daldjoeni, 1998; setyono, yunus, & giyarsih, 2016; sinulingga, 1999; soetomo, 2009; yunus, 2006). while bps (2010) and yunus (2008) also stated that public facility is also an indicator to be considered in terms of village transformation in peripheral area besides the morphological elements. in spatial aspect, rural villages are characterized as low building-density settlement, dominated with agricultural land use, with few public facilities that are difficult to access, and low transportation network connectivity (bintarto, 1986; giyarsih, 2003; jayadinata, 1986; yunus, 2008). otherwise, urban settlements are often characterized with high building density; its land use is dominated with urban uses (nonagricultural) with more complete and easy to access public facilities—as urban area which are commonly defined as the center of services, and more complex, high transportation network connectivity. the characteristic shifts of the elements between urban and rural area forwardly become the indices of the transformation itself. surakarta is one of indonesian cities continuously growing, transforming, and swallowing its surrounding rural villages into urban annexes. the continuous influences of the city towards its rural surroundings contributed to the shifts of the rural spatial characteristics into urban. previous researches conducted in surakarta’s rural villages surroundings by anna, kaeksi, and astuti (2010), astuti (2010), jayanti (2012), oktavia (2010) clarified that spatial transformations constantly occur and transform the previously agricultural land into urban in character. one of the surakarta’s peripheral rural villages undergoing rapid urbanization is gentan which is located adjacent to surakarta city in the south border. based on the preliminary observation of satellite images, gentan village undergoes the most significant transformation— to 58% in the development of built-up area compared to other surakarta’s adjacent rural villages in the last 20 years (1995-2016). this rapid transformation of gentan was triggered by the development of modern/formal settlement of pondok baru permai in 1995 that marked the trend of formal (planned) housing and gated-community development there (fitriastuti, 2010). some prominent previous researches observing the phenomenon of in-situ urbanization in spatial aspect by bentinck (2000), brookfield et al. (1991), zhu (2000) were mainly focused in land-use change on the macro range (regional level) of a city’s peripheral area. while the examination of land-use change on the macro level is effective to trace the trend of urbanization in regional level, those researches did not provide deeper insights about how urbanization could triggered spatial transformation affecting the lives of the village inhabitants, thus needs examination on higher resolution level. however, there are already some previous researches observing the in situ urbanization phenomenon in neighborhood level (higher resolution observation) of a rural village located adjacent to rapidly growing cities. hareedy and deguchi (2011) conducted a study that focuses on the transformation of urban fabric, block configuration, and building characteristic occurred in the peripheral rural village of el-minya city using specific typological approach and village residents’ information to clarify the transformation. this approach will be difficult to replicate and apply to another region with different characteristic. by contrast, kalabamu and bolaane (2013) conducted a study that focus on housing densification in spatial aspect in tlokweng village, bostwana as the accumulation result of changing economic structure in in situ urbanization phenomenon, and not providing broader insights about how the built environment and other spatial elements of the village have changed. this research aims to fill the gap of understanding spatial transformation under in situ urbanization phenomenon in higher resolution scope by taking more applicable, comprehensive and detailed observation of changing rural spatial elements from the urban form quantitative analysis perspectives. this research examined the transformation of gentan village spatial elements characteristic including (1) street network connectivity, (2) land use pattern, (3) building density, and (4) public facilities and the accessibility from 1995 to 2016. the further applications of this research would be easily replicated in different regions and would benefit the urban and regional policy formulators to comprehensively understand the in situ urbanization phenomenon in spatial aspect. this will foster more effective and detailed plan in order to accommodate rapid spatial transformation that occurs in city’s rural village surroundings. http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 | 85 2. data and methods 2.1. research scope (area and period) gentan village administratively belongs to sukoharjo regency. this village located adjacently to surakarta city in the south border (adjacent to laweyan district) has 149.09 hectare in total area. long before the trend of urbanization happened, gentan village consisted of 4 kampungs (traditional settlements) that inhabited by local agricultural communities (figure 1 ) for centuries. figure 1. map of gentan’s constellation with surakarta and its existing condition (author, 2016) gentan village was selected as the representative case study area to study in-situ urbanization in surakarta’s peripheral village for the following reasons: (1) gentan is located adjacent to surakarta city, and (2) it showed the most significant growth of built-up area (58%) from 27% into 85% compared to others surakarta’s adjacent rural villages (figure 2). the year 1995 was chosen as a starting point as it remarked the trend of formal housing and gated-community developments that highly contributed to the transformation of gentan village into urban settlement (duhri, 2015; fitriastuti, 2010). while in overall trend of population growth in all surakarta’s rural adjacent villages, 1995 marked the highest spike to 3% of population growth compared to 1% in common condition. figure 2. research scope justification (author, 2016) 2.2. data in order to identify the spatial transformation of gentan villages, time-series data (1995, 2000, 2005, 2010, 2016) of land use, building, transportation network, and public facilities were used in this research. two types of data resources were used in this research. firstly, the secondary data were obtained from http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 86 | government institutions and satellite images. secondly, primary data were obtained from field verification survey (table 1). two methods namely (1) map interpretation using satellite imageries and digitation into editable map format (figure 3) and (2) statistical data tabulation using microsoft excel were carried out to process the obtained. table 1. research data data sources spot3 10 m panchromatic imagery aug. 1995 & 2000 (digitized into arcgis polygon shapefiles (.shp), result shown in analysis map) spot 3 satellite imagery digitalglobe satellite imagery archives over gentan 2000, 2005, 2010, 2016 (digitized into arcgis polygon shapefiles (.shp), result shown into analysis map) digitalglobe archive—googleearth pro gentan profiling map (hardfile archives) year 1995, 2000, 2005, 2010 gentan administrative office 1995, 2000, 2010 sukoharjo street data bappeda sukoharjo (sukoharjo dept. of development planning) and dpu sukoharjo (sukoharjo dept. of public works) 1995, 2000, 2010 sukoharjo land use data bappeda sukoharjo (sukoharjo dept. of development planning) and bpn sukoharjo (sukoharjo dept. of survey and land) statistical data of gentan village from baki dalam angka 20002016 bps sukoharjo (sukoharjo dept. of statistic) land-use + public facilities field verification data gentan local leaders figure 3. satellite imageries data spot 3 panchromatic 12/8/1995 spot 3 panchromatic 21/11/2000 digitalglobe archive 8/12/2000 digitalglobe archive 08/2005 digitalglobe archive 05/2010 digitalglobe archive 02/2016 as the first step for further analysis, this research used geographical information system by arcgis 10.2.2.to digitalize satellite imagery into digital map. this approach was selected since in one side satellite images supply up-to date information but is restricted by pixel resolution, multispectral classification, and visibility and digitization of aerial photography/imagery (illert, 1991). the intention of map digitization is to http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 | 87 create spatio-temporal data in the form of vector based digital maps to show and measure changes in the research area. in this research, the workflow towards creating the spatio-temporal data is similar with the works of lu et al. (2014), malarvizhi, kumar, and porchelvan (2016), mehmood et al. (2016), ohri and yadav (2012), raza et al. (2016), yang and lo (2002) as they utilized satellite image digitization to analyze changes in urban growth. while those researches were intended to analyze uniquely land use land cover (lulc) change to detect urban sprawl/growth, in this research satellite image was digitized in broader scope including building mapping and land use in the form of polygon, street mapping in the form of arc lines, public facilities in the form of dots combined with information from secondary and field survey data. overlay method was intended to show how the gradual transformation is spatially manifested (figure 4). figure 4. data processing framework data processing was started with delineating the research area by creating a (.kml) form administrative boundary of gentan village. the .kml file was intended to strategically select the area of satellite imageries of digital globe archives and spot 3 for the further analysis and used to delineate the research area for each period year (1995-2016). spot 3 imageries were used to complete the data needed for 1995 timeperiod which is unavailable in google-earth historic view (digital globe archives). after satellite imageries were delineated for each year of research time-period, satellite imageries were being georeferenced in gis to add geographic information. the next step is manual georeferenced-satellite image digitization in arcgis 10.2.2 in order to digitize information of street, building, land use, and public facilities (urban form elements). manual digitization was preferred due to the requirement of broader elements to be mapped (urban form elements). this step was also equipped with data completion from secondary data and field verification data to add more information that is unavailable by only observing satellite imageries. after all the necessary information is completed, a map was produced for each urban form element for each timeperiod. the next step was the ‘overlay’ meaning that each map of urban form elements from each timeperiod was being layered based on the urban form element type. in order to spatially observe the changes, a transformation map was further produced for each urban form element for the further urban form analysis. 2.3. research analysis methods this research carried out a descriptive analysis method to explain the results of each calculation of spatial element transformations and discuss the findings towards its position in literature and previous studies. measurement instruments used for calculation analysis were conducted using statistical data obtained from analysis of (1) building density, (2) land use pattern, (3) spatial accessibility of public facilities, and (4) connectivity. these were compiled from literature review of urban form measurement in higher resolution analysis level based of the work of song and knaap (2004), southworth (1997), berghauser-pont & haupt (2010), brail & klosterman (2001) (figure 5). this higher resolution level urban form analysis fits into the context of this research’s objectives as it used community design perspective and commonly addressed how the area adapt to growth and to study the pattern of development itself. the main focused of this research analysis was to measure the shifts of building density, land use pattern, spatial accessibility, and connectivity into more urban in characters. http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 88 | figure 5. research framework 3. results and discussion 3.1. transformation of street network connectivity the observation of the transportation network connectivity was based in the growth of the street network inside the area, internal and external connectivities. urban settlement is often characterized as the area with complex and dense street network contributing to higher street connectivity (giyarsih, 2003; sinulingga, 1999; yunus, 2008). as opposed, rural is characterized with less street networks and connectivity (giyarsih, 2003). the transformation trend showed that as the street network grows, the external connectivity increases whilst the internal street connectivity declines (figure 6). this result showed that in the last twenty years gentan experienced rising number of access points in external connectivity which means that this village can be easily accessed from its surrounding area. meanwhile, the reduction of internal connectivity was caused by the dominating development of cul-de-sac streets that negatively affects internal circulation (table 2). figure 6. street network transformation map (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 | 89 table 2. street network connectivity transformation (analysis, 2016) connectivity 1995 2000 2005 2010 2016 external connectivity (access points) 18 19 21 23 25 # intersections 43 90 139 166 197 # culdesac 4 9 25 34 42 internal connectivity 0.91 0.91 0.85 0.83 0.82 the development of the street network and the external connectivity transformation across the time could clarify two facts. firstly, urbanization occured in the area as the characteristics shift to resemble urban settlement. secondly, in the last twenty years internal connectivity transformation showed an anomaly. the rise of cul-de-sac street numbers across the time that negatively contributed to the decrease of internal connectivity indicates that the village resembles more to a sub-urban area rather than urban in characteristics. this result has been highlighted by song and knaap (2004) that characterized sub-urban as the area formed by urban sprawl with dwindling streets, many dead-end (cul-de-sac) streets. in gentan village, this type of street was found in gated-community settlement and formal housing imposing high security system, which is also common in spatial manifestation of urban sprawl (song & knaap, 2004; southworth, 1997). this evidence of decreasing internal connectivity implies an indication that this village has transformed more into urban settlement under the great influence of urban sprawl rather than natural growth of traditional settlement that often manifested in the form of housing densification and land subdivision. this result showed different issue occurs in gentan village compared to a previous in situ urbanization research conducted by hareedy & deguchi (2011). they studied the village in el-minya which transformed into urban carrying the characteristic of traditional settlement rather than showing the common characteristic of urban sprawl. 3.2. transformation of building density building density is often described as the ratio of the number of buildings and total area whilst some literature such as song and knaap (2004) and dempsey et al. (2008) stated that the plot density is also an indicator to represent building density using the median value of overall plot density (figure 7). this research combined the two indicators by identifying the numbers of building in the study area, the ratio of the number of buildings per area (ha), median number of plot density, and building coverage. building density itself is an observable indicator in order to clarify in situ urbanization phenomenon as the urban settlement. figure 7. map of building transformation (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 90 | urban settlement is characterized by an area with higher density compared to rural villages (daldjoeni, 1998; sinulingga, 1999; soetomo, 2009; yunus, 2008). as opposed, the rural village settlement is characterized by an area with very low building density (bintarto, 1986). as in situ urbanization phenomenon itself means transformation of rural villages into urban in characters, the shifts of rural characters into urban indicates the urbanization itself. the results showed that gentan experienced escalation in all building density measurements. the rise in total numbers of building, the ratio of numbers of building per hectare, and the building coverage were correlated. the rise of median plot density consequently clarified that in situ urbanization has been occurred in gentan village in the last twenty years (table 3). table 3. building density transformation (analysis, 2016) density 1995 2000 2005 2010 2016 #buildings 1,332 2,455 3,522 3,874 4,442 building coverage (ha) 17.3 30.61 43.07 47.34 57.93 median plot density (%) 65 90 92 92 93 in addition, this research also discovered that the median value major shift of plot density from 1995 to 2000 shows that the village was started to be dominated by the formal settlements and gated community previously consisted and dominated by kampung (organic/informal settlement). the formal settlements in gentan commonly have high ratio of plot density while the organic settlements/kampungs that has been there long before the explosion of formal housing development have lower value of plot density. this finding formed special characters of in situ urbanization occurring in the village from the perspective of building density. the numbers of buildings and the building coverage transformation clarified the urbanization phenomenon. the escalation in plot density indices indicated that there are two types (dualism) of evolving settlements inside the village; traditional (organic/kampung) and modern (formal) (figure 8). finally, the shifts of the density identified shifting domination of the modern ones. compared to previous research, this finding provided different kind of spatial manifestation of in situ urbanization. kalabamu and bolaane (2013) identified the settlement densification under in situ urbanization in tlokweng village in bostwana which was dominated by the infilling development inside the traditional settlement area. according to the research, this was the result of the establishment of housing rentals trend by the village inhabitants in order to improve economic condition. figure 8. dualism of settlement type 3.3. transformation of land use pattern land use is an element of urban form and a vital variable to observe the shifts between rural land use and urban land use that indicate urbanization phenomenon (soetomo, 2009; yunus, 2008). rural land use is often characterized by an area with agricultural use dominance (jayadinata, 1986). in the context of in situ urbanization, land use is the main spatial elements in which its transformation towards more urban land use characteristics indicates the phenomenon of in situ urbanization itself (brookfield et al., 1991). in this research, land use pattern was examined in each sample year to show how the land-use gradually changes over time. the result showed that land use in gentan village changed in pattern from 1995 to 2016 (table 4). it showed that gentan village was previously dominated by the agricultural use and then gradually http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 | 91 dissipated into urban uses. this rapid agricultural land-use conversion clarified that the village transformation into urban settlement (figure 9). table 4. land use pattern transformation (analysis, 2016) land use 1995 2000 2005 2010 2016 area (ha) % area (ha) % area (ha) % area (ha) % area (ha) % agricultural 109.45 73.41 86.59 58.08 64.15 43.03 53.75 36.052 21.53 14.44 residential 32.75 21.97 52.53 35.23 72.46 48.60 79.96 53.632 103.83 69.64 commercial 3.63 2.43 4.39 2.94 5.89 3.95 7.41 4.970 12.87 8.63 public service/facilities 3.26 2.19 4.54 3.05 5.55 3.72 6.73 4.514 9.07 6.08 industrial 0 0.00 1.04 0.70 1.04 0.70 1.24 0.832 1.79 1.20 figure 9. map of land use pattern transformation (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 92 | the research analysis highlighted the biggest land conversion imposed by the residential use, followed by commercial, public services, and the industrial use. by 2016, agricultural use occupied only 14.4% of total gentan village area. the research finding on dominance of residential use converting previously agricultural land use under in situ urbanization indicates that the population explosion driving the new housing expansion is the main force transforming land use. this also indicates that this land use transformation was driven by the continuously expanding major population influx of the neighboring city. 3.4. transformation of public facilities characteristic in 2010, indonesian census board (badan pusat statistik) considered public facilities as the criteria to be assessed in order to decide rural village reclassification through their (1) availability and (2) distance towards them. giyarsih (2003) stated that public facilities are harder to access in rural area than urban. tamin (2000) stated that accessibility towards objects is calculated through the distance, although nonspatial aspects such as individual preferences are also considered affecting the access (table 5). this research identified the transformation of public facilities characteristic through examination of the change in numbers and the median reach-distance of the overall gentan’s existing settlement towards each public facility sub-types. the result showed that most of gentan’s public facilities increased in numbers except for some sub-types; elementary school, church, and puskesmas (district-level health clinic) while the median reach distance picturing spatial accessibility fluctuated across time (figure 10). table 5. public facilities and their accessibility transformation (analysis, 2016) public facilities 1995 2000 2005 2010 2016 # median distance/access (m) # median distance/access (m) # median distance/access (m) # median distance/access (m) # median distance/access (m) commercial facilities traditional market 0 0 0 0 0 0 1 461 1 483 supermarkets 0 0 1 719 1 645 2 507 5 285 small shops 40 81 70 78 90 85 103 86 129 88 religious facilities mosque 8 153 12 120 19 98 24 94 27 91 church 1 513 1 514 1 518 1 534 1 549 health facilities puskesmas (district-level clinic) 1 384 1 420 1 446 1 452 1 474 clinic 1 538 1 650 3 374 3 324 5 319 edcation facilities pre-school (kindergarten) 2 486 2 458 3 344 6 248 6 269 elementary school 2 279 2 328 2 321 2 330 2 340 commercial facilities tended to increase in distance except for swalayan (supermarket) sub-type which showed that the distance towards the facilities were getting further. this could be explained by the centralized growth of commercial facilities along the main street of gentan (songgolangit & mangesti raya street) while the growth of residential use occurred throughout the village area. for religious facilities, the spatial access to the mosque decreased over time as the growth occurred evenly across the village area while the distance towards church increased because the numbers did not grow. health facilities sub-type puskesmas increased in median distance value since for the last twenty years the numbers of the facility did not grow while the health clinic tended to lower in distance as the number grew and equally spread across http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 | 93 the village area. the distance-reduction in education facility occurred in pre-school subtype as the number grows while the elementary sub-type experienced escalation in distance as the number halted. figure 10. map of public facilities transformation (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.4.1.83-96 purnamasari, yudana, and rini / geoplanning: journal of geomatics and planning, vol 4, no. 1, 2017, 83-96 doi: 10.14710/geoplanning.4.1.83-96 94 | 4. conclusion this study aimed to identify the spatial transformation of gentan village under in situ urbanization phenomenon. taken the results together, this research clarified that gentan village has transformed into an urban settlement from previously rural village and justified that in situ urbanization took place in the village. moreover, this research also highlighted several issues from the analysis of gentan’s spatial elements. firstly, external connectivity showed that gentan became more accessible from outside area, but the internal connectivity decreased by the trend of cul-de-sac development that negatively affects the internal circulation. the trend of cul-de-sac development that belongs to the gated community in the village for the last twenty years resembles the trend of sub-urban development. this indicated that the transformation occurred in the area was majorly influenced by the urban sprawl rather than the characteristic of the existing traditional settlement. secondly, while all building density measurements showed escalation for the last twenty years and clarified the urbanization phenomenon, the plot density changes showed that formal settlements dominate gentan village. this finding also indicates that there are dualisms of settlement types in gentan village that form the village transformation towards urban settlements. thirdly, the higher resolution examination of the change in land use pattern showed that the land-use conversion is dominated by the residential uses. this findings indicated that the rapid land use conversion was caused by the population explosion that influenced the needs for new settlement development. the result further suggests that this phenomenon was possibly caused by the influx of new population imposed by the urban sprawl of the neighboring city. fourthly, the enhancement in public facility numbers did not always go in line with their spatial accessibility that shows fluctuation, even as the urbanized village. in addition, the numbers of public facilities grew to support the growth of urban settlement. in the last twenty years, the fluctuation of spatial accessibility stated that the distribution of public facilities was mostly centered along the main street, not evenly across the village. this results and findings provided deeper perspectives to understand the urbanization phenomenon on the neighborhood level that could foster more effective development policies to deal with such transformation. the findings of spatial element transformation also highlighted several issues of in situ urbanization to be recommended for further researches that include; 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(pp. 207–228). ashgate publishing ltd. http://dx.doi.org/10.14710/geoplanning.4.1.83-96 https://doi.org/10.1111/1467-7660.00160 https://doi.org/10.1111/1467-8373.00155 | 77 geoplanning vol 3, no 1, 2016, 77-86 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.1.77-86 monitoring the land use change in campus 2 stkip pgri pontianak a. purwantoa, g. bayuardia a. ikip pgri pontianak, indonesia abstract: the aims of the research are: 1) investigating the changes of land-use occurring at campus 2 of stkip pgri pontianak, 2) determining the tendency towards changes of land-use at campus 2 of stkip pgri pontianak and, 3) mapping the land-use change at campus 2 of stkip pgri pontianak from 2003 to 2011. the methods used in this research were survey and interpretation of the image of a multiple-color composite in 2003, 2008 and 2011 using gis software. the data used were the types of land-use and the width of land-use change area. the data were analyzed by overlay method. the results have shown the following: 1) the changes of land use have been largely from forest land and paddy fields to settlement area; 2) the trend of the change is approaching to the north side, east side, south side and west side of the campus; 3) the characteristics of the extension of land-use changes from 2003 to 2011 are: settlement increased 66,110 m 2 , field service (restaurant) became 10,254 m 2 , the fields had added 17,097 m 2 , paddy field had decreased 25,211 m 2 , the forest area had decreased 104,327 m 2 and educational facilities had increased 35,427 m 2 while police station had extended 650 m 2 . copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): purwanto, a. and bayuardi, g. (2016). monitoring the land use change in campus 2 stkip pgri pontianak. geoplanning: journal of geomatics and planning, 3(1), 77-86. doi:10.14710/geoplanning.3.1.77-86 1. introduction land use change is basically a transition from a certain purpose in using land to another. changes in land use can be a reflection of an area experiencing growth, especially in the growing of physical infrastructure in the form of economic infrastructure, roads and others. in land use development, changes should be distributed in certain places that have good potential. thus, monitoring, data collection and mapping land use change are very important to comprehend the changes. one of the tools that can be used for the puspose is remote sensing-geographic information system (rs-gis). studies on land use change have been developing during the recent years with a variety of methods and cases. several recent studies indicate a strengthening of land-use change identification method by utilizing the technology of remote sensing and geographic information system. in china the study of land use changes on the value of land use spatio temporal gis techniques were conducted in 1992, 1996, 2001, 2004 and 2008 (du et al., 2014). they were able to produce patterns of land, thereby, increasing the efficiency of the investment value of the land. still in china, in yucheng city of shandong province, the study of changes in land use in rural areas is becoming one of the alternatives to see policy changes in land use and land ownership. the study used comparative methods of spatial land use by using map results of image interpretation (liu et al., 2014). in slovakia, similar studies using the spatio temporal gis technique and remote sensing also obtain significant results about the patterns of land use change. the importance of the findings in slovakia can actually help in seeing the history of land use patterns in three different time periods, namely monarchy era, revolutionary era, and the present (kanianska et al., 2014). the studies are interesting to be replicated by adopting the pattern and methods of remote sensing and geographic information system especially to see the dynamics of land use change. rs-gis is a precise and accurate tool to provide information about the article info: received: 21 march 2016 in revised form: 1 april 2016 accepted: 25 april 2016. available online: 30 april 2016 keywords: remote sensing, gis, monitoring, land use change corresponding author: ajun purwanto ikip pgri,pontianak, indonesia email: ajunpurwanto@gmail.com open access http://dx.doi.org/10.14710/geoplanning.3.1.77-86 mailto:ajunpurwanto@gmail.com purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 78 | spatial distribution of land use change covering a wide area. past studies carried out by organizations and institutions around the world have been concentrating on the application of rs-gis for analyzing and evaluating the land use change. geographical information system provides flexible facilities for collecting, storing, displaying and analyzing digital data which is required for the detection of changes in land use (reis, 2008). land use is dynamic, it will change in accordance with the increase of population, social and economic activities. rapid population growth will have implications to the increased need for space to conduct various activities manifested in the land uses. meanwhile, to meet the needs of the land, there are limitations occur in local, physical, geographical, and the ability of governments to provide infrastructure and land use policy instruments. land use policy becomes an interesting subject for discussion in developing countries like indonesia. unclear conditions pushes the need for clear monitoring system (mialhe et al., 2015) clear policy direction (zimmermann et al., 2016), and the clear rule of land transition (romo-leon et al., 2014). sumaatmadja (1988) outlined changes in land use is influenced by the attractiveness of the place, such as: 1) the larger amounts of land available in expansion area, 2) low prices of land in expansion area, so it encourage residents to live in the area, 3) a more pleasant atmosphere, especially in the division still has environmental conditions that are free from all kinds of pollution, 4) are likely to take their education outside of town, 5) approaching the workplace. urban population growth due to natural growth and migration also has implications for the growing population pressure on urban land because of the land requirement for residential activities and other supporting increasing facilities. it becomes a big challenge for planners, city managers and the residents themselves. therefore, this study aims to: 1) determine land use changes occur at campus 2 stkip pgri pontianak, 2) know the trend towards land use change at campus 2 stkip pgri pontianak and 3) to map the area of land use change from 2003 to 2011. study for the development of the campus land use has also been done in purwokerto (munggiarti & buchori, 2015) and bandung (wijaya, 2015). university campus is a strategic area where the dynamics of economic growth affect the use of land. the increasing number of students, along with the related facilities, will create change in both the structure and spatial patterns. in 2003 to 2011, the area of campus 2 stkip pgri pontianak has been as urban expansion area of southern pontianak part that has been developing quite rapidly. the number of buildings is growing comprising the lecture buildings, settlements ones, buildings for businesses and others, showing the dynamics of land use change in the campus area. 2. data and methods the methods used to see the changes in land use were the remote sensing and remote sensing temporal data (du et al., 2014; kanianska et al., 2014; lillesand et al., 2014; liu et al., 2014; munggiarti & buchori, 2015; wijaya, 2015). the data used in this study were multi-color composite images taken from google earth with multi-years of 2003, 2008 and 2011. the method used in this study was a survey of image interpretation and checks. mechanical research was done in several stages as described below. 2.1 the preparatory stage the first was a study of literature, magazines and brochures associated with the object of research. the study was used to supplement the theories related to the study of land use change. second, a composite image of a multi-year available from 2003, 2008 and 2011 was done. these data were used to determine land-use change periodically over the next eight years. the third was to set up a map of west pontianak sub-district administration, to determine the areas experiencing land use change. the fourth was to set up additional equipment used for image interpretation process. 2.2 the interpretation stage delineation/granting outline on the same appearance and differentiate from the appearance of one to another. delineation was done to make residential units or unit blocks habitation. elements of interpretation were used as a reference in determining the block of habitation in the process of image interpretation. determination of the mapping unit was done by photomorphic means mapping unit of the purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 | 79 territory into smaller units groupings based on physical unit that can be observed from the image. block boundary was determined based on clear and unequivocal limits in the form of major roads that were visually observed. delineation of the appearances of changes in land use in the composite image multiyear, in 2003, 2008 and 2011. 2.3 field checks and data analysis field checks were carried out directly to complete the data which could not be gained in the image interpretation process (sutanto, 1998). selection or determination of gcp (ground control point), with data analysis used in this study was the interpretation of using the keys of interpretation, identification of the development direction pattern and overlaying the images of composite multi-years of 2003, 2008 and 2011. the study framework can be seen in figure 1 and the study area in figure 2. figure 1. the study framework satellite image in 2008 multi years composite image pattern intepretation overlay interpretation keys kind of land used change spatial planning and land used change satellite image in 2011 study framework land used change map u rb an p lan n in g p o licy satellite image in 2003 interpretation and delineation purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 80 | figure 2. the study area, pontianak, west borneo (google map, 2015) 3. results and discussion 3.1 land use in 2003 interpretation of satellite image in 2003 was done to make land use map of campus 2 stkip. elements of interpretation were used as a reference in determining the land cover in the process of image interpretation. determination of the land use map unit was done by classification method, it means that mapping unit of the territory into smaller units groupings based on physical unit that can be observed from the image. block boundary was determined based on clear and unequivocal limits in the form of major roads that were visually observed. based on the interpretation of color composite image from google earth 2003 the land use at campus 2 stkip pgri pontianak consists of forest, settlement, wetland, paddy field, and restaurants. it can be seen in figure 3. based on the satellite image interpretation, it is known that the most extensive land use is forest land, which has an area of 962,458 m2 or 77.71 % of the total area of the mapped region. on the other hand, the smallest use was for restaurant, which was only 1,550 m2 or 0.10 %. the results are shown in table 1 and figure 4. table 1. stkip land use in 2003 (analysis, 2015) land use area (m 2 ) % 1. settlement 32,337 2.61 2. service: restaurant 1,550 0.10 3. agriculture: paddy field 78, 698 6.35 wetland 143,985 11.62 4. forest 962,458 77.71 5. others grass 14,877 1.20 road 4,696 0.38 total 1,238,601 100.00 purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 | 81 figure 3. stkip land cover satellite imagery in 2003 (google earth, 2003) figure 4. stkip land use 2003 interpretation result (analysis, 2015) purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 82 | 3.2 land use in 2008 based on the color composite image of google earth in 2008, the land use has undergone a variety of changes, such as for settlement, schools, paddy fields and the police station as can be seen in figure 5. the most extensive land use was still the forest. however, the forest was reduced by 9,408 m2 or 0.76% to 953,050 m2 or 76.95% of the total area being mapped. the smallest land use was for school, which was only 498 m2 (0.04 %). in details, the interpretation results are shown in table 2. figure 5. stkip land cover satellite imagery in 2008 (google earth, 2008) table 2. stkip land use area in 2008 (analysis, 2015) no land use wide (m 2 ) % 1. settlement 42,432 4.50 2. service: restaurant 1,550 0.10 3. agriculture: paddy field 78,698 6.35 wetland 137,197 11.08 4. forest 953,050 76.95 5. others : grass 14,877 1.20 road 4,696 0.38 6. education school 498 0.04 7. police station 5,603 0.55 total 1,238,601 100.00 purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 | 83 3.3 land use in 2011 in 2011, the land use around campus 2 stkip pgri pontianak was classified into settlements, restaurants, farm comprising fields and paddy fields, forest, grass, educational facilities and police stations, the uses of the land can be seen in figure 6. based on the interpretation it is known that the most extensive use was still for forest land. however, it experienced a reduction again from originally 953,050 m2 (77.62%) in 2008 to 861,528 m2 (68.71%). this reduction was 104,327 m2 or 8.42% of the total area being mapped. the smallest land use was for schools, which was only 498 m2 (0.04%). the interpretation results can be seen in figure 7 and table 3. figure 6. stkip land cover satellite imagery in 2011 (google earth, 2011) figure 7. stkip land use 2011 interpretation result (analysis, 2015) purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 84 | table 3. stkip land use area in 2011 (analysis, 2015) land use wide (m 2 ) % 1. settlement 98,447 7.95 2. service: restaurant 11,804 0.95 3. agriculture: paddy field 95,795 7.73 wetland 109,774 8.86 4. forest 861,528 69.56 5. others: grass 14,877 1.20 road 4,696 0.38 6. education facilities: schools 498 0.04 akbid 7,424 0.60 stkip 27,505 2.22 7. police station 6,253 0.50 total 1,238,601 100.00 3.4 changes in land use in the area of campus 2 stkip pgri pontianak changes in land use in fact are complicated as can be seen from the various types of use and extensive changes to the various uses. so it is necessary to be able to know clearly about the change per unit of land use. the following describes the types of land use and the changes (see table 4). the results of the imagery interpretation of 2003, 2008 and 2011 show that the land use in the area of campus 2 stkip pgri pontianak was increasingly varied and complex. the number of units of land use has increased, especially the blocks of land for settlement while experiencing a reduction in forests and fields. the decrease of forest and field areas was due to conversion to settlement, restaurants, educational facilities (schools, academy of midwifery, stkip) and the police stations. the pattern of land use settlement is a manifestation of the people’s activities to utilize the land in order to meet their needs. the use of land for settlements in the study area is ranked at the top in terms of its expansion, and it is also the most extensive change. the high intensity and the change in scale are widely associated with the development or population intervention that is relatively high in the study area. the use of land for settlements was around 32,337 m2 in 2003, and then increased sharply to 101,346 m2 in 2011, meaning that the expansion of settlements was as much as 66,110 m2 or 5.34%. many of the new settlements occupy the agricultural land and forest. this development is unfortunate given that the agricultural land in the study area is very fertile . for the foreseeable future, the remaining agricultural land should be used as urban agricultural land, so that the urban community's food dependency on the supply of the hinterland or the surrounding countryside can be reduced and at the same time can improve the housing environment ecologically. the second most expanded land use was for education, i.e., as much as 35,427 m2 from 2008 to 2011. in general, the growth of land use for educational facilities was on forest areas and some small paddy fields. educational facilities that began to grow at the site of research in 2008-2011 include sma (high school) 8 pontianak, muhammadiyah midwifery academy (akbid) pontianak and the school of teaching and education (stkip) pgri pontianak. meanwhile, the use of land for the police department has also increased, i.e., 650 m2. purwanto and bayuardi / geoplanning: journal of geomatics and planning, vol 3, no 1, 2016, 77-86 doi: 10.14710/geoplanning.3.1.77-86 | 85 table 4. land use change in the area of stkip in 2003-2011 (analysis, 2015) land use area (m 2 ) 2003 area (m 2 ) 2008 area (m 2 ) 2011 change (m 2 ) 2003-2011 1. settlement 32,337 42,032 101,346 + 66,110 2. service: restaurant 1,550 1,550 11,804 + 10,254 3. agriculture : field 78,698 78,698 95,795 + 17,097 wetland 143,985 137,197 109,774 25,211 4. forest 962,458 957,050 858,131 104,327 5. others: grass 14,877 14,877 14,877 road 4,696 4,696 4,696 6.education facilities schools 498 498 + 498 akbid 7,424 + 7,424 stkip 27,505 + 27,505 7. police station 5,603 6,253 + 650 total 1,238,601 1,238,601 1,238,601 0 4. conclusion further conversion of agricultural land and forest to settlement use should be avoided. however, it can be allowed for non-conservation forest. it is intended that the agricultural land can keep producing appropriately to ensure food security. concentration of land conversion in certain areas will lead to the unequal distribution of various fasilities both socially and economically. so, further land use conversion should be directed to cropland and for non built-up uses only. 5. acknowledgments the author(s) would like to thank the directorate of research and community services of the ministry of research, technology and higher education for funding this research through the scheme of young researchers grant in 2015. 6. references du, j., et. al. (2014). urban land market and land-use changes in post-reform china: a case study of beijing. landscape and urban planning, 124, 118–128. kanianska, r., et. al. (2014). land-use and land-cover changes in rural areas during different political systems: a case study of slovakia from 1782 to 2006. land use policy, 36, 554–566. lillesand, t., et. al. (2014). remote sensing and image interpretation. john wiley & sons. liu, y., et. al. (2014). implications of land-use change in rural china: a case study of yucheng, shandong province. land use policy, 40, 111–118. mialhe, f., et. al. (2015). monitoring land-use change by combining participatory land-use maps with standard remote sensing techniques: showcase from a remote forest catchment on mindanao, philippines. international journal of applied earth observation and geoinformation, 36, 69–82. munggiarti, a., & buchori, i. 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(2015). deteksi perubahan penggunaan lahan dengan citra landsat dan sistem informasi geografis: studi kasus di wilayah metropolitan bandung, indonesia. geoplanning: journal of geomatics and planning, 2(2), 82–92. zimmermann, j., et. al. (2016). assessing land-use history for reporting on cropland dynamics—a comparison between the land-parcel identification system and traditional inter-annual approaches. land use policy, 52, 30–40. 73 geoplanning journal of geomatics and planning vol. 10, no. 1, 2023 original research shoreline dynamics in the very small islands of karimunjawa – indonesia: a preliminary study mulyadi alwi 1, bachtiar w. mutaqin 1*, muh aris marfai 1,2 1. coastal and watershed research group, faculty of geography, universitas gadjah mada, yogyakarta 55281 indonesia 2. indonesian geospatial information agency, cibinong 16911 bogor, indonesia doi: 10.14710/geoplanning.10.1.73-82 abstract indonesia is considered one of the biggest archipelagic countries in the world. according to some literature, indonesia has more than 17,000 islands, most of which are classified as small islands. some of these islands have become important areas for tourism, for instance, small islands in karimunjawa. however, some of these islands experienced shoreline changes caused by erosion and accretion. hence, this research aims to map the spatial distribution of shoreline change using the digital shoreline analysis system (dsas) add-in on arcgis. the primary dataset utilized as input consists of sentinel 2a imagery captured over 2017 and 2022. the results showed that around 89 segments, or 51.47% of the total shoreline segments, tend to experience accretion, while the remaining 79 segments, or 45.93%, experience erosion. this finding suggests that most shoreline segments tend to accrete or seaward movement in the research area. the results of this study exhibit notable disparities when compared to the occurrences observed in pandeglang (banten), kuwaru (yogyakarta), buleleng (bali), and east java province, where coastal erosion prevails over accretion. the managers of the islands try to reduce the threat of erosion by constructing dykes and breakwaters. however, these buildings are ineffective due to the relatively simple structures and building materials. therefore, further studies are needed to determine the type and specification of mitigation buildings that are suitable for implementation in that location. copyright © 2023 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction indonesia is one of the largest archipelagic countries in the world, with a total of 17,000 islands. most of the islands in indonesia have an area of less than 2,000 km2 or can be classified as small islands and less than 100 km2, which are called very small islands (big, 2022; mutaqin et al., 2022). furthermore, very small islands are more vulnerable to disasters due to climate change (koroy et al., 2017; miller et al., 2020; mutaqin et al., 2022; mutaqin, handayani, et al., 2021). this is due to the condition of very small islands, which tend to have narrow, isolated landmasses and relatively limited resources (hidayat et al., 2023; wilkinson et al., 2016). in addition, the relatively flat topography of the very small islands also makes this area somewhat prone to disasters (handayani et al., 2022; hidayat et al., 2023; mutaqin et al., 2022; mutaqin, handayani, et al., 2021). small and very small islands have great potential to be utilized for various purposes such as tourism, economic zone, and conservation (miller et al., 2020; mutaqin et al., 2022; wisha et al., 2022; yulianda et al., 2010). for example, the very small islands in the karimunjawa islands are essential tourist destinations in jepara regency (fafurida et al., 2020; pribadi et al., 2020). tourism in the karimunjawa islands has at least been visited by around 30,000 tourists between 2016 and 2020 (balai taman nasional karimunjawa, 2020). this is inseparable from the natural beauty and facilities offered by karimunjawa (figure 1). besides offering beautiful scenery, tourism activities in karimunjawa are supported by facilities such as hotels, inns, and food stalls totaling e-issn: 2355-6544 received: 12 july 2023; accepted: 27 october 2023; published: 31 october 2023. keywords: coastal dynamics, small island developing states, dsas, shoreline, karimunjawa *corresponding author(s) email: mutaqin@ugm.ac.id https://doi.org/10.14710/geoplanning.10.1.73-82 mailto:mutaqin@ugm.ac.id alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 74 115 units by 2021. this shows that karimunjawa has promising tourism potential (lukman et al., 2022; pribadi et al., 2020; setiawan, 2022). (a) (b) courtesy: mulyadi alwi, 2023 figure 1. a) view from cemara besar island and b) hostelries in menjangan kecil island the existence of tourism potential in the karimunjawa islands requires more attention, not only for its development but also for managing the threat of possible disasters. this is necessary because the area consists of small and very small islands which are relatively vulnerable to climate change hazards such as seawater intrusion, erosion, and flooding (appelquist et al., 2016; handayani et al., 2022; koroy et al., 2017; micallef et al., 2018). in addition, the karimunjawa islands area has also been designated as a national park through the decree of the minister of forestry no. 78/kpts-ii/1999 issued on february 22, 1999 (balai taman nasional karimunjawa, 2020), so that protection activities are needed for the ecosystems in those areas. karimunjawa is growing because of the dewadaru airport, karimunjawa harbor, and the development of marine tourism, which has further increased the value and attractiveness of land in the region. as a result, land conversion into built-up land cannot be avoided (amalia et al., 2018; anugrah et al., 2017). currently, apart from tourism, karimunjawa is also used for productive aquaculture (yusuf, 2014). on the other hand, karimunjawa national park has coastal hazards, including environmental damage, beach erosion, and seawater intrusion (muhammad & mardiatno, 2022; purbani et al., 2019). one of the dangerous threats found on the coasts of small islands in karimunjawa is erosion. in some parts of the island, erosion is the main factor causing shoreline changes (figure 2). this can cause losses when dealing directly with vulnerable components such as tourism support facilities. however, previous research only focused on the main islands, e.g., karimunjawa and kemujan, without considering other small islands, which also face severe problems related to coastal erosion (muhammad & mardiatno, 2022; purbani et al., 2019). therefore, this paper aims to identify shoreline changes associated with the distribution of erosion hazard levels in the karimunjawa islands using dsas add-in (marfai et al., 2022; mutaqin, kurniawan, et al., 2021). the results obtained can be used as input for stakeholders in determining the appropriate type of management related to reducing the impact of erosion hazards in the future. 2. data and methods this research focused on the coastal areas of cemara besar, cemara kecil, menjangan kecil, and menjangan besar islands, which are essential islands in tourism activities in karimunjawa (figure 3). quantitative methods utilizing remote sensing and geographic information systems are used to conduct research. the primary data used as input is sentinel 2a imagery with the recording years 2017 and 2022, which can be downloaded at https://apps.sentinel-hub.com/eo-browser/. the image with the year 2017 was chosen because, according to the information collected from the informants, the increase in the number of tourists in karimunjawa began in 2017. the image with the year 2022 was chosen to obtain the latest data sources, which can be accessed free of charge and has a little cloud cover (pribadi et al., 2020). https://doi.org/10.14710/geoplanning.10.1.73-82 alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 75 (a) (b) (c) courtesy: mulyadi alwi, 2023 figure 2. examples of coastal erosion in a) cemara besar, b) cemara kecil, and c) menjangan kecil islands figure 3. study area in the very small island of a) cemara besar, b) cemara kecil, c) menjangan kecil, and d) menjangan besar the two images were then interpreted visually to determine the existence of the shoreline. furthermore, manual digitization was performed using the editing function in arcgis software version 10.4 to obtain shoreline https://doi.org/10.14710/geoplanning.10.1.73-82 alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 76 data for 2017 and 2022. because not all parts of the study location could be identified on sentinel 2a imagery, interpretation was also carried out on maxar technologies' imagery accessible on google earth pro software. the shoreline data obtained were then analyzed using the digital shoreline analysis system (dsas) addin version 5 in arcgis software version 10.4. before further analysis, the two shoreline data must be stored in a private database in a single feature class file. further, a new information column was added to the feature class using the attribute automator function. this new column adds data source acquisition time information and uncertainty values. the uncertainty value indicates the distance around the shoreline to help determine the location of the intersection point between the shoreline and the transect (arjasakusuma et al., 2021; himmelstoss et al., 2021). in addition, a feature class baseline is also needed, which will be used as a starting point in drawing the transect line. making the baseline line can be assisted by processing the 2017 shoreline data using the buffer function as the oldest shoreline (marfai et al., 2022; mutaqin, 2017). adding a new column in the feature class baseline containing id, group, and search information is also necessary. id and group information are used for grouping on the baseline, while search information is used as a reference value to determine the length of the transect line (himmelstoss et al., 2021). hereafter, it was necessary to fill in several options in the default parameter function according to the information stored in shoreline and baseline attributes and then operate the cast transects function with the maximum search distance, transect spacing, and smoothing distance values of 20,000, 500, and 500 m, respectively. there is no specific interval that should be used related to those parameters. users can enter the desired spacing distance in meters between transects along the baseline, depending on their needs in their study area (himmelstoss et al., 2021). the processing results of the cast transects function were used as input to calculate the distance of shoreline changes using the calculate rates function (arjasakusuma et al., 2021; handayani et al., 2022; himmelstoss et al., 2021; marfai et al., 2022; mutaqin, kurniawan, et al., 2021). several types of statistics can be used, such as shoreline change envelope (sce), net shoreline movement (nsm), end point rate (epr), linear regression rate (lrr), and weighted linear regression rate (wlr) (himmelstoss et al., 2021). however, in this case, epr was chosen as a statistic to help identify shoreline changes because it can calculate the value of changes in each shoreline segment (lazuardi et al., 2022; marfai et al., 2022; mutaqin, 2017; mutaqin, kurniawan, et al., 2021). the results were then interpreted visually, where a shoreline with a negative epr indicates erosion, while a positive value suggests accretion (table 1). the research method diagram/workflow related to this research is shown in figure 4. figure 4. the research method diagram/workflow https://doi.org/10.14710/geoplanning.10.1.73-82 alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 77 table 1. category of shoreline changes based on epr value no category of shoreline changes epr value (m/year) 1 very high erosion < -2 2 high erosion -1 to -2 3 moderate erosion 0 to -1 4 stable 0 5 moderate accretion 0 to +1 6 high accretion +1 to +2 7 very high accretion > +2 source: nassar et al. (2018) 3. results and discussion based on the formation process, the karimunjawa islands have several geological formations, namely the karimunjawa formation (ptk), parang (tmpv), alluvium; coastal deposits (qa), and marl, clay, and limestone deposits (tmpg) (mutaqin et al., 2022). the research location, i.e., cemara kecil, cemara besar, menjangan kecil, and menjangan besar, comprises the parang formation with material in the form of tuffs, volcanic breccias, and lava deposits, as well as formations of a younger age composed of alluvium material and coastal deposits (mutaqin et al., 2022). the dominant very small islands material consists of alluvium material and coastal deposits may increase the possibility of coastal erosion (förster et al., 2019; giardino et al., 2018; muhammad & mardiatno, 2022; mutaqin, 2017). the results of shoreline data processing using the dsas add-in, which has been categorized based on nassar et al. (2018), are then visualized in figure 5. our results indicate that approximately 89 segments, or 51.47% of the total shoreline segments, are included in the accretion category, while the other 79 segments, or 45.93%, are included in the erosion category (table 2). this indicates that most of the 2022 shoreline in the study area tends to experience accretion or shifting toward the sea. this result is quite different from what happened in pandeglang (banten), kuwaru (yogyakarta), buleleng (bali), and east java province, which is dominated by coastal erosion than accretion (arjasakusuma et al., 2021; marfai et al., 2022; mutaqin, 2017; mutaqin, kurniawan, et al., 2021). this can be caused by many factors, either natural or human-induced. various natural variables, such as tides, waves, coastal currents, water level, changes in wind direction, storms, and hurricanes-cyclones events, can impact coastal dynamics in the form of erosion and sedimentation (clifton, 2003; marfai et al., 2022; morner, 2017; mutaqin, 2017; mutaqin & ningsih, 2023). the presence of sea walls and other buildings can also affect it (bird, 2019; marfai et al., 2022). related to the phenomenon of climate change, developing countries such as indonesia will be significantly affected and not ready to deal with shocks to social, economic, and environmental systems (koroy et al., 2017; miller et al., 2020; mutaqin et al., 2022). in karimunjawa, climate change will not create new hazards that have never existed but will exacerbate existing hazards, in this case coastal erosion, and create potential hazards in previously unexposed areas (bell et al., 2017; gill & malamud, 2016; muhammad & mardiatno, 2022). in karimunjawa, coastal erosion co-occurs with other hazards (e.g., ecosystem disruption, gradual inundation, seawater intrusion, and tidal floods) may have a multiplier effect than the coastal erosion that occurs individually (appelquist et al., 2016; gill & malamud, 2016; micallef et al., 2018). table 2. distribution of shoreline changes in karimunjawa no category of shoreline changes sums of segment percentages (%) 1 very high erosion 52 30.23 2 high erosion 18 10.47 3 moderate erosion 9 5.23 4 stable 4 2.33 5 moderate accretion 10 5.81 6 high accretion 5 2.91 7 very high accretion 74 43.02 source: data analysis, 2023 https://doi.org/10.14710/geoplanning.10.1.73-82 alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 78 coastal ecosystems have an important role in the dynamics and development of the region in karimunjawa (pribadi et al., 2020). climate change can increase the threat of erosion in karimunjawa, which has a broad impact on coastal areas in the region (appelquist et al., 2016). coastal erosion is closely related to extreme weather due to climate change and emerges as a secondary hazard from extreme weather, especially on exposed shorelines. increased coastal erosion results from stronger storms and higher seas, producing more winds, waves, and floods (wisha et al., 2022). therefore, the survival of coastal communities and the sustainability of coastal systems in karimunjawa are in danger (muhammad & mardiatno, 2022; purbani et al., 2019). figure 5. spatial distribution of shoreline changes in the study area and examples of comparison between 2017 and 2022 shorelines in a) cemara besar (accretion), b) cemara kecil (erosion), c) menjangan kecil (accretion), and d) menjangan besar (accretion) according to information collected from informants, shoreline change in the study area is considered a normal phenomenon that occurs all the time. the informant added that if some shorelines experience landward changes or erosion, other shoreline segments will experience accretion as a form of seeking sediment balance. however, if erosion occurs around a vulnerable component, it can cause a disaster, which can cause property loss (appelquist et al., 2016; marfai et al., 2022; mutaqin, 2017). this condition occurs in some coastal areas in cemara besar and menjangan kecil islands, where buildings are in the form of stalls and hostelries threatened https://doi.org/10.14710/geoplanning.10.1.73-82 alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 79 by erosion. therefore, island managers try to minimize this hazard by constructing dykes and breakwaters (figure 6). however, several structural mitigation buildings are no longer able to function as they should due to changes in the characteristics of hydro-oceanographic variables such as the direction and speed of ocean waves and sediment balance (appelquist et al., 2016; kaharuddin & busthan, 2018; wisha et al., 2022). the observations in the field indicate that the decrease in the effectiveness of the coastal protection structures is also due to the inadequacy of the building structures or the use of relatively simple materials. therefore, further studies are needed to determine the type of mitigation that is somewhat suitable to be applied and with the better materials to be considered (appelquist et al., 2016; marfai et al., 2022; micallef et al., 2018). (a) (b) (c) courtesy: mulyadi alwi, 2023 figure 6. example of structural mitigation from the island managers following the coastal erosion by constructing dykes (a) and traditional breakwaters (b and c) 4. conclusions this research aims to map the spatial distribution of shoreline change using the digital shoreline analysis system (dsas) add-in on arcgis. the results showed that around 89 segments, or 51.47% of the total shoreline segments, tend to experience accretion, while the remaining 79 segments, or 45.93%, experience erosion. some of the erosion that occurs can potentially cause losses because it occurs around vulnerable components in the form of tourist infrastructure such as stalls and hostelries. the managers of the islands try to reduce the threat of danger by constructing dykes and breakwaters. however, some of the buildings that have been built have low effectiveness due to the relatively simple structures and building materials. therefore, further studies are needed https://doi.org/10.14710/geoplanning.10.1.73-82 alwi et al. / geoplanning: journal of geomatics and planning, vol 10, no 1, 2023, 73-82 doi: 10.14710/geoplanning.10.1.73-82 80 to determine the type and specification of mitigation buildings that are suitable for implementation in that location. results from this preliminary research will be useful as a baseline for further research related to coastal hazards in very small islands of karimunjawa. very high-resolution imagery from unmanned aerial vehicles (uav) or remotely piloted aircraft systems services (rpas) may be used to obtain more detailed shoreline data in other very small islands. in addition, further thorough research on the characteristics of hydrooceanographic parameters and their relation to coastal dynamics, especially shoreline changes in very small islands, will be necessary. 5. acknowledgments no funding was obtained for this study. the author thanks rangga for his help and assistance during the data collection, as well as gildcoustic and gilga sahid for their support during the writing process. furthermore, the authors further appreciate anonymous reviewers' valuable remarks on this paper. 6. references amalia, v., purwaningsih, w., benardi, a. i., & others. 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[crossref] https://doi.org/10.14710/geoplanning.10.1.73-82 https://doi.org/10.3390/resources5020021 https://doi.org/10.14710/geoplanning.9.2.73-88 https://doi.org/10.14710/ik.ijms.18.1.20-29 | 17 geoplanning vol 5, no. 1, 2018, 17-34 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.17-34 monitoring and predicting land use-land cover (lulc) changes within and around krau wildlife reserve (kwr) protected area in malaysia using multi-temporal landsat data j. gambo a,b, h. z. m. shafri a , n. s. n. shaharum a, f. a. z. abidin c, m. t. a. rahman c a department of civil engineering and geospatial information science research centre (gisrc), universiti putra malaysia (upm), malaysia b school of general studies, binyaminu usman polytechnic, nigeria c department of wildlife and national parks (dwnp), malaysia abstract: natural and anthropogenic activities surrounding a protected area (pa) may cause its natural area to change in terms of land use-land cover (lulc). thus, there is need of environmental change monitoring within and around pa because of its significant values to ecosystem at conservation scales. effects and influences of local community within and around pa turn into the major problems for natural resource and conservations management as well as environmental impact assessment. ascertaining the complex interface in relations to changes and its driving factors over period of time within and around pa is significant in order to predict future lulc changes, build alternative scenarios and serve as tools for decision making. the main objective of this work was to evaluate temporal change detection and prediction of lulc as well as the trends of changes from 1989 to 2016 within and around krau wildlife reserve (kwr). the cloud issues were mitigated by producing cloud free image and object-based image analysis (obia) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. the comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. the natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. the simulation model of land change modeler (lcm) was utilized with digital elevation model (dem) and past lulc maps to project future lulc pattern using markov chain. the predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of kwr. the study is important for the conservation of habitat species and monitoring the current status of the kwr. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. gambo, j., et al. (2018). monitoring and predicting land use-land cover (lulc) changes within and around krau wildlife reserve (kwr) protected area in malaysia using multi-temporal landsat data. geoplanning: journal of geomatics and planning, 5(1), 17-34. doi: 10.14710/geoplanning.5.1.17-34 1. introduction protected areas (pas) represent a massive investment around the world both at national and international levels in looking after our environment. awareness and dialogue about protection and conservation of these environmentally sensitive areas be well informed and shared understanding among all the beneficiaries involved within and around. natural areas have been effected greatly in many world locations by natural and human activities. thus, monitoring, detecting and forecasting of land features modifications are significant for sustainable management, biodiversity, conservation, and development of pa (bozkaya et al., 2015). geographic spaces which, because of their particular environmental values for conservation purposes, deserved a special forms of safety ranging from total closure, except for protection purposes, to various forms of intervention required to maintain or restore habitats, to direct human use, remove open access article info: received: 20 august 2017 in revised form: 06 march 2018 accepted: 30 april 2018 available online: 30 april 2018 keywords: lulc, obia, protected area, krau wildlife reserve, land change modeler corresponding author: helmi zulhaidi mohd shafri coordinator of remote sensing and gis programme department of civil engineering faculty of engineering universiti putra malaysia (upm) 43400 serdang, selangor, malaysia email: hzms04@gmail.com https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 mailto:hzms04@gmail.com gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 18 | invasive species, re-introduce extirpated spaces, or facilitate visitation by scientists or the public for purposes of research, monitoring and mapping, recreation and education. tourism was also considered to consist of the facilitation of recreational visitation: availability of food services, guide to access roads, accommodation, services, and water supply and regular sanitation is referred to pa (dudley & stolton, 2008). pa is also clearly defined by international union for conservation of nature (iucn) 2008, as a geographical space, dedicated, acknowledged and managed, through legal or other effective means, to achieve the long-term future conservation of nature with associated ecosystem services and cultural values or an area of land and/or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means (dudley & stolton, 2008). unep (2004) described pa as an area of land or sea especially dedicated to the protection and maintenance of biological diversity and conservation of natural and related cultural resources, managed through legal or other positive means. natural forests are the unique land cover types of ecosystems especially in most of the wildlife reserve that provide important ecological services for habitat species within the reserve area. rural community along the pa relies on forest resources for their livelihood (despot belmonte & bieberstein, 2016). frequent changes in pa has led to degradation and fragmentation of wildlife habitat. in malaysia reserved areas have been traditionally established with creation of chior wildlife reserve in 1903. however, based on the master list there are 490 protected areas listed for malaysia: 271 pas for peninsular malaysia, 173 for sabah (including 3 in ft labuan) and 46 for sarawak. these encompass land area (3,510,239 ha) with a total size of 4,586,273 ha (table 1) (interim master list of protected area in malaysia). some pas are administered by the department within the ministry of natural resources and environment (nre) and others are administered and managed at the states and ngos level. according to the nre, terrestrial pas currently cover over 1.8 million ha in peninsular malaysia. they can be divided into four legal categories: areas reserved for a public purpose under the land laws; permanent reserved forests (prfs) under the forestry laws; national parks and state parks under the parks’ laws; and sanctuaries or reserves under the wildlife laws (undp, 2012) kwr is one of the threatened pa in malaysia due to frequent illegal logging (lin, 2016) of important forest tree which alter the condition of natural forest over a decade (ahmad, abdullah, & jaafar, 2012). geospatial technologies have potentials for mapping changes in pas (willis, 2015), environmentally sensitive areas, and biosphere reserve area. mapping and monitoring pas and their surrounding areas at both local and regional scales are crucial given that the vulnerability to anthropogenic activities, including climatic change, and important for conservation and biodiversity management. monitoring using geospatial technologies and field information, can play a significant role in developing baselines for understanding condition of habitats and related species diversity (bush et al., 2017) as well as measures the gain and losses, associated with specific activities around the protected area (nagendra et al., 2013). the challenges for detecting and monitoring of lulc using optical remote sensing data especially in tropical region were the cloud cover and haze (nagendra et al., 2013), but this study utilized image patching using multi-date landsat data. kwr, for example has been particularly input for reason such as recent report about unstop illegal logging and mining around kwr, the use of geospatial data has been used before (ahmad et al., 2012), but limited and outdated information about the loss future prediction and less number of lulc classes by (ahmad et al., 2012), also understanding the current status of natural forest and conditions of habitat species. this could then be of use as updated study of and also utilized the products for proper land use planning, biodiversity and conservation management of kwr based on recent report of illegal logging in kwr (norawi, 2017) and also illegal gold mining lakum forest reserved within the river streams sg. teris around kwr. thus, this study aims to provide the most up-to-date study on the status of kwr and evaluate changes that have occurred over the period and prediction (mishra, rai, & mohan, 2014; kumar et al., 2015; reveshty, 2011). technique of obia will be investigated and compared with traditional pixel-based method to ascertain on the proper method to be used in generating the required information. https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 19 table 1. terrestrial and marine pa coverage in malaysia state terrestrial (ha) marine (ha) johor 235,407.80 61,869.80 kedah 2.00 10,720.10 kelantan 127,946.70 0.00 melaka 106.80 2,401.80 negeri sembilan 57,323.90 1,893.80 pahang 855,160.90 55,800.80 perak 286,673.20 0.00 perlis 4,441.20 0.00 penang 1,414.10 1,339.30 selangor 106,673.10 0.00 terengganu 139,844.10 110,931.40 federal territory 0.00 0.00 kuala lumpur 156.30 0.00 putrajaya 0.00 0.00 labuan 0.00 9,288.30 sabah 1,555,022.70 118,768.50 sarawak 616,288.20 226,914.00 malaysia 3,986,345.60 599,927.80 2. data and methods study area base map was prepared by using subset of landsat satellite image of 2016 (islam et al., 2018). the systematic workflow indicate entirely steps conducted and adopted throughout the research period, starting with selection of study area, data collection (landsat 1989, 2004 and 2016). data analysis were also applied to both imageries, initially from image correction (atmospheric correction and geometric correction). due to presence of cloud cover in 2016 image, image patching was also applied using smartgeo fill tool. the detailed description of the entire methodology workflow was done in sub headings of data and methods as included; data and preprocessing, dealing with cloud and image patching, image classification and change detection approaches. the general systematic flowchart of the steps conducted in this study is shown in figure 1. 2.1. study area the research area of interest is known as krau wildlife reserve (kwr), located nearby mountain benom with a streams/tributaries drained to lompat, teris and krau river in the district of temerloh and jerantut of pahang, malaysia. it is geographically bounded to the south-east of taman negara forest which is the largest natural forest in. kwr covers approximately 62,395 ha as the largest wildlife reserve which makes it the third largest pa in peninsular malaysia (figure 2) with elevation of 2,107 metres ranging from the top of benom mountain in kuala lompat, to 43 metres to the reserve area. the krau wildlife reserve office, institute of biodiversity and national elephant conservation centre located in the southern part with an entrance through lanchang while jenderak wildlife conservation centre is located in the east of the reserve bordering felda jenderak selatan (danced & jabatan perlindungan hidupan liar dan taman negara, 2001). kwr was managed and controlled by the department of wildlife and national parks. because of its diversity of the landscape and biodiversity fullness within such a compact area makes kwr a unique centre of habitat species, natural forest with different flora and fauna (danced & jabatan perlindungan hidupan liar dan taman negara, 2001). in international union for conservation of nature (iucn) management https://doi.org/10.14710/geoplanning.5.1.17-34 https://en.wikipedia.org/wiki/department_of_wildlife_and_national_parks gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 20 | categories for protected area, kwr in list category 1a: strict nature reserve/wilderness area its meant protected area mainly for science or wilderness protection. nevertheless, activities surrounding the area lead kwr to faces many problems related to conservation purposes including encroachment and conversion of natural forest, illegal harvesting of non-timber products, degazettment, and over-hunting (ahmad et al., 2012). 1989 imageries 2004 imageries 2016 imageries mosaic image patching geo smart fill cloud free images subset 1989 subset 2016subset 2004 image segmentations segmented image for 1989 segmented image for 2004 segmented image for 2016 training object selection for segmented images (1989,2004 and 2016) nearest neighbor image classification for segmented images (1989,2004 and 2016) time series classified maps (1989,2004 and 2016) reference points for subset (1989.2004,2016) accuracy assessment (1989,2004 and 2016) results (maps, tables & figures change analysis (1989-2004 and 2004-2016) predictions (2028 and 2040) landsat tm imagery (1989) landsat tm imagery (2004) landsat oli imagery (2016) atmospheric and geometric corrections study area (krau willdlife reserve) figure 1. methodology workflow adopted in this study https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 21 figure 2. the kwr location in peninsular malaysia 2.2. datasets the downloaded landsat 8 oli and landsat tm level-i imagery of the area from u.s geological survey (usgs) website (http://earthexplorer.usgs.gov/) are shown in table 2. to analyze lulc changes in on a yearly basis, the landsat imageries of the year 1989, 2004 and 2016 were obtained with an interval of 15 and 12 years. due to the heavy rainfall and cloud cover in malaysia as tropical region, obtaining optical sensor imageries of 10% cloud free is quite difficult, for this reason in this study four different scene of 2016 landsat 8 oli and two different scene of 2004 landsat tm were utilized and produced 0-1% cloud free image of study area within the same dry seasons between june, july and august, the preprocessing operation started from atmospheric correction and image patching of landsat tm 2004 and landsat oli 2016 due to presence of cloud in area of interest of this study using pci geomatics smart geofill tools. using region of interest (roi) created in google earth pro, the area of interest was subset following the image patching. the completion of pre-processing stages, the study progressed to the image analysis process, first stage image segmentation was conducted by object-based image classification. the segmentation algorithm of multiresolution in ecognition developer 9.0 applied to each individual image for generating image objects. table 2. satellites data used in this study sensor id date acquired time path/row resolution tm 4 landsat 6/15/1989 3:01:32 127/057 30 tm 5 landsat 6/16/1989 2:49:51 126/057 30 tm 5 landsat 8/2/2004 3:07:27 126/057 30 tm 5 landsat 7/18/2004 3:10:35 127/057 30 oli 8 landsat 7/3/2016 3:28:03 127/057 30 oli 8 landsat 6/26/2016 3:21:49 126/057 30 oli 8 landsat 6/26/2016 3:22:12 126/058 30 oli 8 landsat 6/1/2016 3:27:51 127/057 30 the fusing of neighboring segments together to ensure a heterogeneity threshold is stretched to one-pixel image segment within segmentation algorithm. nearest neighbor image classification was applied by following three stages that included creating class hierarchy, training data sets, and training samples were https://doi.org/10.14710/geoplanning.5.1.17-34 http://earthexplorer.usgs.gov/ gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 22 | taken for each lulc type to be classified in the image. testing samples were randomly selected from three different landsat image of the study (waiyasusri, yumuang, & chotpantarat, 2016). with reference to our previous training samples (hackman, gong, & wang, 2017).second stage was the classified images of different dates were used for change detection and landcover projection in land change modeler (lcm) in idrisi selva software. five different lulc types comprising of water, dense forests, urban/built-up, agriculture/sparse forest and bare soil were recognized as the final classes in table 3. table 3. level lulc within and around kwr, as derived from landsat satellitesin 1989, 2004 and 2016 land cover classes descriptions water river, dam, pond, stream, reservoir, pool etc. dense forest natural and dense forest built-up building, road etc. bare soil open land, harvested land etc. agriculture/sparse forest rubber plantation, oil palm plantation, banana plantation, shrubs/grass, cropland, orchard and low density forest etc. 2.3. dealing with cloud and image patching it was possible to utilize uncorrupted landsat scenes within the same season with corrupted one. in each scene, some pixels in the areas with high altitude regions of kwr had been covered by clouds, haze, predominantly in the northern part of the study area. it was easy to identify scenes with cloudy areas because of the available cloud percentage information. however, we had to visually look for areas where pixels had been corrupted by haze or live fires and the resulting thick smoke using false composites (hackman et al., 2017). we used smart geofill tool which allowed user to copy a specified area of an image layer with non-cloud effects, make changes to it and then paste the selection to another layer of the image with cloud effects using color balancing method either overlap or histogram trim (smart geofill geomatica 2015 tutorial), and can also adjust settings for color balance, blend width, contrast, and brightness of the selected area to enhance or adjust its appearance in the destination layer (hruby et al., 2016). meanwhile (hackman et al., 2017) has masked out areas with corrupted pixels before the classification which had an extremely thick layer of cloud. in this study image patching was applied for all available 2004 and 2016 multi-date landsat data images with heavily cloudy (figure 3) showed the 2004 multi-date landsat before image patching and after applied using smart geofill). nonetheless, except for the landsat scenes taken on june 15/16, 1989. figure 3. (a) before image patching, (b) after image patching a b https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 23 2.4. image classification and change detection approaches the main aim of image classification was to automatically categorize all pixels in an image into land cover classes (figures 4). the classification legend was made based on spectral characteristics. image segmentation was conducted by object-based image classification. traditionally, pixel-based image analysis was utilized for image classification on both low and moderate resolution satellites data, obia incorporates not only the spectral information, but also included the shape, size, spatial, texture and contextual of the data. obia merged both spatial and spectral information about the features to extract land use for specific objects (kindu et al., 2013), which grouping many pixels in to one image objects during segmentation stage to avoid the salt-pepper effects (desclée, bogaert, & defourny, 2006). while pixel-based directly focus to one single image objects. in recent research, many studies utilized these technologies such as obia in change detection at different scales, with both low and high resolution satellites sensor such as landsat data (dutta, reddy, sharma, & jha, 2016; waiyasusri et al., 2016; balaji, geetha, & soman, 2016; ranjan et al., 2016) and data like alos (avnir2) was used for land use changes (munthali & murayama, 2011) and the obia approach the same of work by zhang et al (2017) maintained obia approach for change analysis in florida everlades water conservation area using landsat data. the object-based image analysis showed the expansion/reduction of land use types when applied the classified image in change detection algorithms as done by son et al. (2015). the multiresolution segmentation algorithm in ecognition developer 9.0 was applied to generate image objects for each individual image. the segmentation algorithm starts with making homogeneous object clusters with one-pixel image segment, and considerably merges neighboring segments together until a heterogeneity threshold is reached. the heterogeneity threshold identification depended on user-defined scale parameter, as well as the shape and compactness weights. the scale of the segmentation determined the quality of segmentation, and classification. the image segmentation is scale-dependent. for this study different scales were applied before the selection of appropriate classification. figure 4 shows two segmented results with different scales assigned. image (a) has the scale parameter of 40, and maintained the default values of shape 0.1 and compactness equal to 0.5. for image (b) we tried to change different segmentation scale from default of shape, compactness and scale parameter to 70, but the segment between the features have an overlap. in this study we adopted the image (a) segmentation scale because each land use and land cover was segmented properly. figure 4. segmentation testing results using different scale parameters a b 0.1, 05 and 70 0.1, 05 and 70 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 24 | nearest neighbor image classification was done by following three stages that included creating class hierarchy, training data sets, and accuracy assessment. training samples were taken for each lulc type to be classified in the image. following the adaptation of nearest neighbor object based image analysis in this study, a pixel-based image analysis was applied to 2016 oli landsat image using support vector machine (svm) as to compare with obia approach. the comparison in terms of the overall and kappa statistics showed good results for both obia and pixel-based techniques (for obia 90%, 0.87 and svm 98%, 0.87). however, the visualization of two classified image indicated that there were misclassifications between dense forest and agriculture/sparse forest as well as bare soil and built-up area. figure 5. (a) obia classified map, (b) pixel-based svm classified map the pixel-based svm result produced the highest value of overall accuracy and obia classified image obtained lower accuracy. however, the obia classification results showed better accuracy and realistic representations when compared to the topographic map of kwr collected from department of survey and mapping malaysia (jupem) and also google earth map. because of these comparison and validation (figure 5). this study adopted the obia image classification for all three temporal landsat satellite data classifications. 2.5. markov model the stochastic model that the model output is depending on the probabilities of a transition of current change scenarios of pi – j, between states i and j. the multiple land covers land uses categories in a landscape with transition probability pi j , would be the land-cover type (pixels) i probability in time t0 a b https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 25 changes to land-cover type j probability in time t1 is called markov chain model (bozkaya et al., 2015). the markov transitions probabilities, expressed as; (1) the derived transition probabilities is from a sample of transitions that occurred between two certain time intervals data and these probabilities was shown through the matrix p of following transition. the equation below proportion probability of land cover of the second date, and calculated using the equation. (2) where vi j x pi j is the proportion of land cover of the later date, pi j is the matrix of the probability of landcover transition, vi is the proportion of land cover of the current date (vector), i is the type of land cover of the first date, j is the type of land cover of the second date, p11 is the probability that land cover 1 at the first date will change into land cover 1 by the second date, p12 is the probability that land cover 1 at the first date will change into land cover 2 by the second date and so on, and m is the number of land-cover types in the study area (bozkaya et al., 2015). 3. results and discussion 3.1. accuracy assessment of classified images the nearest neighbor obia image classification of the temporal images, generated land cover maps and accuracy assessment report of confusion matrix were performed on (1989, 2004 and 2016) classified images indicated a satisfactory overall accuracy and a kappa statistics as work of (kindu et al., 2013; son et al., 2015; yu, et al, 2016) in (tables 4, 5 and 6). an overall kappa statistics of 0.88, 0.82 and 0.87 was achieved for 1989, 2004 and 2016 lulc with classification accuracy of 91%, 86% and 90% respectively. in table 4 built-up has a lower producer accuracy, followed by dense forest with 71.43% user accuracy. bare soil has producer’s accuracy of 73.33% while dense forest has the highest producer’s accuracy of 100% showed in table 5. all the remaining lulc classes were having their accuracies above 60%. the user’s accuracies of all the lulc types were above 60% with water and bare soil having the highest accuracy of 100%. figure 6. classified lulc maps (1989, 2004 and 2016) 1989 2004 2016 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 26 | table 4. accuracy results for the landsat-5 1989 image derived from obia classification methods note: descriptions of lulc classes; u= unclassified, w= water, bs= bare soil, ds=dense forest, bu=builtup, afs= agriculture/sparse forest. table 5. accuracy results for the landsat-8 image 2004 derived from obia classification methods classified reference producer's accuracy % user's accuracy % u w f bu bu asf total unclassified 0 0 0 0 0 0 0 0.00 0.00 water 0 23 0 0 0 0 23 76.67 100.00 dense forest 0 2 40 1 0 2 45 100.00 88.89 built-up 0 1 0 22 1 1 25 73.33 88.00 bare soil 0 2 0 7 27 3 39 90.00 69.23 agriculture/ sparse forest 0 2 0 0 2 34 38 85.00 89.47 total 0 30 40 30 30 40 170 overall accuracy 86% kappa coefficient 0.82 note: descriptions of lulc classes; u= unclassified, w= water, bs= bare soil, ds=dense forest, bu=builtup, afs= agriculture/sparse forest. table 6. accuracy results for the landsat-8 image 2016 derived from obia classification methods classified reference producer's accuracy % user's accuracy % u w bs df bu asf total unclassified 0 0 1 0 1 0 2 0.00 0.00 water 0 27 0 0 0 0 27 90.00 100.00 bare land 0 0 23 0 1 0 24 76.66 95.83 dense forest 0 1 1 39 0 3 44 97.50 88.64 built-up 0 1 4 0 27 0 32 90.00 81.82 agriculture/ sparse forest 0 1 1 1 1 37 41 92.50 90.24 total 0 30 30 40 30 40 170 overall accuracy 90% kappa coefficient 0.87 note: descriptions of lulc classes; u= unclassified, w= water, bs= bare soil, ds=dense forest, bu=builtup, afs= agriculture/sparse forest. classified reference producer's accuracy % user's accuracy % u w bs df bu asf total unclassified 0 0 0 0 0 1 1 0.00 0.00 water 0 27 0 0 0 0 27 90.00 100.00 bare soil 0 0 40 0 0 0 40 97.56 100.00 dense forest 0 3 0 25 7 0 35 86.21 71.43 built-up 0 0 0 2 23 0 25 76.67 92.00 agriculture/ sparse forest 0 0 1 2 0 39 42 97.50 92.85 total 0 30 41 29 30 40 170 overall accuracy 91% kappa coefficient 0.88 https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 27 3.2. change detection trend analysis of the kwr (within and around) reveals changes in area of the five lulc of 27 year period of the study in table 7 and figure 7 and 8. the land cover changes being taken by both natural activities and community activities surrounding kwr between the study periods were measured by using modifications from the late classified map to early classified map as normally applied by all researches including (islam et al., 2018). table 7 below showed the results changes in hectares and percentages that has been revealed in the past three distinct years of the study generated and computed in terms of maps and tables through lcm. areas covered with dense forest showed an immense changes of -7.05% between 2004 -2016 sooner than 1989-2004 with interval of 15 years period with an only 5.63%. the significant proportions of changes in agriculture/sparse forest from 4137.93 ha in between 1988-2004 to 18772.92 ha between 2004-2016 with relation to earlier trend changes of dense forest to bare soil between the period of 1989-2004. furthermore, there was an increase of built up areas and water. the land cover change detection maps in figure 3 and 4, indicated the changes amongst five classes recognized in this study. 1989 t0 2004 changes result showed that the highest changes was between dense forest to bare soil/open land around the kwr area despite all environmental and biodiversity management measures. however, between the periods of 2004 to 2016 the results indicated that the bare soil area and dense forest were converted more to agriculture/sparse forest with little encroachment around the pa boundary (ahmad et al., 2012; de oliveira et al., 2017; zhang et al., 2017). table 7. lulc changes between the study periods in hectares and percentages land cover classes 1989-2004 2004-2016 1989-2004 2004-2016 area (ha) area (ha) area (%) area (%) water -983.43 2140.92 -0.31 0.67 forest -17996.4 -22629.78 -5.63 -7.05 built-up -2334.78 14436.99 -0.73 4.50 bare soil 17289.18 -12718.80 5.39 -3.96 agriculture 4137.93 18772.92 1.28 5.85 figure 7. lulc changes between 2004-2016 of kwr (within and around) https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 28 | figure 8. lulc changes between 2004-2016 of kwr (within and around) 3.3. gain and losses between 1989 to 2004 and 2004 to 2016 it is clearly indicated from figure 9 that there were significant negative changes and transitions within and around the boundary of kwr for various lulc classes during the period from 1989 to 2004 and 2004 to 2016. the main gain and losses occurred basically between the dense forest to bare soil from 1989 to 2004 while between 2004 to 2016 showed that the losses in bare soil are positively changed to gain in agriculture/sparse forest. this make the analysis valid since changes started from removal of forest at the beginning then planting agriculture products as started by previous study that agricultural activities and illegal logging is the major land use activities taking place around the krau wildlife reserve. figure 8 illustrated the increase and decline that occurred between lulc adopted in this study in hectares from 1989 to 2004 and 2004 to 2016. the green bars represent the gain per class measured in hectares, and the left side brown bars describe the loss (decline) of each class in the same unit. in terms of net changes between the periods of study figure 9 also indicated. between 1989 and 2004 there is increase in the amount of bare soil (20,803 ha), more of the dense forest were proportionally lost to about 39,706 ha. the agriculture/sparse forest had the maximum extent of gains (49,000 ha) between 2004 and 2016 while about 38,324 ha was lost for dense forest within the same period (figure 8). figure 10 and figure 11 indicate the net changes contributions of each land use categories between 1989 to 2004 and 2004 to 2016. 0 20000 40000-20000-40000 water dense forest built-up bare soil agriculture/sparse forest lu lc c las se s gains and losses between 1989 and 2004gains and losses between 1989 and 2004 -1355358 -39706 21678 -83796036 -3524 20803 -33841 37930 0 20000 40000-20000-40000 water dense forest built-up bare soil agriculture/sparse forest lu lc c las se s gains and losses between 2004 and 2016gains and losses between 2004 and 2016 -4042538 -38324 15752 -4593 19007 -20560 7856 -30281 49008 figure 9. land use/land cover gain and losses in (ha) from 1989 to 2004 and 2004 to 2016 https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 29 from figure 8 it is clear that there are significant changes and transitions among various lulc categories during the period from 1989 to 2016. the main changes and transitions are mostly occurred among dense forest, bare soil and agriculture/sparse forest (reddy et al., 2017). 0 6000 12000 18000-6000-12000-18000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es net change between 1989 and 2004net change between 1989 and 2004 -997 -18029 -2343 17280 4090 0 10000 20000-10000-20000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es net change between 2004 and 2016net change between 2004 and 2016 2135 -22572 14414 -12704 18727 figure 10. land use/land cover net changes in (ha) at kwr from 1989 to 2004 and 2004 to 2016 the contributions of other categories to their net change is presented in figure 11 and figure 12 below. it has been clearly shown that dense forest contribute about 6,925 ha to bare soil between 1989 to 2004 and dense forest for both period of study explained the majority of the total increase in agricultural/sparse forest areas (11,868 ha and 16,269 ha). nevertheless, other lulc contribute to the changes throughout the study period as shown in figure 11 and figure 12 respectively. 0-40-80-120-160-200-240-280-320-360-400 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in watercontributions to net change in water 0 -381 -80 -155 -381 0-2000-4000-6000-8000-10000-12000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in dense forestcontributions to net change in dense forest 381 0 384 -6925 -11868 0-200-400-600-800-1000-1200 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in built-upcontributions to net change in built-up 80 -384 0 -1286 -753 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 30 | 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in bare soilcontributions to net change in bare soil 155 6925 1286 0 8913 0 3000 6000 9000 12000-3000-6000-9000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in agriculture/sparse forestcontributions to net change in agriculture/sparse forest 381 11868 753 -8913 0 figure 11. contribution to net changes for all lulc classes from 1989 to 2004 0 200 400 600 800 1000 1200 1400 water dense forest built-up bare soil agriculture/sparse forest lu lc c las se s contributions to net change in watercontributions to net change in water 0 1446 -36 94 631 0-3000-6000-9000-12000-15000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in dense forestcontributions to net change in dense forest -1446 0 -4380 -477 -16269 0 1000 2000 3000 4000 5000 6000 7000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in built-upcontributions to net change in built-up 36 4380 0 3139 6859 0-2000-4000-6000-8000-10000 water dense forest built-up bare soil agriculture/sparse forest lu lc c la ss es contributions to net change in bare soilcontributions to net change in bare soil -94 477 -3139 0 -9948 0 4000 8000 12000 16000-4000-8000 water dense forest built-up bare soil agriculture/sparse forest lu lc c las se s contributions to net change in agriculture/sparse forestcontributions to net change in agriculture/sparse forest -631 16269 -6859 9948 0 figure 12. contribution to net changes in agriculture/sparse forest from 2004 to 2016 https://doi.org/10.14710/geoplanning.5.1.17-34 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 | 31 3.4. prediction the simulation model of lcm was used to simulate lulc modifications pattern (mishra et al., 2014) within and around kwr for 2028 and 2040 based on markov transition probabilities showed in appendix a and b in appendix respectively. the probability of diagonal cells (table 8 and table 9) represent an area which remain under the same class (areendran et al., 2017). however, the predicted lulc (reddy et al., 2017) showed the forest area keep reduced in the next projected time period (2028 and 2040) and has the highest probability of changes from dense forest converted to agriculture/sparse forest and built-up in figure 13. table 8. markov probability of changes for 2028 figure 13. predicted land cover maps of kwr area over 22 years with interval of 12 years (2028 and 2040) markov probability of changes for 2028 lulc class water forest built-up bare soil agriculture water 0.52 0.14 0.08 0.00 0.25 forest 0.01 0.78 0.03 0.01 0.17 built-up 0.00 0.07 0.42 0.05 0.45 bare soil 0.00 0.07 0.17 0.04 0.72 agriculture 0.01 0.12 0.09 0.05 0.73 https://doi.org/10.14710/geoplanning.5.1.17-34 gambo et al. / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 17-34 doi: 10.14710/geoplanning.5.1.17-34 32 | the simulated projected lulc maps in figure 13 can be utilized as tools of decision making toward the protection, conservation and implementation of law enforcement in kwr, because the results indicate that if proper actions not taking the rate of degradation of dense forest will keep increasing and can lead to decline of both plants and animals habitat species (conservation and environmental management division, 2006) malaysia. table 9. markov probability of changes for 2040 markov probability of changes for 2040 lulc class water forest built-up bare soil agriculture water 0.2742 0.2195 0.1056 0.0202 0.3805 forest 0.0129 0.6395 0.0516 0.0189 0.2771 built-up 0.0081 0.1419 0.2319 0.0464 0.5717 bare soil 0.0092 0.1575 0.1458 0.0455 0.642 agriculture 0.011 0.1931 0.1191 0.0434 0.6334 4. conclusion mapping and predicting the lulc changes in a pa is very important for monitoring the activities within and around it. this can minimize the negative impact and help to plan for future managements to safeguard the kwr. the mapping analysis of kwr using multi-temporal satellites data showed and predicted the gradual loss in natural forest area within and around from 1989-2016 and verified by field visits and interviews with kwr officials. oil palm and rubber plantations are one of the main factors leading to the encroachment around the kwr boundary. moreover, the size of low dense forest/agriculture land, of the analyzed years (1989, 2004 and 2016) was increased and was found in predicted results of 2028 and 2040. the changes within and around kwr showed a massive degradation and if left unattended through current situation based on projected land use and land cover, it will be detrimental for biological conservation of wildlife in the pa. the changes and encroachment around kwr boundary have a link with the dynamics of political and social issues of local communities surrounding the wildlife reserve. this study also suggested the implementation of buffer zones which may be one of the key solutions for a better conservation of pas to protect from the negative effects of illegal activities within and around the kwr. it is also recommended that it increase the number of forest rangers in the kwr to monitor the encroachments by local and indigenous communities. future work can improve the work further by utilizing more data from various systems such as radar, lidar and very high resolution data. furthermore the results for the pa analysis in this study can be imported to a gis for further analysis and model development. 5. acknowledgments the authors would like to thank upm for the facilities and funding for research and travel in completing this task. in addition, we thank the department of wildlife and national parks, peninsular malaysia (perhilitan) and the institute of 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10.14710/geoplanning.12.1.79-94 abstract deformation can help predict the presence and severity of an earthquake. sar image data can be used to calculate postseismic surface deformation using the insar and dinsar methods. dinsar (differential interferometric synthetic aperture radar) is a well-established technology for monitoring subsidence and uplift with millimeter precision. this study uses sar imagery to detect surface deformation caused by a magnitude m 6.1 earthquake on december 21, 2015, at 01:47:37 wib in tarakan regency, north borneo. the data used is sentinel-1 satellite imagery in slc (single-look complex) format, with a master image from december 18, 2015 (3 days before the earthquake), and a slave image from january 11, 2016 (21 days after). the interferogram generated by the tarakan earthquake shows deformation patterns radiating in three directions: northeast, southeast-southwest, and southwest-northwest. tarakan city, located south-southwest of the epicenter, experienced the highest subsidence deformation of 0.001–0.035 meters. on december 21, 2015, the tana tidung i regency area, 33 kilometers southwest of the epicenter, showed the highest uplift deformation (0.019–0.079 meters). the largest uplift in tana tidung ii regency (0.069 meters), about 10 kilometers north of the epicenter, occurred near the fault zone. surface deformation due to the tarakan earthquake contributes to seismic hazard assessment in north borneo and indicates other locally active faults. uplift to the east and subsidence to the west of the epicenter suggest an oblique-normal fault, with dominant strike-slip motion and normal (downward) fault blocks to the west. copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction seismotectonic processes triggered by plate movements produce seismic and volcanic activity (hanifa et al. 2019). earthquake events typically alter the shape or position of the earth's surface (deformation) (panuntun et al. 2018). an earthquake typically causes the surrounding earth's crust to deform in both vertical and horizontal directions (sari et al. 2014). this deformation consists of three components: change in location (translation), change in orientation or direction (rotation), and force (strain and stress). deformation can be a major factor in determining the presence and severity of an earthquake (puspita et al. 2024). deformation caused by a big earthquake has a severe influence on the infrastructure and buildings above it. it is commonly recognized that all macro earthquakes (e.g. events whose m > 3.0) cause crustal deformation, but only earthquakes over a particular magnitude usually cause surface rupture (gürpinar et al. 2017). the amount of deformation can be calculated from sar picture data (cahyaningrum, 2024) using the insar and dinsar methods. the worth of e-issn: 2355-6544 received: 23 december 2024; revised: 28 january 2025; accepted: 28 february 2025; available online: 14 may 2025; published: 26 may 2025. keywords: sar imagery, dinssar, deformation, subsidence, uplift, fault *corresponding author(s) email: imanuela.indah@gmail.com https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 80 surface changes can be determined by subtracting or differential insar (dinsar) from three or more radar pictures or by applying a topographic surface model (kurniawan et al. 2016). dinsar (differential interferometric synthetic aperture radar) is a well-developed technology for measuring subsidence with excellent precision in millimeters (islam et al. 2017). this technique employs more than two radar pictures (multitemporal radar images), resulting in temporal decorrelation and atmospheric dishomogeneities that affect interferogram quality. the tarakan earthquake is a rare occurrence, as borneo is indonesia's sole island with a low frequency of seismic activity. however, the tarakan earthquake of magnitude m 6.1 damaged several houses, including two (two) houses in selumit village, central tarakan subdistrict beach, four (four) houses damaged and experiencing landslides in juata kerikil village, and one (one) house under construction that was damaged or collapsed in juata laut village (north borneo regional disaster management agency, 2016). based on damage data from different sub-districts affected by the earthquake, this study uses sar satellite image data to evaluate vertical surface deformation information (souza et al. 2024; gourmelen et al. 2010; bedini 2020) in the form of subsidence and uplift produced by the tarakan earthquake on december 21, 2015. in this study, sar pictures were utilized to detect surface deformation induced by an earthquake that struck most of north borneo province on monday, december 21, 2015, at 01:47:37 a.m., including tarakan, nunukan, and tanjung selor. the bmkg (meteorology, climatology, and geophysics agency) analysis indicates that this earthquake has a magnitude of 6.1 (update). the earthquake's epicenter was on land at coordinates 3.61ols and 117.67obt, or 29 kilometers northeast of tarakan city in north borneo, with a hypocenter depth of 10 kilometers. earthquake shocks were felt in various locations, including tarakan and nunukan iv-v mmi and tanjung selor iii-iv mmi. the earthquake that occurred was an intraplate-type shallow crustal earthquake with a shallow hypocenter caused by active fault activity (center of earthquake and tsunami bmkg, 2016). the earthquake has a land epicenter does not have the ability to create a tsunami. lu et al. 2025 successfully identified the fault movement that caused the 2025 dapu earthquake, as indicated by the presence of ground cracks and water ejection in the dinsar analysis results. research on the use of the dinsar method in determining potential seismic hazard areas has been conducted on the 2017 poso earthquake, the area in the southern part of north lore district is dominated by maximum uplift deformation values and subsidence deformation values which are quite large, causing many buildings to sufferlight to heavy damage (puspita et al. 2024). the dinsar method was also conducted to determine surface defromation and its implications for land degradation after the 2021 flores earthquake (purba et al. 2024) on kalatoa island. the results showed land subsidence of up to 12 cm in garaupa raya village and land uplift of up to 10 cm in lembang mate'ne village. the total area that experienced land subsidence was 39.4 km2 (50.50%), while the area that experienced land uplift was 38.2 km2 (49.02%). calvet et al. 2023 research proves that the dinsar technique to be useful and powerful for the observation and analysis of surface deformation caused by the release of stress during the mw 8.3 illapel earthquake. it proved to be an efficient tool to detect and map the surface deformation with high spatial resolution in an approximate area of 20,000 km². there has been little research into the potential earthquake hazard on borneo island. the island of borneo has long been known as one of the areas in indonesia that is safe from earthquake disasters. previous research on the tarakan earthquake on december 21, 2015 identified micro-faults that caused the earthquake. the results of this research show the direction of fault movement based on the distribution of aftershocks (sriyanto et al. 2016). this research combines differential interferometric synthetic aperture radar (dinsar) analysis, focal mechanism analysis, and correlation with local geological conditions. the results of sar image processing were analysed to determine the value of surface deformation due to the tarakan earthquake. the surface deformation that occurred in the area around the earthquake can provide information related to seismic hazard in the north borneo region, as well as identify the characteristics of the fault that caused the tarakan earthquake. the results of this study are expected to be a reference in improving future earthquake preparedness strategies in the north borneo region. the identification of fault characteristics using the dinsar method can be a verification of the https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 81 results of previous research. the surface deformation caused by the earthquake is a visual representation of the fault movement that causes the earthquake. 2. data and methods 2.1. research location this research is being conducted in the province of north borneo, which has geographical coordinates of 3°18' 00"– 4°24' 00" north latitude and 115°30'00"–118°00' 00" east longitude. the north borneo region is made up of one city and four regencies: bulungan, malinau, nunukan, tana tidung, and tarakan city. the study focused on locations in north borneo province that experienced surface deformation following the december 21, 2015 earthquake. 2.2. the geological structure of borneo island and seismo-tectonics of north borneo borneo formed through the accretion of microcontinental fragments, ophiolite terranes, and island-arc crust onto a paleozoic continental core during the mesozoic era. at the beginning of the cenozoic era, borneo formed part of the promontory of sundaland, which was partly separated from the asian mainland by the protosouth china sea (balaguru et al. 2003). the barito basin in southern borneo is underlain by accreted crust from the meratus mountains in the east and schwaner bedrock of continental origin in the west, and contains a thick and well-exposed succession of cenozoic sediments (witts et al. 2012). to the north, the kutai basin is limited by accreted crust from the kucing plateau (part of the central range) and mangkalihat continental bedrock to the west and north. tarakan basin is farther northerly than kutai and is surrounded by the dent-semporna accreted crust, the sekatak-berau plateau, and the mangkalihat continental basement. the links between these basement terranes are not entirely clear. the tarakan basin is located offshore north-east borneo island, in a structurally complex zone of continental convergence involving subduction of northern sulawesi (hall, 2013; 2019); watkinson et al. 2017). the dominant characteristic of the tarakan basin is the presence of fine to coarse-grained clastic sedimentary rocks with carbonate deposits (hendardi et al. 2024). the temporal and spatial evolution of neogene deformation in the shelf-edge to upper slope region of the tarakan basin reflects the interaction between variations in sediment accumulation rates, the progradation of deltaic sedimentary wedges, mobile shale flows and the growth and linkage of extensional faults. evidence of neogene deformation in the tarakan basin includes growth normal faults, shale rollers and anticlines, mud pipes and volcanoes. deformation was controlled by mobile shale flow across varying dips in the base of the mobile shale surface, gravitational loading and gliding. growth faults formed through tip propagation and segment linkage, as well as late-stage tip retreat and reactivation. the dipping of the base of the mobile shale controls the position, timing and evolution of the growth faults and their associated depocentres (erdi et al. 2023). the barito, kutai and tarakan basins shared a similar tectonic history during the tertiary, characterized by an extensional regime in the paleogene and a compressional regime in neogene and pleistocene time. however, their tectonic origins and styles are dissimilar. several of the borneo deltas (tarakan, baram, west luconia) exhibit large coupled extensional-compressional deformation systems (gorsel, 2018). the kutai basin formed during the early tertiary period, filling up with clastic sediments from west to east (permana et al. 2018). the geological map of north borneo (figure 1) shows that the majority of the region is made up of alluvium deposits, particularly in nunukan, tana tidung, and parts of tarakan city. other areas of tarakan city include the sejau formation, which contains sedimentary, clastic, and flish rock formations (indonesia geospasial, 2020). the eastern half of borneo island has complicated tectonic conditions, making it the most earthquakeprone area on the island. the existence of many descending fault geological formations, as well as several horizontal fault structures, contributes to the zone's earthquake vulnerability. three horizontal faults run across the nunukan-tarakan zone and its environs. to the south are two southwest-southeast trending faults, the mangkalihat fault zone and the maratua fault zone. the mangkalihat fault zone is a continuation of the palu https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 82 koro fault, which runs near tanjung redep. the occurrence of the maratua fault zone is significant because its terminus is located in the ocean near tanjung selor town. meanwhile, to the north of tarakan island, the sempurna fault zone extends from the sulawesi sea to sabah, malaysia, crossing the area around sebatik island. borneo island also contains the tarakan and meratus faults. the tarakan, mangkalihat, and meratus faults are more than 100 kilometers long and have the potential to create earthquakes of magnitude 7. the tarakan horizontal fault may be seen in the northern portion of the island (figure 2), which extends from the mainland to the offshore. the mangkalihat fault, a horizontal fault, is located on the east coast of borneo island. in the southern half of borneo island, there is a stepping fault zone known as the meratus fault, which runs from north-east to south-west (national center for earthquake studies, 2017). however, it remains unclear which fault structure caused the earthquake that struck the majority of east borneo on december 21, 2015. source: modified from google earth, accesed, 2023 source: (indonesia geospasial, 2020) figure 1. north borneo's geological structure tarakan, north borneo, has a very short history of significant earthquakes. tarakan city has seen four significant and devastating earthquakes. the tarakan earthquake on april 19, 1923, had an estimated magnitude of 7.0. the earthquake reached a magnitude of vii–viii mmi, inflicting damage to several homes and ground cracks. second, on february 14, 1925, the tarakan earthquake caused very powerful shaking with an intensity scale of vi–vii mmi, causing numerous buildings to be damaged. the third was the tarakan earthquake on february 28, 1936, which had a magnitude of 6.5 and damaged a number of structures; (north borneo regional disaster management agency, 1956) the fourth was the tarakan earthquake on december 21, 2015. according to the focal mechanism analysis from the global cmt database (figure 3), the tarakan earthquake had a normaloblique fault source mechanism. focal mechanisms of earthquakes are essential for identifying fault planes and characteristics of faults (purba et al. 2025). these characteristics are determined by analysing the strike, dip and information: ktib : long bawan formation (fish, coastal, lagoon, clastic, sedimentary structure) ktme :mentarang formation (sedimentary structure, clastic, fish, neritic, continental slope) mzb :bengara formation (sedimentary structures, clastics, fish, deep sea) qa : alluvium deposits (sedimentary structures, clastics, alluvium) qpi :quarter intrusions (intermediate intrusive rocks, breakthrough igneous rock bodies) tqps : sajau formation (sedimentary structures, clastics, terrestrial fish, fluvial, delta) tes1 : sedimentary, clastic, littoral sandstone. tomi : breakthrough rock (extrusive rock, intermediate, lava, volcanic) tomj : karamuan formation (limestone sediments, reefs) tomj1 : barley volcanic rocks (intermediate extrusive rocks, volcanic polymics) tps : sinjin formation (volcanic pyroclastic intermediate extrusive rock) https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 83 slip parameters of an earthquake, which helps to identify its source and cause. the history of tarakan earthquakes of such magnitude suggests that the region contains active tectonic formations. source: (national center for earthquake studies, 2017). figure 2. fault structure in borneo source: bmkg data catalog and global centroid-moment tensor catalog web search figure 3. spatial distribution of the north borneo earthquake epicenters period 1980 – 2021 and focal mechanism of the tarakan earthquake 2.3. sar image data sar is a microwave-based imager that can penetrate clouds. active sar sensors send pulses and listen for echoes. these echoes are captured in terms of phase and amplitude. the phase is utilized to calculate the distance between the sensor and the target, while the amplitude provides information about the roughness and dielectric constant of the target. several factors influence the interferogram, including earth curvature, topographic effects, atmospheric delay, surface motion, and noise (castaneda et al. 2011). sentinel-1 sar photos can identify changes in the earth's surface on a centimeter scale with adequate processing. in addition, it can be https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 84 used to assess volcanic deformation, subsidence, landslides, and earthquakes. sar data is a digital record of waves (energy) emitted by a radar sensor from a satellite to the earth's surface and reflected back (backscattering) to the satellite's receiving sensor. the sentinel-1 satellite's sar sensor transmits a c-band signal with a wavelength of 5.6 cm. the wavelength of the signal influences its penetration ability; hence, it is critical to understand the wavelength of the sensor when working with sar datasets. c-band sar signals penetrate further into the canopy or surface (nasa’s earth science data system) than x-band signals, but not as deeply as l-band sar signals, which have a wavelength of approximately 25 cm and are better able to penetrate the canopy and return signals from the forest floor. different wavelengths are also sensitive to various degrees of deformation. to detect very minor changes in a relatively short period of time, signals with shorter wavelengths (such as the x-band) are necessary. signals of shorter wavelengths, on the other hand, are more prone to decorrelation caused by minor changes in surface conditions, such as vegetation development. longer wavelengths (such as the l-band) may be required for slower processes that detect motion over longer time periods. the c-band's central position can detect small changes in a short period of time, but it is not as sensitive to small changes as the x-band or as capable of monitoring surface dynamics under the canopy as the l-band. to detect surface deformation induced by an earthquake, at least two sar photos of the same object taken at different times are required: after and before the earthquake. the sar picture at time t before the earthquake is referred to as the master, whereas time t1 after the earthquake is referred to as the slave. the phase value is acquired from both the first and second pass images (figure 4). if the first and second trajectory images have a phase difference, the interferogram will show displacement fringes. the interferogram has two basic fringes: displacement fringes induced by shifting topographic surfaces and topographic fringes caused by topographic forms (campbell et. al 2011). source: analysis, 2025 figure 4. two sar imaging trajectories obtained at time t (before the earthquake) and t1 (after the earthquake) will provide ground surface movement with a phase shift of the sar signal caused by the earthquake). sentinel-1 satellite imagery in single-look complex (slc) format, also known as 2017 copernicus sentinel data (asf hyp3 sentinel-1 burst insar product guide), consists of two satellites, sentinel-1a and sentinel-1b, each carrying a cband synthetic aperture radar (sar) instrument for round-the-clock global photography, eventhrough cloud cover. both satellites orbit in tandem, 180° apart. each satellite may repeat every 12 days, and sentinel-1 can repeat every 6 days thanks to the constellation of two satellites (azhari et al. 2020; braun et al., 2020 téllez-quiñones et al. 2020) sentinel-1 has four observation modes, the main one on land being the interferometric wide swath (iw) mode, which has a spatial resolution of around 5 m x 20 m (islam et al. 2017). surface deformation induced by an earthquake can be detected if there is a phase difference between the master (the first picture acquired) and slave (the second image acquired), as seen in the interferogram created by https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 85 multiplying the amplitude by the phase difference of the signals (hogenson et al., 2020). in this study, two sentinel-1 sar pictures were utilized to assess surface deformation induced by the tarakan earthquake on december 21, 2015. the master data was recorded on december 18, 2015, three days before the earthquake, and the slave data was recorded on january 11, 2016 (21 days later). 2.4 dinsar method differential interferometric synthetic aperture radar (dinsar), as an extension of insar, has become a mature method for monitoring deformations in mining areas (manconi, 2021; jiang et al. 2023; govil et al. 2023). dinsar techniques can be used to monitor topographic change, surface deformation or terrain displacement induced by different phenomena, like earthquakes or seismic activity, magma accumulation due to volcanic lava flow, and particularly, glacier or ice-flow dynamic (téllez-quiñones et al. 2025) using two sar images. the basic purpose of dinsar is to extract the full phase caused exclusively by deformation while deleting or minimizing other contributing components. if a topographic surface module serves as a reference or if three or more radar images are employed, differential insar can be used to determine the changes. the dinsar approach relies on dem (digital elevation model) data to execute differential 2-pass interferometry operations. the dem used is glo-30 (fahrland et al. 2020), with an initial pixel spacing of 1 arcsecond (about 30 meters). the copernicus dem glo-30 is a global digital surface model (dsm) built on the worlddem. worlddem is based on radar satellite data gathered by the tandem-x mission, which has been modified to flatten water bodies, provide steady river flows, and change beaches, coasts, and distinguishing features. the phase information contained in the interferograms of the two sar observations taken at different periods comprises topography, orbital drift, surface deformation, atmospheric influences, and thermal noise (castaneda et al. 2011). this interferogram will show whether there is land subsidence or rising in a specific area. phase wrapping is the result of calculating the height difference that depends on the phase difference between the interferogram intervals (-π, π). the phase change of the signal (∆φ) (equation 1) is affected by the wavelength (λ), displacement (δr), and phase change due to the difference in atmospheric conditions (castaneda et al. 2011) during acquisition by the two radars (α). the phase difference value can be formulated by the following equation (equation 2): ∆𝜑 = 4𝜋 𝛿𝑅 𝜆 + 𝛼………………….………….……………….……..(equation. 1) ∆𝜑 = ∆𝜑𝑓𝑙𝑎𝑡 + ∆𝜑𝑒𝑙𝑒𝑣𝑎𝑠𝑖 + ∆𝜑𝑑𝑒𝑓𝑜𝑟𝑚𝑎𝑠𝑖 + ∆𝜑𝑎𝑡𝑚𝑜𝑠𝑓𝑒𝑟 + ∆𝜑𝑛𝑜𝑖𝑠𝑒 …..….(equation. 2) where ∆𝜑𝑓𝑙𝑎𝑡 is the phase due to topographic influences. using the above equation, the ∆𝜑𝑓𝑙𝑎𝑡 (equation 3) was computed using: ∆𝜑𝑓𝑙𝑎𝑡 = − 4𝜋 𝜆 𝐵𝑛 𝑅𝑡𝑎𝑛𝑡𝑎𝑛 𝜃 …………….……...………............….…….(equation. 3) where ∆𝜑𝑒𝑙𝑒𝑣𝑎𝑠𝑖 is the phase influenced by height. using the above equation, the ∆𝜑𝑓𝑙𝑎𝑡 9 (equation 4) was computed using: ∆𝜑𝑒𝑙𝑒𝑣𝑎𝑠𝑖 = − ∆𝑞 𝑠𝑖𝑛𝑠𝑖𝑛 𝜃 . 𝐵𝑛 𝑅 . 4𝜋 𝜆 ………………………..….....……….(equation. 4) where ∆𝜑𝑎𝑡𝑚𝑜𝑠𝑓𝑒𝑟 is the phase due to atmospheric influences. using the above equation, the ∆𝜑𝑓𝑙𝑎𝑡 (equation 5) was computed using: ∆𝜑𝑎𝑡𝑚𝑜𝑠𝑓𝑒𝑟 = 4𝜋 𝜆 𝑑 ………………………………………….......……(equation. 5) with (bn) is the baseline perpendicular to the slope (perpendicular baseline) (m), (r) is the radar distance to target (m), and (𝜃) is the angle of incidence (degrees). this study used snap software for sentinel-1 sar image data processing and visualization, google earth software for virtually visualizing research data on the earth's surface, and arcgis software version 10.8 for https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 86 raster data operations and data representation. the majority of sentinel-1 image data is processed using the snap program (esa, 2021). furthermore, data is visualized and represented using google earth and arcgis 10.8 software. the picture data processing yielded a surface deformation map (los displacement) from the december 21, 2015 tarakan earthquake. 3. result and discussion 3.1. interferogram phase the phase interferogram is able to show areas that experienced surface resolution due to the poso earthquake on may 29, 2017 (puspita et al. 2024). processing the sar image observation data yields colordifferentiated interferograms displaying the phase shifts of radar waves before and after the earthquake (figure 5). the colorful contours show the interference fringes between the two datasets. the distance between each interference fringe denotes ground motion. the phase difference in the interferogram is represented by blue-topink pixels in the positive phase and blue-to-yellow pixels in the negative phase. the denser the interferogram fringes, the higher the strain on the ground during the earthquake. the tarakan earthquake's interferogram is centered on the main earthquake and extends north-northeast, southeast-southwest, and southwest-northwest. the contrast of the interferogram color repetition (fringe) is not clearly evident, which could be due to air noise, topography influences, or the region's dense vegetation. although the margins are not easily apparent, the distorted portions can still be distinguished. source: analysis, 2025 figure 5. tarakan earthquake interferogram, december 21, 2015 3.2. coherence value map coherence value ranges from 0 to 1, with higher values indicating better interferogram quality. the interferometry procedure is considered good and accurate if the image's coherence value ranges between 0.5 and 1.0. if the value is less than 0.5, the image produced from the interferometry process still contains useful information, (kurniawan et al. 2016) but the image with the coherence value also exhibits an increase in noise level proportionate to the smaller coherence value. areas with high coherence will have clear color contours, whereas freckles areas show very low coherence and noise. typically, a surface that remains constant throughout the difference time picture acquisition produces a very high coherence (jaya et al. 2021). an interferogram's coherence is affected by several factors, including the angle and orientation of the topographic slope (steep slopes lead to low coherence), the nature of the land, the time spacing of the images (longer time intervals lead to lower coherence), and the baseline. https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 87 (a) (b) source: analysis, 2025 figure 6. (a) sar coherence distribution map (b) dem (digital elevation model) map of north borneo figure 6(a) depicts the range of coherence values in the north borneo region, namely the areas surrounding the tarakan earthquake, with coherence values ranging from 0.4 to 0.9. topographic characteristics and forest density influence both high (white) and low (dark gray) coherence values in the north borneo region (figure 6(b)). due to the region's flat topography, white dominates the coherence map in places near the epicenter. some locations with low coherence values (less than 0.4) are generated by sentinel imaging that employs a c-band with a wavelength of 5.4 cm; therefore, the radar waves are insufficient to penetrate the vegetation canopy, resulting in poor interferogram accuracy. areas with coherence greater than 0.8 are situated northeast, southwest, north, south, west, and southeast of the main earthquake, as shown by white pixels. these findings indicate that these places are largely flat with no substantial topographical changes, resulting in numerous reflections of radar signal interference. if deformation happens, areas with high coherence values will produce more interferograms, with fringe lines that are more regular than those with low coherence values. 3.3. deformation phase (wrapped and unwrapped phase) the resulting deformation value remains a deformation phase (wrapped phase) with both negative and positive phase values. figure 7(a) depicts the deformation phase throughout the co-seismic period of the main tarakan earthquake, with an updated magnitude of m 6.1 (yellow star). negative phase values indicate subsidence-related deformation, which is illustrated in red. positive phase values indicate areas of uplift, as illustrated in blue (cahyaningrum, 2024). areas that are white in color, as well as phase points, are often very stable and do not undergo modifications or deformation. the deformed areas are all located some distance from the main earthquake. to measure the amount of deformation in a metric, a computation must be performed using the displacement of the earth's surface formula along the line of sight (los) sensor, as shown in (equation 1). after unwrapping the interferogram, as shown in figure 7(b), the area deformation pattern can be determined even if it is still in phase units. line of sight (los) analysis indicates the satellite's flying direction. if the value is positive, it means that the axis is stretched towards the satellite, indicating land subsidence. if the result is negative, it indicates that the axis is shorter towards the satellite, implying an increase in land level (uplift) (cahyaningrum, 2024). based on this image, the largest land surface rise (uplift) deformation value is 0.117618 meters, which is highlighted in red around the main earthquake. areas undergoing ground subsidence deformation are depicted in blue, with a maximum deformation value of 0.0649612 meters, which is also near the main earthquake. the deformation was directly tied to aftershock activity following the primary earthquake. https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 88 (a) (b) source: analysis, 2025 figure 7. (a) tarakan earthquake deformation phase december 21, 2015 (b) los displacements (changes in deformation) related to the tarakan earthquake on december 21, 2015. the loss displacement map in figure 7(b) depicts the magnitude of deformation change data in various districts and cities in north borneo following the tarakan earthquake on december 21, 2015. tarakan city, located to the south of the main earthquake (yellow star), appears on the map as blue to yellow, suggesting that the deformation change that happened was subsidence (lowering of the land surface), with the deformation value nearly reaching its maximum. tana tidung regency, located just southwest of the main earthquake, is primarily colored red, suggesting that there is deformation change in the form of uplift (an increase in land level) with a value close to its maximum. the same pattern is seen in the tana tidung regency area, which is directly north of the main earthquake, with yellow to red indicating a change in maximum uplift deformation. nunukan regency, located northwest of the main earthquake, displays blue to light orange, suggesting changes in subsidence deformation. the bulungan regency area, located southeast of the major earthquake (bunyu island), appears to be dominated by yellow to orange colors, indicating that uplift deformation has occurred in this location. 3.4. cross section deformation to estimate the deformation value in the areas surrounding the epicenter (gabriel et al. 1989) of the tarakan earthquake on december 21, 2015, a vertical cut (cross-section) was performed in each district area to demonstrate the influence of changes in deformation. the cross section formed generates a cross-section graph, with the y axis representing the value of changes in land surface (deformation) in meters and the x axis representing the length of the cross section in the regency area in kilometers. according to (petersen et al. 2011), the risk of ruptures occurring off-fault is significantly lower than the risk close to the fault. however, the data show that adjacent faults at least 10 km (km) away from the principal fault and with a meter-scale offset on the primary fault can cause displacements of up to 35 cm (cm). the tarakan city area, located around 22 kilometers south of the earthquake’s epicenter (figure 9) exhibits subsidence deformation ranging from 0.001-0.035 meters in the northwest-southeast cross section. the graph along the cross section depicts the subsidence deformation that dominates the tarakan city area, namely the area directly south-southwest of the earthquake’s epicenter. the tana tidung i regency area, located 33 kilometers southwest of the earthquake epicenter, has uplift deformation ranging from 0.019 to 0.079 meters in the northwest-southeast cross section (figure 10). uplift deformation dominates this region, with the highest value displayed by the graph occurring directly southwest of the location of the tarakan earthquake on december 21, 2015. figure 11 depicts a cross-section graphic showing the northwest-southeast cross-section in the bulungan regency area, which is located around 11 https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 89 kilometers southeast of the earthquake epicenter. based on graphic pictures, uplift deformation dominates this area, with major deformations ranging from 0.008 to 0.075 meters. the highest uplift deformation value occurs in the southeastern bulungan region, or precisely to the southeast of the earthquake's epicenter. source: analysis, 2025 figure 8. cross sections were conducted in each district region near the epicenter of the tarakan earthquake on december 21, 2015 source: analysis, 2025 figure 9. vertical cross-section graph of aa' at tarakan city source: analysis, 2025 figure 10. vertical cross-section graph of bb’ in tana tidung i regency https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 90 source: analysis, 2025 figure 11. vertical cross-section graph of cc’ in bulungan regency. source: analysis, 2025 figure 12. vertical cross-section graph of dd’ in tana tidung ii regency source: analysis, 2025 figure 13. vertical cross-section graph of ee’ in nunukan regency figure 12 illustrates the cross-section graph of the northwest-southeast cross section in the tana tidung ii regency area, where the tarakan earthquake occurred. the graph shows that uplift deformation dominates the east segment of this region. the uplift deformation value in this area ranges from 0.001 to 0.069 meters. the largest uplift deformation value is found 10 kilometers north of the earthquake’s epicenter. nunukan regency's southwest-northeast (ee') cross section (figure 13) shows uplift deformation in the southwest and subsidence in the northeast. the uplift deformation range is 0.001–0.062 meters, whereas the subsidence deformation range is 0.001–0.029 meters. maximum uplift and subsidence deformation values occur northwest of the earthquake’s epicenter. the surface deformation values due to the tarakan earthquake on december 21, 2015 provide information on the seismic hazard in the north borneo, especially in tarakan city, nunukan regency, bulungan regency, tana tidung i regency (southwest of the earthquake epicentre), and tana tidung ii regency (north of the earthquake epicentre). previously thought to be an earthquake-safe region, this is no longer the case. the epicentre of the tarakan earthquake, which was located far from the tarakan fault, provides evidence of other locally active faults. in the past year, the indonesian meteorology, climatology and geophysics agency (bmkg) recorded several earthquakes in the north borneo region. on august 10, 2024, at 16:20:23 wita, an earthquake magnitudo m 4.6 occurred on land to the southeast of tarakan city, precisely at 63 km southeast of tarakan city, with a shallow depth of 11 km. the earthquake was felt by people in tarakan city, tanjung selor regency, berau regency, and tana tidung regency with an intensity scale of iii-iv mmi (mercally modified intensity). at this intensity scale, the earthquake was felt quite strongly by many people, the windows/doors rattled, and walls rang. the source of the fault that caused the earthquake was a locally active fault. https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 91 subsidence and uplift deformation due to the earthquake through the dinsar method successfully identified the fault slip characteristics that caused the tarakan earthquake on december 21, 2015. figure 3 is the focal mechanism of the tarakan earthquake obtained from the global cmt catalogue. the focal mechanism parameters show the strike, dip and slip values of two fault planes, where only one fault plane is selected as the main fault plane. research that has been conducted on the distribution of aftershocks from the tarakan earthquake, shows the earthquake slip moves from the epicentre of the main earthquake to the south (sriyanto et al. 2016). based on the results of this study, the main fault plane that caused the tarakan earthquake has a strike value: 3o, dip: 86o, and slip: 169o. the strike, dip, and slip values represent the hanging wall of the fault to the west, and the foot wall of the fault to the east. the slip value on the fault indicates that the fault has an oblique fault mechanism dominated by strike-slip movement. the results of sar image processing using the dinsar method, which shows the surface deformation caused by the tarakan earthquake (figure 8), show that the area east of the epicentre of the earthquake (parts of the eastern part of tana tidung ii regency and bulungan regency) experienced uplift deformation (red colour), and the area west of the epicentre (the western part of tana tidung ii regency, nunukan regency, and tarakan city) experienced subsidence deformation (blue colour). there is a correspondence between the source of the terakan earthquake and the surface deformation caused by the tarakan earthquake. the characteristics of the fault that caused the tarakan earthquake are oblique-normal faults dominated by strike-slip fault movements and normal (downward) fault blocks located to the west. the downward movement of the main fault plane to the west of the epicentre of the tarakan earthquake corresponds to the record of the greatest level of shaking and damage caused by the earthquake, namely tarakan city and nunukan regency on the iv-v mmi shaking level scale. subsidence and uplift deformation will certainly have an impact on the damage to buildings and other infrastructure. earthquake preparedness strategies need to be known by the community and local government in areas prone to subsidence and uplift. moreover, the geological structure in the borneo region can exacerbate the value of subsidence and uplift. the geological structure of north borneo, which is dominated by alluvium deposits and sedimentary rocks, contributed to the land surface deformation that occurred in various regions near the epicenter of the tarakan earthquake on december 21, 2015. alluvium deposits and sedimentary rocks are very soft structures, causing seismic waves to flow more slowly, resulting in larger earthquake shocks. several measures can be taken by the community and local government to reduce the risk of disasters caused by earthquakes, including ensuring that buildings where people live or work have structures that are resistant to earthquake shocks, and are not located close to highlands such as hills, because changes in earthquake deformation can cause landslides. 4. conclusion the interferogram produced by the tarakan earthquake on december 21, 2015, focuses on the primary earthquake in the north-northeast, southeast-southwest, and southwest-northwest directions. the tarakan earthquake caused the highest land rise (uplift) deformation value of 0.117618 meters, as well as the highest land subsidence deformation value of 0.0649612 meters. both of these deformations happen around the main earthquake. northwest-southeast cross sections were taken in tarakan city, tana tidung i, bulungan, and tana tidung ii districts. a cross-section graph showing a northwest-southeast cross section in the tana tidung i area, 33 kilometers southwest of the earthquake epicenter, shows considerable uplift deformation of 0.019 to 0.079 meters. significant uplift deformation in the bulungan regency area, which is approximately 11 kilometers southeast of the earthquake epicenter, ranges between 0.008 and 0.075 meters. the tana tidung ii regency area, which is the epicenter of the tarakan earthquake, shows that uplift deformation dominates the southeast half of the region. the uplift deformation value in this area ranges between 0.001 and 0.069 meters. the maximum uplift deformation value occurs 10 kilometers north of the earthquake's epicenter. the cross-section graph of nunukan regency from southwest to northeast indicates uplift deformation in the southwest and subsidence in the northeast. the uplift deformation range is 0.001-0.062 meters, and the subsidence deformation range is 0.001-0.029 meters. the highest uplift and subsidence deformation values occur northwest of the earthquake's epicenter. the surface deformation values due to the tarakan earthquake on https://doi.org/10.14710/geoplanning.12.1.79-94 pertiwi et al. / geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 79 94 doi: 10.14710/geoplanning.12.1.79-94 92 december 21, 2015 provide information on the seismic hazard in the north borneo, and provide evidence of other locally active faults. uplift deformation due to the tarakan earthquake on december 21, 2015 occurred more to the east of the epicentre, while subsidence deformation occurred more to the west. this is consistent with the focal mechanism of the tarakan earthquake, which indicates oblique-normal faults, with strike-slip fault movements dominating, and normal (downward) fault blocks located to the west. the geological structure of north borneo, which is dominated by alluvium deposits and sedimentary rocks, causes seismic waves to flow more slowly, resulting in greater earthquake shaking, which can increase ground surface deformation. 5. references azhari, m f, karyanto, k., rasimeng, s., & mulyanto, b. s. 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(2012). a new depositional and provenance model for the tanjung formation, barito basin, se kalimantan, indonesia. journal of asian earth sciences, 56, 77-104.[crossref] https://doi.org/10.14710/geoplanning.12.1.79-94 https://doi.org/10.1080/01431161.2020.1727056 https://doi.org/10.3390/rs17071168 https://doi.org/10.1144/sp441.8 https://doi.org/10.1016/j.jseaes.2012.04.022 15 geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 15 30 original research coastal metropolitan dynamics in poland's tricity and indonesia's semarang: ntl, blfei, and obia in google earth engine abdurrahman zaki1*, joanna jaskuła1 1. faculty of environmental and mechanical engineering, poznań university of life sciences, poland doi: 10.14710/geoplanning.12.1.15-30 abstract the increasing global urbanization, particularly in coastal regions, coupled with the risks of climate change and land subsidence, underscores the need to monitor coastal urban development for sustainability. this study focused on the coastal metropolitan regions of poland's tri-city and indonesia's semarang, employing gis, remote sensing (rs), and cloud computing. by integrating nighttime light (ntl) and the built-up land features extraction index (blfei) through google earth engine (gee) and object-based image analysis (obia), the study aimed to gain insights into urban development trends. the methodology encompassed image collection, analysis, and classification over three decades (1992, 2007, 2022). despite efforts to enhance accuracy through built-up masking in subsequent years, the methodology achieved an overall accuracy of 95% for the 2022 maps, while maps in 1992 and 2007 fell short (overall accuracy ranging from 0.81 to 0.90) in comparison. the analysis revealed a gradual expansion of built-up areas in both regions, with gdynia and gdańsk emerging as primary drivers in the tri-city metropolitan region and semarang as the primary driver in the semarang metropolitan region. notably, the semarang metropolitan region exhibited an increase in waterbody areas, attributed to coastal flooding and land subsidence challenges. copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction the world's population is projected to reach between 9.4 to 10.1 billion by 2050 and between 9.4 to 12.7 billion by 2100, according to the united nations (2019a). by 2050, more than 68% of the global population is expected to reside in cities due to urbanization (united nations, 2019b). in many developing countries, urbanization often occurs without proper planning (gumel et al., 2020; sun et al., 2020), resulting in unsustainable urban development (clement & pino, 2023; das et al., 2021; esther, 2022). ultimately, the impacts of urbanization in developing countries could be more severe than those in developed countries (ezadin & faraj, 2022). factors contributing to urban sprawl in developing countries include increasing population, industrialization (hasnine & rukhsana, 2020), the service industry, and real estate development (zhang & pan, 2021), as well as peri-urban and infrastructure development (ahmed et al., 2021). to address sustainable urban development in the future, monitoring the temporal and spatial patterns of urban areas is crucial (dadashpoor et al., 2019b; sumari et al., 2019). however, despite the challenges, cities located along coastal areas are still highly preferred residential areas for urban dwellers (sanders & oliveira, 2020; siegel, 2020; wang et al., 2021). e-issn: 2355-6544 received: 09 september 2024; revised: 08 may 2025; accepted: 09 may 2025; available online: 14 may 2025; published: 26 may 2025. keywords: urbanization, coastal metropolitan, data fusion, obia *corresponding author(s) email: abdurrahman.zaki20@pwk.undip.ac.id https://doi.org/10.14710/geoplanning.12.1.15-30 mailto:abdurrahman.zaki20@pwk.undip.ac.id zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 16 the future sustainability of coastal cities is threatened by the increasing risks of climate change (sanders & oliveira, 2020; wojtowicz-jankowska & kalfouni, 2022), particularly in those located in low-income countries (day et al. 2021). these coastal cities are at risk of land subsidence (cian et al., 2019; hu et al., 2019; wdowinski et al., 2020), sea level rise (qu et al., 2019; taherkhani et al., 2020; valente & veloso-gomes, 2020), rising temperatures (hu, 2021; qi et al., 2022), and pollution (choi et al., 2020; su et al., 2020). to address these issues, adaptation measures to combat climate change, such as adaptive coastal planning (valente & veloso-gomes, 2020; buchori et al., 2022) and community-based adaptation (berman et al., 2020), are needed to prepare for the uncertainties facing coastal cities in the future. in this regard, providing data on changing landscape trends in coastal areas can serve as a monitoring measure and input for coastal adaptation strategies (hu et al., 2021; ragia & krassakis, 2019; vitousek et al., 2023). the monitoring of temporal and spatial changes in the earth's landscape across vast areas is commonly carried out using a geographic information system (gis) and remote sensing (buchori et al., 2015; fahad et al., 2020; liu & yang, 2015; woodcock et al., 2020; zhang, 2020). previous studies have demonstrated that gis and remote sensing can be effectively used to analyze historical land use and land cover (lulc) maps (adnani et al., 2019; viana et al., 2019), predict future lulc maps using the cellular automate (ca) algorithm (hishe et al. 2020; mathanraj et al., 2021; muhammad et al., 2022), and even apply object-based image analysis (obia) for improved accuracy in classifying lulc maps (how et al., 2020; pangastuti & wijayanto, 2021; yadav et al., 2022; zaki et al., 2022). recently, cloud computing, particularly google earth engine, has significantly accelerated image analysis, enabling scholars to analyze large areas at the national or global scale without consuming local computer memory (luo et al., 2021; yadav et al., 2022; zaki et al., 2022; zaki et al., 2023; zhang & li, 2022). when classifying land use and land cover (lulc) maps, a persistent issue is the occurrence of the "salt and pepper effect," which refers to the scattering of misclassified pixels. this phenomenon is particularly noticeable when using pixel-based image analysis, a conventional method for image classification. to mitigate the salt and pepper effect, a more recent classification method known as object-based image analysis (obia) was developed. obia functions by segmenting pixels before the classification process, allowing for the merging of scattered pixels with their homogeneous surrounding environment. in addition to advancing classification methods, efforts to enhance the accuracy of lulc maps have been made through data fusion, which involves the combination of satellite data from various sources to achieve improved results. for instance, prior studies have attempted to fuse landsat image collections with nighttime light datasets using google earth engine (goldblatt et al., 2018; liu et al., 2019). our primary hypothesis centers on the idea that by integrating obia with data fusion, incorporating not only nighttime light data but also a remote sensing index, a scholar can enhance the accuracy of lulc mapping on a broader scale within the framework of google earth engine. a secondary hypothesis aims to demonstrate that urban sprawl is more pronounced in the semarang metropolitan region (indonesia) compared to the tri-city metropolitan region (poland). therefore, the objective of this study is to improve the classification of lulc maps by fusing landsat image collections, integrating nighttime light (ntl) data, and built-up land features extraction index (blfei). this approach enables the analysis of three decades of urban development in medium-sized coastal metropolitan regions in indonesia and poland. however, it is essential to acknowledge certain limitations in this study: 1) social and political factors are not considered in this research, and 2) the categorization of lulc classes is limited to built-up areas, non-built-up areas, and waterbodies. ultimately, this study aspires to contribute to the body of knowledge in gis and remote sensing methodology for monitoring urban sprawl, thereby promoting sustainable coastal urban development. 2. data and methods 2.1. study area the study area of this research includes the coastal areas of the tri-city metropolitan region in poland (figure 1) and the semarang metropolitan region in indonesia (figure 2). these study areas were selected https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 17 because they are medium-sized metropolitan cities in their respective countries. in general, the study area was defined as a 20-kilometer radius from the coastal areas of the tri-city and semarang metropolitan regions. this definition was chosen considering that administrative boundaries do not limit urban growth, and a broader study area is necessary to encompass the urban core and suburban areas in each region. a radius of 20 kilometers was deemed appropriate to cover these aspects in both metropolitan regions. additionally, the study area includes water bodies to account for landscape changes such as land reclamation, harbor construction, and coastal erosion. figure 1. the study area of the tri-city metropolitan region in poland figure 2. the study area of the semarang metropolitan region in indonesia 2.2. data the aim of this research is to monitor urban development over an extended period, and landsat image collections were chosen as the dataset due to their continuous operation since the 1970s, despite differences between each landsat mission. although sentinel-2a satellite image collections were available, they were not utilized in this research as their operational period only started from june 23, 2015. instead, landsat 5 (operational from march 1, 1984, to june 5, 2013; landsat/lt05/c02/t1_l2) and landsat 8 (operational from february 11, 2013; landsat/lc08/c02/t1_l2) provided by the u.s. geological survey (usgs) were used in this study. by utilizing these two landsat datasets, which consist of atmospherically corrected surface reflectance, the research generated maps for three time periods: 1992, 2007, and 2022. the selection of these years was based on available data, taking into account cloud covers and the availability of nighttime light data. the next dataset was the nighttime light data, which indicates urbanization. it included the datasets noaa/dmsp-ols/nighttime_lights with a spatial resolution of 927.67 meters, covering the period from 1992 to 2014, and noaa/viirs/dnb/monthly_v1/vcmslcfg with a spatial resolution of 463.83 meters, spanning from 2014 to 2023 in google earth engine. this dataset was provided by the earth observation group (payne institute for public policy, colorado school of mines). the final dataset comprised a set of randomly selected sample points for both built-up and non-built-up areas within each study area for each observation year (1992, 2007, and 2022), as presented in table 1 and spatially visualized in figure 3. the process of selecting sample points involved gradually adding samples until the resulting land cover map accurately represented the actual earth conditions, as observed from natural color landsat imagery. these sample points were then divided into two sets: 70% for training purposes and 30% for testing (abdi 2020; chenli liu et al. 2020). this division was achieved using the randomcolumn() and filter() functions. the training points were utilized for image classification, while the testing points were used to assess the accuracy of the image classification results. https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 18 table 1. numbers of sample points for built-up (bu) and non-built-up (nbu) areas in each region year tri-city semarang bu nbu bu nbu 2022 179 582 368 550 2007 240 268 218 218 1992 192 157 283 127 to be noted, the number of training samples from 2022 to 1992 decreased due to smaller areas to be classified. this reduction was made taking into consideration the possibility that misclassification could result in a built-up area in a previous year that does not exist in the following year. to anticipate this, the area was limited that would be classified using the boundary of classified built-up areas in the following year of observation. however, the downside is that the number of training samples was up to hundreds, making it not time-efficient for purposes requiring fast processing. figure 3. spatial distribution of training samples in each study area (left: semarang metropolitan region; right: tri-city metropolitan region) 2.3. methods this section describes the methodology used in this study, which generally includes initial image processing (filtering and masking), data fusion, and object-based image classification (obia). the methodology was predominantly implemented using google earth engine (https://code.earthengine.google.com/), and qgis desktop 3.32.1 (https://qgis.org/) for the map layouting. the workflow of this methodology is depicted https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 19 in figure 4. the final output of this process comprises land cover maps delineating built-up areas, non-built-up areas, and water bodies within the two study regions for the years 1992, 2007, and 2022. figure 4. methodological workflow implemented in google earth engine 2.3.1. initial image processing the processes in the initial phase of the methodology involved filtering the landsat image collections, applying a scale factor for both landsat 5 and landsat 8, performing cloud masking, and subsequently applying water masking using mndwi (modified normalized difference water index). initially, the filter date selection for both locations (tri-city and semarang) took into account when both locations are in the summer/dry season. for example, it was referenced in a previous study that indicated the dry season in the java province (where the semarang metropolitan region is located) spans from june to september, with the peak of rainfall in december and january (berliana et al., 2021). on the other hand, another study mentioned that torun (a city located 185 kilometers south of the tri-city) experiences spring from april to october (kejna & pospieszyńska, 2023). in this case, the analyzed period was utilized from april 1st to october 31st for each observation year in both locations to filter the landsat images. secondly, before using the landsat/lt05/c02/t1_l2 and https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 20 landsat/lc08/c02/t1_l2 for calculations, the scale factor must be applied using equation 1 and equation 2: 𝑜𝑝𝑡𝑖𝑐𝑎𝑙 𝑏𝑎𝑛𝑑 = 𝑏𝑎𝑛𝑑 𝑆𝑅_𝐵 × 0.0000275 + (−0.2)………..(eq.1) 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑏𝑎𝑛𝑑 = 𝑏𝑎𝑛𝑑 𝑆𝑇_𝐵 × 0.00341802 + 149)………..(eq.2) where “band sr_b” refers to all optical bands (b1, b2, b3, b4, b5, b7 in landsat 5; b1, b2, b3, b4, b5, b6, b7 in landsat 8), and “band st_b” refers to" thermal bands (b6 in landsat 5 and b10 in landsat 8). thirdly, cloud masking was applied to both dataset using the bitmask for the qa_pixel band to eliminate clouds, cloud shadows, and snow. the specific bitmask used in this step is detailed in table 2. this process resulted in image collections with clear terrain. furthermore, the median value of each pixel was selected, and the image was clipped to the area of interest. table 2. bitmask for qa_pixel in landsat 5 and landsat 8 bit landsat 5 landsat 8 1 dilated cloud dilated cloud 2 unused cirrus 3 cloud cloud 4 cloud shadow cloud shadow 5 snow snow in the final stage of the initial processing, water masking was executed using mndwi (see equation 3), an index proposed by xu (2006) to identify water bodies, where mndwi greater than zero indicates water. in this case, mndwi less than or equal to zero was used to filter the image collections that were previously cloudmasked. the calculation of mndwi was performed using the equation 3, where green and mir represent band 2 and band 5 in landsat 5, and band 3 and band 6 in landsat 8, respectively. 𝑀𝑁𝐷𝑊𝐼 = 𝐺𝑟𝑒𝑒𝑛−𝑀𝐼𝑅 𝐺𝑟𝑒𝑒𝑛+𝑀𝐼𝑅 )………...(eq.3) 2.3.2. data fusion after obtaining the cloudand water-masked landsat image collection, the next step was to integrate this collection with nighttime light data and blfei. for the nighttime light data, the median values of pixels within the period from january 1st to december 31st were selected and cropped using the boundary of the water-masked image of the study area. however, the datasets used in this step had different spatial resolutions (927.67 meters for noaa/dmsp-ols/nighttime_lights and 463.83 meters for noaa/viirs/dnb/monthly_v1/vcmslcfg). then, blfei proposed by bouhennache et al. (2019) which resulted in higher accuracy than some other built-up indexes was calculated. the calculation of blfei was performed using the equation 4: 𝐵𝐿𝐹𝐸𝐼 = ( 𝐺𝑟𝑒𝑒𝑛+𝑅𝑒𝑑+𝑆𝑊𝐼𝑅2 3 −𝑆𝑊𝐼𝑅1) ( 𝐺𝑟𝑒𝑒𝑛+𝑅𝑒𝑑+𝑆𝑊𝐼𝑅2 3 +𝑆𝑊𝐼𝑅1) )………...(eq.4) where the bands used in the formula for landsat 5 and landsat 8 were shown in table 3. finally, landsat images, nighttime light images, and blfei were combined using the "addbands" function in google earth engine. https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 21 table 3. bands in landsat 5 and landsat 8 used for calculating blfei landsat tm landsat oli band wavelength (micrometers) band wavelength (micrometers) green 2 0.52-0.60 3 0.53-0.59 red 3 0.63-0.69 4 0.64-0.67 swir 1 5 1.55-1.75 6 1.57-1.65 swir 2 7 2.08-2.35 7 2.11-2.29 source: https://www.usgs.gov/faqs/what-are-band-designations-landsat-satellites 2.3.3. object-based image analysis the final step was to conduct object-based image analysis (obia) to monitor urban development patterns. initially, image segmentation was performed to cluster similar pixels from the result of data fusion into polygons, which is an essential step in obia. in this case, it was used a size of 15 and "hex" as the grid type when performing super pixel clustering based on simple non-iterative clustering (snic) in google earth engine. the result of image segmentation is illustrated in figure 5. figure 5. segmentation result (left) and google satellite imagery (right) in an area located in the northern tri-city, poland and then, some polygons were selected as training regions based on the previously prepared random training points. using these training regions, classification using the random forest algorithm was performed to simulate lulc maps. in this case, the number of trees set for the random forest algorithm was 50, referring to junaid et al., (2023). since the water bodies were excluded from the initial stage, blank data in the study area was set as water bodies and combined with the resulted lulc from the obia. finally, all results of calculations in google earth engine were exported to google drive to be further downloaded, visualized, and analyzed in qgis desktop 3.32.1. 3. result and discussion this section comprises a description of the results from the analysis between urban development in the a medium-sized coastal metropolitan region in poland and indonesia. the results include interpretations of the data used during the data fusion process, such as landsat images, the ntl, and the blfei. additionally, there are discussions about the lulc maps resulting from the object-based image analysis (obia) method in google earth engine, representing urban development in both study areas. this section is followed by a discussion of these results in relation to other relevant studies to understand how they correlate with the existing body of knowledge. https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 22 3.1. results the coastal metropolitan regions exhibited gradual expansion, with green open spaces converted into built-up areas and the construction of toll roads further stimulating rapid urban growth. overall, the development of built-up areas in both regions aligned with the primary transportation networks connecting other cities. this expansion is illustrated in figure 6 and figure 7, which depict each study area using blfei and ntl. the data shows a varying range across the years, with the variance in ntl attributed to advancements in satellite technologies that have resulted in higher spatial resolution, particularly noticeable when comparing the 2007 and 2022 data. blfei effectively highlights built-up areas through its reddish coloration on the map. additionally, these maps reveal an expanding coastline in both regions due to harbor development and land reclamation. in the semarang metropolitan region, however, land subsidence and coastal flooding are particularly pronounced in the northeastern coastal area. figure 6. blfei and nighttime light data in the tri-city metropolitan region figure 7. blfei and nighttime light data in the semarang metropolitan region https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 23 figure 8. identification of built-up expansion in the tri-city metropolitan region figure 9. land cover map vs. google satellite image in tri-city’s cities figure 8 and figure 10, resulting from the application of obia to the combination of all landsat bands, ntl, and blfei, illustrate the evolution of built-up areas in both study regions. in the tri-city metropolitan area, gdynia and gdańsk act as primary drivers of urban development, with sopot serving as a connector between these cities and forming a cohesive metropolitan zone. the presence of harbors in both gdynia and gdańsk, along with an international airport in gdańsk, significantly fuels urban expansion. the dominant housing type in poland, characterized by numerous apartments and multi-story buildings, contributes to more compact urban development. in contrast, in the semarang metropolitan region, semarang city is the main driver of growth, linking neighboring cities such as kendal, demak, and ungaran. the harbor and domestic airport in semarang also play crucial roles in spurring urban expansion. however, the prevalence of single-family houses in indonesia, which is more prone to causing urban sprawl, contrasts with the denser housing patterns seen in poland. additionally, figure 9 and figure 11 provide a detailed comparison between the land cover map and google satellite images for each city in the study areas. https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 24 figure 10. identification of built-up expansion in the semarang metropolitan region figure 11. land cover map vs. google satellite image in semarang the lulc maps resulting from this study exhibit varied overall accuracy (oa) statistics, following a similar pattern for both study areas: the oa of the subsequent year is higher than that of the preceding year. the methodology achieved an overall accuracy of 95% for the 2022 maps. the oa for the tri-city metropolitan region in 1992 and 2007 stands at 81% and 87%, respectively, as shown in table 4. similarly, for the semarang metropolitan region, the oa in 1992 and 2007 is 87% and 90%, respectively, presented in table 5. despite the expectation to increase the oa of an lulc map by masking it based on the built-up areas in the following year, the accuracy of the lulc maps in 1992 and 2007 still falls short compared to the oa in 2022. https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 25 table 4. overall accuracy of land cover maps in the tri-city metropolitan region 1992 2007 2022 bu nbu bu nbu bu nbu bu 42 5 60 9 43 7 nbu 8 15 5 30 5 173 oa 0.81 0.87 0.95 table 5. overall accuracy of land cover maps in the semarang metropolitan region 1992 2007 2022 bu nbu bu nbu bu nbu bu 71 2 62 1 95 5 nbu 9 5 8 18 7 149 oa 0.87 0.90 0.95 in both study areas, there is a general increase in built-up areas and a decrease in non-built-up areas, as illustrated in table 6. in the tri-city region, built-up areas increased by 32.75 square kilometers or 34.45% during 1992-2007 and by 68.88 square kilometers or 53.89% during 2007-2022. conversely, in semarang, builtup areas expanded by 142.80 square kilometers or 58.60% in the 1992-2007 period and by 99.31 square kilometers or 25.70% in the 2007-2022 period. this indicates a faster rate of built-up development in the tricity region during the 2007-2022 period, while in the semarang metropolitan region, the acceleration was observed in the 1992-2007 period. however, in the semarang metropolitan region, there is an increase in the area of waterbodies due to the well-known issues of coastal flooding and land subsidence. table 6. changes in area for each land cover class in both study areas year tri-city semarang built up non-built up waterbodies built up non-built up waterbodies 1992 (km2) 95.06 1617.52 1303.09 243.69 2172.79 2342.57 2007 (km2) 127.81 1588.27 1298.84 386.49 2003.31 2369.15 2022 (km2) 196.69 1520.70 1297.30 485.80 1848.02 2425.29 1992-2007 (δ) 32.75 -29.25 -4.25 142.80 -169.48 26.58 2007-2022 (δ) 68.88 -67.57 -1.54 99.31 -155.29 56.14 1992-2007 (%) 34.45 -1.81 -0.33 58.60 -7.80 1.13 2007-2022 (%) 53.89 -4.25 -0.12 25.70 -7.75 2.37 3.2. discussion similar data fusion techniques were employed by goldblatt et al. (2018) who combined nighttime light data with a landsat 8 image collection. using pixel-based image analysis, they achieved accuracies ranging from 80.5% to 87.2% for a nationwide built-up classification. on the other hand, while liu et al. (2019) were able to produce built-up maps with an overall accuracy of at least 94.7%. their approach relied on viirs nighttime light data, which offers higher spatial resolution and has been available since 2014. this is in contrast to the dmspols nighttime light data, which was available from 1992 to 2014 and was used for generating the maps in 1992 and 2007 in our research. differing from the aforementioned studies, this research employed an object-based image analysis (obia) approach—a method designed to mitigate the salt-and-pepper effect when classifying high-resolution images. it involved the classification of fused images from landsat, nighttime light data, and blfei, resulting in maps with accuracies ranging from 0.81 to 0.95. however, it's important to note that the fusion of these three images did not achieve the expected high accuracy for all years. notably, it was successful only for the year 2022, despite our efforts to enhance accuracy by incorporating built-up masking from the 2022 map when classifying land covers in 2007 and built-up masking from the 2007 map when classifying land covers in 1992. nevertheless, the utilization of the obia method in google earth engine appeared promising, as it offers a simpler workflow https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 26 compared to implementing it in the orfeo toolbox (otb) of the qgis software, as demonstrated in a previous study (zaki et al., 2022). in addition to the accuracy assessment, both the tri-city and semarang metropolitan regions have experienced rapid urban expansion, signifying an increase in urban density in the city centers and a sprawling process in their suburban areas. this comparison indicates that the distance between urban centers in a metropolitan region influences the dynamics of urban development. on one hand, the tri-city metropolitan region comprises gdańsk and gdynia, situated only about 22 kilometers apart as two major urban centers in 1992, with sopot located in between, playing a significant role in driving urban growth. on the other hand, the semarang metropolitan region primarily relies on semarang as the sole main driver of urban development within its area. kendal to the west and demak to the east serve as neighboring cities, with both cities being approximately 30 kilometers away from semarang. however, it's worth noting that, in general, urban development in both metropolitan regions follows transportation networks that connect urban centers to surrounding cities. in the case of semarang, there is a phenomenon known as "desa kota" indicating a blurred distinction between suburban and urban areas (mcgee, 2022). this phenomenon has not been observed in the tri-city metropolitan region. it is worth noting that coastal urban areas are particularly susceptible to flood risks, especially with the increasing threat of climate change, exacerbated by land subsidence. a previous study analyzed land subsidence occurring in gdańsk and gdynia in 2018 and 2020 (rajaoalison & knez, 2021). however, based on our observations, the severity of land subsidence in these areas does not appear to be as pronounced as what has been observed on the coast of the semarang metropolitan region. in the semarang region, some residents' houses are already experiencing flooding, leading to forced migration or the necessity to raise the height of roads and homes over time (buchori et al., 2018; buchori et al., 2021). this trend is evident in the resulting maps from our study, showing that in the eastern part of the semarang metropolitan region's coast, water bodies have been expanding since 1992 to 2022. in contrast, such a phenomenon has not been observed along the coast of the tri-city metropolitan region. the lulc classes employed in this study are limited to three categories: built-up, non-built-up, and waterbodies. each yearly image resulted from the median value of landsat images taken between april and october. however, due to variations in the agricultural cycle across years, there are instances when extensive harvested agricultural lands are observed. the fluctuating vegetation patterns throughout the years prompted our focus on a limited range of land cover types, predominantly built-up and non-built-up areas. in future research, conducting a more detailed lulc classification involving categories like bare lands, forest lands, and agricultural lands would facilitate a more comprehensive analysis. secondly, green spaces (non-built-up areas) in 2022 were assumed to be the same in 2007 and 1992, implying that there were no afforestation activities within the two study areas. this assumption was made because of observed misclassifications of built-up areas in 2007 and 1992. therefore, they opted to mask out nonbuilt-up areas in 2022 when classifying land covers in 2007 and to mask out non-built-up areas in 2007 when classifying land covers in 1992. this technique is primarily applicable for classifying land cover changes in developing countries, where it is common for urban areas to continuously expand, and instances of building deconstruction being replaced by vegetation are relatively rare. thirdly, the blfei and ntl datasets were not normalized before they were merged with the cloudand water-masked landsat image collection in the methodology. consequently, the obtained results suggest that future scholars investigate whether normalization of these two variables could lead to higher accuracy in the final lulc maps. this suggestion is supported by some research demonstrating that normalization positively affected the classification performance using machine learning algorithms (raju et al., 2020; singh & singh, 2020). lastly, this research was limited to using the random forest algorithm for lulc classification. however, gaining a deeper understanding of the principles and concepts underlying each machine learning algorithm https://doi.org/10.14710/geoplanning.12.1.15-30 zaki and jaskuła / geoplanning: journal of geomatics and planning, vol 12, no 1, 2025, 15 – 30 doi: 10.14710/geoplanning.12.1.15-30 27 would be advantageous for selecting the most suitable algorithm or even testing each to determine the one with the highest accuracy. in the future, exploring the use of the segment anything model (sam), an artificial intelligence-based image segmentation system developed by meta ai, could be considered for analyzing urban area maps (giannakis et al., 2023; ren et al., 2023; wang et al., 2023; zhang et al., 2023). moreover, future research could perform the projection of future lulc maps; and incorporate 3d building footprint data as it reveals the vertical structure of buildings in urban areas, serving as an additional variable for data fusion. 4. conclusion this study attempted to enhance the accuracy of land use and land cover (lulc) mapping in the coastal areas of metropolitan regions in indonesia and poland over three decades (1992, 2007, and 2022). the approach involved data fusion between nighttime light and landsat image data, along with object-based image analysis (obia), conducted efficiently using the google earth engine cloud computing platform. despite efforts to improve accuracy through built-up masking in subsequent years, the accuracy assessment of lulc maps revealed varied overall accuracy patterns in both study areas. the methodology achieved an overall accuracy of 95% for the 2022 maps, while maps in 1992 and 2007 fell short (overall accuracy ranging from 0.81 to 0.90) in comparison. notably, the semarang metropolitan region exhibited an increase in waterbody areas, attributed to coastal flooding and land subsidence challenges, highlighting the complex dynamics between urbanization and environmental factors. the analysis uncovered a gradual expansion of built-up areas in both regions, indicating urban development stimulated by primary transportation networks to surrounding cities. in the tri-city metropolitan area, gdynia and gdańsk emerged as primary drivers, with sopot acting as a crucial connector, forming a cohesive metropolitan zone. the presence of a harbor in gdynia played a significant role in influencing urban expansion. conversely, in the semarang metropolitan region, semarang city took the lead, linking neighboring cities (kendal, demak, ungaran), with the harbor in semarang contributing significantly to surrounding urban development. generally, as a metropolitan region in a developing country, semarang, with its larger population, has experienced a larger area converted into built-up areas in the same period compared to the more developed tri-city in poland. in conclusion, the study demonstrated a unique approach using obia and data fusion, providing insights into urban development dynamics. while successful in 2022, the fusion of images faced challenges in other years. the expansion of water bodies in the semarang metropolitan region emphasizes the urgency of addressing climate-related risks in coastal urban planning and the need for adaptive strategies. 5. acknowledgments the authors would like to thank the editors and reviewers for their valuable comments, which have contributed to improving the quality of the manuscript. 6. references abdi, a. m. 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https://doi.org/10.26789/ijg.2020.01.004 https://doi.org/10.3390/land10111275 57 geoplanning journal of geomatics and planning vol. 11, no. 1, 2024 original research optimizing gistaru: evaluating a gis-based platform's contribution to indonesian spatial planning for smart city development rini rachmawati1,2*, rizki adriadi ghiffari1,2, ach. firyal wijdani3, maryam qonita3, novirene tania4, bitta pigawati5 1. spatial planning laboratory, department of development geography, faculty of geography, universitas gadjah mada, yogyakarta, indonesia, 55281; 2. smart city, village, and region research group, department of development geography, faculty of geography, universitas gadjah mada, yogyakarta, indonesia, 55281; 3. graduate program on regional development, faculty of geography, universitas gadjah mada, yogyakarta, indonesia, 55281; 4. undergraduate program on regional development, department of development geography faculty of geography, universitas gadjah mada, yogyakarta, indonesia, 55281; 5. department of urban and regional planning, faculty of engineering, universitas diponegoro, semarang, indonesia, 50275. doi: 10.14710/geoplanning.11.1.57-70 abstract gistaru (geographic information system for spatial planning) is a web-based and gis-based information system in indonesia that makes it easy for the public to access spatial information. in gistaru, there are an online spatial plan (rtr online) and an interactive detailed spatial plan (rdtr) application. this research aims to; identify and analyze the use of the online spatial plan (rtr online) website and interactive detailed spatial planning (rdtr interactive) application in gistaru and as well as identify the requirements for developing the website and application. in addition, the research outcomes are analyzed in terms of their contribution to the development of smart cities. data was collected through in-depth interviews and website searches related to gistaru, rtr online, and rdtr interactive at the national, provincial, and district or city levels. in-depth interview analysis is carried out through selected cases. the results showed that most regions in indonesia have an integrated spatial pattern with gistaru. however, there were inconsistencies in the rtr online and rdtr interactive data entry in gistaru. in the meantime, relatively few spatial structure data are integrated with rtr online. the interactive detailed spatial plan application is now operational in the majority of indonesian provinces. the only province in which all regions have implemented interactive rdtr is dki jakarta. this geographic information system for spatial planning is very useful for achieving the goal of smart cities, particularly related to public services in the smart governance dimension. copyright © 2024 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction spatial planning is a process that involves a diversity of actors and activities toward ensuring them all, and this process aims to create diverse recreational spaces that are efficient and equitable, respectful of privacy and individuality, and environmentally friendly (haughton et al., 2009). indonesian law number 26 of 2007 concerning spatial planning states that spatial planning, in general, has the meaning of a system of spatial planning processes, space utilization, and space utilization control (law of the republic of indonesia number 26 year 2007 on spatial planning). spatial planning is carried out to produce general spatial plans and detailed spatial plans. e-issn: 2355-6544 received: 24 january 2023; accepted: 05 march 2024; published: 08 march 2024. keywords: spatial planning; gistaru; web gis; detailed spatial plan, smart city *corresponding author(s) email: rinirachma@ugm.ac.id https://doi.org/10.14710/geoplanning.11.1.57-70 mailto:rinirachma@ugm.ac.id rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 58 spatial planning including its legal instruments is the basis of a policy that is needed to ensure the certainty of the effective use of resources to maintain, restore, and enhance biodiversity and ecosystems (albert et al., 2020). as they contain legally binding guidelines for both governments and residents, every aspect of land use that is influenced by either spatial planning or land administration needs to be recognized, recorded, and standardized (indrajit et al., 2020). the outcomes and effectiveness of participatory processes are typically described in spatial planning literature in terms of the improvement and tenacity of the governance system, the empowerment of the community, the operability of strategies, and action during the strategic plan's implementation phase (lingua & caruso, 2022; rahmawati et al., 2018). the implementation of spatial planning in indonesia is regulated in government regulation number 21 of 2021. the regulation states that the implementation of spatial planning is an activity that includes the regulation, guidance, implementation, and supervision of spatial planning (government regulation number 21 of 2021 concerning the implementation of spatial planning). the form of spatial planning development is through the development of spatial planning information and communication systems. the development of spatial planning information and communication systems is an effort to develop quality, up-to-date, efficient, and integrated spatial planning information, and communication systems. the development of spatial planning information and communication systems is carried out through the provision of databases and information on spatial planning by developing an electronic system network and disseminating spatial planning information to the public. gistaru (gis for spatial planning) is a form of spatial planning information and communication system presented by the ministry of agrarian affairs and spatial planning/national land agency (atr/bpn). gistaru was presented as part of the implementation of the new concept of system-based activity permits and the change of location permitting authority into space utilization approval. this is a mandate from the amendment of law number 26 of 2007 concerning spatial planning to law number 11 of 2020 concerning job creation (zulkarnain & priyanta, 2021). gistaru is intended to support local governments in planning, especially in terms of structuring the area. the application is expected to assist spatial planners to conduct a comprehensive area study and coordinate regional development plans to align with the development vision (arkdata, 2021). as a geographic information system (gis) based application, gistaru is directed to provide convenience for the public or investors to access spatial planning data, including detailed land use zoning maps (sutanta et al., 2021; utami et al., 2021; wiwaha et al., 2020). the use of web-gis in spatial planning through gistaru provides advantages in the form of an inexpensive process, centralized data can be linked to related ministries/institutions, and easy to access and integrate with other map sources (haryanti, 2018). the gis as a mapping tool assume geographic positions (oliveira et al., 2022). the preparation of the database is carried out by considering the regulation of atr/bpn no. 1 of 2018 concerning guidelines for the preparation of provincial, regency, and city spatial plan and atr/bpn regulation no. 16 of 2018 concerning guidelines for the preparation of detailed spatial plan (rdtr) and regency/city zoning regulations for development and guidelines and regulations on the preparation of rdtr in the regions ( regulation of the minister of agrarian affairs and spatial planning/head of the national land agency of the republic of indonesia number 16 of 2018 concerning guidelines for the preparation of detailed spatial plans and district/city zoning regulations). however, this regulation is no longer valid and has been changed to ministerial regulation of atr/bpn no. 11 of 2021 concerning procedures for compilation, review, revision, and issuance of substance approval for provincial, regency, city spatial planning and detailed spatial plans (regulation of the minister of agrarian affairs and spatial planning/head of the national land agency of the republic of indonesia number 11 of 2021 concerning procedures for the preparation, review, revision, and issuance of substance approval of provincial, regency spatial plans). gistaru consists of two applications, namely the online spatial plan (rtr online) and the interactive detailed spatial plan (rdtr interactive). both applications are new things that are important to study. spatial planning products contained in gistaru include detailed spatial plans, regional spatial plans, spatial plans, national strategic areas, island/islands spatial plans, and national spatial plans, all of which can https://doi.org/10.14710/geoplanning.11.1.57-70 rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 59 be accessed by the public via https:// /gistaru.atrbpn.go.id/. currently, out of a total of 70 detailed spatial plans that have been issued, 32 detailed spatial plans under gistaru have been integrated into the online single submission (oss) system and will continue to be developed (directorate general of spatial planning ministry atr/bpn, 2021). there are still many local governments that have not utilized gistaru to provide spatial data for each region (hary,anti, 2018). meanwhile, on the other hand, the digital transformation presented through gistaru is expected to facilitate the licensing process for the implementation of existing spatial planning. considering the important role of gistaru as a form of digital transformation involvement in spatial planning, research is needed to review the progress of gistaru that has been going so far. disclosure of spatial planning information is one of the challenges in the implementation of spatial planning in indonesia. the transparency of the process of various spatial planning policies, both the regional spatial plan (rtrw) and the detailed spatial plan (rdtr) is an important thing to realize the implementation of quality spatial planning both from the aspect of regulation as well as guidance and supervision. one form of improving spatial planning is through the development of spatial planning information and communication in the form of developing an electronic system. through simple online interfaces, the established technology foundation makes it easier to incorporate spatial datasets into the spatial data information. similar to that, it offers a user-friendly interface that enables users to browse, evaluate, and enjoy its content (vaitis et al., 2022). currently, the ministry of agrarian affairs and spatial planning/national land agency (atr/bpn) is developing gistaru as a gis web-based application to make it easier for the public to access spatial planning maps. however, web-based applications, including gistaru, generally seem to be fragmented, making it difficult for users to obtain the information and services they want (mustofa, 2020). in addition, many local governments have not utilized gistaru in providing spatial data which has an impact on confusion in the community (haryanti, 2018). on the other hand, user needs for the provision of land information service portals are increasing. thus, research is needed to find out more about the gistaru application, both in terms of utilization and development needs in the future. also, how the online spatial planning (rtr online) application and interactive spatial detail plan (rdtr interactive) in gistaru have been implemented and utilized. this research aims to; identify and analyze the use of online spatial plan (rtr online) website and interactive detailed spatial planning (rdtr interactive) application in gistaru and identify what is needed to develop the website and the application. while existing studies have often focused on the developmental aspects of specific applications, this research seeks to identify patterns, challenges, and future development needs that can be generalized for the advancement of digital spatial planning systems on global scale. the findings aim to provide useful insights that go beyond the local context and offer applicable knowledge for researchers, policymakers, and practitioners involved in the larger field of geospatial technology and land-use planning by addressing the gaps in development and utilization in gistaru. in addition to adding a layer of depth to the scientific discourse, the analysis of the difficulties faced by web-based applications such as gistaru and the changing demands of users makes our study relevant and beneficial to a broader readership outside the confines of product development. 2. data and methods the research was carried out through collection and analysis of secondary data obtained through the website. apart from that, in-depth interviews were also conducted with several relevant sources. the secondary data gleaned from websites related to gistaru, rtr online, and rdtr interaktif at the national, provincial, district, and city levels. the data also be quantitatively and qualitatively analyzed. in addition, a spatial analysis was conducted to illustrate the distribution of usage of the gistaru, rtr online, and rdtr interaktif applications. the quantitative analysis technique consists of a data analysis of the number of provinces and regencies/cities that have implemented rtr online and rdtr interaktif. the primary data gathered from indepth interviews with selected informants i.e. with big (geospatial information agency), particularly ptra (spatial mapping center and atlas), the department of human settlements, spatial planning, and land affairs of dki jakarta, and the department of land and spatial planning of the special region of yogyakarta. the https://doi.org/10.14710/geoplanning.11.1.57-70 rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 60 results of the in-depth interview be analyzed qualitatively. on the basis of the results of the spatial and quantitative analysis, it will be possible to qualitatively identify locations (provinces and districts/cities) where the application and utilization of online rtr and interactive rdtr are already optimal. 3. result and discussion 3.1. gis for spatial planning (gistaru), online spatial planning (rtr online) and interactive spatial detail plan (rdtr interactive) as a support for the industrial revolution 4.0, spatial planning plays a role in connecting and integrating planning between sectors. the emergence of geospatial technology spawned a one-map policy that incorporates the internet of things (iot), big data, cloud computing, machine learning, and artificial intelligence (ai). in the field of spatial planning, one of the derivative products of the one-map policy is the spatial geographic information system, or gistaru (pratikno et al., 2020). it is anticipated that this web application will support electronic integrated investment licensing or online single submission (oss). online single submission is an application for a business license submitted via an integrated electronic system (djasriain, 2022). gistaru is a web-based application that provides access to spatial planning-related geospatial data. gistaru includes national regional spatial plans, border area spatial plans, national strategic area spatial plans, provincial spatial plans, regency/city spatial plans, and detailed spatial plans. gistaru is operationally managed by the map studio, directorate general of spatial planning, ministry of agrarian and spatial planning/national land agency or called atr/bpn in indonesia (wahyuni et al., 2019). the ministry of agrarian and spatial planning/national land agency (atr/bpn) is the agency that creates, manages, and oversees the gistaru application. the geospatial information agency (badan informasi geospasial / big) as the agency in charge of maps in indonesia only has a role as a provider of base maps (results of in-depth interviews). there is no direct cooperation from the ministry of atr/bpn with big regarding the request for data/base maps as the source of gistaru data. data from big can be accessed openly, through the geospatial information agency’s portal, called inageoportal. however, it is not certain whether the base map data used in gistaru is a base map from big. it is not possible to confirm whether the base map that has been approved by big is the map that is used as the input data in the planning maps displayed in gistaru. it is possible that the base map used is the map of the relevant local government itself. following are the results of in-depth interviews with informants from the center for spatial mapping and atlas, geospatial information agency: “because big is still unable to provide all possible base maps, regions can apply to make their own base maps. big has 5 days to decide whether to grant permission or not. regions that apply can also ask for assistance from geospatial information agency for the required base map specifications” however, until now no one has been able to guarantee whether the map inputted by the local government into the rtr online or rdtr interactive database is the same base map as the one that has been validated by big. this is still something that is not certain regarding the source of this gistaru data. at the licensing service practice stage, gistaru through the interactive rdtr is used as the basis for approval of the suitability of space utilization for a type of activity plan. interactive in the context of utilizing web gis is considered the most comprehensive method to accommodate increasingly dynamic spatial challenges based on data entry that can be analyzed more deeply (lorek & horbiński, 2020). this system does not run alone, but is integrated with other licensing systems, such as simbg for building permit services, and amdalnet for environmental feasibility approval. however, gistaru through the interactive rdtr is the first screening related to licensing activities in a location, especially urban areas (soekemi, 2022). gistaru supports the spatial planning process, by facilitating access and understanding for the community in planning activities that utilize space (indrajit et al., 2021; rahmawati et al., 2018). gistaru is a spatial planning (rtr) application created to display layers of spatial data that have been inventoried by the ministry of agrarian and spatial planning. https://doi.org/10.14710/geoplanning.11.1.57-70 rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 61 the layer used comes from the network node of the ministry/institution and local government that is the guardian of the data. gistaru is divided into 4 sub-platforms, including (directorate general of spatial planning ministry atr/bpn, 2022): 1) rtr online to access general spatial plan data such as national, provincial, and city or district spatial plan; 2) rdtr interaktif to access detailed spatial planning data, and equipped with locationbased and activity-based search features; 3) sifataru: a web-based and webgis-based spatial information system which contains data and information on the development of the implementation of spatial utilization in the national territory, islands/islands, and national strategic areas (ksn) as well as the spatial plan synchronization program with development plans that are integrated with multilateral information systems. sifataru consists of synchronization module of spatial utilization program, monitoring and evaluation module of spatial planning implementation, and spatial utilization map; and 4) moduleprotaru: application of data and information management related to the progress of spatial planning activities and regional space utilization carried out by the provincial, district, and city governments. technical instructions on how to use the sitaru application are explained online, an example is the one implemented at the yogyakarta city land and spatial planning office (rachmawati, 2021). it is explained that some of the key steps in using the application are; first, open the play store on the android phone; second, type sitaru in the search field, then install; third, activate the gps, then open the sitaru application from the android phone. the user can directly select the desired location to find the zone and its status. however, not many people recognize this application and use it, for that social media can be used to expand its use (rachmawati, 2021). 3.2. utilization of rtr online (online spatial planning) and rdtr interactive (interactive detailed spatial plan) applications in the gis for spatial planning (gistaru) regarding the use of the rtr online application and rdtr interactive application, data collection is carried out through data searches on the internet. according to the results of a data search conducted on the gistaru.atrbpn.go.id page, regional spatial planning which is already integrated with gistaru's rtr online can be seen in table 1. the results of the data search (see table 1) show that most regions in indonesia have integrated spatial pattern data with gistaru, but only a small number of regions have not been fully integrated, such as north sumatra province, riau province, dki jakarta, southeast sulawesi province, west sulawesi province, papua province, and west papua province. meanwhile, not much spatial structure data is integrated with gistaru's rtr online. of the 34 provinces in indonesia, only eight provinces have 100% availability of spatial structure data, namely jambi province, banten province, west java province, east java province, special region of yogyakarta, west kalimantan province, province, south kalimantan, and east kalimantan province. there are 14 provinces in indonesia where the total availability of spatial structure data is still 0%. identification of the availability of detailed spatial plan data in gistaru's rtr online shows that east java province is the province with the most data availability in indonesia, namely 40 rdtr data. this number is very unequal compared to other provinces where most of the data availability is below 10 and some provinces also have no data available. rtr online, as shown in figure 1, can be used by business actors to find out information about space allocation in a location based on spatial plans that have become legal products such as government regulations, presidential regulations, provincial regulations, and regency/city regional regulations (kementerian investasi/bkpm, 2021). especially for business activities located in areas that do not yet have an oss-integrated detailed spatial plan. for areas or locations that still do not have a detailed spatial plan yet, the suitability of space utilization activities is carried out using the approval mechanism for the suitability of space utilization activities (pkkpr) issued through the oss system, either automatically or through preliminary assessment (ministry of investment/bkpm, 2021). https://doi.org/10.14710/geoplanning.11.1.57-70 rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 62 source: gistaru.atrbpn.go.id/rtronline/, 2022 figure 1. the rtr online web-gis application table 1. total availability of spatial pattern, spatial structure, and detailed spatial plan data on gistaru's rtr online in indonesia province total district/city rtr online spatial pattern % spatial structure % detailed spatial plan aceh 23 23 100% 21 91% 3 sumatera utara 33 29 88% 22 67% 4 sumatera barat 19 19 100% 17 89% 4 sumatera selatan 17 17 100% 14 82% 3 riau 12 7 58% 0 0% 4 kepulauan riau 7 7 100% 5 71% 3 jambi 11 11 100% 11 100% 1 bengkulu 10 10 100% 9 90% 0 bangka belitung 7 7 100% 6 86% 5 lampung 15 15 100% 14 93% 2 banten 8 8 100% 8 100% 3 jawa barat 27 27 100% 27 100% 12 jawa tengah 35 35 100% 32 91% 11 jawa timur 38 38 100% 38 100% 40 dki jakarta 6 0 0% 0 0% 0 yogyakarta 5 5 100% 5 100% 7 bali 9 9 100% 0 0% 5 nusa tenggara barat 10 10 100% 0 0% 6 nusa tenggara timur 22 22 100% 0 0% 8 kalimantan barat 14 14 100% 14 100% 7 kalimantan selatan 13 13 100% 13 100% 4 kalimantan tengah 14 14 100% 8 57% 2 kalimantan timur 10 10 100% 9 90% 4 kalimantan utara 5 5 100% 5 100% 1 gorontalo 6 6 100% 0 0% 1 sulawesi selatan 24 24 100% 0 0% 11 sulawesi tenggara 17 16 94% 0 0% 0 sulawesi tengah 13 13 100% 0 0% 5 sulawesi utara 15 15 100% 0 0% 0 sulawesi barat 7 6 86% 1 14% 1 maluku 11 11 100% 0 0% 1 maluku utara 10 10 100% 0 0% 1 papua 29 21 72% 0 0% 2 papua barat 12 9 75% 0 0% 3 source: gistaru.atrbpn.go.id/rtronline/, 2022 https://doi.org/10.14710/geoplanning.11.1.57-70 about:blank about:blank rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 63 the rdtr interactive web-gis application, as shown in figure 2, is specifically intended for business actors to find out the types of activities that are permitted at a location in an area that already has a detailed spatial plan. the ease of accessing the information on the allocation of a location helps businesspeople determine the location of their business. after confirming the location designation through the rdtr interactive, business actors can immediately apply for the suitability of spatial use activities using the automatic confirmation of the suitability of space utilization activities (kkkpr) issued through the oss system (ministry of investment/bkpm, 2021). interactive rdtr is currently being implemented in 27 provinces in indonesia. the number of cities/districts that have implemented interactive rdtr from each province is still diverse, with a total of 77 cities/districts in indonesia. the only province where all regions have implemented interactive rdtr is dki jakarta. unlike other provinces, dki jakarta has one rdtr data covering the entire region. source: (gistaru.atrbpn.go.id/rdtrinteraktif/ on 12 may 2022) figure 2. the rdtr interactive web-gis application the detailed spatial plan in the rdtr interactive for administrative areas in the form of a municipality has covered the entire area within the city, while for an area in the form of a district the detailed spatial plan only covers a certain area. for example, sleman regency has 2 detailed spatial plans (rdtr sleman barat rdtr sleman timur) in rdtr interactive and there are still many areas within sleman regency that are not included in the scope of the detailed spatial plan. meanwhile, the city of yogyakarta has one detailed spatial plan data covering the entire city area. the district with the most detailed spatial planning data on this interactive rdtr site is pasuruan regency, with 4 rdtr. according to the results of a data search conducted on the gistaru.atrbpn.go.id, a detailed spatial plan that is already integrated with gistaru's rdtr interactive can be seen in table 2. the difference in data between rtr online and rdtr interactive is something that is encountered in this data search process. even though it is a site in the same application (gistaru), there are quite a lot of differences in the data displayed. for example, for the malang municipality, in the rdtr interactive, there is only one detailed spatial plan data, namely the central malang rdtr, while there are six detailed spatial plan data of malang municipality can be found in the rtr online. based on the data search, it can also be seen that even though the title of the site is rdtr interactive, the number of rdtr (detailed spatial plan) in rdtr interactive is much less than the rdtr (detailed spatial plan) displayed on rtr online. this is an important input for the development of gistaru. the level of data integration is one of the keys to using web gis for multi-planning needs, especially development planning at the regional level (zhou et al., 2017). furthermore, the level of data integration in the use of web gis can be developed to the point where every user can access information with a mobile phone (kalinka et al., 2020). in the context of spatial planning, web gis can be integrated with reporting features to monitor frequent land use violations (sejati et al., 2020). https://doi.org/10.14710/geoplanning.11.1.57-70 about:blank rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 64 table 2. data on the implementation of rdtr interactive in indonesia province municipality/ total detailed spatial plan (rdtr) district aceh aceh tengah 1 rdtr takengan aceh barat 1 rdtr meulaboh bali badung 3 rdtr kuta, rdtr kuta utara, rdtr kuta selatan denpasar 1 rdtr wp utara dki jakarta jakarta pusat 1 rdtr dki jakarta jakarta utara jakarta barat jakarta selatan jakarta timur d.i. yogyakarta gunungkidul 1 rdtr siung-wediombo sleman 2 rdtr kawasan sleman timur, rdtr kawasan sleman barat kota yogyakarta 1 rdtr kota yogyakarta gorontalo gorontalo 1 rdtr kota gorontalo jambi sungai penuh 1 rdtr kota sungai penuh jawa barat kab. bandung 2 rdtr bojongsoang, rdtr tegalluar sumedang 3 rdtr perkotaan sumedang, rdtr ujungjaya, rdtr wp paseh subang 1 rdtr kotabaru patimban kota bandung 1 rdtr kota bandung kota cirebon 1 rdtr kota cirebon kota depok 1 rdtr kota depok jawa tengah banyumas 1 rdtr perkotaan purwokerto batang 1 rdtr tulis cilacap 1 rdtr perkotaan cilacap jepara 1 rdtr jepara purworejo 1 rdtr purworejo kutoarjo sragen 1 rdtr kawasan perkotaan sragen sukoharjo 1 rdtr perkotaan kartasura jawa timur lamongan 1 rdtr paciran pasuruan 4 rdtr grati, rdtr gembol, rdtr pandaan, rdtr wonorejo kota kediri 1 rdtr kota kediri kota malang 1 rdtr malang tengah kota pasuruan 1 rdtr kota pasuruan kalimantan barat ketapang 2 rdtr perkotaan ketapang, rdtr kuala tolak kualasatong kalimantan tengah kotawaringin timur 2 rdtr kawasan perkotaan mentawa bru ketapang, rdtr kpi bangendang gunung mas 1 rdtr gunung mas kalimantan timur kutai timur 2 rdtr bengalan kaliorang, rdtr kawasan perkotaan sangatta kepulauan bangka belitung bangka 2 rdtr perkotaan merawang, rdtr perkotaan sungailiat belitung timur 2 rdtr gantung, rdtr perkotaan manggar kepulauan riau bintan 2 rdtr tanjunguban, rdtr wp teluk lobam kuala sempang tanjung pinang 1 rdtr tanjung pinang lampung tanggamus 1 rdtr ginting maluku kota ambon 1 rdtr pusat kota mabon maluku utara halmahera selatan 2 rdtr bwp kawasan perkotaan wayauwa bibinoi, rdtr kawasan perkotaan labuha nusa tenggara barat bima 1 rdtr wp kecamatan monta lombok tengah 1 rdtr sekitar kek mandalika lombok utara 1 rdtr tanjung kota bima 2 rdtr bwp mpunda, rdtr wp rasanae barat nusa tenggara timur alor 1 rdtr perkotaan kalabahi ende 2 rdtr ende, rdtr ende kalimutu nagekeo 1 rdtr perkotaan mbay papua jayapura 1 rdtr bwp sentani papua barat fakfak 1 rdtr fakfak teluk wondama 1 rdtr rasiei riau pelalawan 1 rdtr langgam siak 2 rdtr sekitar ki tanjung buton, rdtr siak sri indrapura dumai 2 rdtr pekotaan dan industri kota dumai, rdtr medang kampai sulawesi selatan barru 1 rdtr kawasan emas barongkong luwu 1 rdtr perkotaan belopa luwu utara 1 rdtr perkotaan masumba maros 1 rdtr moncongloe pinrang 1 rdtr perkotaan pinrang soppeng 1 rdtr perkotaan watansoppeng sulawesi tengah poso 1 rdtr tentena parigi moutong 1 rdtr perkotaan parigi banggai laut 2 rdtr banggai kawasan ii, rdtr kawasan perkotaan banggai sumatera barat sijunjung 1 rdtr muaro sijunjung kota payakumbuh 1 rdtr kota payakumbuh sumatera selatan banyuasin 1 rdtr pangkalanbalai sumatera utara batubara 3 rdtr batubara, rdtr kuala tanjung, rdtr wp tanjung tiram dan talawi tapanuli selatan 1 rdtr batangtoru tapanuli utara 1 rdtr tarutung sipoholan siatasbarita kota medan 1 rdtr kota medan source: (gistaru.atrbpn.go.id accessed on 12 may 2022) https://doi.org/10.14710/geoplanning.11.1.57-70 about:blank rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 65 rdtr interactive only contains rdtr (detailed spatial plan) that has been integrated with the oss system, while on the rtr online menu, the rdtr (detailed spatial plan) displayed is all rdtr (detailed spatial plan) that has been determined by perda (regional regulations) or perkada (regional head regulations) (results of in-depth interview with informants). it can be seen in comparison from table 1 and table 2, in the d.i.yogyakarta region, of the six detailed spatial plans (rdtr) that can be seen in the rtr online, only four detailed spatial plans (rdtr) can be seen in the rdtr interactive. this is because of the six rdtr data that have been set, only four have been integrated with the oss system. rtr online and rdtr interactive are here to answer the government's commitment to integrating spatial planning into online spatial planning permits as mandated in law no. 11 of 2020 concerning job creation article 14 paragraph 2. however, in practice, a lot of spatial information in the regions is not available and cannot be displayed in gistaru. this is still an obstacle in the development of gistaru. the local government hopes that this interactive and online context can really be applied in the form of data synchronization and the availability of online integration services as conveyed by an informant from the department of human settlements, spatial planning, and land affairs of dki jakarta as follows: for the development of gistaru, it is necessary that the data can be synchronized and there is an online integration service, not after a regional regulation is passed then the map is being completed and submitted. our rdtr adheres to the indonesian standard classification of business fields (kbli), which is dynamic. so, in the future, gistaru should be more dynamic. 3.3. the need to develop rtr online (online spatial planning) and rdtr interactive (interactive detailed spatial plan) applications in the gis for spatial planning (gistaru) based on the data search results, in the gistaru, users can only search data and retrieve data information. no menu can facilitate users to be ability to interact in two directions. in addition, there is also no menu to download existing information, application users can only view the information. therefore, in the future, the application needs to be developed again so that it allows for interaction. the results of interviews with informants showed that the gistaru is quite capable of getting information about spatial planning. however, a menu still needs to be added to be able to connect directly to the oss application. the addition of the menu that connects directly to the oss application will be very useful for users, especially those who will carry out permits in space utilization. users can first check whether the space utilization plan to be carried out is in accordance with the spatial plan set by the government. with this menu, users do not need to open two applications simultaneously to perform permissions. in indonesia, the detailed spatial plan (rdtr) currently has included three-dimensional information such as basic building coefficient (kdb) and building floor coefficient (klb). however, rdtr interactive in gistaru can only display detailed spatial plan (rdtr) in two dimensions. if three-dimensional information can be displayed, users can easily access information in how the form of the polar money plan in the rdtr, including the maximum number of floors and the maximum building area in one plot. one of the uses of the gistaru is to support sectoral digital-based licensing services through the arrangement of approvals for the suitability of space utilization. however, this system has not been fully integrated with the building approval process through simbg and the environmental service approval process through amdalnet (amir et al., 2022; soekemi, 2022). full integration of the three systems is needed to support the efficiency of the space utilization permit process in online-single submission (oss). 3.4. gis for spatial planning (gistaru) and smart city development the existence of gistaru, rtr online, and rdtr interactive can provide benefits for users to obtain spatial information and spatial permits easily and through online systems. this indicates that public services https://doi.org/10.14710/geoplanning.11.1.57-70 rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 66 provided by the government online can improve government performance in the provision of good public services and support the achievement of smart cities, especially the dimensions of smart governance. smart government is one of the elements that must be fulfilled to realize a smart city. in general, smart government is a term that refers to the effective implementation of ict in public services and government (giffinger et al., 2007). that means the government in smart city program promotes digital technology toward better city management (rachmawati, 2019a). smart government harnesses the power of "data" to improve public services; enable an integrated service experience; to engage with citizens; to formulate policies; and to implement solutions for the welfare of the community. by providing easy access to services and information, smart governance is a way for the government to utilize new technology to better serve the community (allwinkle & cruickshank, 2011; rachmawati et al., 2022). dki jakarta has developed its own online rdtr system via the jakarta satu portal in order to accommodate the limitations of gistaru's existing functions (jakartasatu.jakarta.go.id). jakarta satu is an integrated monitoring system constructed using data from all regional government agencies in a single base map as a reference for dki jakarta provincial government decision-making. the utilization of rdtr online in jakarta satu is intended to expedite the licensing of businesses in dki jakarta. it is hoped that integrating the spatial data of the jakarta spatial planning area with the dki jakarta one stop service (ptsp) will make the permit control process more transparent to the general public. transparency is regarded as a crucial aspect of smart city development, particularly the smart governance dimension (johannessen & berntzen, 2018). through the jakarta one portal, dki jakarta is regarded as a model for developing smart cities. by developing smart rdtr 2022, more functions that cannot be displayed systemically in gistaru, such as information on independent city plans (irk), confirmation of suitability of space utilization activities (kkkpr), and building intensity calculation simulations, can be accommodated. thus, the function of providing public services to support enhancing the performance of government services can be realized, and smart governance has been implemented with regard to public services. currently, a number of regions have been unable to implement the online rtr and interactive rdtr in this gistaru. therefore, the relevant ministry, in this case the ministry of agrarian affairs and spatial planning/national land agency (atr/bpn), must conduct an evaluation of its implementation. however, each region must strive to maximize the delivery of public services. similarly with regard to the provision of these regulatory details. to support the success of every local government agency, including the development of electronic-based public services, each region must have collaborative and inventive leadership. to support the success of each local government agency, including the development of electronic public services, each region requires leadership that is collaborative and innovative (rachmawati et al., 2022). 3.5. discussion according to informant interviews, the rtr online in the gistaru can only display information in one direction and has not been integrated. on the rdtr interactive, however, the menu display is interactive and more useful. users of rdtr interactive are already conscious of an area's zoning regulations. in addition, it is possible to determine whether the land use plan that adheres to the indonesian standard field classification standard is in accordance with the spatial designation plan. however, if the zoning regulations are classified as conditional and limited, it still does not specify what requirements must be met and what the land use restriction is. a government employee can use an online spatial data visualization tool as a data communication tool. online spatial data visualization technology provides free and open access to data at the point of use and can provide better data information than other methods of data presentation, particularly for non-experts (armstrong-moore et al., 2021). unfortunately, the relevant agencies do not fully comprehend the existence of the rtr online and rdtr interactive applications in gistaru. not all regions have filled in data on rtr online and rdtr interactive, as evidenced by search results. due to the newness of the two applications, it is also necessary to improve their comprehension. local governments require technical assistance for its implementation. https://doi.org/10.14710/geoplanning.11.1.57-70 rachmawati et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 57-70 doi: 10.14710/geoplanning.11.1.57-70 67 numerous nations have implemented spatial planning reforms with substantial ramifications for their ability to promote integrated, adaptive, and collective planning decisions (nadin et al., 2021). building a planning support system that is integrated with building and transportation simulations to support the design of urban systems can be accomplished by reviewing the most recent trends from platforms associated with geographic information systems and spatial planning or modeling (yoshida et al., 2020). in the past, participatory gis has been emphasized as a change from traditional mapping and gis. participatory geo-information has evolved and can improve participatory spatial planning, including via participatory gis (mccall & dunn, 2012). it is also expected that gistaru will be an application that supports spatial modeling and participatory gis. the spatial modeling aspect has been noticed, but not the participatory content. this is due to the fact that this application continues to provide unidirectional information. there are not enough references related to rtr online and rdtr interactive in gistaru so comparisons with the results of previous studies are difficult to do. as a reference point for additional research, it is necessary to examine examples from other countries. in addition, references linking urban planning and smart cities are still extremely limited. meanwhile, synchronization between regional development plan products such as the long-term and medium-term development plans (rpjpd and rpjmd) was already carried out when preparing the smart city masterplan, and vice versa (rachmawati, 2019b). 4. conclusion in conclusion, our investigation highlights the discrepancies that currently exist in gistaru's rtr online and rdtr interactive data input. despite these obstacles, the majority of indonesian regions have effectively incorporated spatial pattern data into the gistaru framework, suggesting that the adoption of this framework is trending in the right direction. still, there isn't much connection between gistaru's rtr online component and spatial structure data. with the interactive rdtr currently widely used in all indonesian provinces, the application's regional acceptance has advanced significantly. surprisingly, dki jakarta is unique in that all areas have completely adopted the interactive rdtr capability. despite the drawbacks of gistaru's unidirectional information display in some applications, it is critical to acknowledge the system's critical role in furthering the goal of smart city development. the gis for spatial planning is a valuable tool for achieving smart city goals, especially when it comes to improving public service performance in the context of smart governance. with further work to be done on integrating data and application functionality, gistaru's influence on indonesia's smart city development trajectory might grow as continue to optimize the system. in the future, a good online spatial planning model needs to be implemented through activating online rtr and interactive rdtr in every municipality and regency, also for the provincial level. on the other hand, users of this application also need to get sufficient information to be able to access it. the results of this research reveal more practical and implementable matters. however, from a scientific perspective, it can be explained that spatial planning has so far been available in the form of blueprints or documents that are rigid and difficult to upgrade. currently, through advances in information and communication technology (ict), changes in spatial planning can be made in a more dynamic way. apart from that, it also makes it easier to access spatial planning products online. real things have changed a lot in the current era of ict development. future research needs to carry out case studies in one of the cities, districts or provinces in more detail to reveal the level of success in providing online rtr and interactive rdtr data, which has not been done in depth in this research. 5. acknowledgments the research was funded by the faculty of geography, 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(2017). construction of a spatial planning system at city-level: case study of “integration of multi-planning” in yulin city, china. habitat international, 65, 32–48. [crossref] zulkarnain, c. s. a., & priyanta, m. (2021). kewenangan pemerintah daerah dalam penataan ruang kawasan perdesaan: implikasi perubahan pasca undang-undang cipta kerja (the authority of the local government in rural area spatial planning: post-implications of indonesian law number 11 year 2020). bina hukum lingkungan, 5(3), 416– 431. https://doi.org/10.14710/geoplanning.11.1.57-70 https://doi.org/10.1016/j.habitatint.2017.04.015 | 131 geoplanning vol 5, no. 1, 2018, 131-146 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.1.131-146 why do spatial data and information have a significant role in spatial planning process? the investigation of spatial data and information usage in indonesian spatial planning policies a. yudono a,b a department of urban studies and planning, university of sheffield, united kingdom b department of urban and regional planning, brawijaya university, indonesia abstract: in spatial planning processes, the different aspects of human interactions involving political circumstances, social, economics, historical and cultural objectives can be understood through maps or spatial visualisations, because those media can illustrate abstract phenomena into visual images. spatial data has a role to play in spatial governance by providing thematic spatial information and analysis at all authority scales (masson-vincent, 2008). furthermore, spatial data and information are prerequisites for any participation in planning deliberation helping to create consensus (campbell & masser, 1995). spatial data and information currently have a role in communicating with all stakeholders (i.e. local authorities, private sectors and communities) whose interests are in development proposals in particular areas in order to decide implementation, priorities in local geographical areas (dühr, 2007). this paper investigates the role of spatial data and information in indonesian spatial planning process using archival research method. the empirical studies take a qualitative approach in analysing the results of data collection from fieldwork observation through collecting legal documents and internal institutional reports. synchronization and consistency between development plan and spatial plan must be ensured in every interrelated spatial policy, so that the various implementation efforts do not lead to conflict. furthermore, spatial data and information has a crucial role in translating the development strategies into the implementation of the development programme for the implementation of the government's agenda. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): yudono, a. (2018). why do spatial data and information have a significant role in spatial planning process? : the investigation of spatial data and information usage in indonesian spatial planning process. geoplanning: journal of geomatics and planning, 5(1), 131-146. doi: 10.14710/geoplanning.5.1.131-146 1. introduction physical space can be seen being where social systems interact, involving humans with social, economic and environmental aspects (hall, 2010). these interaction do not always take place in balanced ways that automatically and mutually benefit all parties, because of different capabilities, interests and the cumulative nature of survival in the geospatial world. hence, space needs to be organised so as to maintain ecological balance and provide support for human and other living organisms in producing and maintaining optimal living conditions (chadwick, 1971; mcloughlin, 1969; meadowcroft, 2002). spaces for human living as a dynamic circumstance, needs to be planned in ways that not only reflects the quality and coherence of a tier planning programs (national to sub-national planning levels), but also reflect the quality of spatial planning components. that is, the qualities of the space itself are determined by the realisation of the harmony and balance of the space utilisation in relation to economic, social and environmental carrying capacity factors (faludi, 2000). the spatial planning should be based on understanding of the potentials and limitations of the natural environment and the socio-economic development activities in particular areas, as well as the current demands and the preservation of the environment in the future (hall, 2010). thus, ideally, available built open access article info: received:07 august 2017 in revised form: 10 dec 2017 accepted: 28 april 2018 available online: 30 april 2018 keywords: spatial data, information, development plan, spatial planning corresponding author: adipandang yudono university of sheffield, sheffield, united kingdom email: ayudono1@sheffield.ac.uk https://doi.org/10.14710/geoplanning.5.1.131-146 mailto:ayudono1@sheffield.ac.uk yudono/ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 132 | space and environmental conservation need to be set out in an entire spatial planning system at all government levels. in spatial planning processes, the different aspects of human interactions involving political circumstances, social, economics, historical and cultural objectives can be understood through maps or spatial visualisations, because those media can illustrate abstract phenomena into visual images (dühr, 2007; stephenson, 2010). furthermore, the spatial visualization can assist in mediating planning debates (healey, 1997), setting planning agendas (forester, 1982) and incorporating various viewpoints of planning stakeholders (robbins & cullinan, 1996). spatial data has a role to play in spatial governance by providing thematic spatial information and analysis at all authority scales (masson-vincent, 2008). furthermore, spatial data and information are prerequisites for any participation in planning deliberation helping to create consensus (campbell & masser, 1995). spatial data and information currently have a role in communicating with all stakeholders (i.e. local authorities, private sectors and communities) whose interests are in development proposals in particular areas in order to decide implementation, priorities in local geographical areas (dühr, 2007). as an essential planning element, spatial visualisation and spatial information can help to achieve spatial planning consensus by shaping attention to relevant spatial issues, communicating strategic planning messages and stimulating planning actions at different government levels or within the private sectors or amongst communities (dühr, 2007). spatial visualization has a significant role in integrating different governmental viewpoints for achieving planning goals from national to sub-national levels. 2. data and methods this paper investigates the role of spatial data and information in indonesian spatial planning process using archival research method. the empirical studies take a qualitative approach in analyzing the results of data collection from fieldwork observation through collecting legal documents and internal institutional reports. archival research of legal documents had the purpose of providing a basic background of the policy context to gain a comprehensive understanding of the role of spatial data in spatial planning policy formulation that has been conducted in the past and likely to be conducted in the future. the primary documents used as the principal references for this study are as follows: 1. geospatial information act no.4/2011 2. governmental regulation no.9/2014 of detailed geospatial information act no.4/2011 implementation 3. governmental regulation no.8/2013 of spatial planning map guides 4. spatial planning act no.26/2007 5. governmental regulation no.15/2010 of spatial planning act practices 6. national development and planning system law no.25/2004 7. law no.17/2007 of indonesian long-term development plan 2005-2025 some of these legal documents have been collected by downloading from the official indonesian government websites. other relevant documents that cannot be directly accessed were obtained by writing a formal request letter to the relevant agencies. all documents were downloaded and/or scanned and then stored on a laptop, an external hard disk and cloud storage as a backup. 3. results and discussion 3.1. the indonesian spatial planning policies in general, the comprehensive planning policies in indonesia is divided into three parts: the development plan, the spatial plan, and the state budget allocation plan. in practice, the three systems are related and complement urban and regional development and planning. 1. the development plan the development plan is a translation of the values contained in the indonesian constitution (the constitution 1945), and ratified in law no. 25 of 2004 on national development planning system (sistem https://doi.org/10.14710/geoplanning.5.1.131-146 yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 133 perencanaan pembangunan nasional, sppn). sppn is a replacement of the outlines of state policy, garisgaris besar haluan negara (gbhn) as a result of the indonesian constitutional 1945 amendments. sppn is the implementation of indonesia's development direction for the long-term development period of 20 years, known as the national long-term plan (rpjpn). this development period is divided into five-year durations known as medium-term development plans (rpjmn). finally, the detailed rpjmn is the government’s annual implementation plan referred to as the government work plan (rkp) at the province, regency and municipality levels. 2. the spatial plan the spatial plan is a guideline for the optimal harmonious utilization of natural resources, as well as the basis for the country's development priorities in guiding the development of infrastructure and shaping the spatial structure and land use plan. the spatial structure plan relates to the public service networks connected by the infrastructure networks system between different governmental administrative areas (national strategic sites, provinces, regencies and municipalities). the land use plan is concerned with environmental protection and built environment areas. within this consideration, spatial data and information have a significant role in providing spatial structure and utilization visualization. the indonesian spatial plan is stipulated in the law. no. 26 in 2007 and categorized in a hierarchical system at national, province, regency and municipality levels. 3. the state budget allocation plan the implementation of the development plan translated into the spatial plan depends on the state budget allocation that is approved and distributed by the central, province and local government levels. the budget for the operationalization of development and planning is known as the national and sub-national (i.e. province, municipality and regency levels) state budget. the state budget allocation plan stipulated in law no.17 / 2003 of state budget at each government level. the relationship between the development plan, the spatial plan and the state budget allocation plan can be seen in figure 1. it shows that the use of spatial data and information is crucial for translating the "language of rpjpn and rpjmn" into the context of development that is tailored to a geographic region. spatial data and information contained in the spatial plan will identify priorities in determining the amount of the budget to be approved. from three indonesian development and planning aspects, very relevant to the focus of this research on spatial data and information usage concerns on the development plan and the spatial plan. thus, this section will focus on both plans. the next section discusses in more detail procedures for the operationalization of the development plan. 3.2. the indonesian development plans the indonesian development plan commenced a new phase in 2005, with the renewed indonesian developmental vision, marked by fundamental changes in the indonesian political and governmental system. during 1998-2004 periods, the indonesian government had commenced the government transition which transformed the indonesian government system from centralistic to decentralization system. in this period, many laws and regulations were enacted and the indonesian constitution 1945 was amended four times. one of the fundamental transformations during the governmental transitions was the enactment of the indonesian long-term development plan for 2005-2025. this plan is the reference for all components of indonesian society (government, communities, and businesses) in realizing ideals and national objectives in accordance with the vision, mission and agreed goals, so that all efforts of development actors are synergistic and coordinated. the objectives of the indonesian long-term development plan of 2005 – 2025 are to achieve an independent, fair, developed nation as a foundation for the next phase of development towards a just and prosperous society in the republic of indonesia under pancasila*) and the indonesian constitution 1945. the translation of the vision, mission and goals of long-term development plan are summarized in the following table 1. https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 yudono/ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 134 | level development plan spatial plan state budget allocation plan central government provincial government regency/municipality government figure 1. the relationship between the development plan, the spatial plan and the state budget allocation plan (source: yudono, 2016) national spatial planning (rtrw nasional) map scale 1:1,000,000 national budget plan (rapb nasional) 1. provincial long term development plan (rpjp provinsi) 2. provincial medium term development plan (rpjm provinsi) 3. provincial short term development plan (rkp proivinsi ) provincial spatial planning (rtrwp) map scale 1:250,000 provincial budget plan (rapb provinsi) 1. regency / municipality long term development plan (rpjp kabupaten/ kota) 2. regency / municipality medium term development plan (rpjm kabupaten/kota) 3. regency / municipality short term development plan (rkp kabupaten/kota ) regency/municipa lity spatial planning (rtrw kabupaten/kota ) map scale 1:50,000 for regency and map scale1:25,000 for municipality regency/munici pality budget plan (rapb kabupaten/ kota) 1. national long term development plan (rpjp nasional) 2. national medium term development plan (rpjm nasional) 3. government agenda plan/ national short term development plan (rkp nasional) https://doi.org/10.14710/geoplanning.5.1.131-146 yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 135 table 1. the vision, mission and goals of the indonesian long-term development plan 2005-2025 (source: the ministry of home affairs, 2017, translated to english by the author in 2016) vision indonesia as an independent, progressive, fair and prosperous nation mission 1. realizing a society that has good morality, ethics, culture, and is based on the philosophy of pancasila*) 2. realizing an energized nation; 3. creating a democratic society based on law; 4. realizing a secure, peaceful, and united indonesia; 5. achieving equitable development and justice; 6. realizing a beautiful and sustainable indonesia; 7. realizing indonesia as an independent island state, advanced, powerful, and based on national interests; 8. realizing indonesia plays a significant role in the international community. goal highlights 1. the realization of indonesian society that has good morality, ethics, culture, and civilized; 2. establishing a nation that is competitive to achieve a society that is more prosperous; 3.the realization of a democratic indonesia, based on law and justice; 4. the realization of security and peace for all people and the integrity preservation in the territory of the republic of indonesia and the sovereignty of the country from all threats, both from domestic and overseas; 5. the realization of the construction of a more equitable and fairer; 6. the realization of making indonesia beautiful and sustainable; 7. the realization of indonesia as an archipelagic nation independent, advanced, powerful, and based on national interests; 8. the realization of the increased role of indonesia in the international community. planning and development oversight in the period of 2005 – 2025 begins with the implementation of the direct election of the heads of government from central to sub-national levels, and continued with the preparation of the national development plan based on law no. 25 of 2004. in 2010, law no.17/2010 ratified the basic planning policy and the national long-term development plan (rjpn) with five-year medium-term development plan (rpjmn) scenarios. the idea of 20 year period of the national long-term development plan can be explained that the onset of urbanization in developing countries can lead to a doubling of cities population size over the following 15-20 years (clarke, 1992). this trend produces increased demands for meeting human needs such as residential, commercial and community services. since land is a key element of all urban development, spatial plans, which typically intend to control the built environment, designate land uses, capacities for development and urban area utilization, are extremely important to national government. the rpjpn 20052025 sets policy directions and the priorities to be pursued in the national mediumterm development plan (rpjmn), formulated for five-year periods between 2005-2025. the five-year period, like the 20-year plan, can be derived from clarke’s (1992) studied. he argues that the doubling of population numbers of a particular regions in the next 20 years is likely to lead to long-term social and environment instabilities as well as monetary problems at national, province and local scales. but planning and development programs should focus on short-to-medium term (5-10 year) policies and strategies to monitor urban and regional development. for indonesian case, a further reason for the five-year periods of the medium-term development plan (rpjm) is related to the period of office of the indonesian president to implement his/her agenda while the short term development period of one year is related to presidential cabinet work in realizing the president’s vision and mission during one period of office. *) pancasila is indonesian state philosophy which has meaning in every indonesian life aspect, society and the state should be based on the value of the divinity, humanity, unity, democracy and social justice. https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 yudono/ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 136 | 3.3. how can spatial data and information contribute to the indonesian development plan process? the indonesian ministry of national development and planning (badan perencanaan and pembangunan nasional, bappenas) report as the ministry that is responsible for preparing indonesian development plan, commissioned a study from the indonesia infrastructure initiative (indii) in 2010 on gis for infrastructure development. the study examines the potential for gis usage to support bappenas performance, including the preparation of rpjpn, rpjmn and rkp. this section discusses spatial data and information usage in indonesian development plans in the light of this report. as discussed in the previous section, the indonesian development plan is divided into three development plans according to the difference in the time period of development: namely, rpjpn which covers 20 years; rpjmn for periods of five years; and rkp for periods of one year. the study by indii of spatial data and information potential usage in the national rpjpn can be seen in figure 2 and for the national rpjm and rkp can be seen in figure 3. figure 2 shows that spatial data and information usage or can benefit from geographical information systems (gis) in translating the vision and mission of rpjpn 2005 2025 is relevant at the stages of prioritizing development programs and for appraisal of development outcomes. further evaluations of development activities that have the potential for spatial data and information usage are the rpjmn evaluation of prior periods for feedback and improvement in the next rpjm period, as well as the evaluation of public-private partnerships (pkps) programs. the potential for spatial data and information usage in the spatial development system can be implemented by examining the formulations of regional development priorities and development projects approval set in rpjpn 2005-2025. spatial analysis of the particular regional characteristics can be identified as socio-economic development issues as the basis for deciding priority development programs in the selected regions. once indonesian ministry of national development and planning has completed formulating the rpjmn, special ministries set up their program proposals. after each ministry finished developing their program proposals, the program proposals are delivered to the particular divisions under bappenas for audit and assessment. the division assesses every system proposition against various criteria: rpjmn program preferences and particular aims, while the deputy of funding and the minister of finance audit the source and strategies to allocate in the government work plan (rkp). furthermore, the draft of usage plan and financing for rkp are submitted to the house of representatives (dpr) for report. in this case, spatial analysis potentially will be used in auditing and assessing program proposals to implement in rpjmn and rkp agenda (see figure 3). overall, spatial data and information usage in development plan documents are not stated explicitly, but examination of the indii indicates that there is potential for spatial data and information to be used in the audit, assessment and evaluation activities which consider to the development plan goals. in the comprehensive indonesian development and planning context, development policy language needs to be translated into spatial planning policy language requiring spatial information visualization for the implementation of the government’s agenda. further spatial data and information usage in indonesian spatial plans will be discussed in the next section. 3.4. the indonesian spatial planning system the previous section has already mentioned that in terms of the comprehensive indonesian spatial policy, the manifestation of the development plan is the spatial plan, rencana tata ruang wilayah (rtrw). it becomes the guidelines for all government levels to manage natural resources optimally and sustainability with attention to disaster risk, and as well is the basis for the development of national welfare. in terms of the spatial planning practices in indonesia (including the regulation, development, implementation, and monitoring), the government has enacted law no.26 of 2007. the law regulates the spatial planning system at the national, province and municipality also regency levels (see figure 4). the spatial plan makes both general and detailed plans of particular areas. a general spatial plan consisting of spatial structure plan and a land use plan, which is formulated based on administrative areas. the spatial structure plan guides the public service networks that are connected by the infrastructure networks system between different governmental administrative areas (national strategic sites, provinces, https://doi.org/10.14710/geoplanning.5.1.131-146 yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 137 municipalities and regencies); and the land use plan is defined as a land use planning concerned with environment protection and built environment areas. the detailed spatial plan for a particular area is based on the strategic value of the local approaches and activities with zoning schemes. the preparation of a detailed plan is intended as a spatial plan management tool, and as a basis for setting zoning regulations. the zoning regulations set the terms and conditions for the control of land utilization for each block/zone designated in the detailed spatial master plans. spatial structure and land use plan visualization are specified government regulation no. 8 of 2013 (pp 8/2013). pp no. 8 / 2013 concerns methods for creating the spatial planning maps in relation to the level of map accuracy, including: 1. geometric accuracy geospatial reference system, scale and mapping unit. 2. details of the spatial planning element maps and symbols. the relationship between the spatial plan maps and the elements of the spatial planning system the national spatial plan (rtrw nasional); the provincial spatial plan (rtrw provinsi); and the municipality / regency spatial plan, (rtrw kota or rtrw kabupaten) – will be discussed in the next section. national longterm development plan (rpjpn) 2005-2025 bappenas studies and define programmes providing guidelines to sectoral ministries for strategic planning (renstra) formulating programme priorities list sectoral ministries submit project proposals previous programmes 2004-09 other proposals evaluating results of 2004-2009 prioritise & approve projects pkps evaluate government funding? nongovernment funding? prepare tender for project finance department for approval can benefit from gis no yes yes no figure 2. potential spatial data and information usage fit in the national long-term development plan (rpjpn) (source: indii, 2010) https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 yudono/ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 138 | project proposals from ministries review & evaluation workshop with stakeholders & technical experts national medium-term (rpjmn) programme priorities objectives: economic growth poverty reduction fiscal outlook for 5 years potential for private funding work packages with recommendations review of project finance by deputy for funding & finance minister draft rkp deputies reviews work packages review by house of representative (dpr) annual government programmes (rkp) figure 3. potential spatial data and information usage fit in the national medium-term development plan (rpjpmn) and annual government work plan (rkp) (source: indii, 2010) 1. the national spatial plan the indonesian national spatial plan stipulated under government regulation no. 26 of 2008 (later will be abbreviated into goi, 2008) as a reference for government agencies at all levels to determine the location and spatial utilization of the government’s agenda and programs. the purpose of national spatial planning reflects the integration of development sectors, regions, and between stakeholders. policy and national spatial planning strategies are formulated by considering science and technology as ways of making plan, availability of data and information, as well as finance for development. the national spatial plan is formulated for a period of 20 years illustrating the spatial dimension of the long-term development plan visions. the national spatial plan has functions in supervising spatial plans at the provincial and municipality / regency levels to guarantee adherence to laws and consistency amongst systems of planning and advancing congruity of arrangements and activities amongst areas. it also takes the lead in giving providing basic information and data on the conditions of recent spatial development. https://doi.org/10.14710/geoplanning.5.1.131-146 yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 139 the contents of the national spatial plan consist of the spatial structure plan, the land use plan, the establishment of national strategic sites and the national governmental program indicators. in terms of translating the visualization of the national spatial plan into the map, there are two elements which are regulated by pp no.8 / 2013: the national structure plan and the national land use plan. the fundamental aspects of the national spatial plan maps use 1: 1,000,000 map scale. (see figure 5). therefore, a study of spatial data and information usage in the national spatial plan needs to focus on both elements. the national spatial structure plan looks at national urban systems associated with the rural population in a service area and the main infrastructure network systems which have national socio-economic impact. the national urban system consists of urban areas with a covering national and local scale activity centers. the activity center is supported and equipped with a regional infrastructure network, with levels of service tailored to the hierarchy of activities and service needs. the major infrastructure networks are a primary system developed to unify the territory of the republic of indonesia and in addition, to serve national scale activities, including transportation, electrical and energy, telecommunications and water resources network systems. meanwhile, the national land use plan describes the land use plan, either for national strategic built environment utilization or protected areas. the definition of a national protected area is an area in which development is either not permitted or is restricted. it is a space which functions mainly for protecting the health of the environment including natural resources and artificial resources, cultural heritage and history, as well as to reduce the impact of natural disasters. built environment areas have a national strategic value developed to support the functions of national defense and security, regional strategic industry, urban and metropolitan areas, and agricultural regions according to the legislation of licensing and management of a government authority (see figure 6). national spatial plan island-specific spatial plan national strategic regional spatial plan provincial spatial plan provincial strategic regional spatial plan regency/municipal spatial plan regency/municipal general spatial plan regency/municipal detailed spatial plan urban area spatial plan an urban area within a regency or urban area stradding multiple provinces, regencies, or municipalities metropolitan spatial plan one or more contiguous urban areas (population of 1 million or more) that are particularity important from a policy perspective rural area spatial plan a spatial plan for an agriculture area defined as a sub-district or multiple villages within one regency or stradding multiple provinces, regencies, or municipalities agropolitan spatial plan one or more regencies that are particularly important from a policy perspective central government provincial government regency/ municipal government urban areas rural areas source: (ministry of land, infrastructure, transport and tourism, 2017) figure 4. the hierarchical spatial planning system in indonesia https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 | 140 figure 5. the indonesian national structure plan map (source: big, 2008, with permission to re-publish from big in 2016) yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 141 figure 6. the indonesian national land use plan map (source: big, 2008, with permission to re-publish from big in 2016) https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 | 142 2. the provincial spatial plan the provincial spatial plan is the reference for province and local government agencies (i.e. municipality/ regency levels) for determining land use and strategic locations. the provincial spatial plan enshrines integrated alignment and balanced development amongst municipalities / regencies’ regions, as well as to synchronize different developmental sectors. the duration of the implementation of provincial spatial plans aligns with the 20 years at the national level. the provincial spatial plan has similar content elements as the national level: the spatial structure plan, land use plan, the establishment of a strategic sites plan, and land use direction and controls. for the purpose of this research, analysis of the contents of spatial plans focuses on the spatial structure and land use plans either at province or regency / municipality levels. the provincial structure plan is the embodiment of the urban system within the province and the infrastructure network of the province being developed to integrate entire areas at a province level. spatial structure takes the form of a regional hierarchy starting with the primary activity centers characterized as urban activities, and moving to tertiary activity centers characterized as areas developed predominantly by a particular sector, for example agriculture. the linkages between activity centers in the province are made by the network systems of transportation, energy and electricity, telecommunications, and water resources (including the entire upstream dam / watersheds reservoir areas). the provincial land use plan is a picture of the provincial land use system, either having functions for protecting designated areas or built environment utilities. provincial protected areas are ecologically protected areas in which the ecosystem covers more than one regency / municipality and the management is the authority of the provincial government. built environment areas are defined as residential, commercial, mining exploration, industrial estates and tourist resorts areas, having a strategic value for the provincial economy. in terms of translating the visualization of the provincial spatial plan into the map, like the national level, there are two elements regulated by pp no.8 / 2013: the provincial structure plan and the provincial land use plans. the basic provincial spatial maps are at a scale of 1: 250,000. for provinces with coastal and marine areas, the spatial plan map must be equipped with bathymetry data. for areas bordering other provinces, the spatial plan maps are prepared after the province government coordinates with the adjacent provincial government. information on the provincial spatial plan maps shows the borders of two or more provinces with a five-kilometer buffer along the borderlines as a neutral area. coordination between adjacent provincial governments is a crucial point for spatial plan integration. relevant to the research topic, spatial data between adjacent provinces is sensitive to potential conflicts, for instance, in land disputes. thus, open data with specifically spatial data development and sharing is important as a geographical visual communication to achieve spatial plan integration and consensus. 3. the municipality and regency spatial plans the municipality and regency spatial plan act as the guideline for local governments (municipality and regency, also district (kecamatan) levels) to set the development locations, as well as for local government planning programs. in terms of translating the visualization of the municipality and regency spatial plan into the map, like the national and provincial levels, there are two elements regulated by pp no.8 / 2013: the municipality and regency structure plan and the land use plan. the maps are at a scale of 1: 25,000 for municipality and 1:50,000 for regencies. furthermore, for municipalities and regencies with coastal and marine areas, the spatial plan maps of the regency and municipality must be equipped with bathymetry data. for regency/municipality areas bordering other regency/municipality areas, spatial plan maps are prepared after the regency/municipality coordinates with the adjacent regency/municipality. information on the municipality / regency spatial plan maps shows the borders other municipalities / regencies, with a 2,5 kilometers buffer along the border lines as a neutral area. in the provincial, municipal and regency spatial plan ratification process, spatial data and information need to be included as a matter of technical spatial plan maps completeness before spatial plan documents can become regulations and need to have been checked by the indonesian national mapping agency (badan informasi geospasial, big) before plans are ratified. big supervision procedure of spatial plan maps will be discussed in the next section. yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 143 3.5. big oversight of the formulation of spatial planning policy maps when law no. 26 of 2007 on spatial planning was enacted, all levels of government were required to make spatial plans, including spatial planning maps, for a period of 20 years. the regulation of spatial plan maps is regulated by article 14 sections 5b which states that "general spatial plan comprise of planning areas which wide-scale on the spatial plan map needs details of spatial plan policy formulation prior to implementation." article 14, section 7 "further provisions on the level of accuracy of the spatial planning map is set by government regulation." the mechanisms for developing spatial maps are set out in policies from derived spatial planning law, namely government regulation (pp) no. 8 of 2013 concerning the accuracy of the spatial plan maps. this pp covers technical preparation of spatial maps ranging from the required thematic maps in spatial plans, reference system, map scale, mapping units and symbols. it also covers associated procedures for the supervision of the preparation of spatial plan maps in order to acquire technical recommendations from big under regulation of the head of big no. 6 of 2014. the supervision by big of spatial plan map production aims to ensure technical accuracy and valid spatial data and information as a reference for spatial plan implementation. the inspection of spatial plan maps covers six aspects based on big, 2014 (translated by the author in 2016): 1. geometric position of base map from big; 2. completion and updated basic spatial data assessment for the base map defined by big; 3. completeness of thematic maps with in accordance with the ministry of public works regulation no. 20 / prt / m / 2007; 4. the consistency of spatial plan maps with spatial plan documents that include spatial structure, land use and special areas / strategic plans adjusted with base maps and thematic maps; 5. the consistency of spatial plan maps with provincial/municipal/regency legislation/regulation according to the spatial structure, land use and special areas/strategic plans that meet with the existing regulations; 6. cartographic presentation with the assessment of symbols, colors, and notation in agreement with government regulation no. 8 of 2013. generally, the supervision procedure of spatial plan maps provides clear guidelines, but obstacles are encountered is the consultation process carried out directly with the mapping agencies nationwide. the supervision method through direct face-to-face contact between applicants and official big staff has been a major obstacle to map development process spatial plans until today, because of it takes high cost and longtime spatial plan process. there is a need for alternative ways of consulting on the production of the spatial maps, for instance, by electronic supervision (e-supervision) method. 3.6. synchronizing the indonesian development plan with spatial plan spatial data or information usage has a crucial role in spatial planning processes to translate the vision, mission and strategy of the long-term development plan (rpjp) and medium-term development plan (rpjm) into the spatial plan (rtrw). for instance, in rpjp and rpjm, the role of spatial data and information is to describe the general conditions of a region, where the rtrw is translated in the terminology of the region's profile (see figure 7). aspects of the rpjp and rpjm, analysis of strategic issues, policy direction and development strategy is translated into the rtrw with the inclusion of spatial information in the discussion of strategic issues, spatial structure and land use plans. especially for rpjm, which requires more detailed information for the five-year period of rpjp, an indication of the priority program plans and funding needs to be translated in the land use plan directives enshrined in rtrw (main five-year indication programs). the detailed relationship between indonesian development and spatial plan can be seen in the following figure 8. in summary, synchronization and consistency becomes imperative in every interrelated spatial policy, so that the various implementation efforts do not lead to conflict. in addition, spatial data and information has a crucial role in translating development strategies into the implementation of development programs in the spatial planning context. the development plan and spatial plan policy decided by the government has an impact on civil society, especially since the 1992 un declaration on sustainable development https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 yudono/ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 144 | emphasized creating a good governance agenda in which implementation should involve communities in urban and regional planning. long-term development plan introduction general condition strategic issues analysis vision and mission policy direction development steps and priority spatial plan introduction law and regulation references to formulate spatial plan geographic condition strategic issues and maps spatial plan purpose spatial plan policy and strategic spatial structural plan land use plan regional strategic determinations spatial utility plan direction (5 years main indication programmes) spatial control development direction medium-term development plan introduction general condition financial management description and financial framework strategic issues analysis vision, mission, aim and target policy direction and strategic general policy and local development programmes priority indication programme plan + budget demands figure 7. relations between long-term development plan – spatial plan – medium term development plan (source: rizal, 2008, translated to english by the author in 2016) https://doi.org/10.14710/geoplanning.5.1.131-146 yudono / geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 | 145 national spatial plan strategic plan of ministry and central government agencies national spatial plan national medium-term development plan national short-term development plan provincial spatial plan provincial medium-term development plan provincial short-term development plan strategic plan of ad-hoc team that comprises of local government institutions to serve spatial planning regency/ municipal spatial plan regency/ municipal medium-term development plan regency/ municipal short-term development plan strategic plan of ad-hoc team that comprises of local government institutions to serve spatial planning work plan of local government institutions to serve spatial planning provincial spatial plan municipality spatial plan island-specific spatial plan national strategic regional spatial plan provincial strategic regional spatial plan municipality detailed spatial plan municipality strategic regional spatial plan regency spatial plan regency detailed spatial plan regency strategic regional spatial plan work plan of local government institutions to serve spatial planning work plan of ministry and central government agencies s p a ti a l p la n 9. mayor/ head of regency 10. regency/ municipal bappeda 11. regency/ municipal bkprd 12. regency/ municipal skpd 5. governor 6. provincial development planning agency 7. provincial bkprd 8. provincial skpd 1. president and ministry 2. state ministry of national development planning 3. national coordination of spasial planning 4. ministry national provincial regency/ municipality transcribe transcribe transcribe reference reference reference reference reference reference reference reference guidance guidance guidance guidance guidance guidance guidance guidance guidance reference development plan according to law no.24/2005 and law. 32/2004 spatial plan according to law no.26/2007 and ministry of public work regulation no.15,16 and 17/prt/m/2009 regency spatial plan a.1 regency spatial plan a.2 provincial spatial plan a.1 provincial spatial plan a.2 figure 8. the detailed relationship between the indonesian development plan and the spatial plan at all governmental levels (source: rizal, 2008, translated to english by the author in 2016) 4. conclusion the significant laws relating to the indonesian planning and development system is law no. 25/2004 on the national development planning system, sistem perencanaan pembangunan nasional (sppn), as a replacement of the outlines of state policy, garis-garis besar haluan negara (gbhn) as a result of the indonesian constitutional 1945 amendments. the success of sppn is supported by the state budget allocation plan stipulated in law no.17 / 2003 of state budget at each government level. the details of sppn development programs are embodied in the development of the long-term development plan, rencana program jangka panjang (rpjp), with the strategy stages undertaken in the five-year medium term development plan, rencana program jangka menengah (rpjm) form and the details of every annual stage of strategy in the government work plan, rencana kerja pemerintah (rkp). the work plan for the direction of development of sppn is a-spatial, and then implemented in spatial form in spatial planning documents, rencana tata ruang wilayah (rtrw) in accordance with law. no. 26/2007. spatial information visualization in development plan documents are not stated explicitly, but studies by indii indicate that there is potential for spatial data and information to be used in the audit, assessment and evaluation of development plan goals. the spatial plan is formulated as general and detailed plans of particular areas. a general spatial plan is based on the governmental administrative area with the planning contents essences coming from the spatial structure plan and land use plan. government regulation no. 8 of 2013 (pp 8/2013) sets out the methods for creating spatial planning maps and stipulates the level of map accuracy according to levels of government and purposes of the maps as set out in the spatial planning law. synchronization and consistency between development plan and spatial plan must be ensured in every interrelated spatial policy, so that the various implementation efforts do not lead to conflict. furthermore, spatial data and information has a crucial role in translating the development strategies into the implementation of the development program for the implementation of the government's agenda. https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.14710/geoplanning.5.1.131-146 yudono/ geoplanning: journal of geomatics and planning, vol 5, no 1, 2018, 131-146 doi: 10.14710/geoplanning.5.1.131-146 146 | 5. acknowledgments this paper is part of the author’s phd research. within this consideration, the author would like to thanks to the indonesian endowment fund for education (lpdp scholarship) to support this financial research, also thank you to my supervisor, dr. alasdair rae, with his patient, always support and motivate me to do this research. 6. references (new) badan informasi geospasial (big), (2014), the head of big regulation no. 6 of 2014, tata cara konsultasi penyusunan peta rencana tata ruang (procedure for spatial plan formulation supervision). campbell, h., & masser, i. 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(2008). mensinkronkan perencanaan pembangunan dan perencanaan keruangan di indonesia pola hubungan sistem perencanaan pembangunan nasional (uu no. 25/2004) dengan sistem penataan ruang (uu no. 26/2007) (synchronizing development system and spatial planning system. working paper prepared in ministry of national planning and development, jakarta, indonesia. robbins, e., & cullinan, e. (1996). architecture why architects draw, edward robbins. 1994. the mit press, cambridge, ma. 300 pages. isbn: 0-262-18157-6.39.95. bulletin of science, technology & society, 16(1–2), 63–64. [crossref] stephenson, j. (2010). people and place. planning theory & practice, 11(1), 9–21. [crossref] vincent, m.m, (2008), governance and geography explaining the importance of regional planning to citizens, stakeholders in their living space, boletin de la a.g.e.n, no.46, pp. 77-95. yudono, a, (2006), teknologi, informasi dan perencanaan di indonesia: quo vadis one map policy? (technology, information and planning in indonesia: quo vadis one map policy?), lsc insight-the contemporary policy issues in indonesia, vol.1, no.10. https://doi.org/10.14710/geoplanning.5.1.131-146 https://doi.org/10.1016/0197-3975(92)90045-z https://doi.org/10.4324/9780203965818 https://doi.org/10.1080/713691907 https://doi.org/10.1080/01944368208976167 https://doi.org/10.4324/9780203861424 https://doi.org/10.1007/978-1-349-25538-2 https://doi.org/10.1177/027046769601600117 https://doi.org/10.1080/14649350903549878 123 geoplanning journal of geomatics and planning geoplanning: journal of geomatics and planning, vol. 12, no. 1, 2025, 123 138 original research the ecological impact of urban expansion in oasis environments: a combined cartographic and landscape analysis assoule dechaicha1*, walid arab2, djamel alkama3 1. department of architecture, institute of urban techniques management, university of m’sila, algeria. 2. department of city management, institute of urban techniques management, university of m’sila, algeria 3. department of architecture, faculty of sciences and technology, university of guelma, algeria doi: 10.14710/geoplanning.12.1.123-138 abstract current patterns of urban expansion are mostly considered as impulsive, leading to numerous negative consequences for our biosphere. this disturbing fact has not gone unnoticed in the sprawling oasis cities of the algerian sahara, where palm groves are the primary victim. furthermore, any approach taken to preserve oasis ecosystems requires considering ecological variables. in this perspective, the present study seeks to highlight the spatial growth process of the laghouat oasis in southern algeria, during the period 1986 2019. the methodology employed is based on the supervised classification of four landsat satellite images to initiate a cartographic analysis and a landscape quantification of the land use evolution. the analysis is focused on two luc classes: built-up areas and palm groves. the results of the cartographic analysis highlighted two completely opposite spatiotemporal trends: a significant growth of built-up areas, against a considerable decline in palm groves. the monitoring of the landscape metrics also revealed two different behaviours: growth by densification and elongation of the urban fabric, versus progressive morcellation and fragmentation of the palm groves. these findings pointed to the negative impact of uncontrolled extensions on both the palm groves and the overall oasis ecosystem. this research highlighted the importance of landscape metrics in assessing various forms of spatial urban growth and may support urban planners in choosing the optimal solutions for oasis sustainability. copyright © 2025 by author, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction the unbridled growth of urban centres is currently one of the major facts characterising human development (un-habitat, 2020). the environmental repercussions have been proven to be negative, continuing to adversely affect both local and global ecosystems. the irresponsible expansion of cities significantly contributes to the aggravation of environmental issues that increasingly threaten the equilibrium of our biosphere: depletion of natural resources, greenhouse gases emissions, climate change, and biodiversity loss (un environment, 2019). the biodiversity is considered to be the first casualty of the uncontrolled urban growth, a phenomenon that is particularly pronounced in developing countries (mcdonald et al., 2013). the oases of the algerian sahara are not exempt from this situation. urban settlements are continuously encroaching upon these sensitive areas, harming the palm groves that constitute the essential biotope of the oasis ecosystem (kouzmine, e-issn: 2355-6544 received: 25 september 2024; revised: 08 may 2025; accepted: 16 may 2025; available online: 16 may 2025; published: 28 may 2025. keywords: spatial growth, palm grove, oasis ecosystem, satellite image, landscape metrics *corresponding author(s) email: dechaicha@univ-msila.dz https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 124 2012). the ecological homogeneity that has distinguished these human settlements since their genesis is currently threatened by the excessive expansion of built-up areas (côte, 2012; jouve & jouve, 2012). eco-responsible management of oasis resources essentially requires an understanding of the oasis urbanisation phenomenon and the transformations it generates (jia et al., 2004; c. liu et al., 2021; y. liu et al., 2019). nevertheless, the scale of these kinds of transformations is not appropriately addressed in urban planning documents. these documents have primarily been developed to quantitatively respond to the demands imposed by urbanisation. as a result, many researchers and practitioners have raised concerns about their effectiveness, particularly in terms of controlling the impacts on local environmental potential (gherbi, 2015; boumedine, 2013). the application of urban landscape ecology methodologies enables an enhanced comprehension of the environmental challenges currently facing vulnerable regions, including oasis areas (jia et al., 2004; y. liu et al., 2019). urban landscape ecology is a discipline that emerged from the integration of landscape ecology and urban ecology. this association allows for the study of the structures and forms of urban landscapes while also facilitating an understanding of the conditions necessary for the flourishing of biodiversity and for the equilibrium of ecosystems (norton et al., 2016; wu, 2013). therefore, this discipline provides a conceptual and methodological framework for spatial analysis and evaluation of landscape transformations that impact ecological processes and ecosystem stability, particularly those related to ecological continuity and connectivity (breuste et al., 2008; mörtberg et al., 2007; wu, 2013). uncontrolled urbanisation is widely recognised as one of the primary exogenous factors disrupting the continuity of ecological processes, by the colonisation of natural and semi-natural areas, and the fragmentation of ecological entities and corridors that host and ensure the sustainability of biodiversity (haddad et al., 2015; theodorou, 2022). to understand the impacts of uncontrolled urbanisation on oasis ecosystems, the present study consists of an analysis of the spatial evolution of laghouat oasis in southern algeria, during the period 1986 2019. the starting hypothesis posits that an ecological approach can lead to the estimation of landscape transformations associated with the city's spatial growth. the methodology requires the creation of diachronic cartography to track urban evolution and the quantification of the spatial changes characterising each study stage. this approach is based on the use of satellite images and the application of landscape metrics. the research is organised into four distinct sections. the first section addresses the theoretical foundation of the study, while the second section explores the subject area and the methods employed. the third section presents and discusses the collected data, and the final section summarises our findings and includes the conclusion. diachronic cartography to identify landscape transformations, the study of spatiotemporal dynamics characterising landscapes can be conducted by highlighting the diachronic change in biophysical land use. this allows the identification of converted soils and the quantification of their extent (herold et al., 2002; lausch et al., 2015; norton et al., 2016; wilson et al., 2003). this kind of research is practically based on change mapping using satellite imagery and geographic information systems gis (buyantuyev & wu, 2007; lechner et al., 2012; southworth et al., 2002). the level of analysis is that of the urban scale, which covers the whole urban perimeter and offers a synoptic view of the phenomenon studied. at this level of analysis, the landscape is perceptible to the local community and constitutes a major concern for researchers and actors engaged in spatial planning and ecosystemic sustainability (forman & forman, 1995; norton et al., 2016; turner & gardner, 2015a). from an ecological point of view, understanding the spatial dynamics that characterise territories necessitates quantitative approaches developed in landscape ecology (lausch et al., 2015; turner & gardner, 1991). regarding urbanised territories, they are characterised by their heterogeneity and the complexity of their landscape compositions (leitão & ahern, 2002; young et al., 2009). the use of landscape metrics offers the possibility of describing urban landscapes and measuring the changes affecting them over time (breuste et al., 2008; huang et al., 2007). these metrics were developed and applied from the 1980s onwards to describe natural landscapes and were later generalised to a variety of study fields (aguilera et al., 2011; turner & gardner, 1991). two aspects can be analysed: landscape composition, which highlights the diversity and abundance of the https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 125 fragments constituting the landscape; and spatial configuration, which focuses on the shape and spatial arrangement of these different fragments. three scales of analysis can be considered: the fragment scale (patch scale), the landscape class scale, and the overall landscape scale (turner & gardner, 2015b). these metrics are calculated essentially by the classification of satellite images and the use of gis. satellite images are employed as input support; gis makes the mapping process, calculating these indices, and visualising the findings (mcgarigal et al., 2012; o’neill et al., 1988). numerous studies have demonstrated the utility of landscape metrics in monitoring the sprawl of urbanised areas and estimating the associated landscape transformations (cai et al., 2024; cyriac & firoz c., 2022; herold et al., 2002; trinder & liu, 2020; wahyudi et al., 2019; wang et al., 2018). as for oases areas, studies using this approach are also numerous. studies have revealed the detrimental effects of human activity on the fragmentation of oases (amaya et al., 2024; ming et al., 2008; xie et al., 2016; zhang et al., 2010), while others have sought to assess spatiotemporal changes affecting oases on a broader scale (chen et al., 2023; jia et al., 2004; khebour allouche et al., 2021; li et al., 2001; sun & zhou, 2016; xie et al., 2014). however, analysis that based solely on monitoring landscape index behaviour may encounter interpretative limitations, particularly in terms of locating areas affected by the change (cyriac & firoz c., 2022; garcía-pardo et al., 2022; herold et al., 2005; lausch et al., 2015; ramachandra et al., 2012). a cartographic analysis could complement this analysis, providing a more comprehensive understanding of the landscape under investigation. this study aims to address this gap by integrating cartographic and quantitative methods to identify and quantify the changes affecting the landscape. in the context of algerian oases, this combined methodology may offer valuable insights into the current state of the land. this research underscores the importance of landscape metrics for eco-responsible spatial planning. 2. data and methods 2.1. study area the city of laghouat is situated 420 km south of the capital algiers, at a longitude of 2° 56' east, and a latitude of 33° 46' north, with an average altitude of 767 meters. it lies in the southern foothills of the saharan atlas, a mountain range separating the vast desert from northern algeria. laghouat is an ancient oasis and chief town of the wilaya (district) that bears its name. the old city is located on the western bank of the oued m'zi, a dry riverbed. it is surrounded to the south by the oued messaad mountain, as well as to the west and northwest by the djebel lahmar mountain, which forms part of the saharan atlas' djebels ammour mountain range figure 1. laghouat climate is an arid environment with very hot and dry summers and chilly and reasonably mild winters. source: authorship from google satellite map imagery, 2024 figure 1. the study area (authorship from google satellite map imagery) https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 126 the discovery of large hydrocarbon deposits and the designation of laghouat city as a chief town of the wilaya in 1974 significantly enhanced the city's economic and administrative status. laghouat has experienced a remarkable demographic boom, with its population increasing from nearly 26 000 inhabitants in 1966 to over 210 000 in 2019, a more than 8-fold increase over half a century. this demographic boom has not been matched by adequate urban infrastructure, leading to numerous challenges, particularly in the protection of the palm grove and the safeguarding of the unique local oasis landscape. these issues are central to ongoing scientific and professional discussions regarding the sustainability of this ancestral oasis (benarfa et al., 2018; benkouider et al., 2013; rezzoug, 2013). 2.2. data used to initiate the cartographic and landscape analysis, we explored the usgs (united states geological survey) free archive. four landsat satellite images were acquired corresponding to the following dates: 1986 (8 april, tm5 sensor), 1997 (21 march, tm5 sensor), 2008 (20 april, etm+ sensor), and 2019 (18 march, olitirs sensor). the spatial resolution of these images is of the order of 30 m, with spectral richness covering the visible and near-infrared bands. auxiliary documents are also used as reference maps. these include parcel plans from 1986, 1998, 2006, and 2018, and historical images provided by the usgs that correspond to the same acquisition dates as the selected images. it is essential to highlight that the spatial resolution of the images employed at this level of study, which pertains to the overall macro-form of the oasis, is potentially useful. however, for studies at finer scales, such as neighbourhoods, this spatial quality may not be relevant due to the “mixel” effect that can distort the quality of the desired thematic maps (lu & weng, 2007; phiri & morgenroth, 2017; wahyudi et al., 2019; wulder et al., 2012). the free software qgis (congedo, 2021) and fragstat (mcgarigal et al., 2012) were utilised during this research to perform the classification procedure and calculate landscape metrics for each study interval. 2.3. methodology the monitoring and evaluation of spatiotemporal transformations characterising laghouat oasis from 1986 to 2019 require the creation of thematic maps for the years 1986, 1997, 2008, and 2019. in the first step, these maps will undergo a diachronic analysis of the land use evolution, in order to then proceed to the calculation and evaluation of landscape metrics for each evolutionary stage. an image classification process is performed with the objective to create thematic maps. this procedure was carried out through the following steps: 2.3.1. pre-processing and normalisation of acquired images this step consists of a geometric correction and radiometric calibration of the raw images to homogenise their radiometry and ensure a better superposition of the different scenes collected. the study area is defined and extracted using a polygonal cut-out window that encompasses the entire oasis and its surrounding environment. the area of interest is situated between longitudes 2°48ʹ50ʺ and 2°53ʹ55ʺ east, and latitudes 33°45ʹ41ʺ and 33°50ʹ14ʺ north, based on the wgs 84 datum, zone 31 north. a false colour composition was subsequently selected figure 2, utilising the band combinations (4-3-2) for the tm and etm+ images and (5-4-3) for the oli-tirs (congedo, 2021). 2.3.2. classification, enhancement, and validation of classification this operation consists of generating a thematic map that represents the state of land cover at a specific date, by assigning to each pixel its corresponding biophysical land cover (richards & jia, 2006). given our field expertise alongside the nature of the reference maps utilised, the supervised classification approach is adopted in the present study (du et al., 2014; weng, 2007). four thematic classes have been predefined using the fao nomenclature (fao, 2016): (1) urbanised areas (built and developed areas); (2) palm grove (phoeniculture); (3) bare soil (unbuilt and uncultivated soils); (4) peripheral vegetation (of farms outside the urban perimeter). it is important to note that this region is crossed by two dry wadis: the wadi of m'zi to the east, and the wadi of messaad to the south of the city. these wadis are considered as bare soils. the training areas samples were https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 127 constructed by digitizing representative polygons of different classes using the “region growing” algorithm. the supervised classification is performed using the maximum likelihood algorithm (congedo, 2021; phiri & morgenroth, 2017). subsequently, a post-classification enhancement is applied to the maps generated by the classification to make them homogeneous and comparable. this enhancement involves correcting the confusion induced by the classification and the elimination of isolated pixels by applying a thresholding of 3x3 pixels (manandhar et al., 2009). the resulting maps undergo an accuracy assessment to ensure they are suitable for analysis. the confusion matrix and kappa index (k) are calculated. these two indicators provide information on the classification quality obtained. the classification is statistically accepted when k value is greater than or equal to 0,8. to validate the classification, 200 control points were randomly generated on the thematic maps and matched with the reference documents (congalton, 1991). 2.3.3. calculation and monitoring of landscape metrics considering that the current study focuses the development of land use classes, the analysis is restricted to the class level. to characterise the spatiotemporal dynamics of the laghouat oasis, six landscape metrics were used. these metrics include the class area (ca), the percentage in the landscape (plan), the number of patches (np), the mean patch area (area_mn), the aggregation index (ai), and the normalised landscape shape index (nlsi). the first four indices are compositional metrics, while the last two serve as spatial configuration indicators. a detailed description of these indices and their interpretations is provided in table 1 (mcgarigal et al., 2012; o’neill et al., 1988). table 1. description of selected metrics metric description interpretation ca (ha) 𝑪𝑨 = ∑ 𝒂𝒊𝒋 ( 𝟏 𝟏𝟎. 𝟎𝟎𝟎 ) 𝒏 𝒋=𝟏 aij: area (m2) of patch ij. ca ≥ 1 ca is a surface indicator of composition. it indicates the spatial extent of a class in the landscape. monitoring this index allows to reveal spatial trends (growth/decrease). pland (%) 𝑷𝑳𝑨𝑵𝑫 = 𝑷𝒊 = ∑ 𝒂𝒊𝒋 𝒏 𝒋=𝟏 𝑨 (𝟏𝟎𝟎) pi: proportion of the landscape occupied by the class i. aij: area (m2) of patch ij. a: total landscape area (m2). 0 < pland ≤ 100 pland is an indicator of dominance in the studied landscape. np (units) 𝑵𝑷 = 𝒏𝑰 ni: number of patches constituting the class i. np ≥ 1 ni reveals the abundance/rarity of fragments belonging to the same landscape class. the evolution of ni indicates the growth or decrease of the patches constituting the landscape area_mn (ha) 𝑨𝑹𝑬𝑨_𝑴𝑵 = ∑ 𝒙𝒊𝒋 𝒏 𝒋=𝟏 𝒏𝒊 xij: total class area (m2) of patch ij. ni: number of patches constituting the class i. area_mn ≥ 0 area_mn combined with np indicates the spatial trend: growth/decrease, densification/disaggregation. ai (%) 𝑨𝑰 = [ 𝒈𝒊𝒊 𝒎𝒂𝒔→𝒈𝒊𝒊 ] (𝟏𝟎𝟎) gii: number of like adjacencies between pixels of patch type (class) i. max-gii: maximum number of like adjacencies between pixels of patch type (class) i. 0 < ai ≤ 100 a synthetic index indicating the landscape configuration: compactness/fragmentation. ai increases: compactness of fragments increases, and vice versa. nlsi (without unit) 𝒏𝑳𝑺𝑰 = 𝒆𝒊 − 𝐦𝐢𝐧 𝒆𝒊 𝐦𝐚𝐱 𝒆𝒊 − 𝐦𝐢𝐧 𝒆𝒊 e: total length of the edge of class i. min e: minimum total length of the edge of class i. max e: maximum total length of the edge of class i. 0 ≤ nlsi ≤ 100 nlsi decreases: compactness increases and fragments are tending towards aggregation; nlsi increases: the studied class is tending towards landscape fragmentation. https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 128 it is important to note that each of these metrics may have limitations in terms of interpretation when considered in isolation, without reference to other indices. correlating these metrics may be necessary for meaningful spatial analysis (feng et al., 2018; mcgarigal et al., 2012; o’neill et al., 1988). source: authorship from landsat imagery, 2024 figure 2. images used in false colour compositing of landsat imagery, combination of bands (4-3-2) for the 1986, 1997 and 2008 images, and the bands (5-4-3) for the 2019 image 3. results and discussion 3.1. validation of the classification the supervised classification has resulted in four thematic maps corresponding to the years 1986, 1997, 2008 and 2019 figure 3. the accuracy assessment results are shown in table 2. table 2. classification accuracy of the resulting thematic maps type of assessment 1986 1997 2008 2019 overall accuracy (%) 94.10 91.38 89.66 92.54 accuracy of 'urbanised area' class (%) 95.22 92.51 89.19 92.48 accuracy of the 'palm grove' class (%) 91.15 88.62 91.52 90.66 kappa index (k) 0.93 0.91 0.89 0.92 the calculation of the confusion matrix for the four maps obtained has demonstrated a satisfactory level of accuracy, both for the overall accuracy (94.10, 91.38, 89.66, 92.48 respectively) and for the accuracy of the classes targeted in this study. notably, the accuracy for the urbanised areas was 95.22, 92.51, 89.19, and 92.48 respectively, while the accuracy for palm grove was 91.15, 88.62, 91.52, and 90.66 respectively. the kappa index https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 129 (k) also indicated an acceptable level of accuracy; with values of 0.93, 0.91, 0.89, and 0.92 for the years 1986, 1997, 2008, and 2019 respectively. this level of performance has been achieved through post-processing enhancements consisting of the correction of some classification confusions between the built-up area class and the bare soil class, mostly located outside the urban area. 3.2. land use evolution cartography: two opposite trends four thematic maps were generated from the satellite image classification presented above figure 3. these maps illustrate the spatiotemporal land use evolution characterising laghouat oasis during the period from 1986 to 2019. figure 3. spatiotemporal evolution of land use classes between 1986 and 2019 the diachronic interpretation of these maps reveals a notable growth of the urban fabric, characterized by variations in both form and direction, alongside a consistent reduction in the palm groves area. several landscape forms can be distinguished. the 1986 map shows a balanced landscape between the two spatial components, the city, and the palm grove. the latter occupies the eastern part of the oasis and has a relatively compact form. the built and agricultural spaces appear to be distinct, with only a few small fragments located within the palm grove, in close proximity to the urban fabric. the eastern strip of the palm grove is largely devoid of buildings, presenting itself as a homogeneous and continuous spatial entity. this state of spatial compactness has been maintained to this date due to the regulations in effect during this period, specifically the 1984 regulation that prohibited construction within this green zone. during this first period, the urban fabric is situated in close proximity to the palm grove, representing the original form of oasis morphology. however, the presence of several urban fragments detached from the central city is evident. these fragments constitute the newly urbanized areas, which are typically located to the north https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 130 of the northern palm grove and to the east of the city, extending from the old fabric along the national road nr° 01. the 1997 map illustrates the beginning of sprawl of the urbanized area class. this sprawl is oriented in two main directions: north-westward from the city, and south-eastward in continuity with the old urban fabric. it is also noticeable that the inner parts of the palm grove have started to be encroached upon by the built-up class, affecting both the northern and southern palm groves. this marks the onset of a conversion process of agricultural land, initiated by the repeal of the 1984 local regulations designed to protect the palm grove. furthermore, the spatial growth form of the city is increasingly leaning towards morphological fragmentation, as evidenced by the emergence of newly built fragments on the outskirts of the city. this fragmentation impacts the compactness of the urban macroform, leading to a gradual elongation of the urban perimeter. the newly urbanized areas, especially those in the northwest and south of the city, are shown to be expanding according to the 2008 map. the new districts of the northwest have recorded dynamics of spatial densification due to the realization of the housing and important infrastructure programs launched during this same period (the first five-year program 1999 2004, and the second program 2005 2010). the growth of informal residential neighborhoods, which spread particularly in the southern and western peripheral regions, has also contributed to the sprawl of the urban class during this third period of evolution. the vegetation has continued to lose surface area, particularly along the northern and southern borders of the two palm groves. the latest map of 2019 clearly shows a significant expansion of the urban patch, with a larger scale than in previous periods. the built-up areas have reached the natural boundaries of the oasis: the “djebel lahmar” to the northwest and the peripheral farms to the south. reaching these natural limits has led to the densification until saturation of the urban fabric in these parts of the city. a new spatial growth process is also noticed in the southern zone, marked by the appearance of new built-up areas that have evolved along the “district road cw230”, extending beyond the territorial limits of laghouat municipality. following this urbanization axis has imparted a more elongated form to the urban macroform. conversely, we also notice the disappearance of several green fragments in the hearts of the two palm groves. the green urban fabric has clearly become fragmented due to the encroachment of built-up areas. overall, the changing cartography illustrated in figure 3 reveals a clear expansion of urbanized areas against a significant decline in the palm grove. the sprawl of the built-up class has followed three main directions: northwest reaching the physical limit constituted by the mountain of djebel lahmar; southwest along the cw230 road axis; and into the palm grove itself. this sprawl has become more pronounced during the last period (2008 2019), resulting in a more elongated urban macroform. in contrast, a decrease in the land area has characterised the palm grove class. the loss of agricultural land began first (between 1986 and 1997) with the construction of plots bordering the built-up areas, followed by an axial penetration into the palm grove from the old urban fabric. this conversion process of agricultural land continued steadily until urbanisation of the palm grove heart and the disappearance of large green plots. 3.3. landscape quantification of land use change this part of the analysis seeks to describe the landscape forms characterising the laghouat oasis over the period 1986 2019, by monitoring landscape composition indicators (ca, pland, np and area_mn), followed by synthetic configuration indicators (ai and nlsi). the aim is to measure the surface portion occupied by each luc class on the one hand, and the level of compactness or spatial fragmentation of the studied luc classes on the other 3.3.1. landscape composition evolution: ca and pland. the temporal evolutions of land use classes and the corresponding changes in their spatial extent are analysed across three interval 1986 1997, 1997 – 2008, and 2008 2019. the analysis will then focus solely on the two classes targeted in the present study (built-up areas and palm groves).the results of the evolution of https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 131 land use class areas are illustrated in table 3 and figure 4. table 3 shows the area quantification of the land use classes calculated from the thematic cartography presented in figure 3. figure 4 specifically highlights the evolution of the two classes targeted in this study: the built-up area and the palm grove. continuous growth has been observed in the built-up area class. the area expanded from 300.33 hectares in 1986 to 1647.09 ha in 2019, with intermediate values of 518.67 ha in 1997 and 1002.51 ha in 2008. the surface area gains were 218.34 ha from 1987 to 1997, 483.84 ha from 1997 to 2008, and 644.58 ha from 2008 to 2019. these gains correspond to annual surface area consumption estimates of +19.85 ha, +43.99 ha, and +58.60 ha for the periods 1986 to 1997, 1997 to 2008, and 2008 to 2019, correspondingly. an increasing rate of annual evolution is noticed for this class of buildings. in contrast, the palm grove class has been marked by a continuous decrease in its surface area. it decreased from 243.81 ha in 1986 to 122.94 ha in 2019, passing through 213.75 ha in 1997 and 155.34 ha in 2008. the losses in area for this class are as follows: -30.06 ha during the period (1986 1997), -58.41 ha during the second period (1997 2008), and -32.40 ha during the last period (2008 2019). the rates of annual loss vary across these periods, with recorded annual losses of -2.73 ha/year, -5.31 ha/year and -2.95 ha/year for the periods 1986 1997, 1997 – 2008, and 2008 2019 respectively. the most accelerated rate of loss corresponds to the second period between 1997 and 2008, with an estimated annual loss of 5.31 ha/year. the third class, peripheral vegetation, has recorded the following values: 45.45 ha, 41.76 ha, 15.48 ha, and 62.19 ha for the years 1986, 1997, 2008 and 2019, respectively. this green class has recovered in the last period (2008 2019) after experiencing a decline in the first two study periods. the area of the bare soil class has steadily decreased in favour of the other classes, with the following decreasing values: 2932.02 ha in 1986, 2747.43 ha in 1997, 2347.92 ha in 2008, and 1689.39 ha in 2019. in figure 4 shows the spatial evolution dynamics of the two classes examined in this study: the built-up areas and the palm grove. table 3. evolution of the class areas (ca) between 1986 and 2019 land use class class area (ha) annual evolution (ha/year) 1986 1997 2008 2019 1986 1997 1997 2008 2008 2019 built-up area 300.33 518.67 1002.51 1647.09 +19.85 +43.99 +58.60 palm grove 243.81 213.75 155.34 122.94 2.73 5.31 2.95 peripheral vegetation 45.45 41.76 15.48 62.19 0.34 2.39 +4.25 bare soil 2932.02 2747.43 2347.92 1689.39 -16.78 -36.32 -59,87 figure 4. spatiotemporal evolution of ca and pland indices between 1986 and 2019 in terms of land area, the proportion occupied by urbanised areas has recorded a remarkable gain. the recorded percentages are as follows: 4.55 % in 1986, 7.86 % in 1997, 15.18 % in 2008, and 24.95 % in 2019, i.e., a global gain estimated at 448.43 % and a multiplication of surface area exceeding 5 times (5.48) compared to the https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 132 surface area measured in 1986. conversely, the palm grove has seen a decline in its surface share, with the following decreasing percentages: 3.69 % in 1986, 3.24 % in 1997, 2.35 % in 2008, and 1.86 % in 2019. approximately 49.57 % of the agricultural area disappeared between 1986 and 2019, decreasing from 243.81 ha in 1986 to 122.94 ha in 2019. returning to the thematic cartography, it can be seen that the lost agricultural areas are mostly converted into built-up area or have become uncultivated plots suitable to be built on in the future. 3.3.2. np and area_mn indices the spatiotemporal evolution of the last two indicators of oasis landscape composition np (number of fragments) and area_mn (mean patch area) is illustrated in figure 5. the correlation of these two indices reveals the spatial trend characterising each of the two classes studied within the urban perimeter. figure 5. spatiotemporal evolution of np and area_mn between 1986 and 2019 the number of urbanised fragments recorded by np has exhibited a fluctuating trend: an increase from 54 to 83 fragments between 1986 and 1997, followed by a steady decline during the subsequent study periods (with 50 fragments in 2008 compared to 12 fragments in 2019). concurrently, the mean size of these built fragments (area_mn) has shown a consistent increase over the entire study period, but with a less pronounced rhythm distinguishing the first period (1986 1997). the values obtained are as follows: 5.56 ha in 1986, 6.25 ha in 1997, 20.05 ha in 2008, and 77.26 ha in 2019. this growth is characterised by an amplification of the pace, which is more pronounced in the last period (2008 2019). the increase in the number of built-up fragments accompanied by a slight increase in the mean size of these fragments means the emergence of new, smaller, and less dense urbanised areas in the peripheral zones (new town). conversely, the decrease of np during the last two study intervals (1997 2008 and 2008 2019) coupled with a clear increase of the area_mn index indicates a merging process of certain urban fragments to form larger entities. the new urbanised areas have undergone a process of densification and spatial continuous growth to join the neighbouring fragments and to form a cohesive built entity. as for the palm grove class, the number of agricultural fragments has increased throughout the entire study period. the number of fragments np rose from 21 in 1986 to a total of 83 in 2019, with intermediate values of 45 in 1997 and 72 in 2008. however, the rate of growth for np has relatively decreased during the last period (2008 2019) compared to previous periods. additionally, the mean size of agricultural fragments area_mn has continuously declined. the measured values are as follows: 11.61 ha, 6.25 ha, 2.16 ha, and 1.48 ha for the years 1986, 1997, 2008, and 2019 respectively. an increase in the np index accompanied with a decrease in the area_mn index over the entire study period is noted. this finding indicates that the palm grove class is being decomposed into smaller fragments with significant losses in the profile of the built-up class. the pace of this conversion trend in agricultural areas began to slow down from 2008. https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 133 3.3.3. the spatial configuration indices ai and nlsi in order to better describe the spatiotemporal dynamics of landscape classes evolution, it is important to complete the analysis by monitoring the synthetic configuration indices. the results of the calculation for the ai and nlsi indicators are shown in figure 6; illustrates the varied behaviour of the indices monitored for the two classes targeted in this study. for the first class, that of built-up areas, the aggregation index ai exhibited relative stagnation during the initial study period, with closely aligned values of 73.57 % in 1986 and 73.74 % in 1997. however, from the second study period onwards, ai starts to register rising values: 85.48 % in 2008 and 90.89 % in 2019. concurrently, the nlsi index of the same class has also recorded stagnation during the first period, maintaining a value of 0.26 for the two years 1986 and 1997, and then began to have decrease values for the following periods, reaching 0.15 in 2008 and 0.09 in 2019. the stagnation of the compactness indices for the built-up area class means that the spatial growth has followed a balanced pattern, characterised by both continuous extensions of the existing urban fabric and discontinuous extensions into new peripheral areas, a result supported by the increase in the number of fragments outlined above. the rise in ai values, coupled with the decrease in nlsi values during the second and final study periods indicates that the compact mode is more characteristic. the urban extensions took place either by densification of vacant pockets or by spatial continuity with existing areas. from 1997 onwards, the urban fabric began to have more compact forms. figure 6. evolution of configuration indices ai and nlsi between 1986 and 2019 in contrast to this development, the palm grove class has experienced a steady decrease in its ai index, with values of 82.80 %, 76.74 %, 71.42 %, and 66.29 % recorded in the years 1986, 1997, 2008 and 2019 successively. the nlsi index has also been marked by a constant growth during the same study periods, with measured values of 0.17, 0.23, 0.29, and 0.34 in 1986, 1997, 2008 and 2019 respectively. the consistent decrease of ai values, coupled with the continuous increase in nlsi index indicates that the vegetation class has progressively lost its compactness, leading to more fragmented configurations of the green space. the rise in nlsi also means that the shapes of the green fragments tend to have more complex geometric forms, as the edge lengths increase. this evolution indicates that the green fragments have been subject to a nibbling process at their edges, a consequence of the encroachment of built-up areas. the results of the present study align with previous research that applies landscape metrics in oasis contexts. the decline in urban vegetation within urban perimeters has also been documented in other algerian cities (dechaicha & djamel, 2021; gherraz et al., 2020; teqwa bechaa et al., 2024). in the arab and north african context, the detrimental effects of urbanisation have been corroborated by several studies (dechaicha et al., 2021; gad, 2015; khebour allouche et al., 2021; riad et al., 2020). additionally, the landscape fragmentation of oases resulting from accelerated urbanisation has been demonstrated in studies conducted on a global scale (amaya et al., 2024; chen et al., 2023; liu et al., 2021; tang et al., 2019; xue et al., 2022). compared to previous studies, the spatial decline and landscape fragmentation of the palm groves in our case study are more pronounced, with a loss of 49.57% of the surface area and an almost 20% increase in landscape https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 dechaicha et al. / geoplanning: journal of geomatics and planning, vol 12, no. 1, 2025, 123 138 doi: 10.14710/geoplanning.12.1.123-138 134 fragmentation. this deterioration is attributed to insufficient control over urban development in the vicinity of the historic palm grove. 4. conclusion the goal of the current study was to draw attention to the landscape changes that have characterized the growth of laghouat oasis from 1986 to 2019. two methods were employed: a cartographic analysis of land use change and a quantitative analysis based on the measurement of landscape metrics at each stage of evolution. the results of the cartographic analysis have revealed an important sprawl of the urban fabric, against progressive retreat of the urban vegetation represented by the palm groves. three main directions have oriented the spatial growth of the urbanised areas: first, from the national road rn 01 towards the northwest, reaching the physical limits defined by djebel lahmar mountain; second, towards the southeast, along the cw 230 road axis, until crossing the territorial boundaries of the city, resulting in a more elongated urban macroform due to these extended developments; and third, encroachments into the palm groves, starting with the conversion of the border plots to later invade the core areas of the palm groves. the results of the landscape quantification have highlighted two distinctly contrasting spatial processes: a significant growth of built-up areas, which increased during the last period (2008 2019), in contrast to the gradual decline of agricultural areas which were more pronounced during the second study period (1997 2008). the results of the landscape metrics have also shown two distinct spatial trends: a morphological compactification trend of urbanised areas versus a continuous process of fragmentation and landscape desegregation of agricultural areas. the built-up class has experienced two successive modes of spatial growth: an initial period of spatial discontinuous growth, materialised by the appearance of new urbanised areas detached from the existing fabric, followed by a period of continuous growth materialised by the densification of new areas and the filling of unbuilt gaps. this is reflected in the increase in the average sizes of urban fragments. this growth pattern is corroborated by the results of the landscape configuration indices ai and nlsi. the urban sprawl is characterised by two growth modes: a relatively disaggregated form, followed by a densification process in continuity with the existing neighbourhoods. in contrast, the urban vegetation has undergone a reverse process: continuous loss of surface area and compactness. the palm grove has undergone a landscape fragmentation process over the course of the research period, due to the uncontrolled growth of builtup areas. between 1986 and 2019, the laghouat landscape underwent a full transformation. it went from a relatively balanced form between the urban fabric and the palm grove, to an unbalanced form characterized by the predominance of the urbanised areas class over that of green areas. the latter has undergone a twofold spatial process: nibbling of the bordering areas which are in close contact with the built-up areas, and the conversion of the plots located in the heart of the northern and southern palm groves, which has led to the loss of the original form of the oasis. the negative ecological impact of urban dynamics on oases has also been highlighted in other studies concerned with monitoring oasis spatial dynamics. the extent of this impact is more noticeable in saharan oases, where urban expansion is less controlled. the research conducted has highlighted the value of using landscape metrics as a tool to analyse the spatial forms brought about by the expansion of urban areas and assess how these forms affect the oasis potential. these measures can be useful tools to aid in optimizing spatial design decisions for an oasis sustainability standpoint. 5. references aguilera, f., valenzuela, l. m., & botequilha-leitão, a. 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[crossref] https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.14710/geoplanning.12.1.123-138 https://doi.org/10.1016/j.jaridenv.2015.12.002 https://doi.org/10.1007/s10661-022-10038-3 http://hdl.handle.net/2436/92006 https://doi.org/10.1007/s11806-010-0322-x 205 geoplanning: journal of geomatics and planning, vol. 11, no. 2, 2024, 205-222 original research urban sprawl symptoms in bandar lampung suburban area, indonesia zulqadri ansar 1,2*, walter t. de vries1 1. technical university of munich, germany 2. institut teknologi sumatera, indonesia doi: 10.14710/geoplanning.11.2.205-222 abstract this research investigates the phenomenon of urban sprawl in a medium-sized metropolitan area, specifically bandar lampung. it identifies the primary characteristics of urban sprawl and its impact on suburban development. the goal is to pinpoint the symptoms of urban sprawl through its spatial patterns, which may form systemically or sporadically, and predict their occurrence. the underlying theory is that urban sprawl symptoms can be observed in the rapid population growth and land use change in suburban areas. using statistical and spatial analysis (geographic information system), we studied the population growth rate and land use alterations in bandar lampung and its suburbs over the past decade. our study reveals that the population in the suburbs is growing faster than in the city. over a decade, there has been a land use change to 1255 ha of built-up land. this change is strongly associated with the development of public infrastructure and road networks. we recommend implementing smart growth strategies to manage urban sprawl in medium-sized cities in indonesia. additionally, we provide a critical review of the causal relationships driving urban sprawl and its widespread impacts copyright © 2024 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction massive urban expansion beyond city boundaries may potentially trigger further informal urban sprawl (brueckner, 2001; clawson, 2014; pendall, 1999). urban sprawl is a form of unplanned, unstructured, and seemingly random urban expansion in suburban areas (brueckner, 2001; clawson, 2014; kironde, 1997; pendall, 1999; tian, guo, et al., 2017), which have significant effects on several factors. these include physical, economic, and social aspects. this transformation is manifested as a structural change in the spatial economy from agriculture to manufacturing and service industries, an exponential growth of population, and an increase in land prices along with land use dynamics. combined these may eventually result in potential disasters, such as floods (winarso et al., 2015). urban sprawl and urban-rural land conversions have significantly increased in indonesia since the reformation era in the 90s, particularly in jakarta and on java. one of the root causes of this phenomenon is the high population density on java, which has put enormous pressure on land resources since the early 2000s (verburg et al., 1999). this is partly due to an active decentralization policy, and more recently, due to increased investments in mega infrastructure both outside of java as well as on java itself. several studies suggest that the most visible effect is the massive land conversion and socioeconomic changes from agriculture to urban settlement, industrial activities, and other activities (hudalah et al., 2013). there is a considerable amount of research focusing on urban sprawl characteristics in indonesia's large cities such as jakarta, surabaya, and medan (budiyantini & pratiwi, 2016; firman, 1998, 1997, 2000; hudalah et al., 2007; legates & hudalah, 2014; verburg et al., 1999). despite this, there remains a gap in when it comes to the understanding of urban sprawl in medium-sized metropolitan areas. it's therefore crucial to investigate e-issn: 2355-6544 received: 20 decembet 2023; revised: .07 february 2024.; accepted: 27 may 2024; available online: 30 november 2024; published: 04 december 2024 keywords: sprawl, suburban, medium-sized metropolitan *corresponding author(s) email: zulqadri.ansar@tum.de https://doi.org/10.14710/geoplanning.11.2.205-222 mailto:zulqadri.ansar@tum.de ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 206 whether these medium-sized cities share common traits with their larger counterparts or if they possess unique dynamics, growth patterns, and challenges. since the era of decentralization, these medium-sized cities have experienced significant expansion. yet, their capacity to manage this growth is limited, making them vulnerable to the negative impacts of urban sprawl, including environmental degradation and congestion. this research aims to generate novel insights to academic literature and planning practice. according to dardak et al. (2006), urban areas in indonesia are considered metropolitan if they have a population of over 1 million people and a population density above 150 people/km2, with 75% of the population working in non-agricultural fields. the size of several metropolitan areas in indonesia varies significantly, with jakarta being the largest at 10 million people and a density of 16,937 people/km. bandung, surabaya, and medan have a population of around 2,500,000 people, while makassar, semarang, and palembang have approximately 1,500,000 people. some cities have recently been added to the metropolitan category with populations of about 1 million, such as batam, padang, bandar lampung, and pekanbaru. moreover, there are several cities expected to be classified as metropolitan soon, as their populations are nearly exceeding 1 million, including malang, samarinda, balikpapan, and banjarmasin, among others. these cities are commonly known as "medium-sized metropolitan cities." urban sprawl is a complex and multifaceted phenomenon that has physical, environmental, and socioeconomic effects. contemporary urbanization has been characterized by urban sprawl, which is an extensive form of land use for urban purposes that has detrimental environmental effects (nuissl & siedentop, 2021). urban sprawl is characterized by the rapid transformation of land use/land cover into urban built-up areas, which has caused a significant increase in the built-up area (sahana et al., 2018). the transformation has led to a decrease in urban open space, the transformation of prime agricultural land and wetlands into built-up areas, and changes in densities of resident al housing and buildings, topography, and infrastructure. urban sprawl is characterized by low-density, scattered, leapfrog development, strip development, and discontinuous expansion into suburban areas. low-density housing is a typical characteristic of urban sprawl, often associated with inefficient use of land resources (guan et al., 2020; shi et al., 2023). scattered and leapfrog development is another characteristic of urban sprawl, referring to the lack of a clear pattern in urban development. this type of development leads to spatial inefficiencies and excessive spatial expansion due to pressures to provide land to cater to the rapid growth of the population's need for housing (frenkel & ashkenazi, 2008; nuissl & siedentop, 2021; pendall, 1999). strip development is another characteristic of urban sprawl, referring to the development of long, linear strips of urban development along highways and arterial roads. this type of development tends to be ad hoc and unplanned, leading to spatial inefficiencies and excessive spatial expansion (ewing, 2008). another key characteristic of urban sprawl is the lack of continuity in expansion. this is characterized by the unstructured, random, and unplanned expansion of urban areas, which contributes to changes in land use patterns of various shapes and sizes (tian, ge, et al., 2017; yasin et al., 2021). rapid land use changes, discontinuous or jumping development patterns, no green open space, following the transportation axis, separation between urban and residential land uses, and low accessibility are additional indicators of urban sprawl (frenkel & ashkenazi, 2008; guastella et al., 2019). the non-spatial characteristics of urban sprawl include absolute population growth, the emergence of a divide between formality and informality, and ineffective property land rights institutions (wu et al., 2006). urban sprawl has negative impacts on the environment, economy, and society, including ecological fragmentation and loss of biodiversity, as well as an increase in greenhouse gas emissions, traffic congestion, and air pollution. moreover, it concentrates wealth in certain areas while cutting off low-income communities from resources, exacerbating social and economic inequality. the definition of urban sprawl and the differentiation between urban and suburban areas are pivotal. these distinctions can be made through various methods (mikelbank, 2004; stokes & seto, 2019). one approach involves using the administrative boundary to separate the city from the surrounding districts. another factor is population density, as urban zones typically exhibit higher density, while suburban areas are less densely populated. land use and economic activities can also create distinctions, with urban areas offering more variety, and suburbs often dominated by housing and minimal economic activities. finally, the availability of https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 207 transportation infrastructure and the area's proximity to the city center can be indicative. suburban zones typically rely more on private transport for accessing jobs and services in the city center. these factors contribute to defining and distinguishing urban and suburban areas. this study specifically investigates how urban sprawl occurs in medium-sized metropolitan areas, with bandar lampung as a sample area. the primary goal is to discern the key drivers, patterns, and areas affected by urban sprawl. specific objectives encompass assessing the influence of infrastructure development, government policies, and demographic shifts on urban expansion. the research employs geographic information systems (gis) and remote sensing technologies to map alterations in land use and urban boundaries, aiming to understand the characteristics of urban growth, particularly in suburbs transforming from rural to urban. an additional objective is to formulate evidence-based recommendations for urban planning and policy adjustments to manage and mitigate the impacts of urban growth. the findings can contribute to a sustainable urban growth strategy for bandar lampung in particular and similar medium-sized cities in general. to achieve these objectives, the research poses the following research questions: what are the spatial manifestations of urban sprawl in medium-sized metropolitan areas? are these spatial patterns systematic or sporadic? is urban sprawl more prevalent in flat topography areas, or does it follow the expansion of roads and infrastructure? how can the observed manifestations of urban sprawl be interpreted and predicted? bandar lampung, situated at the most southern side of sumatra, is a city characterized by its diverse geography, encompassing beaches, lowlands, and hills. this variety in landscape shapes the trajectory of the city's development, rendering it an ideal subject for studying urban expansion. with a population exceeding one million, the city exhibits rapid urbanization and population growth, mirroring the evolution of medium-sized cities throughout indonesia. the city's substantial economic growth is primarily attributable to its role as a principal nexus between java and sumatra. infrastructure initiatives such as the trans sumatra toll road and the establishment of new university campuses on the city's periphery are impelling urban expansion, stimulating land development and real estate investment, and reshaping the city's physical layout. however, this rapid expansion engenders environmental concerns, including potential damage to biodiversity and habitat alteration. these issues are particularly pertinent in bandar lampung, and understanding their implications is critical. with indonesia's shift towards decentralization, cities like bandar lampung now wield greater autonomy in managing their urban development. this grants bandar lampung a unique position as a case study for examining the impact of local governance on urban growth. rapid growth presents urban planning with substantial challenges, particularly in managing urban expansion and its impact on transportation, housing, and public services. despite its significance, bandar lampung has not been as extensively studied as cities like jakarta or surabaya. consequently, this research could contribute valuable new data and insights that could be applied to other cities in indonesia and elsewhere experiencing similar urbanization patterns. the selection of bandar lampung as the research site was influenced by its distinct characteristics and pressing urban expansion issues. moreover, the potential contributions of these findings to the body of urban planning literature and policy development for similar urban environments globally were also considered. 2. data and methods 2.1. study area bandar lampung witnessed an annual population increase averaging 18,907 individuals over the past decade, the city's expansion has transcended its administrative boundaries. the soekarno hatta highway, bisecting the city, delineates the urban and suburban areas. the city's land area is distinctly divided, with the eastern side, comprising over 50% of the total land, primarily utilized for agricultural purposes, whereas the western side is urbanized. in recent times, bandar lampung has seen an influx of land interventions and developmental initiatives. these encompass the construction of infrastructural projects on the city's periphery, the establishment of the trans-sumatra toll road, and the inception of the new town development project, which relocated provincial government buildings and spurred the creation of new growth centres. the construction of the sumatra institute of technology (itera) campus triggered a surge in land prices in the vicinity. https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 208 furthermore, the development of airan raya hospital and the elevation of radin intan state islamic institute to university status contributed to the city's expansion. the lampung regional police headquarters (polda lampung) was also constructed adjacent to the itera educational area (figure 1). figure 1. study area 2.2. data the data referred to in this context is a collection of different types of information that can be used to understand and analyse a particular area. these data types include population data, which indicates the number of people living in a given area, as well as land use data, which reveals how the land is being used and for what purposes. topographic data is also included, providing a detailed understanding of the physical features of the land, such as elevation and slope. the data is further categorized into three main types: quantitative data, which is numerical in nature and can be easily measured and analysed; qualitative data, which provides a more subjective understanding of the area and can be more difficult to quantify; and spatial data, which provides information on the location and shape of objects and can be represented in the form of a point, a line, or a polygon. • the first dataset consists of population data for bandar lampung and its suburbs, obtained from statistical data released by the central bureau of statistics (badan pusat statistik). this data can be accessed freely at http://www.bps.go.id/. the population data, collected between 2012 and 2022, includes 33 villages or suburbs, 13 of which are in south lampung municipality and 20 in bandar lampung municipality. • the second dataset is land use and land cover data obtained from aerial and satellite imagery of the suburbs of bandar lampung. the dataset is available in the open repositories of the united states geological survey (usgs, https://www.usgs.gov/) . the usgs publishes aerial photography data with mediumgrade quality that is freely accessible by all parties. the satellite used by the usgs is landsat, and the name of the aerial photo is landsat images. the aerial images were collected in 2012 and 2022, enabling us to monitor land cover with medium resolution. • the third dataset is topographic data, released by the geospatial information agency of the republic of indonesia. it can be obtained for free by downloading the data at https://tanahair.indonesia.go.id/. this information is downloaded in tif format and then changed into shapefile format by arcgis. • the fourth dataset is infrastructure data, including road development and public facility development in the suburbs of bandar lampung. high-resolution aerial photographs collected from the national institute of aeronautics and space of indonesia (lapan) were used to obtain this data. this aerial photo is a pleiades aerial photo. the data was obtained free of charge for research purposes by requesting data collection from lapan. the aerial image was taken in the last year's aerial photo (2022). https://doi.org/10.14710/geoplanning.11.2.205-222 http://www.bps.go.id/ https://www.usgs.gov/ https://tanahair.indonesia.go.id/s ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 209 2.3. methodology the current study utilizes a blend of spatial and quantitative approaches to comprehend the phenomenon of urban sprawl. spatial techniques, including geographic information system (gis) and remote sensing, enable geographic visualization of urban growth and the tracking of urban development over time. gis is a software designed for storing, managing, and analyzing geospatial data. in this research context, it integrates data such as land use, population, and infrastructure into a single system. it facilitates complex spatial analysis, helping in identifying patterns and trends in the data. remote sensing is a technique that collects data about the earth's surface using satellites or aircraft. in this study, it has been employed to track land use changes over a decade. simultaneously, quantitative methods measure population growth rate, land conversion area, and analyze patterns based on variables like topography, public infrastructure, and road network. the findings are then visualized through mapping, infographics, or tables. these two methodologies collectively aid in understanding the process of urban sprawl occurring in bandar lampung's suburbs (figure 2 and table 1). there are several causes described in literature. each of these are addressed hereunder. government policies. government policies can inadvertently promote urban sprawl through subsidies for public infrastructure and services, affordable housing, low land prices, and transportation policies. poorly integrated policies across different dimensions can also contribute to urban sprawl. adequate transportation policies are crucial in preventing urban sprawl. settlement development programs and governments' inability to meet housing needs can also be factors. moreover, local governments often lack experience in managing urban growth (gómezantonio et al., 2016; habibi & asadi, 2011; hosseini & hajilou, 2019; majewska et al., 2022; nazarnia et al., 2019; taiwo, 2022; yasin et al., 2021). urban sprawl is often attributed to weak planning laws and single-use zoning by many experts. development policies, such as zoning, urban growth boundaries, or development control, are often absent, according to some experts. several studies have found that incorrect perceptions and policies related to urban growth contribute to urban sprawl. this is often caused by ineffective urban planning and land use policies, accompanied by poor decision-making in a region, particularly with regards to controlling urban growth and land use (vargas-hernández & zdunek-wielgołaska, 2021; weilenmann et al., 2017; xi-liu & qing-xian, 2018; yasin et al., 2021). mobility and private vehicle. population mobility and the use of private vehicles are the primary causes of urban sprawl in cities. private vehicles are a defining feature of urban sprawl. this type of development is characterized by roads and pathways that prioritize private vehicle use to access shopping and other activity centers. because of segregated development patterns, residents in peripheral areas become reliant on private vehicles, resulting in high transportation costs and lengthy commutes. as mobility increases, so does private vehicle use, leading to traffic congestion, air pollution, and other negative impacts. this problem is exacerbated by inadequate transportation infrastructure. private vehicle use encourages low-density development patterns and leads to the expansion of urban areas into surrounding rural areas, resulting in more cars and air pollution. the likelihood of urban sprawl increases with the number of private vehicles owned (almeida santos et al., 2018; ewing & rong, 2008; firman, 2002; gómez-antonio et al., 2016; hakim & parolin, 2009; hölzl, 2018; majewska et al., 2022; manesha et al., 2021; mehriar et al., 2020; restivo et al., 2019; slaev et al., 2018). ineffective urban planning. urban planning and control are essential to mitigate urban sprawl. however, several factors contribute to it, including inadequate urban planning policies, weak local governance, and ineffective urban growth control policies. inadequate policies and weak governance can encourage uncontrolled development, including in rural areas that should be preserved. government policies that prioritize integration, homeownership, and stability also contribute to urban sprawl. to promote sustainable urban development, it is crucial to understand the needs of both urban and rural land users. ineffective growth control policies can lead to uneven growth and exclusion of low-income groups and minorities. policies that encourage development in peri-urban areas are also causes of urban sprawl. irregular urban land use patterns and a lack of spatial sense can also contribute to sprawl. this is often accompanied by rigid planning (bidandi & williams, 2020; gómezantonio et al., 2016; mustafa & teller, 2020; nazarnia et al., 2016; nuissl & siedentop, 2021; xi-liu & qingxian, 2018). https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 210 land price. land prices significantly influence urban sprawl through several mechanisms. firstly, high land values in city centers often push people to seek cheaper alternatives, typically located in the suburbs. this shift develops pressure for land development, leading to urban sprawl. secondly, fertile agricultural land in suburban areas is usually less expensive than city-center land. consequently, it becomes a prime target for developers looking for affordable land for their projects. the resulting conversion of this agricultural land into residential or commercial spaces accelerates urban expansion. lastly, land in areas affected by urban sprawl often transitions from agricultural to non-agricultural uses. this change is driven by the higher market value of nonagricultural uses, prompting landowners to pursue greater economic returns (elmanisa et al., 2016; karakayaci, 2019; pendall, 1999). topography. topography and landscape features play a crucial role in shaping urban growth patterns. cities located on plains have an advantage due to the availability of land for urban development, which can increase economic growth. however, areas with higher levels of terrain undulation are less suitable for largescale spatial expansion. this can encourage denser and more intensive spatial forms within the district. physical constraints such as terrain undulations and landscape features can influence the direction of urban development and the pattern of urban growth in the region (herold et al., 2003; nandi & dewiyanti, 2019; yang et al., 2023). population growth. urban sprawl is characterized by rapid population growth in the suburbs, often caused by people moving from urban centers to more financially affordable suburbs. population growth has important spatial implications, particularly in terms of economic inequalities. several studies indicate that urbanization leads to uncontrolled urban sprawl, which poses global challenges, climate change, and urban poverty. rapid urbanization is a result of an increasing number of urban dwellers, with people migrating to cities for various reasons such as limited land in the city, a desire to be closer to nature, or to avoid the traffic, crime, and noise of the city. however, suburban residents can still access urban areas with their private vehicles. this phenomenon has the potential to disrupt sustainable urban growth due to the expansion of land beyond its availability, characterized by population and employment densities located in city centers and suburbs that are functionally and spatially linked. the expansion of land beyond its availability is a challenge to sustainable urban growth (bagheri & tousi, 2018; carlucci et al., 2018b; dura-guimera, 2003; frenkel & orenstein, 2012; guastella et al., 2019; guite, 2019; hatab et al., 2022; liu et al., 2018; nuissl & siedentop, 2021; pendall, 1999; tian et al., 2017; yiran et al., 2020; yue et al., 2016). in addition to the causes, several authors describe the impacts of urban sprawl. each of these are dewc4ribed hereunder. land conversion. urban sprawl can have both direct and indirect impacts on the environment. these include the loss of agricultural land and its conversion to residential or commercial use. however, building access roads and supporting infrastructure often require tilling and dredging of land, further damaging the environment. the conversion of agricultural land to urban use also comes at the expense of other areas such as forests, wetlands, and grasslands, and can lead to the loss or damage of free ecosystem services such as flood control and water purification. the destruction of wildlife habitats is another obvious environmental impact of widespread building development. remaining areas of wildlife habitat may be too small to support all the native species that lived there before or may be widely separated from each other. these conditions force wildlife to traverse dangerous, human-dominated landscapes in search of food and mates, which can disrupt ecosystem balance and reduce the availability of natural resources for communities. another direct impact of such conversion is the loss of agricultural land, resulting in farmers losing their livelihoods and investments in agricultural infrastructure such as irrigation (bae & chang, 2019; cho et al., 2010; firman, 2000; hanham & spiker, 2005; harrison & donnelly, 2011; lennert et al., 2020; mohammady & delavar, 2015; sahana et al., 2018; schuster olbrich et al., 2022; van metre et al., 2000; verburg et al., 1999; yue et al., 2016). transportation and energy consumption. urban sprawl leads to negative impacts on transportation, public health, and the environment. this includes increased reliance on private cars, longer commutes, higher costs, traffic congestion, and air pollution. as urban areas expand, they replace open spaces with concrete and asphalt, leading to a decrease in wildlife habitats and biodiversity. longer distances between destinations also result in higher fuel consumption and increased energy consumption, which contribute to climate change and the depletion of natural resources. transportation has major impacts on energy consumption and carbon emissions. https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 211 more people in an area means more energy is needed, causing increased emissions, energy use, traffic congestion, and destruction of ecosystems. rapid and sporadic urbanization causes even greater negative impacts (cartone et al., 2021; hanif, 2018; kakar & prasad, 2020; mohammady & delavar, 2016). congestion and pollution. urban sprawl can lead to traffic congestion and air pollution due to the reliance on private vehicles for commuting, shopping, and schooling. the lack of adequate public transportation exacerbates the problem. this results in longer travel times to work and an increase in private vehicle use for trips to commerce centers, which contributes to traffic congestion and air pollution from vehicle emissions. these negative effects reduce the quality of life in urban areas, as evidenced by studies. to mitigate these effects, it is crucial to improve public transportation options and promote alternative modes of transportation (bart, 2010; daniels, 2001; kakar & prasad, 2020; krishnaveni & anilkumar, 2020; rubiera-morollón & garrido-yserte, 2020). 2.4. identifying the manifestation of urban sprawl symptoms this study explores urban sprawl by focusing on key indicators like land use change and population increase. the former refers to the transformation of non-urban land, such as agricultural or forested areas, into urban spaces, including residential or commercial zones. the latter denotes a higher population growth rate in suburbs compared to urban areas. we employed a quantitative and spatial methodology to discern the indications of urban sprawl in bandar lampung's peripheral areas. the quantitative technique scrutinized population growth, and the spatial method inspected shifts in land use. the assessment of population growth served as the primary indicator, facilitating the recognition of urban sprawl symptoms. our analysis unfolded over three phases. initially, we assembled data encompassing the population and area of bandar lampung city and 12 suburban villages under the administration of the south lampung regency for a decade (2012-2022), although the data extended only until 2021. following this, we conducted a statistical analysis of the population, covering aspects such as growth, density, average growth, and growth rate. finally, we compared the trend of population growth rates in urban and suburban areas over the previous decade. in addition to the population analysis, we investigated changes in land use over the last decade. this involved a comparative study of land cover in 2012 and 2022. the data analysis process consisted of three steps. initially, we digitized 2012 and 2022 satellite images of bandar lampung into vector data using arcgis mapping software. we then classified the vector data based on land cover criteria. finally, we overlaid the 2012 and 2022 land cover data to comprehend the changes. this approach produced a map displaying the distribution of land that underwent functional changes over a decade. the spatial method effectively observes changes in land use representing the sprawl phenomenon; however, its success is significantly dependent on the quality of the acquired satellite images. 2.5. identifying the variations in and inter-relations between independent and dependent variables the focus of this study is the exploration of the nexus between various independent variables and the dependent variable, which is the transformation of land use from 2012 to 2022. several independent variables are considered in this study, such as topography, roads, public facilities, land price, land type, and regional administrative boundaries. the study seeks to understand how these variables influence land conversion, a process denoting the shift from undeveloped to developed areas, such as residential zones. data for this study was procured from a previous analysis comparing the landscape of 2012 to that of 2022. the first independent variable in our study is topography, which relates to the landscape's physical features, including elevation, slope, and terrain type. topography can influence the pattern and speed of urban sprawl, with areas characterized by gentle slopes and fewer physical barriers often experiencing swift and extensive changes in land use. the examination of topographic variables occurred in three primary stages. the first step was processing dem/srtm data in vector format (tiff) to create topographic maps. the second step was reclassifying this data by elevation class and converting it into polygon format (shapefile). lastly, we https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 212 overlayed the topographic map with the land use change map from 2012 to 2022. this process is crucial for understanding how topography impacts land use change. the second independent variable refers to public facilities, such as universities, hospitals, and office parks. the expansion of public infrastructure into suburban regions can act as a catalyst for urban growth and changes in land usage. in order to investigate this variable, the coordinates for each public facility were processed using arcgis, a geographic information system software. around these coordinates, a 'buffer' or radius of approximately 1000 meters was created to represent the service area of the public infrastructure. this buffer map was then combined with a map of land that had undergone conversion between 2012 and 2022. the result was a map highlighting areas where land use changes correlated with the type of public infrastructure present. this procedure is key in identifying trends in land use near specific public infrastructure projects. the third independent variable in this study is road infrastructure. this refers to the primary transportation routes in an urban environment. the construction or expansion of roads can enhance accessibility and result in changes in the function of adjacent land. this variable's value was obtained through three crucial stages: digitizing the road infrastructure lines into arcgis, creating a 'buffer' or radius of about 500 meters around the road network map, and superimposing this buffer map with the land use change map. the end product is a map of land use change based on road classification. this procedure is crucial for identifying land use change trends along specific road classes. table 1. research design research targets data requirements data source data collect data analysis approach analysis techniques output identifying the manifestation of urban sprawl symptoms • population in 2012 • population in 2022 • central bureau of statistics secondary quantitative • statistic urban population growth • landsat imagery in 2012 • landsat imagery in 2022 • pleiades imagery in 2022 • https://www.usgs.gov/ (web of u.s geological survey) • lapan (national institute of aeronautics & space of indonesia) secondary spatial analysis • gis technique (classified, overlayed) • quantitative analysis land use change map identifying the variations in and interrelations between independent and dependent variables • topography spatial data • land use change 2012 &2022 • big (geospatial information agency of indonesia) • results of land use change analysis from 2012 2022 secondary spatial analysis • gis technique (classified, overlayed) • quantitative analysis distribution of land conversion based on topographic classification • public facilities coordinate • land use change 2012 &2022 • bappeda (regional development planning agency) • results of land use change analysis from 2012 2022 secondary spatial analysis • gis technique (classified, overlayed) • quantitative analysis distribution of land conversion based on public facilities classification • road infrastructures • land use change 2012 &2022 • bappeda (regional development planning agency) • results of land use change analysis from 2012 2022 secondary spatial analysis ● gis technique (classified, overlayed) • quantitative analysis distribution of land conversion based on road infrastructure classification https://doi.org/10.14710/geoplanning.11.2.205-222 https://www.usgs.gov/ ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 213 figure 2. theoretical framework design 3. results and discussion 3.1. population growth bandar lampung's population statistics indicate an increase of 216,860 people over the past eight years, or an average of 27,107 people per year. the combined population of bandar lampung and its suburbs is 1,253,582, with 70% living in urban areas and 30% in suburban areas. as described in the previous chapter, the soekarno-hatta bypass highway separates urban and suburban areas: urban areas are located to the west of the bypass highway, while suburban areas are located to the east. table 2. population growth, density & growth rate 2013 – 2020 2013 2014 2015 2016 2017 2018 2019 2020 density (people/km) growth rate core 741.84 756.54 771.23 785.70 800.02 814.11 828.06 877.22 5,913 18.25% suburban 294.88 299.23 304.16 309.77 314.87 321.08 325.20 376.36 2,501 27.63% subdistricts 200.20 204.15 208.06 212.03 215.89 219.70 223.44 263.35 villages 94.68 95.08 96.10 97.74 98.98 101.38 101.76 113.01 table 2 shows that the total population in urban areas remains higher than in the suburbs. table 3 shows that the population density in urban and suburban areas in 2020 is different. urban areas have a higher population density of 5,913 people/km2, covering 51.63 km2, while suburban areas have a lower population density of 2,501 people/km2, covering 98.86 km2. despite the lower density, suburban areas have a higher population growth rate than urban areas. from 2013 to 2020, the growth rate trend in the two areas is also different, with urban areas having a growth rate of 18.25% and suburban areas having a growth rate of 27.63%. this indicates that the population increase in suburban areas from 2013 to 2020 has been at a faster rate than in urban areas. 3.2. land conversion the second manifestation of suburban sprawl in bandar lampung is land conversion. this has been identified through a spatial approach that observes the conversion of agricultural land into built-up areas in the suburbs. this observation was conducted by comparing two land use maps from 2012 and 2022, which covered a total area of 15,047 hectares. https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 214 table 3. land cover in suburb of bandar lampung 2012 2022 no land cover in 2012 area (ha) change 2012 2022 1 water bodies 5.99 5.55 -0.43 2 open spaces 178.57 125.26 -53.31 3 agriculture plantation 3,534.06 3,261.46 -272.60 4 residential 3,013.39 4,268.18 1,254.79 5 swamp 4.12 4.12 0.00 6 agriculture fields 3,152.17 2,732.70 -419.48 7 grassland 1,401.43 1,312.37 -89.06 8 agricultural fallow 3,757.49 3,337.57 -419.92 total 15,047.23 15.047,22 0 sources: rtrw of lampung province figure 3. land use 2012 figure 4. land use 2022 figure 5. land use change 2012 2022 figure 3, 4, and 5 visually illustrate the land use maps in 2012 and 2022, respectively. the maps provide a clear visual representation of the changes in land use over the course of a decade. it is worth noting that residential areas are shown in brown, while agricultural land is shown in ivory. upon comparison of the two maps, it becomes apparent that there has been a significant conversion of agricultural land into built-up areas. specifically, the data shows that 1,254.79 hectares of agricultural land were converted into built-up areas over the course of the decade, averaging roughly 125 hectares per year. this represents a substantial loss of agricultural land, which has important implications for food production and the environment. interestingly, the https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 215 conversion of land was dominated by residential areas, which rapidly expanded beyond the administrative boundaries. in 2012, the residential area was 3,013 hectares, but by 2022, it had increased by 4,268 hectares. meanwhile, agricultural land drastically declined to 2,732 hectares. this data is presented in table 3, which provides a more detailed breakdown of the changes in land use over the decade. 3.3. the variations and inter-relations between independent and dependent variables 3.3.1. land conversion and topography the analysis results are derived by utilizing the land use map, which provides critical information about the land conversion process based on topography class. figure 6 presents a detailed overview of the land conversion, highlighting the various regions where changes have occurred throughout the area. figure 6. land use change based on topography upon examining figure 6, it becomes evident that the majority of the land conversion occurred at an elevation of 51-100 meters above sea level (masl), accounting for a total of 901 hectares or 72% of the conversion. additionally, another 331 hectares, or 27%, were converted at an elevation of 101-200 masl. interestingly, the areas classified as high topography, which are above 200 masl, accounted for less than 1 hectare or approximately 0.3% of the overall land conversion. it is evident that the differences in elevation play a significant role in the conversion process, indicating a direct correlation between elevation and land use changes. moreover, the data highlights the need for more in-depth studies into the effects of topography on land use conversion processes, which could help develop more effective land use policies and strategies. 3.3.2. land conversion and infrastructure the next analysis carried out is the analysis of the type of infrastructure on land use change in the suburban area of bandar lampung, the aim is to find out how much the development of various types of infrastructure contributes to land use change in the area. the table 4 presented below provides an overview of the extent of land conversion that has taken place in the vicinity of recently constructed infrastructure. the land conversion is categorized into two distinct groups: road connections and other urban facilities, which include universities, hospitals, government buildings, and flyover roads. it is important to note that the observational radius of the infrastructure varies depending on the type of road. arterial roads, for instance, span an impressive 500 meters to the left and right, while collector roads span 200 meters to the left and right. these observational radii are significant because they directly impact the extent of land conversion that has taken place in the area. as such, it is crucial to understand https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 216 the implications of this land conversion, particularly with respect to environmental degradation and urban sprawl. table 4. land use change based on road infrastructure and public infrastructure road classification (radius 500 m) area (ha) public infrastructure (radius 1 km) area (ha) arterial primary road 183.91 toll gate 27.47 arterial secondary road 14.58 high education facilities 130.25 primary collector road 61.57 health facilities 53.52 secondary collector road 710.67 market 52.76 industrial park 13.73 government district 45.60 total 970.73 total 323.34 upon thorough analysis, it was discovered that 26% of the total land conversion, equivalent to 323.34 hectares, occurred around new infrastructure projects. this indicates that the majority of the land was repurposed for these projects. moreover, 77% or approximately 970.73 hectares are situated around or along road networks, underscoring the significance of road networks in facilitating land conversion. to provide a more comprehensive understanding of the situation, figure 7 and 8 have been included, which shows the percentage distribution of different types of infrastructure on land conversion in the peri-urban area of bandar lampung. this visualization serves as a valuable resource for anyone seeking to gain a deeper understanding of the underlying dynamics of land conversion in this area. figure 7. land use change based on road infrastructure figure 8. land use change based on public infrastructure 3.4. discussion urban sprawl, the uncontrolled expansion of urban development, affects many cities worldwide, including bandar lampung, indonesia. this phenomenon involves the rapid transformation of rural and agricultural land into urban areas, leading to increased car dependency and reduced population density. characterized by the swift conversion of land use into urban built-up areas, urban sprawl significantly increases these developed areas (sahana et al., 2018). this study aims to understand the occurrence, influencing factors, and growth patterns of urban sprawl in bandar lampung. by combining field research and literature findings, this study analyses quantitative data on land use change and population density, comparing them with previous research theories. it pays particular attention to the impact of development policies and infrastructure development on urban expansion. the study concludes by providing practical, evidence-based recommendations for policy makers, urban planners, and other stakeholders to manage future urban sprawl. this research offers a unique geographical and social perspective on urban sprawl in medium-sized cities, providing in-depth knowledge of the phenomenon. it offers critical insights that can impact urban planning and https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 217 policy-making for similar sized cities. the study expands our understanding of how urban sprawl happens, specifically how infrastructure and topographic factors influence the development of medium-sized cities. this knowledge can aid in creating more effective strategies to control urban sprawl. a unique aspect of this research is the application of the spatial analysis method of buffer and overlay analysis. this approach produces more accurate and reliable results, crucial for urban planning decisions. the study also provides a comprehensive analysis of urban sprawl drivers, using a combination of quantitative and qualitative methods. this approach allows for a deeper understanding of the complexity of the urban sprawl phenomenon. lastly, the research utilizes advanced gis and photo-imaging technologies to analyze spatial patterns and the impact of infrastructure on urban sprawl. this approach offers a current perspective that improves the reliability and accuracy of the results. 3.4.1. symptoms and process urban sprawl, or urban expansion, is defined by higher population growth rates in the suburbs compared to the city centre. this research indicates a faster growth rate in the suburbs, with ratios of 27.63% and 18.25% respectively. urban expansion is often a result of people relocating from the city centre to the more financially accessible suburbs (bagheri & tousi, 2018; carlucci et al., 2018a). this shift increases housing demand in the suburbs (carlucci et al., 2018a). in bandar lampung, suburban land is less expensive and housing construction is largely subsidized by the government, making it more affordable. the availability of land also influences population growth and contributes to urban sprawl (nandi & dewiyanti, 2019). bandar lampung's suburbs offer plentiful land, attracting urban residents with more affordable prices and larger land areas. the population density of an area often reflects the type of development. the study area shows a stark contrast between the suburban and urban areas. the suburban areas have a much lower population density of about 2,501 people/km, compared to 5,901 people/km in the urban areas. this low population density results in a sparse settlement distribution, contributing to the outward expansion of urban areas (shi et al., 2023). however, challenges arise when urban expansion occurs without proper control or planning. unchecked growth can lead to urban sprawl, limited access to public services, increased infrastructure costs, and loss of green space. if based on appropriate spatial planning, low-density development can be beneficial. unfortunately, low-density urban growth is often poorly planned and managed. ideal planning should manage urban growth to maintain quality of life, support sustainable development, and ensure equitable access to public services. despite low density, access to essential facilities such as health, education, clean water, and sanitation is crucial in supporting quality of life. therefore, urban planning needs to consider how to integrate low-density suburban areas with the infrastructure of larger cities, such as transport and public services. contemporary urbanisation is characterised by urban sprawl, which is an extensive form of land use with negative environmental impacts (nuissl & siedentop, 2021). rapid population growth has spurred increased demand for housing and land in peri-urban areas for residential, commercial, and office activities. due to the low population density, land availability in peri-urban areas is limited. this has significant implications for urban planning, including the loss of open land. this study found that high population growth in peri-urban areas has led to a significant reduction in agricultural land. in the past decade, there has been a notable conversion of land use in the suburbs of bandar lampung. more than 1,250 hectares of agricultural land, plantations, and pastures were converted to residential land. on average, 125 hectares of open land are converted to residential land each year, reflecting the rapid urbanisation of the area. out of the 1,250 hectares converted, 89% was agricultural land turned into developed land. developers often prefer transforming paddy fields, as these have an abundant groundwater supply near the surface, eliminating the need for deep excavation to access water. furthermore, the soil structure of paddy fields ensures high stability for housing foundations. conversely, other open land types like wetlands and peatlands possess irregular soil structures and inadequate groundwater conditions. the expansion of urban areas onto agricultural land poses a threat to the livelihoods of farmers in peri-urban regions. a decrease in agricultural land can significantly impact food production, leading to reduced food availability for the community. this can directly affect the income of individuals in peri-urban areas (bae & chang, 2019). https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 218 the subsequent symptom is the evolution of facilities and road infrastructure. the study revealed that 95% of the converted land is situated along road infrastructure, with the remainder around public facilities. this indicates that road infrastructure on the city's periphery is vital for accommodating urban growth. convenient access to major roads is essential for urban expansion and land conversion. an article by ewing & rong (2008) highlights that road development typically occurs in long, linear patterns along motorways and arterial roads. this type of development tends to be unplanned, featuring jumps and ribbon patterns, which can result in spatial inefficiencies and sprawl. 3.4.2. urban sprawl manifestation the research demonstrates that the urban sprawl in bandar lampung is evident and quantifiable. data indicates a significant geographical expansion of the city boundary, with an increase in the built-up area of 1255 ha over the past decade. the transition in land use from agricultural to residential, educational, commercial, and industrial signifies the city's rapid and often unpremeditated growth, pointing to urban sprawl. the study also reveals a population growth rate of 27.63% in bandar lampung's suburbs. this statistic is a testament to the general urbanization trend, where residents are moving from dense city centers to expansive suburbs. factors driving this trend include the quest for affordable housing, improved quality of life, and infrastructure development such as roads and public transportation, which facilitate access to the city center. these findings align with numerous references that define urban sprawl attributes. the literature indicates that urban sprawl typically involves vast geographical expansion, increased land use, and population dispersion to suburban areas (brueckner, 2000; johnson, 2001; tian et al., 2017) further emphasize that sprawl often stems from infrastructure development that enables population mobility and easy access to urban resources. however, unique factors emerge in the specific context of indonesia, where dynamics such as local government policies and regional economic factors significantly influence urban sprawl patterns. for instance, in bandar lampung, local government policies that encourage infrastructure development without sufficient planning may have expedited urban sprawl beyond what is typically seen in developed countries' cities. urban sprawl in bandar lampung is evident through substantial geographical expansion and land use change, substantiated by data demonstrating a shift from agricultural to more urbanised uses of land. this local context provides a unique perspective to this phenomenon, highlighting the necessity to consider local factors when understanding and managing urban sprawl. this growth can jeopardise the livelihoods of farmers in the surrounding areas. there could be a significant reduction in agricultural land, impacting food production and availability. this, in turn, directly affects the income of people living in these areas (bae & chang, 2019). 3.4.3. systemic or sporadic this data demonstrates that urban sprawl in bandar lampung exhibits a mixed pattern, with certain aspects being systematic and others sporadic. systematic patterns of expansion emerge through the land use changes along the arterial and collector road corridors, marked by a significant rise in residential and commercial development. on the other hand, sporadic patterns of sprawl are observed as well, especially in areas less accessible to major development plans. development in these regions appears spontaneous and irregular, frequently driven by uncoordinated land speculation and property investment. the observed patterns mirror those observed in other global cities. urban sprawl often unfolds systematically due to government policies and substantial infrastructure investments, according to nazarnia et al. (2019). conversely, the literature also acknowledges that sprawl can transpire sporadically, particularly in rapidly growing cities with lax land use regulations (vargas-hernández & zdunek-wielgołaska, 2021; xi-liu & qing-xian, 2018). economic, social, and political factors often dictate whether urban sprawl is systematic or sporadic, as highlighted by nuissl & siedentop (2021). in bandar lampung the presence of both patterns suggests variations in local policy effectiveness and responses to swift economic pressures. urban sprawl in bandar lampung is a blend of systematic and sporadic trends. these tendencies underscore the need for a more dynamic and responsive planning approach, as well as further research to gain a deeper understanding of how policy, infrastructure, and economic factors interact to shape urban development patterns. https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 219 3.4.4. how can sprawl be explained and predicted? this study reveals that factors such as topography, road development, and public infrastructure significantly influence the patterns of urban sprawl in bandar lampung. data analysis indicates that areas with a flat, easily developed topography tend to experience faster urban expansion compared to regions with more challenging contours. this implies that the ease of development affects the pace and direction of urban expansion. infrastructure development, especially roads, and public facilities like schools and health centres are key predictors of urban development. areas newly accessible by arterial and other collector roads often become prime locations for new residential development, attracting more residents to settle outside the city centre. these findings corroborate existing theories in urban studies and planning literature. the literature has consistently recognized the importance of topography in urban development. research by herold et al. (2003) and yang et al. (2023) demonstrate how topography influences not just physical development but also infrastructure costs and land use. this affirms that favorable topography can expedite urban sprawl by lessening the costs and technical challenges of development. moreover, the urban expansion model explained by brueckner (2000) and the springboard development theory (kakar & prasad, 2020; mohammady & delavar, 2016) illustrate that infrastructure, particularly roads, play a pivotal role in shaping the patterns and extent of urban sprawl. new roads and transport infrastructure make previously inaccessible or less desirable areas more accessible, promoting faster and farther urban expansion from the city centre. the findings validate these theories by demonstrating that the manifestations of urban sprawl in bandar lampung, in terms of topography and infrastructure, align with principles identified in recent literature. these insights can aid in more sustainable urban planning and development. a deeper understanding of these factors can help stakeholders make better-informed decisions about infrastructure development. 4. conclusion urban sprawl refers to the uncontrolled and unplanned expansion of urban areas into neighboring rural regions. this process is typically characterized by the transformation of rural land into low-density residential, commercial, or industrial developments. urban sprawl introduces a variety of economic, social, and environmental impacts. it's essential to manage this process effectively to mitigate potential negative consequences. urban expansion in bandar lampung has largely occurred to the east, in suburban areas where public infrastructure development has occurred and led to considerable land conversion of approximately 1255 hectares. the direction of urban expansion has been guided by public infrastructure development, including roads and educational facilities. housing demand and population growth rates are significant factors influencing the magnitude of urban expansion. urban sprawl typically develops in the direction of new infrastructure and, without proper planning, can result in unregulated expansion. land prices, particularly in areas where they are lower, significantly influence the direction of sprawl. the private sector, driven by profit, often spearheads this process, while the government's involvement remains limited. these factors together indicate a deviation from the intended controlled urban growth, suggesting the failure of the initiative. urban expansion often leads to an increased dominance of the private property sector in spatial development, thereby reducing the role of local governments. this imbalance can result in the private sector governing land use decisions, often at the expense of coherent urban planning and enforcement. efforts by the government to increase low-cost housing also contribute to urban sprawl, pushing low-income households into new suburbs. this study aims to explore and elucidate the symptoms and processes of urban sprawl by identifying its spatial manifestations, patterns, and directions of expansion in mid-sized metropolitan regions. the research contributes to the scientific field by providing tangible evidence on the dynamics of urban sprawl and the factors propelling it in mid-sized cities, with a particular focus on the roles of infrastructure and landscape. moreover, it offers valuable insights into the efficacy of current urban planning strategies. future studies could explore strategies to regulate urban sprawl and promote sustainable urban development. this could encompass enhancing land use regulations, improving government oversight of the private sector, or advocating for urban planning strategies that prioritize sustainable development. strategies could also include advocating for higher https://doi.org/10.14710/geoplanning.11.2.205-222 ansar and de vries / geoplanning: journal of geomatics and planning, vol 11, no 2, 2024, 205-222 doi: 10.14710/geoplanning.11.2.205-222 220 densities and mixed-use development to mitigate urban sprawl. additionally, evaluating the effects of urban sprawl on different socio-economic groups and investigating strategies to alleviate its negative impacts would be beneficial. 5. references almeida santos, j., sanches amorim, m. c., & hoyos guevara, a. j. de. 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[crossref] https://doi.org/10.14710/geoplanning.11.2.205-222 https://doi.org/10.1007/s40808-016-0209-4 https://doi.org/10.3390/su12104097 https://doi.org/10.1088/1755-1315/286/1/012031 https://doi.org/10.1016/j.landurbplan.2018.09.025 https://doi.org/10.1007/978-3-030-77712-8_20 https://doi.org/10.1068/b260555 https://doi.org/10.3390/ijerph16081350 https://doi.org/10.3390/su12166551 https://doi.org/10.1016/j.scitotenv.2018.02.170 https://doi.org/10.1080/1747423x.2022.2086312 https://doi.org/10.3390/su15021020 https://doi.org/10.1080/09654313.2018.1465530 https://doi.org/10.1088/1748-9326/aafab8 https://doi.org/10.1016/j.cities.2016.01.002 https://doi.org/10.1177/0042098015615098 https://doi.org/10.1177/0042098015615098 https://doi.org/10.1021/es991007n https://doi.org/10.1007/s10668-020-00623-2 https://doi.org/10.1016/s0959-3780(99)00175-2 https://doi.org/10.1016/j.landurbplan.2016.08.002 https://doi.org/10.1016/j.habitatint.2015.05.024 https://doi.org/10.4324/9780203962985 https://doi.org/10.3390/land12030721 https://doi.org/10.4314/gjg.v12i1.1 https://doi.org/10.1016/j.habitatint.2016.06.009 | 213 geoplanning vol 4, no. 2, 2017, 213-224 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.2.213-224 slum revitalizing plan of baghdadiyah by spatial re-modeling configuration h. m. taki a,b a king abdulaziz university, department of urban and regional planning, jeddah, saudi arabia b university of indonesia, department of geography, depok, indonesia abstract: baghdadiyah is a neighborhood of jeddah downtown, kingdom of saudi arabia. inhabitants of this area come from multi-ethnic and mostly low-income residents. the high density of the area affects land use more concisely. consequently, the beauty of the city which was used to be historic sites of jeddah is down-grading. this study proposed to revitalize city plan for a better quality of life. the aim of this study was to re-shape a slum area and to improve the spatial configuration of urban structure based on the recent condition. the method involved several approaches: recognizing a theoretical basic concept of the slum, applying spatial reconfiguration, analyzing the existing conditions and re-constructing new syntactical properties. the result of this study shared a new design of slum revitalizing plan for baghdadiyah configuring dominated patterns of integration and connection. adequate transport networks would reshape the city building profile. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to site (apa 6th style): taki, h.m. (2017). slum revitalizing plan of baghdadiyah by spatial re-modeling configuration, 4(2), 213-224. doi: 10.14710/geoplanning.4.2.213-224 1. introduction rapid urbanization is changing the structure of urban space by affecting the physical conditions of the environment. this process is particularly intense in the city namely the emergence of slums. increasing slum area appears in all the developing countries almost every year and it becomes important that the government takes a step wise to intervene this case. referring to the circumstances in the locality of the city, the slums based on (un-habitat, 2004) was defined as the area inner the city that is closely related to the perception of poverty as the common characteristics of ownership and tenure insecurity lack of access to basic services. huchzermeyer (2011) described slum settlements as a widespread phenomenon that occurs in many countries and involves many aspects. slums are not recognized and handled by public authorities with government policies as an integral part of a municipal management. this creates problems both socially and environmentally coupled with the unavailability of investments in these settlements (taki et al., 2017). hence, the importance of handling this problem especially in solving social problems, environment, and design needs an alternative responsive design strategy to provide social sustainability in the environment and the slum revitalizing plan is part of the solution to this problem (gencer, 2013; james, 2012; makinde, 2012). slum revitalizing plan according to (lutafali & khoja, 2011), refers to the physical development plans to improve or redevelop the conditions of informal housing areas. the revitalizing process revolves around understanding the role of the user which initiates transformation process in slum revitalizing areas. the slums of the residents are an important contributor to the development of the city by rendering their services to citizens and organization (bhaskar & chikarmane, 2012; fox, 2014; golubchikov & badyina, 2012). to bring these weak parts into the mainstream of society, it is essential to give them proper shelter. article info: received: 4 dec 2016 in revised form: 2 feb 2017 accepted: 24 juny 2017 available online: 3 nov 2017 keywords: slum revitalizing plan, spatial configuration, syntactical properties, spatial connectivity, integration corresponding author: herika muhamad taki king abdulaziz university, jeddah, saudi arabia email: htaki0001@stu.kau.edu.sa open access http://doi.org/10.14710/geoplanning.4.2.213-224 http://doi.org/10.14710/geoplanning.4.2.213-224 mailto:htaki0001@stu.kau.edu.sa taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 214 | the contributions of slum residents to the city's economy as industrial workers, workers on the construction site, the housekeeper, fabric-pickers, peddlers and various small-scale trade are indispensable. baghdadiyah located in jeddah downtown, kingdom of saudi arabia as one of the slums areas where the predominant form of settlement was unplanned. it is considered as smaller fragments of the street network. this situation coupled with a high population density causes physical and social problems as well as it causes an impact to poor connections between the surrounding urban street network and interior layouts of an area. referring to unplanned settlements project of jeddah by karimi et al. (2015), some problems appear in this area such as; (1) degraded health, environmental, socio, physical, and economic circumstances;(2) nonexistence of good maintenance; (3) unlawful occupation of government and private land; (4) unreliability from claiming area proprietorship; (5) concentration of non-saudi inhabitant; (6) inner infrastructure (the lack connected with the neighboring areas). some previous studies defined the slum (isunju et al., 2011; khalifa, 2011; kohli et al., 2012), some studies to assess social dimension (faye et al., 2011; king et al., 2013; h taubenböck & kraff, 2013) and others to improve the environmental aspect (rapoport, 2016; turley et al., 2013; wells et al., 2016). most researches use the social and economic approach as a basis to undertake the rehabilitation of slums areas. the examination using spatial configuration was rare. however, it can be done using the technique of space syntax developed by (hillier et al., 1993). the technique establishes causality between the configuration of space and natural movement. each causality was established between the space configuration and behavior patterns implying the cause and effect. study of the slum areas using the space syntax analysis conducted by (hernbäck, 2012) examines the impact of urban form on co-presence in open space for informal settlements in pune, india. the study was carried out to compare two distinct types of urban environments; one with slums. officially unplanned area was gradually developed with an irregular road network and one area of slums more deliberately was planned with a more regular road grid. the measurement approaches utilized space syntax, organized observations, and correlations investigations. this study followed previous researches but carried out a different method. this study has remodeled slum area and compared the syntactical properties between a new model and existing condition to achieve sustainable solutions. 2. data and methods 2.1. study area and data source bagdhadiyah is a neighborhood of jeddah downtown, located 21.54 latitude and 39.20 longitudes, positioned on the eastern coast and the largest port of the red sea (figure 1). jeddah has a population of 2,867,446. it is an important commercial center as well as the main gateway to mecca (islamic holiest city). the secondary data were collected for implementation of slum revitalizing plan based on the requirement from project archives of private consultant in 2016. these data were (a) location data of study area, (b) transportation data such as street network; (c) spatial data such as building categories, and land use. 2.2. method the method for developing the slum revitalizing plan for baghdadiyah neighborhood was based on the space syntax technique, which combines integration and connectivity measurements. integration assessment was performed to identify the design to-movement potential of a space. meanwhile, the connectivity analysis was used for counting deep all line up and gets that property of the line. taken together, these results ultimately contributed to the remodeling configuration. the literature review was conducted to gain consensus understanding on the discussion of the slum area. the model should be developed based on the environmental approach and sustainable spatial planning. the planning steps of slum revitalizing plan for badgdadiyah were described in figure 2. http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 | 215 figure 1. the map of study area (modified from google earth) figure 2. flowchart of the study http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 216 | 2.3 spatial re-modeling configuration the relationship between the syntactical properties (integration) and movement (pedestrian & vehicular) as well as the land uses was largely corroborated in the literature of space syntax. it is adopted by the current research as the initial base assumption to establish a methodology to assess and develop the initial proposal of a contemporary design for baghdadiyah. the most appropriate proposal is expected to attain integration of the urban axes and the new plan (figure 2). baghdadiyah plan which represents its alley ways network was drawn as a geometrically closed polygon layout in dwg format, imported to syntax2d 1.2 in a dxf format, then all-line-axial-map is automatically generated. this map was reduced to a fewest-line-map which covers the spatial system with as few lines as possible to let any part of the system to be seen from a line and at the same time ensure the minimized depth between all pairs of lines. the minimal version of the fewest-line map was selected rather than the subset version. it gives more natural image that approximates what was done by syntax researchers. finally, an analysis was run to calculate the values of syntactic properties. two syntactical properties (integration and connectivity) were measured. the connectivity means to count deep all line up and obtain a property of the line that can be viewed in the system. it relates to many lines of intersection and located to the depth of the middle area. connectivity measures the number of immediate neighbors that are directly connected to space. integration is the fewer intervening lines which needs to be passed through to go from a line to every other line (hillier et al., 1987). in a more recent meaning, integration measures the distance from each spatial element to all others in a system (up to a certain radius and given a definition of distance) and so corresponds to mathematical closeness. according to this measure, the spaces of any spatial system can be hierarchical starting the maximum integrated (red axial lines) to the maximum segregated one (blue axial lines) (klarqvist, 1993). the literature of space syntax stated that integration represents the to-movement potential of a space, and choses the through-movement potential, and points out also that the two measures correspond to two basic elements in any trip: selecting a destination from an origin (integration). with the purpose of discourse the problem of the slum areas, an unconventional spatial analytic methodology was established to demonstrate the most essential routes of the road or street in each of the settlements (taki et al., 2017). the isolated and intensified core of the settlement was identified by local accessibility analysis. based on the analysis, a plan was decided upon to realign and link the smaller fragments to the larger structure of city-wide routes. 3. results and discussion 3.1. identifying physical condition of baghdadiyah baghdadiyah located on the east side of the red sea, is an area densely populated with a variety of backgrounds country. most the dweller is middle class and poor. this area is surrounded by the main street transport; therefore, it is a strategic value of this area. densely settlements are concentrated in the center of the area with smaller houses, while large houses are located on the sides of those areas. the streets network in this area is homocentric. there are two main streets that intersect in the middle of the area, and each of them has small street branches (figure 3). this small street indicates a very dense concentration of buildings with extremely narrow spaces between buildings that lead to the formation of small and much-branched streets. many buildings in baghdadiyah are old, unfit and bad categories, as shown in figure 4 with a dark red color. they are mostly located in the middle area with a pattern of small and split form features. some of the site blocks consist of small and densely buildings that are not neatly arranged while surrounding site blocks are neatly constructed buildings. http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 | 217 figure 3. the streets network of baghdadiyah figure 4. the building categories of baghdadiyah overall land use at this location (figure 5) was dominated residential areas (yellow color) with the location at the center of the area. other land use such as commercial (red) was located along side of the http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 218 | street, the open space and vacant, while warehousing, offices, and industry lied spread alongside the main street. the figure 6 showed that many numbers of intervening lines or depth and the large numbers led to much space segregations, as shown on the identification of the circle. it shows the amount of space with categories slum. this location is a target and categorized as a slum area. therefore, it needs serious attention to immediate revitalization. figure 5. the land use of baghdadiyah figure 6. the potential area to be revitalized in baghdadiyah http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 | 219 3.2. spatial configuration analysis of existing condition existing condition is the current physical form of the area consisting of the location structure among buildings and street network patterns. understanding the existing condition is important to build the new or proposed model since it is expected to improve the quality of life. in this section, the syntactical properties (connectivity and integration) have been used as an analysis using space syntax technique. the connectivity analysis of baghdadiyah shown in figure 7 revealed that the number of lines was directly joined with it. the connectivity was related to many lines of intersection and was located to the depth of the middle area. the red color representing lines or roads with good connectivity was only visible in the middle. as opposed, the other of the lines had blue color which means poor category of connectivity. the category of lines color was blue, green, yellow and red. figure 7. the connectivity of baghdadiyah (own analysis, 2016) http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 220 | global image integration in figure 8 showed that good integration is shown as a red line. by contrast, dominant blue color stated the poor area. the integration of poorly visible especially the area of the center where many lines are formed caused considerable depths and increase segregation in the system. many blue lines on the edge, green and yellow lines dominated the whole area. from the analysis of connectivity (figure 7) and integration (figure 8) of the existing area by deep or shallow in each line from other lines, it can be discussed that this area is categorized as a slum and soon need to be revitalized, especially in the central of the area. the enormous numbers of lines with dense and short distance were the evidence. figure 8. the integration of baghdadiyah (own analysis, 2016) 3.3. a new model for slum revitalizing plan of badgdadiyah the new model is a proposed design to replace the current condition. the new model is designed with the aim of reducing the very tight spacing between buildings as well as facilitating the movement of transport traffic by creating simpler street network patterns but is expected to enlarge the value of connectivity and integration. the focus of the revitalizing area is that the detected location has a low syntactical properties value and is proven by green and blue color lines in the result of space syntax technique. connectivity of new model is taken by line and network improvement and presented in the system. it is expected to increase the connectivity of new space. in the figure 9, the reduction in the blue color as a http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 | 221 connectivity0 20 1 7 13 19 25 31 37 43 49 55 connectivity sign of poor connectivity appeared. on the contrary, closer to the red color, a sign of improvement especially overall connectivity area can be observed. analysis of integration of the new models is expected to add shallow from the lines and on the other hand, can reduce the deep of all line. in figure 10, there is an addition of red color lines in the middle of the street intersection, while the blue and green lines are reduced. this result marked improvement in terms of integration. figure 9. the connectivity of new model of baghdadiyah (own analysis, 2016) http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 222 | figure 10. the integration of new model of baghdadiyah (own analysis, 2016) 3.4. the comparative analysis of syntactical property between existing and a new model. the final analysis step of this study is to obtain a full comparison between the existing condition (actual) and the new model (proposed). a comparative analysis was conducted by looking at the syntactical properties. in table 1, a valuable difference in connectivity and integration based on average, maximum, minimum and standard deviation was shown. table 1. the syntactical property between existing (actual) and new model (own analysis, 2016). syntatical property average maximun minimum sd actual proposed actual proposed actual proposed actual proposed connectivity 4,189 4,441 9 12 1 1 1,790 2,299 integration 68,353 86,453 242,405 211,939 15,868 9.5 43,036 42,686 a comparative analysis above states that there are differences of connectivity average between existing (4,189) and proposed model (4,441). it means that connectivity of the new model was better than the existing condition. the analysis of integration shows the actual average (68,453) and the new model http://doi.org/10.14710/geoplanning.4.2.213-224 taki/ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 213-224 doi: 10.14710/geoplanning.4.2.213-224 | 223 (86,453) in syntactical property. this means that the existing is narrower than the new model. in addition, this analysis is expected to make it more accessible. previous research that has specifically studied the slum area using space syntax techniques was performed by (hernbäck, 2012). his research aimed to find the most significant physical differences between study areas that were proved to be the hierarchical nature of the street network. a clearer hierarchy in an unplanned area implies the space in which women are generally located at lower levels of public function. the regular street grid of the planned area resulted in a more equitable distribution of public functions. in this way, the built environment in unplanned areas reflects and reproduces gender relations in the use of public space. meanwhile, the result of this research showed the difference of synthetic properties between existing condition and new model where the syntactical properties value of new model was better than the existing condition. it indicated the expressive profit achieved through intervention slum revitalizing plan, in terms of integration and connectivity urban framework with buildings and streets. moreover, the proposed street in the new model should correspondingly improve the interaction between buildings, which will be more connected, especially in areas where there was an economic and social gap. the recommendation of this study is that, this elaborated proposal for baghdadiyah is not only to offer a relation between the buildings and streets with urban fabric but also to take this opportunity to reduce most of the city's problems, to bring the city back as a key element of space articulation. 4. conclusion the existing spatial configuration of baghdadiyah is dominated by segregation because the depth of each line. in this spatial configuration, it is indicated by the presence of the slum area and the need to obtain immediate attention for revitalizing the area. the results of the comparative analysis showed the new model having more connectivity compared to the existing condition since the shallow is decreased and the deep is reduced. also, the new model is dominated by integration. since the integration value is increased, the deep among lines is reduced. finally, a new model performs better than the existing condition. in addition, the new model is highly potential to be proposed to revitalize the slum area. the intended benefits of this study are to eliminate the degradation quality of environmental, physical, social, health, and economic conditions, to improve attractive physical territory and the value of land, to guarantee the security of the land tenure, to prevent the fragmentation of non-saudi inhabitants, to add inner infrastructure and to connect to the neighboring areas. 5. references bhaskar, a., & chikarmane, p. 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(2016). environmental psychology. environmental health: from global to local, 203. http://doi.org/10.14710/geoplanning.4.2.213-224 https://doi.org/10.1016/j.puhe.2011.03.008 https://doi.org/10.1016/j.habitatint.2010.03.004 https://doi.org/10.1007/s11205-013-0320-0 https://doi.org/10.1016/j.compenvurbsys.2011.11.001 https://doi.org/10.30871/jagi.v1i01.346 https://doi.org/10.1007/s10901-013-9333-x https://doi.org/10.1108/meq.2004.15.3.337.3 37 geoplanning journal of geomatics and planning vol. 9, no. 1, 2022 original research 3d modelling of boscha observatory with tls and uav integration data gusti a. j. kartini1*, naura d. saputri1 1. department of geodetic engineering, faculty of civil engineering and planning, institut teknologi nasional bandung, indonesia doi: 10.14710/geoplanning.9.1.37-46 abstract the bosscha observatory is southeast asia's first modern astronomical observatory. this observatory is located exactly on the lembang fault in west java, indonesia. its existence on the fault line makes bosscha observatory very vulnerable to disasters, which in the future will cause severe damage to the cultural heritage building. one way to protect the preservation of cultural heritage buildings is through 3d digital documentation. with 3d shapes, we can obtain precise visual and geometric data that can be used to monitor the building's condition. there are two technologies will be used in this study, terrestrial laser scanner (tls) and unmanned aerial vehicle (uav) photogrammetry. tls systems can capture millions of points representing 3-d coordinates at extremely high spatial densities on complex, multidimensional surfaces within minutes. uav photogrammetry can generate 3d point cloud in centimeter-level precision. the results of data integration between tls and uav have been implemented successfully and can be used as one of the measurement techniques supporting 3d modeling and compensating for the shortcomings of each tool. this three-dimensional model can be used to create a cylindrical portion of a building and the roof of a hemispherical building; the texture and color of the building's details, such as windows, doors, and stairs, can be produced with an rmse error value of 0.025 meters. copyright © 2022 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction the bosscha observatory is southeast asia's first modern astronomical observatory, having been designated as a national cultural heritage site in 2004 and a national vital object in 2008. this observatory is located exactly on the lembang fault in west java, indonesia (figure 1). according to research, this fault can produce earthquakes with a magnitude of 6.5–7.0 on the richter scale (daryono et al., 2019). its existence on the fault line makes bosscha observatory very vulnerable to disasters, which in the future will cause severe damage to the cultural heritage building. because of the importance of cultural heritage buildings for the future, protection is needed to prevent damage and destruction (batur et al., 2020; chmutina et al., 2020). one way to protect the preservation of cultural heritage buildings is through digital documentation. typically, image-based technology or lasers have been utilized in the documentation of cultural heritage structures. with the aid of this technology, the documentation results are not only 2d but also 3d. the researcher was able to accurately document their subjects using 3d digital data formats (dostal & yamafune, 2018). with 3d shapes, we can obtain precise visual and geometric data that can be used to monitor the building's condition (kushwaha et al., 2020). the high precision of measurement makes it possible to investigate the deformations and damages of historic objects (kwoczynska et al., 2016). wirnajaya et al. (2019) conducted 3d mapping at the bosscha observatory using terrestrial laser scanner (tls) technology to produce the majority of 3d point cloud shapes. the absence of point cloud data on the roof of the bosscha observatory is a limitation of their research. in other studies, to obtain the top or difficulte-issn: 2355-6544 received: 24 september 2021; accepted: 22 november 2022; published: 29 november 2022. keywords: terrestrial laser scanner, unmanned aerial vehicle, data integration, point cloud, 3d model *corresponding author(s) email: ayujessy@itenas.ac.id https://doi.org/10.14710/geoplanning.9.1.37-46 mailto:ayujessy@itenas.ac.id kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 38 to-scan areas of an object, additional tools such as cranes are required (büyüksalih et al., 2020). tls scanning is relatively more expensive in terms of cost, but it has many advantages, including the ability to map quickly and in large quantities, to provide position, intensity, and rgb information, and to produce relatively precise measurements (wu et al., 2021). tls systems can capture millions of points representing 3-d coordinates at extremely high spatial densities on complex, multidimensional surfaces within minutes (gallay et al., 2015). the technology can quickly determine the precise coordinates of points that represent the surface of an object (klapa et al., 2017). source: daryono et al., 2018 figure 1. the location of the bosscha observatory (6°49'28.97" s 107°37'01.59" e) is on the lembang fault trajectory. the lembang fault is illustrated with a black line that stretches for 29 km in addition to tls, maharani et al. (2020) have used unmanned aerial vehicle (uav) photogrammetry to document the bosscha observatory and produce its full 3d shape. the resulting photographs were then combined and converted into a point cloud shape using agisoft metashape in this study. in other studies, photography-based technologies are commonly used to document heritage building (febro, 2020; manajitprasert et al., 2019; themistocleous, 2020). this is because uav photogrammetry is relatively inexpensive, in addition to being simple to operate and capable of producing high-quality 3d models (manajitprasert et al., 2019; a murtiyoso et al., 2019). 3d point clouds can be reconstructed from uav images with satisfactory accuracy; these images can generate centimeter-level precision (arnadi murtiyoso & grussenmeyer, 2017; pan et al., 2019). in a separate study, a combination of tls and uav was utilized to create 3d documentation of historical buildings. ulvi (2021) combines uav and tls data because tls is incapable of obtaining roof and tower area. according to the findings of ulvi (2021) it is known that the two techniques can complement each other. hu et al. (2016) merged point cloud data from multiple technologies on the liyang yi temple building, wudang mountain, shiyan, hubei province, central china, to determine the building's complete shape. complex architectural structures may be restored using a combination of different technologies (ağca et al., 2020; li et al., 2021; liang et al., 2018). the integration of tls and uav is possible based on previous research. the bosscha observatory has point cloud data and photographs, but there is no research on creating 3d models of the building, so this study will attempt to combine the two data sets. the results of this study will be compared with data from wirnajaya et al. (2019) to determine if the results of this integration are better to those of previous studies. https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 39 2. data and methods we utilized secondary data from previous studies for this investigation. in march of 2019, scans were performed using a topcon gls-2000 tls. this results of the study conducted ten scans in the.e57 data format (figure 2a). in april 2019, scanning with a dji mavic 2 pro uav and ground control point (gcp) coordinate measurements were performed. the scan produced 334 images in .jpeg format and 31 coordinate points in the wgs 84/utm zone 48s system (figure 2b). afterward, data processing is performed on each data set. the cloud-to-cloud method is used to perform a registration process for tls data processing in cyclone 2020, after which the accuracy of the values generated by the registration process is evaluated. then, a filtering process is applied to eliminate noise from building objects that are no longer required or will be removed in order to concentrate on the desired area. (a) (b) source: analysis, 2022 figure 2. (a) tls scan result and (b) uav photogrammetry scan result. https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 40 furthermore, photogrammetric data processing is carried out on agisoft metashape. the first step in uav data processing is the align photos procedure, which is used to identify the image's points. this method matches points from two or more photographs. this process will generate a useful initial three-dimensional model in the form of a sparse point cloud for the subsequent stage. following the process of aligning photos, the georeferencing stage is performed to provide a three-dimensional x, y, and z coordinate reference for the aligned photos. this phase also includes the marking of photographs, which is used to identify gcp points on the photograph. the location of the marker is determined by the point measured in the field using an object that is readily identifiable. then, the process of optimizing cameras is executed, which aims to realign the photos from the preceding process (marking photos and geofencing), which are adjusted to the precision of the camera's position from the selected coordinate system. in addition, the process of forming dense point clouds produces point clouds with a greater density than sparse point clouds. the integration of tls and uav data is the subsequent step, which is performed on cloudcompare. generally, processing is performed using the method depicted in figure 3. the entered data are already in the same coordinate system, wgs 84/utm zone 48s, because the research area is in lembang, west java. the data format generated by the tls data processing is a point cloud in e57 format, while the data format generated by the uav data processing is a dense point cloud in e57 format. the e57 format was selected because it is a compact and vendor-neutral file format used for storing and exchanging three-dimensional (3d) imaging data, including point clouds, images, and metadata. numerous applications support the e57 data format. the next step is data integration using merge points, so that the data generated by tls and uav are merged into a single set. to combine the two measurements' data, it is essential that the resulting point cloud data have the same coordinate system. using the point pairs picking registration method, the data integration procedure is carried out by selecting the elements of the most prominent object between the two datasets. on the edges (edges of the building) and corners of the building, it is possible to select object elements. there are a total of 10 points used in the registration process. this registration procedure results in an accuracy of 0.025 meter. the total number of points resulting from this integration is 213,286,187. source: analysis, 2022 figure 3. tls and uav data integration process on cloud compare. https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 41 following the successful completion of the tls and uav data integration processes, the meshing process is the next step. the meshing procedure seeks to reconstruct the 3d model created by combining tls and uav data. this stage of meshing aims to also bind the data point cloud into a triangular shape and generate a threedimensional model's surface area. plugins for poison surface reconstruction are used to generate the mesh. this plugin has a dense point cloud density and works well with closed objects. the parameter used is the octree depth; the greater the value of the octree depth, the finer the mesh results; however, the greater the value used, the greater the time and memory requirements. in this study, a depth of 11 octrees was used to generate a shape that closely resembles the actual situation. 3. result and discussion the build texture is the final step following the formation of the mesh. the objective of this step is to add color and texture to the three-dimensional (3d) model created in the previous step so that it closely resembles the appearance of the real object. the portion of visible sky (qpcv) parameter is used to calculate the illumination from point clouds (or mesh nodes) to provide a texture, color, and light that closely resembles the actual situation on the ground. this parameter works well with objects that have closed shapes; otherwise, the produced light will reach points in the front and back, resulting in unreal (unlikely) results and a lack of contrast. more data will produce smoother results, but it will take longer and require more memory. figure 4 is a visual representation of the registration, meshing, and texturing processes. (a) (b) (c) source: analysis, 2022 figure 4. (a) the results of the tls and uav registration processes, (b) meshing using octree depth 11, and (c) texturing objects. as the final phase of this processing, a solid three-dimensional model of the bosscha observatory building is created using the sketchup pro after the texture formation stage of tls and uav data integration has been completed. as a result of the tls and uav data integration process, a point cloud representing a solid 3d model of the building was produced as shown in figure 5. the tls measurement data obtained from wirnajaya et al. (2019) proved to be insufficient, particularly on the building's roof. this is possible because the acquisition process is influenced by the tool's distance from the object, which impacts the angle at which the object is captured. according to reshetyuk (2009), if the distance between the instrument and the object is too near, the viewing angle will be reduced. this will have an effect on the roof structure of the bosscha observatory, which cannot be modeled accurately. on the other hand, the distance between the tool and the object is an essential planning parameter prior to tls acquisition. according to achille et al. (2015), as distance increases, so does resolution. to take measurements of relatively small objects, it is necessary to readjust the correct distance; in this case, the scanned object has a surface area of approximately 552 m2. even though there are limitations on the measurement distance, the obtained rmse results are relatively accurate, at 0.015 meters. https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 42 (a) (b) source: analysis, 2022 figure 5. bosscha observatory 3d model (a) front view and (b) rear view created using sketchup pro. compared to the maharani et al. (2020), study, the uav measurement data applied in this research is slightly different. in this study, there were only 334 photos and 31 gcp points, compared to 362 photos and 38 gcp points in maharani et al. (2020). due to the lack of overlapping images, the difference in the number of photographs that used will impact the alignment procedure. in maharani et al. (2020), gcp points were only distributed on the cylindrical portion of the building because the roof was constantly moving for observatories purposes. the variance in gcp points influences the gcp marking procedure. as shown in figure 6, there is no gcp point available at the building's rear, so this component cannot be properly bonded during the georeferencing procedure. due to these disparities in data, an rmse of 0.3 meters was obtained in this study, which is significantly different from the rmse generated by tls. https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 43 source: analysis, 2022 figure 6. distribution of gcp points at the bosscha observatory. the red circle shows areas that do not have gcp points due to differences in data between this study and maharani et al. (2020). in the process of integrating tls and uav data, the tls data is used as a reference because its rmse value is significantly better than that of the uav data. however, because the data on the roof of the building cannot accurately represent the actual object, uav data is utilized to compensate for the shortcomings of the tls data on the roof. using the 10 points that are identical in both sets of data, it is possible to integrate the data and obtain an rmse value of 0.025 meters. even though the rmse uav value is measured in centimeters, the tls and uav integration results are measured in millimeters. this is in accordance with the statement mikrut et al. (2014) that the accuracy of the final object can be improved by combining laser scanning and photogrammetry. to determine the accuracy of the three-dimensional model derived from the integration of tls and uav data, the distance between the integrated model and the tls registration results on objects visible in both models is compared. comparing the distance between the two models yields an rmse of 0.001 meters. the distances between the two models are compared in table 1. based on the differences in distance between the two models, a statistical method was used to assess if the tls and uav integration results differed significantly from the tls data. calculations for the statistical test were performed using the t-distribution with a 95% confidence interval. the results of statistical test calculations utilizing the t-distribution method indicate that all measurement results from the three-dimensional model of the bosscha observatory have been accepted, i.e., they are already within the interval of lower values and upper https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 44 values in comparison to the results of the comparison of distance sizes from registration measurements of data processing tls. this indicates that there is no significant difference between the two data sets. table 1. distances between the two models, where xi is the size of the average distance of the 3d model as a result of data integration obtained from three measurements; xi-yi is the size of the average distance from the registration results of tls data processing no objects tls data integration xi-yi (m) (xi-yi)² (m) xi (m) yi (m) 1 a01 1.844 1.845 -0.001 0.0000004 2 a02 1.853 1.851 0.002 0.0000028 3 a03 2.371 2.372 0.000 0.0000001 4 a04 2.371 2.370 0.001 0.0000018 5 b01 1.257 1.256 0.002 0.0000028 6 b02 1.257 1.258 -0.001 0.0000010 7 b03 1.594 1.593 0.001 0.0000010 8 b04 1.593 1.592 0.001 0.0000010 9 c01 1.479 1.478 0.000 0.0000001 10 c02 0.938 0.940 -0.002 0.0000054 11 c03 2.095 2.094 0.001 0.0000004 12 c04 1.258 1.259 -0.001 0.0000010 13 c05 1.255 1.253 0.002 0.0000054 14 c06 1.255 1.254 0.001 0.0000018 15 d01 1.598 1.597 0.000 0.0000001 16 d02 1.599 1.598 0.001 0.0000010 17 d03 1.255 1.253 0.001 0.0000018 18 d04 1.256 1.256 0.001 0.0000004 19 e01 1.599 1.598 0.001 0.0000018 20 e02 1.600 1.600 0.000 0.0000000 21 e03 1.685 1.685 0.000 0.0000001 22 e04 1.684 1.683 0.001 0.0000018 23 f01 2.292 2.293 -0.001 0.0000004 24 f02 2.291 2.291 0.000 0.0000000 25 f03 6.016 6.014 0.002 0.0000028 26 f04 0.216 0.214 0.001 0.0000018 27 g01 6.016 6.016 -0.001 0.0000004 28 g02 0.212 0.209 0.003 0.0000071 1.59524e-06 rmse 0.001263027 source: analysis.2022 4. conclusion the results of data integration between tls and uav have been implemented successfully and can be used as one of the measurement techniques supporting 3d modeling and compensating for the shortcomings of each tool. the 3d model of the exterior of the bosscha observatory produced by the integration process and tls and uav data can be used to approximate actual conditions on the ground. this three-dimensional model can be used to create a cylindrical portion of a building and the roof of a hemispherical building; the texture and https://doi.org/10.14710/geoplanning.9.1.37-46 kartini and saputri / geoplanning: journal of geomatics and planning, vol 9, no 1, 2022, 37-46 doi: 10.14710/geoplanning.9.1.37-46 45 color of the building's details, such as windows, doors, and stairs, can be produced with an rmse error value of 0.025 meters. there is no statistically significant difference between the comparison of the tls distance size and the tls and uav data integration distance size, based on the results of statistical tests. more research is required to determine how to combine different technologies so that the complete shape of an object can be created by utilizing the strengths of each technology. 5. acknowledgments this work was supported by kampus merdeka competition program research grant 2021, geodetic engineering, institut teknologi nasional bandung. 6. references achille, c., adami, a., chiarini, s., cremonesi, s., fassi, f., fregonese, l., & taffurelli, l. 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[crossref] https://doi.org/10.14710/geoplanning.9.1.37-46 https://doi.org/10.1016/j.culher.2018.03.004 https://doi.org/10.1088/1755-1315/500/1/012073 https://doi.org/10.3390/app9183640 https://doi.org/10.1515/ipc-2015-0008 https://doi.org/10.5194/isprs-archives-xlii-2-w17-225-2019 https://doi.org/10.1111/phor.12197 https://doi.org/10.3390/rs11101204 https://doi.org/10.1080/01431161.2020.1834164 https://doi.org/10.24853/nalars.19.1.41-48 https://doi.org/10.3390/s22010265 geoplanning vol 2, no 1, 2015, 22-29 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning | 22 open access konversi lahan pertanian pada koridor jalan soloyogyakarta di kabupaten klaten s. rahayua, i.rudiartob, pangic a universitas diponegoro, indonesia, email: sri.yksmg@yahoo.com b universitas diponegoro, indonesia, email: irudiarto@yahoo.com c universitas diponegoro, indonesia, email: folder.pangi@gmail.com abstract: this research aims to assess the conversion of agricultural land in the soloyogyakarta corridor of klaten regency (kabupaten) during 1994-2013. it is important because the solo-yogyakarta arterial road in kabupaten klaten affects the land use of the surrounding area. this research has used spatial analysis methods, i.e. interpreting the landsat and alos satellite imageries, overlaying land use maps, and comparing the land use map and the spatial plan map (rtrw) of kabupaten klaten. the results show that conversion of agricultural land during the period of 1994-2013 was 424.82 ha, most of which was converted into residential and industrial use. so, the average loss of agricultural land has been about 22.35 ha/year while residential land has increased by 19.84 ha/year. the largest conversion happened in the banaran village. meanwhile, land use that was not in accordance with the rtrw was 69.15 ha (0.90 %). © 2015 gjgp undip. all rights reserved. abstrak: keberadaan jalan solo-yogyakarta di kabupaten klaten mempengaruhi penggunaan lahan di daerah sekitarnya. penelitian ini bertujuan untuk mengkaji konversi lahan pertanian di koridor jalan tersebut dalam kurun waktu 1994-2013. analisis spasial dilakukan dengan cara interpretasi citra landsat dan alos, tumpang susun (overlay) peta penggunaan lahan yang dihasilkan, serta pembandingan peta penggunaan lahan dengan peta rencana tata ruang wilayah (rtrw) kabupaten klaten. hasil penelitian menunjukkan konversi lahan pertanian selama tahun 1994-2013 mencapai 424,82 ha, sebagian besar lahan tersebut berubah menjadi lahan permukiman dan industri. laju penyusutan lahan pertanian mencapai 22,35 ha/tahun, sedangkan lahan permukiman meningkat 19,84 ha/tahun. konversi lahan pertanian terbesar terjadi di desa banaran. secara keseluruhan, penggunaan lahan yang tidak sesuai dengan rtrw kabupaten klaten mencapai 69,15 ha (0,90%).%). © 2015 gjgp undip. all rights reserved. 1. pendahuluan proses pembangunan yang ada, tingginya laju pertumbuhan penduduk dan aktivitas manusia yang semakin meningkat akan mempengaruhi penggunaan lahan pertanian yang ada di suatu wilayah. selain itu, penggunaan lahan yang ada di suatu wilayah juga dipengaruhi oleh keberadaan prasarana dan sarana, khususnya prasarana dan sarana transportasi. salah satu pengaruhnya adalah mendorong terjadinya info artikel; diterima: 27 maret 2015 hasil revisi : 10 april 2015 disetujui: 25 april 2015 publikasi on-line: 30 april 2015 kata kunci: konversi lahan pertanian, kabupaten klaten, citra satelit, sig article info; received: 27 march 2015 in revised form: 10 april 2015 accepted: 25 april 2015 available online: 30 april 2015 keywords: agricultural land convertion, klaten regency, satellite imagery, gis mailto:sri.yksmg@yahoo.com mailto:irudiarto@yahoo.com mailto:folder.pangi@gmail.com geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 23 konversi lahan pertanian ke penggunaan non pertanian seperti untuk permukiman, industri, jasa dan lain sebagainya. konversi lahan pertanian menyebabkan penyempitan luas lahan pertanian yang ada. semakin sempitnya luas lahan pertanian yang ada dan masih besarnya penduduk yang menggantungkan hidupnya dari lahan pertanian akan dapat menyebabkan pembangunan pertanian menghadapi masalah yang berat di masa yang akan datang. hal ini dapat menyebabkan ketahanan pangan terganggu. permasalahan ini tidak dapat dibiarkan dan harus mendapat perhatian yang sebaik-baiknya. lahan pertanian perlu dijaga kelestariannya agar mampu menunjang proses pembangunan yang bersifat jangka panjang. salah satu usaha yang dapat dilakukan untuk mengembangkan sektor pertanian adalah dengan cara melindungi lahan pertanian yang produktif agar tidak beralih ke penggunaan lahan non pertanian. sebagai daerah yang lokasinya sangat strategis, daerah sepanjang koridor jalan solo-yogyakarta di kabupaten klaten merupakan salah satu daerah yang mengalami penyusutan lahan pertanian. penyusutan lahan pertanian ini terjadi seiring dengan pertambahan penduduk dan proses pembangunan yang ada di sepanjang koridor tersebut. menurut topografinya, koridor jalan solo-yogyakarta ini merupakan wilayah yang datar. sedangkan dari jenis tanahnya, daerah ini merupakan daerah yang subur dan produktif, sehingga sangat disayangkan apabila lahan pertanian yang subur terkonversi menjadi lahan non pertanian. penelitian ini bertujuan untuk mengkaji konversi lahan pertanian di koridor jalan solo-yogyakarta, kabupaten klaten. konversi lahan pertanian yang ada dilakukan dengan memanfaatkan citra penginderaan jauh dan sistem informasi geografis (sig). pemanfaatan citra satelit dikarenakan citra satelit dapat menyajikan gambaran obyek, daerah dan gejala di permukaan bumi secara lengkap dengan wujud dan letak obyek yang mirip dengan keadaan sebenarnya di medan (sutanto, 1986). 2. data dan metode wilayah penelitian meliputi desa-desa di sepanjang jalan solo – yogyakarta yang berada di kabupaten klaten yang terkena pengaruh dari adanya jalur utama tersebut yang meliputi 59 desa di 11 kecamatan. gambar 1. lokasi wilayah studi (rtrw kab. klaten 2011-2031) http://id.wikipedia.org/wiki/topografi geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 24 tabel 1. daftar desa/kelurahan wilayah studi (rtrw kab. klaten 2011-2031) no kecamatan desa no kecamatan desa 1 prambanan kebon dalem kidul 31 delanggu butuhan 2 kotesan 32 banaran 3 sanggrahan 33 karang 4 geneng 34 delanggu 5 kemudo 35 sabrang 6 taji 36 gatak 7 tlogo 37 kepanjen 8 kebonarum gondang 38 karanganom blanceran 9 jogonalan somopuro 39 klaten selatan trunuh 10 tangkisan pos 40 sumberejo 11 gondangan 41 merbung 12 bakung 42 tegalyoso 13 karangdukuh 43 gayamprit 14 plawikan 44 jetis 15 kraguman 45 klaten tengah buntalan 16 prawatan 46 mojayan 17 wonoboyo 47 bareng 18 ceper mlese 48 semangkak 19 jombor 49 kabupaten 20 dlimas 50 klaten 21 jambu kulon 51 tonggalan 22 meger 52 klaten utara sekarsuli 23 klepu 53 bareng lor 24 ngawonggo 54 karanganom 25 kuncen 55 ketandan 26 wonosari wadung getas 56 belang wetan 27 tegalgondo 57 jonggrangan 28 delanggu bowan 58 gergunung 29 dukuh 59 jebugan 30 jetis pendekatan yang digunakan dalam penelitian ini adalah pendekatan diskriptif kuantitatif. pemetaan penggunaan lahan di daerah penelitian dilakukan dengan interpretasi citra satelit landsat dan citra satelit alos. interpretasi citra dilakukan dengan menggunakan software er mapper 7.0. sedangkan pengolahan dan analisis data dilakukan dengan menggunakan bantuan program sig yaitu sofware arc gis. pengolahan dan analisis data dalam penelitian ini meliputi: a) identifikasi penggunaan lahan tahun 1994 dan 2013 identifikasi penggunaan lahan dilakukan dengan menginterpretasi citra satelit tahun 1994 dan tahun 2013. interpretasi citra satelit adalah upaya pengenalan obyek yang tergambar pada citra satelit dan penilaian arti pentingnya obyek tersebut (sutanto, 1986). guna mengecek kebenaran hasil interpretasi citra maka dilakukan survai atau cek ke lapangan secara langsung. survai lapangan ini juga untuk mengumpulkan data yang tidak dapat diperoleh dari citra satelit. untuk melihat citra daerah penelitian yang telah di cropping, dapat dilihat pada gambar 2. geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 25 gambar 2. citra daerah penelitian yang telah di cropping (hasil analisis, 2013) b) analisis konversi lahan pertanian analisis ini dilakukan dengan mengoverlay peta penggunaan lahan di koridor jalan solo-yogyakarta kabupaten klaten tahun 1994 dengan tahun 2013 yang diperoleh dari interpretasi citra satelit. hasil overlay adalah peta perubahan penggunaan lahan pertanian di koridor jalan solo-yogyakarta kabupaten klaten antara tahun 1994 -2013. dengan analisis spasial pada peta tersebut, akan diketahui luas dan distribusi spasial perubahan penggunaan lahan pertanian yang terjadi. c) analisis penggunaan lahan dengan rencana tata ruang wilayah (rtrw) kabupaten klaten. analisis ini dilakukan untuk mengetahui apakah penggunaan lahan yang ada sesuai dengan peruntukan lahannya. analisis ini dilakukan dengan mengoverlay peta penggunaan lahan yang telah dihasilkan dengan peta rencana pola ruang kabupaten klaten tahun 2011-2031. 3. hasil dan pembahasan 3.1. identiikasi penggunaan lahan tahun 1994 dan 2013 hasil klasifikasi penggunaan lahan dari citra landsat dihasilkan 5 jenis penggunaan lahan yang berada di wilayah studi yaitu penggunaan lahan industri, permukiman, lahan terbuka, lahan pertanian dan vegetasi lain. gambar 3. peta penggunaan lahan tahun 1994 gambar 4. peta penggunaan lahan tahun 2013 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 26 penggunaan lahan di koridor jalan solo–yogyakarta yang berada di kabupaten klaten pada tahun 1994 di dominasi oleh penggunaan lahan sawah yaitu 4.248,55 ha atau 55,23 %. sedangkan penggunaan lahan untuk permukiman sebesar 3.095,11 ha atau 40,24%. penggunaan lahan untuk permukiman yang luas ada di kecamatan klaten utara dan kecamatan klaten tengah. tabel 2. jenis dan luasan penggunaan lahan tahun 1994 (hasil analisis, 2013) no jenis penggunaan lahan luas (ha) prosentase 1 industri 113.30 1,47 2 lahan terbuka 146.77 1,91 3 pemukiman 3095.11 40,24 4 pertanian 4248.55 55,23 5 vegetasi lain 88.53 1,15 jumlah total 7692.26 100,00 pada tahun 2013, penggunaan lahan pertanian di daerah penelitian masih mendominasi yaitu 49,71 %, lahan permukiman 45,14 % dan penggunaan lahan lainnya tidak banyak mengalami perubahan luasan untuk masing-masing jenis penggunaan lahan dijelaskan pada tabel berikut ini: tabel 3. jenis dan luasan penggunaan lahan tahun 2013 (hasil analisis, 2013) no jenis penggunaan lahan luas (ha) prosentase 1 industri 196.16 2,55 2 lahan terbuka 109.41 1,42 3 pemukiman 3472.2 45,14 4 pertanian 3823.73 49,71 5 vegetasi lain 90.76 1,18 jumlah 7692.26 100 3.2. konversi lahan pertanian dari tahun 1994 sampai 2013 konversi lahan pertanian di peroleh dari hasil overlay peta penggunaan lahan daerah penelitian tahun 1994 dan peta penggunaan lahan tahun 2013. besarnya konversi lahan di sajikan dalam tabel 4. tabel 4. perubahan penggunaan lahan (konversi) pertanian di koridor solo – yogya, kab. klaten (hasil analisis, 2013) no jenis penggunaan lahan luas lahan pada tahun perubahan penggunaan lahan (ha) rata-rata perubahan pertahun (ha) 1994 2013 1 industri 113.30 196.16 82.86 4.36 2 lahan terbuka 146.77 109.41 -37.36 -1.96 3 pemukiman 3095.11 3472.2 377.09 19.84 4 pertanian 4248.55 3823.73 -424.82 -22.35 5 vegetasi lain 88.53 90.76 2.23 0.12 jumlah 7692.26 7692.26 geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 27 gambar 5. grafik perubahan lahan di koridor jalan solo–yogyayakarta, kab. klaten (hasil analisis, 2013) berdasarkan tabel 4 diatas dapat diketahui bahwa selama periode 1994 – 2013 perubahan penggunaan lahan yang paling besar adalah lahan untuk pertanian yaitu berkurang sebesar 424.82 ha atau berkurang 9,99 % dibandingkan luas lahan pertanian pada tahun 1994. rata-rata penyusutan lahan pertanian adalah sebesar -22,35 ha/tahun, besarnya konversi lahan pertanian tersebut harus mendapatkan perhatian, karena hal ini tentunya akan dapat mengancam usaha untuk mencapai ketahanan pangan. disamping itu juga dikarenakan lahan pertanian yang ada di koridor jalan soloyogya ini mayoritas merupakan lahan pertanian yang subur. penyusutan luas lahan pertanian yang ada di daerah penelitian karena berubah ke penggunaan lahan lain diantaranya adalah berubah ke lahan permukiman sebesar 377.09 ha (rata-rata penambahan adalah 19,84 hatahun). besarnya pertambahan luas lahan pertanian ini disebabkan karena pertambahan jumlah penduduk yang ada. disamping itu juga disebabkan daya tarik yang ada di daerah penelitian diantaranya adalah:  daerah penelitian merupakan daerah yang relatif datar, sehingga aman dari bencana alam seperti longsor bahkan juga aman dari bahaya banjir.  memiliki aksesibilitas yang baik, karena daerah ini dilalui jalan utama yang menghubungkan kota solo dan provinsi yogyakarta. aksesibilitas didaerah ini juga didukung oleh moda transportasi angkutan umum yang banyak.  kedalaman air yang relatif dangkal dan kualitas airnya yang cukup baik. perubahan penggunaan lahan yang lain adalah perubahan lahan industri yang rata-rata bertambah setiap tahunnya sejumlah 4,36 ha, lahan untuk vegetasi lain bertambah 0,12 ha/tahun serta lahan terbuka berkurang 1,96 ha/tahun. lokasi konversi lahan pertanian yang terjadi di koridor solo–yogyakarta tersebut di gambarkan dalam peta konversi lahan pertanian sebagai berikut ini. gambar 6. peta sebaran lokasi konversi lahan pertanian di koridor solo-yogyakarta periode tahun 1994-2013 (hasil analisis, 2013) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 28 desa yang paling besar mengalami konversi lahan pertanian adalah desa banaran kecamatan delangu. konversi lahan pertanian yang ada di daerah ini diantaranya berubah menjadi permukiman, perdagangan dan jasa, pom bensin bahkan gudang. gudang yang ada di desa ini adalah gudang alfamart. pembangunan gudang ini juga memicu konversi lahan-lahan sawah yang ada disekitarnya. lahan sawah yang berada di belakang gudang ini pada saat ini sudah berubah menjadi perumahan. selain itu juga harga lahan menjadi naik karena sudah terbangunnya beberapa fasilitas yang ada disekitarnya khususnya jalan. hal ini lebih lanjut dapat memicu terjadinya transaksi jual beli sawah yang ada disekitarnya. konversi lahan yang terjadi di daerah penelitian perlu memdapat perhatian, khususnya tingginya konversi lahan pertanian ke non pertanian yang didominasi berubah ke lahan permukiman. mengingat lahan pertanian di daerah penelitian merupakan lahan pertanian yang subur. hal ini perlu dipertimbangkan karena setiap kebijakan pembangunan yang ada harus berwawasan lingkungan dan berkelanjutan. 3.3. kesesuaian penggunaan lahan dengan rtrw kabupaten klaten sampai pada tahun 2013, sebagian besar (9910 %) penggunaan lahan di sepanjang koridor solo– yogyakarta, kabupaten klaten masih sesuai dengan rencana pola ruang kabupaten klaten tahun 2011 – 2031. jumlah tersebut di mungkinkan akan berkurang jika pemanfaatan lahan di koridor ini tidak di monitoring dengan ketat. perlu langkah antisipasi oleh pemerintah setempat agar pembangunan yang dilakukan di masa yang akan datang lebih diarahkan ke lahan-lahan yang bukan merupakan lahan sawah, khusunya sawah yang beririgasi. tabel 5. kesesuaian lahan dengan rtrw kabupaten klaten (hasil analisis, 2013) kesesuaian luas (ha) prosentase sesuai 7623.11 99.10% tidak sesuai 69.15 0.90% jumlah 7692.26 100% gambar 7. lokasi sebaran konversi lahan yang tidak sesuai dengan rtrw (hasil analisis, 2013) geoplanning: journal of geomatics and planning vol 2, no 1, 2015, 22-29 rahayu et al. | 29 4. kesimpulan berdasarkan hasil analisis dapat disimpulkan bahwa keberadaan jalan solo-yogya dan peningkatan aksesibilitas pada ruas jalan tersebut ternyata merupakan daya tarik di daerah penelitian yang pada akhirnya memicu terjadinya konversi lahan. selama periode tahun 1994 sampai 2013 lahan pertanian di koridor tersebut berkurang sebesar 424.82ha, dengan rata-rata perubahan pertahun 22,35 ha/tahun. sebagian besar lahan pertanian berubah ke lahan permukiman sebesar 377.09 ha, dengan rata-rata bertambah 19.84 ha/tahun. desa yang mengalami perubahan lahan pertanian terbesar adalah desa banaran di kecamatan delanggu. kesesuaian penggunaan lahan di daerah penelitian dengan peta rencana tata ruang kabupaten klaten diketahui bahwa lahan yang penggunaanya tidak sesuai dengan rtrw kabupaten klaten adalah sebesar 69.15 ha (0.90%). berdasarkan hasil penelitian tersebut maka ke depan perlu memperketat aturan pemberian ijin pendirian bangunan di atas lahan pertanian, khususnya lahan pertanian yang subur. 5. daftar pustaka aronoff, s. (1989). geographic information systems : a management perspective. wdl publ., ottawa. buiten, h.j. (1993). image interpretation : visual or digital? dalam : buiten, h.j. dan j.g.p.w. clevers (eds), land observation by remote sensing : theory and applications. gordon and breach science publ. amterdam. bing hui, dkk. (2012). analysis of land use change characteristics based on remote sensing and gis in the jiuxiang river watershed. dalam internasional journal on smart sensing and interlligent systems, vol. 5, no, 4. bulan desember 2012. diakses tanggal 10 maret 2013. danoedoro, p. (1996). pengolahan citra digital : teori dan aplikasi dalam bidang penginderaan jauh. fakultas geografi universitas gadjah mada, yogyakarta. hord, r.m. (1986). remote sensing methods and applications. john wiley & sons, new york. jensen, j.r. (1983). urban/suburban land use analysis. dalam colwell, r.n., manual of remote sensing. vol ii, american society of photogrammetry, virginia. jensen, j.r. (1996). introductory digital image processing : a remote sensing perspective, second edition. prentice hall, new jersey. lillesand, t.m. dan r.w. kiefer. (1979). remote sensing and image interpretation. terjemahan : dulbahri, p. suharsono, hartono dan suharyadi, 1990. gadjah mada university press, yogyakarta. lindgren, d.t. (1985). land use planning and remote sensing. martinus nijhoff publishers, dordrecht. mengistu d. a. dan ayobamit. (2007). application of remote sensing and gis inland use/land cover mapping and change detection in a part of south western nigeria. dalam journal: african journal of environmental science and technology vol. 1 (5), pp. 099-109, december, 2007. diakses tanggal 10 maret 2013 meyer, w.b. dan turner ii.l. (1994). changes in land use and land cover a global perspective. cambridge university press, cambridge. purwadhi, s. h. (2001). interpretasi citra digital. gramedia, jakarta. sitorus, s.r.p. (1995). evaluasi sumberdaya lahan. tarsito, bandung. sutanto. (1986). penginderaan jauh jilid 1. gadjah mada university press, yogyakarta. warpani, suwardjoko. (1990). merencanakan sistem perangkutan. bandung. penerbit itb bandung. 124 | geoplanning vol 2, no 2, 2015, 124-137 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.2.2.124-137 pemetaan neraca sumberdaya air kabupaten sabu raijua, nusa tenggara timur, indonesia r. dwihatmojo a, d. maryanto a a badan informasi geospasial, indonesia abstract: the balance of water resources reflects changing water resource potential within a certain time. sabu raijua regency is a regency in east nusa tenggara province that has physical problems in the water supply. this study aims to identify the potential, utilization, and the balance of water resources in sabu raijua regency. the method used is quantitative and spatial analysis to calculate the potential, utilization, and balance water resources. the results showed potential and utilization of water in sabu raijua. the balance of water resources showed greater ‘aktiva’ than ‘pasiva’, and there is still a balance of 415,453,645.75 m3/year. this condition also shows that sabu raijua regency has a surplus of water resources within a period of one year. however, in a certain period (july-september) it experiences a deficit of water resources so that it needs good water resource management to anticipate the problem. abstrak: neraca sumberdaya air menggambarkan perubahan potensi sumberdaya air dalam kurun waktu tertentu. kabupaten sabu raijua merupakan salah satu kabupaten di provinsi nusa tenggara timur yang memiliki permasalahan fisik dalam penyediaan air. penelitian ini bertujun untuk melihat potensi, pemanfaatan, dan neraca sumberdaya air di kabupaten sabu raijua. metode yang digunakan adalah analisis kuantitatif dan spasial untuk menghitung potensi, pemanfaatan, dan neraca sumberdaya air. hasil penelitian menunujukkan besarnya potensi dan pemanfaatan air di kabupaten sabu raijua. neraca sumberdaya air menunjukkan aktiva lebih besar dibanding pasiva sehingga masih terdapat saldo sebesar 415.453.645,75 m3/tahun. kondisi ini juga menunjukkan bahwa kabupaten sabu raijua mengalami surplus sumber daya air dalam kurun waktu satu tahun. namun pada bulan tertentu (juliseptember) terjadi defisit sumberdaya air sehingga dibutuhkan arahan pengelolaan sumberdaya air untuk mengantisipasi permasalahan tersebut. copyright © 2015 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): dwihatmojo, r., & maryanto, d. (2015). pemetaan neraca sumberdaya air kabupaten sabu raijua, nusa tenggara timur, indonesia. geoplanning: journal of geomatics and planning, 2(2), 124-137. doi:10.14710/geoplanning.2.2.124-137 1. pendahuluan kabupaten sabu raijua merupakan salah satu kabupaten yang berada di provinsi nusa tenggara timur. kabupaten sabu raijua terdiri dari dua pulau utama yakni pulau sabu dan pulau raijua. pulau di kabupaten sabu raijua sebagai pulau terluar sehingga memiliki peranan strategis sebagai wilayah perbatasan negara. sumberdaya alam merupakan salah satu isu strategis dalam pengelolaan wilayah, perencanaan pembangunan melihat kondisi dan potensi wilayah yang dimiliki. proses memahami kondisi eksisting dan prediksi mengenai dinamika sumberdaya air untuk berbagai kegiatan sangat dibutuhkan (jain et.al, 2010). article info: received: 20 august 2015 in revised form: 10 september 2015 accepted: 1 october 2015 available online: 31 october 2015 keywords: potential water, balance water resources, sabu raijua regency corresponding author: roswidyatmoko dwihatmojo geospatial information agency indonesia email: roswidyatmoko.dwihatmojo@big.go. id open access info artikel: diterima: 20 agustus 2015 hasil revisi: 10 september 2015 disetujui: 1 oktober 2015 publikasi on-line: 31 oktober 2015 kata kunci: potensi air, neraca sumberdaya air, kabupaten sabu raijua kontak penulis: roswidyatmoko dwihatmojo badan informasi geospasial, indonesia email: roswidyatmoko.dwihatmojo@big.go .id http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 125 salah satu permasalahan yang dihadapi adalah kemiskinan dan keterbatasan sumberdaya khususnya air, oleh karena itu dipandang perlunya upaya untuk melihat potensi dan pemanfaatan sumberdaya air. kabupaten sabu raijua sebagai daerah otonomi baru berdasarkan undang-undang nomor 52 tahun 2008 memandang perlu melakukan penyusunan neraca sumberdaya alam spasial kabupaten sabu raijua demi tercapainya kelestarian fungsi ekosistemnya. untuk mendukung keberhasilan usaha tersebut perlu diketahui lokasi keterdapatan potensi dan kondisi sumberdaya alam yang ada di suatu wilayah sehingga dapat dibuat perencanaan yang tepat dalam pengembangan wilayah tersebut (gambar 1). salah satu alternatif untuk mendukung pengembangan pemanfaatan potensi sumberdaya alam yang ada di suatu wilayah dapat dilakukan melalui penyusunan neraca sumberdaya alam. penyusunan neraca sumberdaya alam merupakan modal atau langkah awal untuk pemanfaatan sumberdaya alam dan untuk menghitung ketersediaan sumberdaya serta potensi pendapatan daerah yang dapat dihasilkannya. selain itu dataset meteorologi sangat dibutuhkan untuk menyusun potesi sumberdaya air di suatu daerah (remesan dan holman, 2015). penyusunan neraca sumberdaya alam ini berkaitan dengan bagaimana pengelolaan sumberdaya alam yang dapat menguntungkan baik secara ekonomi dan lingkungan serta adanya kelangsungan bagi kesejahteraan masyarakat untuk generasi sekarang dan generasi penerusnya. gambar 1. peta administrasi kabupaten sabu raijua (badan informasi geospasial, 2004) berdasarkan latar belakang tersebut, penelitian sebagai salah satu upaya untuk mengetahui besaran dan sebaran potensi sumberdaya air daerah untuk menyusun strategi pemanfaatan dan pengelolaan sumberdaya air di daerah. aspek penggunaan lahan juga diperhatikan sebagai dasar dalam menyusun strategi interaksi sumberdaya air dengan pola pemanfaatanya (yang, 2015). penelitian ini bertujuan untuk mengetahui potensi sumberdaya air, pemanfaatan sumberdaya air, dan menghitung neraca sumberdaya air untuk evaluasi sumberdaya air kabupaten sabu raijua. 2. data dan metode neraca spasial menekankan penyusunan informasi neraca dengan memanfaatkan informasi keruangan atau geospasial. neraca sumberdaya air spasial adalah “timbangan”, yang disusun untuk mengetahui besarnya cadangan awal sumberdaya air atau potensi air yang dinyatakan dalam aktiva, dan besarnya http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 126 | pemanfaatan / penggunaan air yang dinyatakan dalam pasiva serta dinyatakan dalam sistem koordinat tertentu. perubahan cadangan dapat diketahui melalui besarnya sisa cadangan yang dinyatakan dalam saldo dalam suatu daerah dan dalam suatu kurun waktu karena air bersifat dinamis sehingga analisis multivariat sangat dimungkinkan untuk melihat dan mengukur keberlanjutan manajemen sumberdaya air (hunter et al., 2015) kerangka neraca sumberdaya air dalam bentuk model tabulasi statistik berupa tabel diskontro (sebelah menyebelah) seperti dalam neraca keuangan. penyusunan neraca sumberdaya lahan sebenarnya adalah memberikan informasi tentang perubahan potensi sumberdaya air dalam suatu kurun waktu yang dinyatakan dalam aktiva dan pasiva. 2.1 potensi sumberdaya air (aktiva) a) air permukaan berdasarkan data yang tersedia, perhitungan debit dapat dilakukan antara lain dengan cara berikut ini :  analisa regional  estimasi debit pada suatu lokasi berdasarkan atas perbandingan luas das dari pos duga air dengan lokasi yang akan dihitung dan dengan atau tidak mempertimbangkan faktor curah hujan.  data debit yang tersedia harus memenuhi kriteria panjang data minimal 10 tahun dan telah dilakukan pengujian data.  metode hujan – limpasan berhubung data curah hujan umumnya tersedia dengan periode yang lebih panjang dari data debit, maka estimasi debit diperoleh dengan cara perhitungan analisa sintetis berdasarkan data curah hujan yang terjadi. data curah hujan telah dilakukan pengujian data dan merupakan data curah hujan rata-rata di das (diperoleh berdasarkan hasil perhitungan poligon thiesen, isohyet). dalam metode ini subyektivitas dan asumsi bahwa trend curah hujan dan debit mempunyai trend yang sama, apabila data curah hujan dan debit menunjukan trend yang berbeda maka sebaiknya cara ini tidak dilakukan.  distribusi normal perhitungan debit andalan persatuan waktu (setengah bulanan atau sepuluh harian atau bulanan) dengan menggunakan metoda distribusi normal q80 = qrata-rata 0,84 * sd dimana : q80 = debit setengah-bulanan 80% qrata-rata = debit rata-rata untuk setengah-bulanan sd = deviasi standar sd = ( ( σ (xi xm)2 ) / (r-1)0,5 dimana : xi = nilai data untuk setengah bulanan i; xm = rata-rata untuk semua nilai x; r = jumlah tahun data. b) air bawah tanah a. perhitungan cadangan air bawah tanah diperlukan data-data tebal akuifer, sebaran akuifer dan transmisibilitas akuifer baik akuifer tidak tertekan maupun tertekan. untuk bisa terpenuhinya data ini sangat sulit , maka cadangan airtanah disetarakan dengan imbuhan air tanah yang berasal dari air hujan. b. air hujan sebagian menjadi air permukaan dan sebagian meresap kedalam tanah. perkiraan awal imbuhan dapat di hitung dengan mengambil prosentase tertentu dari curah hujan rata-rata tahunan (rf) yang meresap ke reservoar air bawah tanah. ketelitian metode ini tergantung pada angka prosentase imbuhan yang terpilih. imbuhan pada akuifer dapat dihitung sebagai berikut: rc = rf x a x rc (%) rc = imbuhan ( m3 /tahun ) http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 127 rf = curah hujan rata-rata tahunan di daerah tingkapan dihitung dengan metode isohyet dan poligon thiessen. a = luas area/ tadah (m2) dihitung dengan planimeter, tidak termasuk sawah irigasi. rc = prosentase imbuhan. imbuhan tersebut ditambah perhitungan imbuhan dari infiltrasi rata-rata (ir) dari daerah sawah yang terletak pada daerah akuifer. jika padi hanya ditanam pada saat musim hujan (1 kali panen) diperlukan hitungan imbuhan dengan menggunakan prosentase imbuhan (rc %). c. keseimbangan air di cekungan keseimbangan air dapat digunakan untuk menghitung imbuhan dengan; formulasi (walton 1970); rf = ro + eta + ab + qg ± δsm ± δsg dimana : rf = curah hujan rata-rata tahunan ro = limpasan ir permukaan, diukur secara langsung dari aliran dasar pada stasiun pengukur sungai. eta = evapotranspirasi nyata. ab = pengambilan airtanah qg = airtanah yang mengalir di daerah batas cekungan dengan menggunakan persamaan darcy. δsm = perubahan dalam simpanan kelengasan tanah, dihitung dengan keseimbangan kelengasan tanah. δsg = perubahan dalam cadangan airtanah formulasi darcy; qg = t.i.l dimana qg = airtanah yang mengalir di daerah batas cekungan t = keterusan (m2/hari) i = gradient hidrolika l = lebar akuifer dalam metode ini semua komponen dihitung kecuali δsm, δsm. δsg ini akan seimbang sepanjang tahun, artinya akan positif pada musim hujan dan negatif pada musim kemarau. cara lain salah satunya adalah dengan pendekatan water balance model thornwite matter. dengan pendekatan ini bisa diketahui besarnya runoff bulanan dan besarnya air yang tertahan (detention) dalam bulanan. runoff merupakan aliran langsung setelah hujan dan aliran air sungai yang muncul dari mata air. air detention merupakan air perkulasi yang kemudian mengisi air tanah. dengan perkiraan besarnya perkulasi ini kita bisa memperkirakan potensi airtanah atau bisa dipakai sebagai pedoman nilai aman besarnya airtanah yang dapat diambil. data yang diperlukan dalam metode ini adalah data hujan bulanan rata-rata, suhu bulanan rata-rata untuk menghitung penguapan, data penggunaan lahan dan data jenis tanah. pendekatan ini dipakai untuk menghitung imbangan air dalam satu-satuan daerah aliran sungai dan kurang disarankan untuk batas wilayah administrasi, seperti formulasi berikut: p = i + aet + of + d sm + d gws + gwr dimana : p = presipitasi i = intersepsi aet = aktual evapotranspirasi http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 128 | of = overlandflow d sm = perubahan kelengasan tanah d gws = perubahan cadangan airtanah gwr = aliran airtanah 2.2 kebutuhan sumberdaya air (pasiva) dalam perhitungan neraca air sebaiknya perhitungan kebutuhan air diperhitungkan dapat memenuhi kebutuhan air multi guna. kebutuhan air tersebut dapat dikelompokkan menjadi dua bagian yaitu:  kebutuhan air non-irigasi meliputi antara lain : kebutuhan air baku untuk domestik dan non-domestik (pelayanan umum), kebutuhan air industri, kebutuhan air untuk perikanan, kebutuhan air untuk peternakan, dll.  kebutuhan air untuk irigasi merupakan kebutuhan air untuk pertanian yang disesuaikan dengan pola tata tanam (luas tanam, jenis tanaman dan tingkat pertumbuhan), kondisi jaringan irigasi. dalam perhitungan kebutuhan air pada tiap wilayah sungai ditetapkan sebagai fungsi perkalian antara jumlah pemakai air atau luas daerah irigasi dan satuan kebutuhan air persatuan waktu. a) kebutuhan air irigasi data air untuk irigasi sudah ada pada masing-masing dinas pengelolaan sumber daya air provinsi atau kabupaten. penggunaan air untuk irigasi padi diperhitungkan berdasar luas sawah irigasi teknis, semi teknis dan sederhana yang terdapat dalam das yang bersangkutan. penggunaan air untuk irigasi yang dipergunakan dalam waktu satu tahun sehingga pengaruh lama tanaman dan prosentase (%) intensitas tanaman harus diperhitungkan. perhitungan penggunaan air untuk padi per tahun adalah : a = l x i t x a a = pengunaan air irigasi dalam l = luas daerah irigasi ( ha ) i t = intensitas tanaman dalam prosen (%) musim/ tahun a = standar penggunaan air ( 1 l/det/ha ) atau a = 0,001 m/det/ha x 3600 x 24 x 120 hari / musim b) kebutuhan air bersih rumah tangga (domestik) kebutuhan air bersih rumah tangga adalah air yang diperlukan untuk rumah tangga yang diperoleh secara individu dari sumber air yang dibuat oleh masing masing rumah tangga seperti sumur dangkal, perpipaan atau hidran umum atau dapat diperoleh dari layanan sistem penyediaan air minum (spam) pdam. sumber air baku yang dipakai oleh pdam terdiri dari air tanah, air permukaan atau gabungan dari keduanya. kebutuhan air bersih rumah tangga, dinyatakan dalam satuan liter/orang/hari (l/o/h), besar kebutuhan tergantung dari kategori kota berdasarkan jumlah penduduk (tabel 1): tabel 1. kebutuhan air bersih rumah tangga menurut kategori kota (dirjen cipta karya, 2006) no kategori kota jumlah penduduk (jiwa) kebutuhan air bersih (l/o/h) 1 semi urban 3.000 – 20.000 60 90 2 kota kecil 20.000 – 100.000 90 110 3 kota sedang 100.000 – 500.000 100125 4 kota besar 500.000 – 1.000.000 120 150 5 metropolitan > 1.000.000 150 200 dengan asumsi kenaikan kebutuhan air bersih 1 % per tahun, maka kebutuhan air bersih pada tahun 2010 serta prediksinya untuk tahun 2014, 2019 dan 2029 berdasarkan kategori kota, diuraikan pada tabel 2 berikut: http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 129 tabel 2. kebutuhan air bersih rumah tangga per orang per hari (dirjen cipta karya, 2006) keterangan: hasil perhitungan dengan kenaikan kebutuhan air bersih 1 % per tahun kebutuhan air bersih rumah tangga diperhitungkan pula untuk kehilangan air yang terdiri dari : (1).kehilangan dalam proses sebesar 6 %; (2).kehilangan air tidak terhitung yaitu sebesar 25 %. c) kebutuhan air perkotaan (komersial dan sosial) – non domestik kebutuhan air perkotaan, yaitu untuk komersial dan sosial seperti: toko, gudang, bengkel, sekolah, rumah sakit, hotel dan sebagainya diasumsikan antara 15% sampai dengan 30% dari total air pemakaian air bersih rumah tangga (asdak, 2002). ternyata makin besar dan padat penduduknya cenderung lebih banyak daerah komersial dan sosial, sehingga kebutuhan untuk air komersial dan sosial akan lebih tinggi jika penduduk bertambah. d) kebutuhan air industri survei kebutuhan air industri diperlukan untuk menentukan rata-rata penggunaan air pada berbagai jenis industri tertentu. perhitungan kebutuhan air industri dapat diperhitungkan berdasarkan atas:  jumlah karyawan  luas air industri  jenis/tipe industri. e) kebutuhan untuk peternakan perhitungan kebutuhan air rata-rata untuk peternakan tergantung pada jumlah dan jenis ternak (tabel 3). kebutuhan air rata-rata untuk ternak ditentukan dengan mengacu pada hasil penelitian dari fidp (ditjen. pengairan, 1992) yang di muat dalam technical report national water resources policy. secara umum kebutuhan air untuk ternak dapat diestimasikan dengan cara mengalikan jumlah ternak dengan tingkat kebutuhan air berdasarkan persamaan berikut ini:  )3()3()2()2()1()1( pqpqpqqe  keterangan : qe = kebutuhan air untuk ternak, (lt/hari). q(1) = kebutuhan air untuk sapi, kerbau, dan kuda, (lt/ekor/hari). q(2) = kebutuhan air untuk kambing, dan domba, (lt/ekor/hari). q(3) = kebutuhan air untuk unggas, (lt/ekor/hari). p(1) = jumlah sapi, kerbau, dan kuda, (ekor). p(2) = jumlah kambing, dan domba, (ekor). p(3) = jumlah unggas, (ekor). tabel 3. kebutuhan air untuk ternak (dirjen cipta karya, 2006) jenis ternak kebutuhan air (lt/ekor/hari) sapi/kerbau/kuda 40 kambing/domba 5 babi 6 unggas 0,6 kriteria kota rentang penduduk (jiwa) kebutuhan air (l/o/h) tahun 2010 tahun 2014 tahun 2019 tahun 2029 desa 3.000 --20.000 60 62 66 72 kota kecil 20.000 --100.000 90 94 98 108 kota sedang 100.000 --500.000 100 104 109 120 kota besar 500.000 --1.000.000 120 125 131 144 metropolitan > 1.000.000 150 156 164 180 http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 130 | f) kebutuhan untuk perikanan kebutuhan air untuk perikanan diperkirakan berdasarkan luas kolam, tipe kolam serta kedalaman air yang diperlukan. kebutuhan ini meliputi kebutuhan untuk mengisi kolam pada saat awal tanam dan penggantian air. penggantian air bertujuan untuk memperbaiki kondisi kualitas air dalam kolam. kebutuhan air untuk perikanan untuk selanjutnya dapat di hitung dengan menggunakan rumus sebagai berikut: 10000)( 1000 )(  fpa fpq qfp keterangan : qfp = kebutuhan air untuk perikanan, (m3/hari), q(fp) = kebutuhan air untuk pembilasan, (lt/hari/ha), a(fp) = luas kolam ikan, (ha). g) kebutuhan untuk penggelontoran atau pemeliharaan sungai. menurut peraturan pemerintah republik indonesia nomor 38 tahun 2011 tentang sungai, aliran pemeliharaan sungai adalah aliran air minimum yang harus tersedia di sungai untuk menjaga kehidupan ekosistem sungai. perlindungan aliran pemeliharaan sungai dilakukan dengan mengendalikan ketersediaan debit andalan 95%, yaitu aliran air (m3/detik) yang selalu tersedia dalam 95% waktu pengamatan, atau hanya paling banyak 5% kemungkinannya aliran tersebut tidak tercapai. dalam hal debit andalan 95% tidak tercapai, pengelola sumber daya air harus mengendalikan pemakaian air di hulu. dengan demikian besarnya kebutuhan air untuk pemeliharaan sungai dihitung berdasarkan debit andalan q95% dari data ketersediaan air yang ada. direktorat teknik penyehatan, ditjen. cipta karya, (1993) menetapkan unit kebutuhan air sebesar: 10,5 m3/kapita/bulan (atau 350 l/kapita/hari). angka ini akan menurun sampai 300 l/kapita/hari pada tahun 2015 selaras dengan pembangunan sistem-sistem pembuangan air (sewerage systems) dan makin banyaknya penduduk yang memanfaatkan sistem tersebut. diagram alir penelitian dapat dilihat pada gambar 2 di bawah ini. gambar 2. diagram alir penelitian (analisis, 2015) peta batas das peta rbi skala 1:25.000 peta hidrogeologi skala 1:250.000 peta kerja peta ishoye t peta penutup lahan skala 1:25.000 peta pasiva sd air tentatif peta aktiva sd air tentatif peta pasiva sd air cek lapangan data curah hujan data aliran permukima n potensi mata air & potensi akuifer lokasi pos duga air data debit sungai tabel neraca sd air http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 131 3. hasil dan pembahasan 3.1 analisis potensi sumberdaya air (aktiva) di kabupaten sabu raijua a) air hujan hasil perhitungan dengan menggunakan metode thiesen terhadap data curah hujan tersebut, menunjukkan bahwa potensi tebal curah hujan di kabupaten sabu raijua adalah sebesar 1097,2 mm/tahun atau 504.633.554,76 m3/tahun. berdasarkan data tersebut dapat diketahui pula bahwa, curah hujan yang terjadi di kabupaten sabu raijua bagian barat (termasuk p. raijua) lebih tinggi dibandingkan di sebelah timur. curah hujan dapat diklasifikasikan menjadi klas rendah (< 1500 mm/tahun), sedang (1500-2500 mm/tahun) dan tinggi (2500-5000 mm/tahun). berdasarkan seluruh hasil perekaman curah hujan dari 4 stasiun yang ada, menunjukkan bahwa curah hujan rata-rata yang terjadi diseluruh kabupaten sabu raijua termasuk dalam kelas rendah. kondisi tersebut tentu akan mempengaruhi potensi sumber daya air baik permukaan, maupun bawah permukaan di wilayah kabupaten sabu raijua, karena air hujan merupakan sumber utama dari kedua jenis potensi air tersebut. b) air permukaan ketersediaan air permukaan di kabupten sabu raijua diperoleh dari perhitungan data hujan setelah dikurangi dengan evapotranspirasi dan pengisian/peresapan air tanah. pada periode satu tahun, seluruh kecamatan di sabu raijua umumnya kemungkinan masih terdapat air permukaan terjadi pada bulan januari-juni dan oktoberdesember, artinya bahwa pada bulan bulan tersebut curah hujan yang terjadi dapat menutupi untuk kebutuhan evapotranspirasi dan resapan. pada bulan-bulan juliseptember nilai potensi air permukaan nol (atau negatif) menunjukkan bahwa pada bulan tersebut hujan yang terjadi tidak cukup untuk memenuhi kebutuhan evapotranspirasi dan resapan ke dalam tanah. potensi air permukaan paling banyak terdapat di kecamatan sabu barat, yaitu kurang lebih 146.538.495,49 m3/tahun. disamping karena curah hujannya relatif lebih tinggi di daerah ini, luas wilayahnya juga relatif besar dan mempunyai formasi geologi yang koefisien/prosentase imbuhannya cukup kecil seperti misalnya formasi bobonaro. namun demikian fluktuasi potensi air permukaan tersebut cukup tajam, karena pada saat kemarau terjadi defisit air. kecamatan yang mempunyai potensi air permukaan terkecil terdapat di kecamatan sabu timur dengan potensi sebesar kurang lebih 26.383.608,01 m3/tahun. empat kecamatan lainnya mempunyai potensi air permukaan berkisar antara lebih dari 36. m3/tahun hingga kurang dari 70.000.000 m3/tahun. pola ketersediaan potensi air permukaan mengikuti pola curah hujan, yaitu berbentuk huruf v, karena air permukaan bersumber terutama dari air hujan. c) mata air rata-rata debit total seluruh mata air di kabupaten sabu raijua berkisar 92,27 l/det, dengan total potensi airnya adalah sebesar 2.909.827 m3/tahun. secara temporal, debit mata air polanya mengikuti pola tebal curah hujan, yaitu naik pda musim hujan dan menurun pada musim kemarau. dari sisi spasial (adminsitrasi), pemunculan air tanah (mata air) paling banyak berada di wilayah kecamatan hawu mehara. mata air yang debitnya relaif besar antara lain mata air lok eimada dan limagu di sabu timur, mata air depe ae di hawu mehara dan mata air menia di raijua. d) air tanah formasi batuan paling luas adalah kompleks bobonaro, yakni kurang lebih 217.578.530,46 m2, namun nilai prosentase imbuhan hanya sebesar 5%, sedangkan formasi paling sempit arealnya adalah formasi ofu, yaitu sekitar 9.868.125,46% dengan prosentase imbuhan hanya 5%. selain dipengaruhi oleh nilai prosentase imbuhan, besarnya resapan air hujan juga dipengaruhi oleh besarnya curah hujan itu sendiri. hasil perhitungan besarnya air yang meresap ke dalam tanah menunjukkan bahwa formasi batu gamping koral yaitu mencapai kurang lebih 55.499.245,38 m3/thn, yang tersebar di bagian utara dan barat pulau sabu, dan disekeliling pulau raijua. adapun yang terkecil terdapat pada formasi ofu yaitu sebesar 491.008,92 m3/thn, yang secara spasial berada di bagian selatan pulau sabu. adapun total besarnya volume resapan seluruh kabupaten adalah sebesar 80.756.150,17 m3/thn. volume resapan inilah yang berpotensi menjadi air tanah yang diperhitungkan dalam neraca sumber daya air. http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 132 | kecamatan sabu barat mempunyai potensi air tanah paling banyak yaitu 29.292.781,88 m3/tahun, sedangkan paling kecil potensinya yaitu kecamatan sabu liae sebesar 2.896.244,84 m3/tahun. secara temporal pola ketersediaan potensi air tanah mengikuti pola curah hujan, yaitu berbentu huruf v. hal ini disebabkan karena sumber air tanah berasal dari air hujan. dari uraian tiap sumber daya air, ketersediaan sumber daya air di kabupaten sabu raijua dapat dikompilasi menjadi sebuah tabel aktiva. seperti pada tabel 4 merupakan jenis sumber daya air dalam satuan m3/tahun untuk masing masing kecamatan. potensi sumber daya air baik permukaan maupun bawah permukaan paling tinggi adalah kecamatan sabu barat yakni sebesar 176.215.489,53 m3/tahun, sedangkan potensi yang paling kecil adalah sabu timur yaitu sebesar 37.850.996,87 m3/tahun. namun demikian kecamatan sabu timur mempunyai potensi mata air yang paling tinggi yaitu sekitar 559.716,48 m3/tahun. gambar 3 di bawah menunjukkan peta potensi air kabupaten sabu raijua. tabel 4. aktiva sumber daya air kabupaten sabu raijua (m3/thn) sumberdaya air sabu barat sabu tengah sabu timur sabu liae hawu mehara raijua air hujan 203.453.857,74 60.342.145,54 42.816.462,93 54.244.145,85 89.464.217,55 54.312.725,14 air permukaan 146.538.495,49 39.133.687,46 26.383.608,01 41.169.556,47 69.265.349,42 36.518.308,98 mata air 384.212,16 3.162,24 559.716,48 56.920,32 388.006,85 278.909,57 air tanah 29.292.781,88 13.571.455,76 10.907.672,38 2.896.244,84 12.987.546,94 11.100.448,37 aktiva 176.215.489,53 52.708.305,47 37.850.996,87 44.122.721,63 82.640.903,21 47.897.666,91 gambar 3. peta potensi air kabupaten sabu raijua (analisis, 2014) http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 133 3.2 analisis kebutuhan sumberdaya air (pasiva) di kabupaten sabu raijua a) air domestik penggunanan air untuk keperluan domestik paling banyak di kecamatan sabu barat sebesar 893.257,20 m3/tahun yang disebabkan jumlah penduduk tinggi sehingga kebutuhan perkapita juga lebih tinggi. urutan jumlah penggunanan selanjutnya dari besar ke yang lebih kecil yaitu hawu mehara (346.326,60 m3/tahun); sabu liae (199.421,40 m3/tahun); raijua (172.637,70 m3/thun); sabu timur (166.878,00 m3/tahun); dan paling sedikit adalah sabu tengah (162.782,70 m3/tahun). kebutuhan domestik yang terutama adalah mandi, cuci dan kakus. berdasarkan hasil perhitungan di atas, total penggunaan air untuk domestik di kabupaten sabu raijua adalah 1.941.304,60 m3/tahun. b) air non domestik penggunaan untuk non domestik sebenarnya mencakup banyak kegiatan, namun keterbatasan data maka hanya diperhitungkan kegiatan pendidikan dan kesehatan. penggunaan untuk non domestik diperhitungkan jumlah murid sedangkan di bidang kesehatan diperhitungkan jumlah puskesmas. total penggunaan air untuk kebutuhan non domestik sebesar 114.015 m3/tahun. c) air untuk peternakan hasil perhitungan kebutuhan air untuk peternakan diketahui bahwa kecamatan sabu barat merupakan wilayah paling banyak mempunyai ternak, baik sapi, kerbau maupun kuda hingga ternak unggas sebesar 169.953.855 m3/tahun. kecamatan sabu timur meskipun variasi hewan ternaknya lebih banyak dibanding raijua namun jumlahnya sedikit maka kebutuhan airnya paling kecil yaitu sebesar 28.290,20 m3/thn. d) air untuk pertanian data luas sawah irigasi diperoleh dari data dalam angka dan hasil interpretasi citra. total kebutuhan air untuk pertanian yaitu sebesar 22.560.768.00 m3/tahun. penggunaan air tertinggi di kecamatan sabu barat yaitu sebesar 9.476.352,00 m3/tahun karena luas sawah paling besar. disusul kemudian kecamatan sabu tengah dengan penggunaan air sebesar 8.356.608,00 m3/tahun, dan yang paling sedikit adalah kecamatan raijua dan hawu mehara karena tidak terdapat sawah irigasi. e) air untuk industri data jumlah karyawan industri untuk menghitung penggunaan air untuk industri diperoleh dari bps (kecamatan dalam angka), sedangkan standar penggunaan air untuk industri dapat diperkirakan dengan menggunakan besaran kebutuhan rata-rata yaitu 500 liter/hari/karyawan (pu, 2005). jumlah total pemakaian air untuk industri di kabupaten sabu raijua sebesar 961.593,00 m3/tahun. air tersebut paling banyak digunakan di kecamatan sabu barat yaitu sebesar 913.047,50 m3/tahun, disusul kemudian sabu tengah (20.440,00 m3/tahun), raijua (10.950,00 m3/tahun), sabu timur (10.767,50 m3/tahun) dan sabu liae (6.387,50 m3/tahun). tabel 5. pasiva sumber daya air kabupaten sabu raijua (m3/tahun) penggunaan sabu barat sabu tengah sabu timur sabu liae hawu mehara raijua domestik 893.257,20 162.782,70 166.878,00 199.421,40 346.326,60 172.637,70 non domestik 35.014,45 13.661,95 16.019,85 19.359,60 19.713,65 10.245,55 industri 913.047,50 20.440,00 10.767,50 6.387,50 0 10.950,00 peternakan 169.953,86 47.657,25 28.290,20 65.951,27 47.176,40 45.729,76 pertanian 9.476.352,00 8.356.608,00 2.923.776,00 1.804.032,00 0 0 jumlah 11.487.625,01 8.601.149,90 3.145.731,55 2.095.151,77 413.216,65 239.563,01 berdasarkan hasil perhitungan yang disajikan pada tabel 5 dapat diketahui bahwa penggunaan terbesar sumber daya air berada di wilayah kecamatan sabu barat yakni sebesar 11.487.625,01 m3/tahun, kemudian berurutan hingga ke yang paling sedikit yaitu kecamatan sabu tengah (8.601.149,90 m3/tahun), sabu timur (3.145.731,55 m3/tahun), sabu liae (2.095.151,77 m3/tahun), hawu mehara (413.216,65 m3/tahun) dan paling kecil raijua (239,563.01 m3/tahun). bidang pertanian secara umum merupakan penggunaan air terbesar dibanding bidang lainnya, di bawahnya kemudian bidang domestik, peternakan, industri dan non domestik (gambar 4). http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 134 | gambar 4. peta pemanfaatan air (analisis, 2014) 3.3 neraca sumberdaya air di kabupaten sabu raijua neraca sumber daya air merupakan imbangan antara ketersediaan dan penggunaan sumber daya air. hasil perhitungan neraca sumber daya air kabupaten sabu raijua disajikan dalam bentuk tabel diskontro (sebelah menyebelah) seperti yang disajikan tabel 6 di bawah. tabel 6. neraca sumber daya air kabupaten sabu raijua (analisis, 2014) kecamatan : hawu mehara sumber daya air potensi (m3/th) penggunaan volume (m3/tahun) air hujan 89.464.217,55 domestik 346.326,60 air permukaan 69.265.349,42 non domestik 19.713,65 mata air 388.006,85 industri air tanah 12.987.546,94 peternakan 47.176,40 jumlah a. permukaan + a. tanah 82.640.903,21 jumlah 413.216,65 kecamatan : raijua sumber daya air potensi (m3/th) penggunaan volume (m3/tahun) air hujan 54.312.725,14 domestik 172.637,70 air permukaan 36.518.308,98 non domestik 10.245,55 mata air 278.909,57 industri 10.950,00 air tanah 11.100.448,37 peternakan 45.729,76 jumlah a. permukaan + a. tanah 47.897.666,91 jumlah 239.563,01 http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 135 kecamatan : sabu barat sumber daya air potensi (m3/th) penggunaan volume (m3/tahun) air hujan 203.453.857,74 domestik 893.257,20 air permukaan 146.538.495,49 non domestik 35.014,45 mata air 384.212,16 industri 913.047,50 air tanah 29.292.781,88 peternakan 169.953,86 pertanian 9.476.352,00 jumlah a. permukaan + a. tanah 176.215.489,53 jumlah 11.487.625,01 kecamatan : sabu liae sumber daya air potensi (m3/th) penggunaan volume (m3/tahun) air hujan 54.244.145,85 domestik 199.421,40 air permukaan 41.169.556,47 non domestik 19.359,60 mata air 56.920,32 industri 6.387,50 air tanah 2.896.244,84 peternakan 65.951,27 pertanian 1.804.032,00 jumlah a. permukaan + a. tanah 44.122.721,63 jumlah 2.095.151.77 kecamatan : sabu tengah sumber daya air potensi (m3/th) penggunaan volume (m3/tahun) air hujan 60.342.145,54 domestik 162.782,70 air permukaan 39.133.687,46 non domestik 13.661,95 mata air 3.162,24 industri 20.440,00 air tanah 13.571.455,76 peternakan 47.657,25 pertanian 8,356,608,00 jumlah a. permukaan + a. tanah 52.708.305,47 jumlah 8,601,149.90 kecamatan : sabu timur sumber daya air potensi (m3/th) penggunaan volume (m3/tahun) air hujan 42.816.462,93 domestik 166.878,00 air permukaan 26.383.608,01 non domestik 16.019,85 mata air 559.716,48 industri 10.767,50 air tanah 10.907.672,38 peternakan 28.290,20 pertanian 2.923.776,00 jumlah a. permukaan + a. tanah 37.850.996,87 jumlah 3.145.731,55 total aktiva 441.436.083,62 total pasiva 25.982.437,87 nilai aktiva dihitung dari jumlah air permukaan dan air tanahnya saja, karena air hujan merupakan sumber dari kedua jenis air tersebut. berdasarkan hasil perhitungan tersebut, dapat diketahui bahwa aktiva lebih besar dibanding pasiva sehingga masih terdapat saldo sebesar 415.453.645,75 m3/tahun. kondisi ini juga menunjukkan bahwa kabupaten sabu raijua mengalami surplus sumber daya air dalam kurun waktu satu tahun. demikian pula bila dilihat setiap wilayah kecamatan kondisi aktiva masih lebih besar dari pasiva sehingga kabupaten sabu raijua masih mempunyai daya dukung untuk menyangga kehidupannya (gambar 5). http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 136 | gambar 5. peta neraca air kabupaten sabu raijua (analisis, 2014) hal yang perlu untuk dicermati lebih lanjut secara temporal pada tiap kecamatan selain terjadi surplus tetapi juga terjadi defisit. sebagai indikator awal hasil perhitungan bulanan terhadap besarnya hujan bila dibandingkan dengan besarnya evapotranspirasi dan resapan, menunjukkan bahwa pada bulan juli, agustus, dan september mengalami defisit. artinya air hujan yang jatuh pada bulan tersebut tidak dapat memenuhi kebutuhan untuk evapotranspirasi dan resapan. jadi secara umum dari perhitungan data aktiva, telah terjadi defisit. hasil perhitungan neraca air spasial (aktiva, pasiva, dan saldo) menunjukkan bahwa setiap kecamatan mengalami defisit pada bulan juli, agustus dan september. kecamatan-kecamatan yang defisitnya bertambah panjang yaitu sabu liae (oktober), sabu timur (oktober) dan sabu tengah (oktober dan november). 4. kesimpulan curah hujan di seluruh kawasan kabupaten sabu raijua termasuk dalam kelas yang rendah yang mempengaruhi kondisi sumberdaya air di kabupaten tersebut. hasil penelitian juga menunujukkan besarnya potensi dan pemanfaatan air di kabupaten sabu raijua. neraca sumberdaya air menunjukkan aktiva lebih besar dibanding pasiva sehingga masih terdapat saldo sebesar 415.453.645,75 m3/tahun. kondisi ini juga menunjukkan bahwa kabupaten sabu raijua mengalami surplus sumber daya air dalam kurun waktu satu tahun. penggunaan sumber daya air untuk bebagai keperluan adalah berkisar 25,98 x 106 m3/tahun, dengan bidang pertanian mempunyai proporsi terbesar yaitu berkisar 22,6 x 106 m3/tahun. bila ditinjau secara tahunan, sumber daya air di kabupaten sabu raijua masih mengalami surplus, namun secara lebih detil, bila dilihat secara bulanan, seluruh kecamatan mengalami defisit pada bulan juli hingga september. 5. daftar pustaka asdak, c. (2002). hidrologi dan pengelolaan daerah aliran sungai. yogyakarta: gadjah mada university press. bakosurtanal et.al. (2004). petunjuk teknis neraca sumberdaya alam spasial nasional. bakosurtanal. (2001). neraca sumberdaya air spasial nasional. bps kabupaten kupang. (2013). hawu mehara dalam angka 2013. bps kabupaten kupang. http://dx.doi.org/10.14710/geoplanning.2.2.124-137 dwihatmojo dan maryanto / geoplanning: journal of geomatics and planning, vol 2, no 2, 2015, 124-137 doi: 10.14710/geoplanning.2.2.124-137 | 137 ----------------------------. (2013). raijua dalam angka 2013. bps kabupaten kupang. ----------------------------. (2013). sabu barat dalam angka 2013. bps kabupaten kupang. ----------------------------. (2013). sabu liae dalam angka 2013. bps kabupaten kupang. ----------------------------. (2013). sabu raijua dalam angka 2013. bps kabupaten kupang. ----------------------------. (2013). sabu tengah dalam angka 2013. bps kabupaten kupang. ----------------------------. (2013). sabu timur dalam angka 2013. bps kabupaten kupang. br, s. h. (1993). analisis hidrologi. jakarta: pt. gramedia departement pekerjaan umum. (2006). standar kebutuhan air rumah tangga: ditjen cipta karya hunter, et al. (2015). a dynamic, multivariate sustainability measure for robust analysis of water management under climate and demand uncertainty in an arid environment. water, 7(11): 59285958. jain et al., (2010). simulation of runoff and sediment yield for a himalayan watershed using swat model. journal water resources, 2: 267-281. remesan, r., & holman, i. p. (2015). effect of baseline meteorological data selection on hydrological modelling of climate change scenarios. journal of hydrology, 528, 631-642. sekretariat negara. (2011). peraturan pemerintah republik indonesia nomor 38 tahun 2011 tentang sungai yang, et al. (2015). simulation of groundwater-surface water interactions under different landuse scenarios in the bulang catchment, northwest china. water. 7(11): 5959-5985 http://dx.doi.org/10.14710/geoplanning.2.2.124-137 | 229 geoplanning vol 5, no 2, 2018, 229-236 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.5.2.229-236 analyzing land use pattern changes in mukim pengerang, johor, malaysia n. che’mana, a. f. salihina adepartment of urban and regional planning, faculty of built environment and surveying, university teknolog malaysia, malaysia abstract: urbanization and urban land-use transition have a competitive environment to ensure and provide good facilities for citizen benefit. thus, quantifying the spatiotemporal pattern of urbanization is important for understanding its ecological impacts and can provide basic information for appropriate decision-making. the growth of urbanization in mukim pengerang, johor, has undergone rapid changes in agriculture, settlements, townships and various activities. the changes of the land uses are due to the rapid economic development, which are the refinery and petrochemical integrated development (rapid) project and pengerang integrated petroleum complex (pipc). the industrialization projects boost the growth in land property and commercial which progressing in rapid development since the year 2012. therefore, the main aim of this paper is to quantify the changes in landscape pattern or land use pattern between the year 2008 and 2017 occurred in mukim pengerang. in monitoring the spatial pattern changes, and the changes of landscape structure, the metrics landscape were analyzed with determination of the shanon diversity index (shdi), the number of patches (np), edge density (ed) and total edge (te) in the period of 8 years. the results show that the changes occurred with the three types of land use showed significant changes in the types of land use which are forest, agricultural and built-up area. the result of shdi analysis shows the increment value between the year 2008 and 2017. this situation illustrates that the higher value of shdi for an area, resulting in the higher level of land use. this is because the growing pattern of land use is reflected by a large number of patches due to the diversification of land use activities in the area. as a result, from the metrics statistics test verifies there was a significant change in land use that took place within 8 years. copyright © 2018 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): che’man, n., & salihin, a. f. (2018). analyzing land use pattern changes in mukim pengerang, johor, malaysia. geoplanning: journal of geomatics and planning, 5(2), 229-236. doi: 10.14710/geoplanning.5.2.229-236. 1. introduction city development and shifts of urban use are a challenge to ensure citizens’ welfare is adequately protected and available sufficiently. the rapid growth in malaysia's is due to advances in economic development, particularly in the industrial sector such as the rubber and oil palm industries. thus, as the industrial sector-based economic is growing, a township in malaysia has started to growth drastically since the early 1970s. in line with the global economic growth and the high labor market in developed countries, had caused population migration to major cities to seize employment opportunities in the industrial sector. according to (toosi et at., 2012), more than 50 percent of the world's population moves from rural to urban areas. to the extent that economic growth is reflected in urban growth, it is often manifested in changes in land use patterns. in general, some amount of growth can be captured in the existing building stock and associated land use patterns, but increasing growth tends to induce land use change. therefore, urban development plays a role in providing economic facilities where it can support human life. in providing facilities for citizens, it requires a large land use spaces since the land usage is increasing from time to time. the changes in land use tend to occur due to urban development to meet the interests of the population. increasing urban population increases the demand for land for urban activity (samat, et al., 2011). thus, the study on land use changes is quite interesting and important because, landscape fragmentation and the impact of such change is a growing need to uphold the natural biodiversity of the region. geospatial technology used in this study could signify the importance of land cover changes over article info: received: 19 july 2018 in revised form: 20 sept 2018 accepted: 1 oct 2018 available online: 25 oct 2018 keywords: land use pattern change, fragstat, urbanization corresponding author: noordini binti che man department of urban and regional planning, faculty of built environment and surveying, universiti teknologi malaysia email: b-noordini@utm.my open access http://doi.org/10.14710/geoplanning.5.2.229-236 http://doi.org/10.14710/geoplanning.5.2.229-236 mailto:b-noordini@utm.my che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 230 | the pengerang area, which possibly helps to assess the dynamics changes of the area. it also could help to reveals if there is a reduction in natural vegetation cover in the study area. in this paper, the level of land use change in mukim pengerang, johor malaysia was examined by using landscape metrics analysis method in fragstat 4.2, a spatial pattern analysis software. landscape metrics analysis is used to obtain the applicable changes using the patch analysis. the use of spatial landscape metrics analysis techniques is to examine the percentage of changes of land use type and the shape of spatial changes in understanding the form and stage of land use change within the eight years. this process has led to land-use change or landscape fragmentation. landscape metrics is an approach to estimate the landscape pattern. various matrices are available for the examination of the relationship between spatial structures. it is also used in this study to understand patterns of land use change and to give a clear picture of the surrounding change and the comparison within the 2 years. there are several researches by previous researchers related to landscape pattern/fragmentation. li et al. (2017) has characterized the landscape patterns in beijing city, china during 2000 and 2010 using four landscape metrics, i.e. patch density (pd), edge density (ed), shannon’s diversity index (shdi) and the aggregation index (ai) which two of the metrics used are similar with the research (pd and shdi). as result, new construction land was found in the original forest land and grassland, leading to a slight increase of pd and shdi. as overall results, showed that landscape patterns in beijing city were greatly changed along with the process of urbanization during 2000–2010 and showed obvious spatial differentiation. similar research using landscape metrics by (liu et al., 2010) which examine the size, pattern and nature of land use changes. the study demonstrating of landscape metrics which could show the characteristics of the urban expansion in lianyunggang, china. as result, every expansion of urban development had their different modes and types of land use which can show the different changes of landscape patterns. a study related to landscape pattern using land use and land cover (lulc) analysis by jaybhaye et al. (2016) reveals that there was reduction in natural vegetation cover from 1989 to 2015 in anjaneri hill, india. the fragmentation analysis for the study area was based on the parameters of class area, percentage of land, number of patches, patch density, total edge length, edge density, and largest patch index. the results obtained from the study revealed an increase in the fragmentation and significant degradation of forests. similarly, pang et al. (2010) investigated the changing characteristics of landscape patterns in zoige county, from 1986 to 2005. through analysis of lulc driving forces, finally got the conclusions: the climate change and human disturbance factors, including increasing temperature, over-grazing, drainage of water systems, were both responsible for the wetland degradation in zoige county. the study of lulc along with fragmentation at the landscape level can help improve understanding of the pace at which conversion of landscape elements is happening and the impacts on ecosystem services as studies of lulc are courser in nature and would not show how each land use is reducing in size, proximity and shape among other things that determine ecosystem services as result on study by tolessa et al. (2016). the study was conducted to examine composition and configuration of forested landscape in the central highlands of ethiopia using satellite images of over a period of four decades, and fragstat raster dataset was used to analyze fragmentation. nong et al. (2014) investigated urban growth patterns of the hanoi capital city of vietnam from 19932001 which to quantify the speed, growth modes, and resultant changes in landscape pattern of urbanization and examine the diffusioncoalescence and the landscape structural homogenization processes in hanoi. through the landscape pattern analysis and comparison with other cities, the result show that the urbanization in hanoi is limited by its infrastructure systems which make the urban growth not evenly distributed, limiting their competitive advantage disproportionately high transport cost, growing congestion and land market distortions. kabba & li (2011) investigated land use changes, and their ecological effects in wuhan (1987-2005) by using remote sensing techniques extracted land use data, whilst the spatial analyst software, fragstats quantified ecological metrics at both landscape and class levels. the results showed increased urban and agricultural land uses (1987-2005); with urban land increasing more than 250 percent. other than that, socioeconomic factors and ecological metrics indeed explained land use changes and their effects in wuhan. other related research by karami (2014), was carried out in the zagros vegetative region in the west of iran to quantify structure and spatial pattern of land uses and forest fragmentation in the zagros mountains region. the mosaic analysis method was used for quantifying landscape metrics. the result of http://doi.org/10.14710/geoplanning.5.2.229-236 che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 | 231 the study shows that the fragmentation of natural land uses such forest and rangelands should be reducing and maintain large patches of natural vegetation to sustainable land management in this region. singh et al. (2014) study presents the results of a set of landscape metrics derived from remotely sensed data aiming to characterize the historical trends of landscape changes in the allahabad district in the period 1990–2010. this study demonstrates the probable use of remote sensing, gis and fragstat in assessing spatial structure and change in landscape. interest on the study on landscape fragmentation is not only cover on land use changes but also influence on disease emergence. a study by ferrell & brinkerhoff (2018) which to identify patterns and drivers of vector-borne disease risk that may operate at different scales in virginia. although the study is not related or similar to this research, but it is quite interesting which the land use or land cover variables could be used for other type of researches. these and other examples show that the landscape fragmentation research attracting more researcher and moving towards interdisciplinary endeavors. 2. data and methods 2.1 study area mukim pengerang located in the east of johor state. there was a rapid development in surrounding areas that become a new growth area in the state of johor and intended to place as the catalyst for growth in johor. among the major developments or mega projects in the region are the development of the gas and oil industry, refinery and petroleum integrated development (rapid) and pengerang integrated petroleum complex (pipc) (figure 1). it is about 20,000 acres and the construction started in the year 2012 and growing rapidly which influenced the development of the surrounding area. in the meantime, it encouraged the provision of housing and institutional requirements for the residents. therefore, there is an extension of land use for land development and land use change as a result of the major developments. total populations in this mukim are 125,544 people (year 2010) and the population density is 82.53 people per km2. with the economic development and increasing of population, the surrounding land use in mukim pengerang has changed between 2008 and 2017. figure 1. industrial project of pipc effect the surrounding of mukim pengerang mukim pengerang http://doi.org/10.14710/geoplanning.5.2.229-236 http://doi.org/10.14710/geoplanning.5.2.229-236 che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 232 | 2.2 methods in monitoring spatial pattern changes, land use data was prepared using esri arcgis 10 software to oversee the classification of land use classes. geographical information systems (gis) approaches have added a new dimension to the understanding of these changes, not least the urban landscape (wu, 2008; wu et al., 2006; yuan et al.,2005). gis could offer the platform on which data on such images are stored, processed and analyzed for decision making. the land use data collected and prepared for this study was in shapefile format and classified by classes. there are several land uses classes that used in this study as shown in table 1. based on the land use data (figure 2), the dominant land use classes in this area for both years are agriculture which is about 223,292.90 acres (year 2008) and 220,078.35 acres (year 2017). while, for other land use classes which is built-up area 12,648.99 acres (year 2008), 21,709.25 acres (year 2017) and forest 28,829.41 acres (year 2008), 28,016.05 acre (year 2017). as shown in this data, the acreage of agriculture becomes decreased as the built-up area becomes expanded in 2017. thus, (table 1) shows the agriculture land use class has converted into a built-up area for development in this area. table 1. land use classification land use classes description agriculture almost all are green gardens, small size corn and fruit gardens that are generally located in gardens. forest mixed of plants with a higher density of trees and plants. built-up residential, commercial, industrial, transportation and facilities. figure 2. raster image of mukim pengerang, 2008 and 2017 http://doi.org/10.14710/geoplanning.5.2.229-236 che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 | 233 to obtain the applicable changes using matrix statistical analysis, patch analysis was used. the used of spatial metrics analysis techniques is to examine the percentage of changes in the type of land use and the shape of spatial change (figure 2). this spatial change study can be applied using gis to make the results more efficient. this is because by using gis, spatial metrics changes not only be produced in the category of classes but also by the diversity of area which either homogeneous or heterogeneous. classification technique is too accurate in ensuring precise change-detention results. furthermore, the data collected in two differences years (2008 and 2017) give a comparison of the fragmentation result between both years. spatial pattern analysis software fragstats 4.2 is applied to calculate landscape metrics of each class type and total landscape after it was converted into raster image from shapefile format. fragstat 4.2 provides a very comprehensive set of spatial statistics and descriptive metrics of the pattern at the patch, class, and landscape levels (haines-young & chopping, 1996). in analyzing the fragmentation of landscape in the study area and correlated the changes throughout the years, quantify landscape metrics was used at both landscape and class levels. there are several class-level metrics as shown in table 2. table 2. class-level metrics index (unit) formula description np (number of patches) (#) where: ni = number of patches of the corresponding land use class is the number of patches of the corresponding patch type (class). higher nump indicates greater fragmentation. pd (patch density) where: ni = number of patches of class a= total of the class area (m2) the equals the number of patches of the corresponding patch type divided by total landscape area (m2). pland (%) where: tla= total landscape area it equals the percentage of the landscape comprised of the corresponding class type. shdi (shanon diversity index) m= number of patches included pi= proportion of the landscape occupied by patch type (class) i shannon’s diversity index is the amount of patch per individual the value of shdi increases if the number of patches increases and the broad distribution borders between classes increases over time. source: (mcgarigal, 2002) and (mcgarigal & marks, 1995) *ln pi http://doi.org/10.14710/geoplanning.5.2.229-236 http://doi.org/10.14710/geoplanning.5.2.229-236 che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 234 | 3. results and discussion 3.1. fragmentation analysis in 2008 and 2017, the most changing classes (agriculture, forest, and built-up area) are chosen to compute spatial landscape at class level by means of fragstat software. based on the result (table 3), in mukim pengerang, agriculture is represented as a dominant class of landscape because it has a larger percentage of total area. meanwhile, the statistic of agriculture showed that the percentage of landscape (pland) index decreased from 81.38 to 78.6, while the number of patches (np) increased from 228 to 427 during the whole period from 2008 to 2017. this combination result shows that there are breaking up of the agriculture areas into small areas. besides, patch density (pd) result shows non-isolated in certain areas that caused break up patches. this shows that agriculture land use becomes decrease because of development expanded to the surrounding of the study area throughout the years. table 3. class-level metrics of 2008 and 2017 land use class area (ca) 2008 class area (ca) 2017 np (#) 2008 np (#) 2017 pd 2008 pd 2017 pland (%) 2008 pland (%) 2017 agriculture 94,690.34 93,792.79 228 427 0.37 0.19 81.378 78.6 forest 11,305.26 11,124.26 141 1,025 0.88 0.12 9.716 9.57 buit-up 3,874.74 4,689.51 8,322 6,948 1.18 5.82 3.33 3.93 in regards to forest area during period 2008-2017, the number of patches (np) increased from 141 to 1025. similarly, the percentage of the landscape (pland) index decreased from 9.72 to 9.57. thus, it shows that forest land use becomes decreased and breaking up into smaller patch caused by the development of the surrounding area. while the value of the built-up area metrics shows a change in the increasing percentage of landscape index (pland) from 3.33 to 3.93. these show that urban development in the study area has taken place. meanwhile, the number of patches (np) has also decreased from 8,322 to 6,948. however, the pd value for built-up is increasing. this shows that urban change is increasing by 2017 and the shape of the development density is a group based on patch saturation value is increasing in 2017. table 4. metrics of landscape structure for selected indices at the landscape level, 2008 and 2017 9 np (#) pd shdi 2008 10,089 8.67 0.696 2017 8,187 6.86 0.818 for shdi, based on the value for 2008 and 2017, it shows an increasing value (table 4). this situation illustrates that the higher the shdi value for land use, the higher the level of land use compositions. this is because the growing pattern of land use is reflected by a large number of patches due to the diversification of land use activities within a given area. based on this study results, it shows an agreement with the findings of singh et al. (2014) and tolessa et al. (2016). these study and some previous study results related to land use changes therefore proved the capability of remote sensing and gis to quantify changes in natural resource over time. 4. conclusion in conclusion, based on the result, land use change in mukim pengerang is more frequent in 2008 compared to 2017. this resulted in the use of agricultural land and forests in 2017 due to urban development caused by the development of the petroleum industry project. overall, from the metrics statistics, it was found that there was a significant change in land use over a period of 8 years. this study is an analysis that aims to know and understand the land use structure against the effects of ecology. this study was also conducted to determine the level of land use change. this is because measuring land use change or landscape is very important for understanding the structure of land use against relevant ecological effects. http://doi.org/10.14710/geoplanning.5.2.229-236 che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 | 235 5. references ferrell, a., & brinkerhoff, r. 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(2005). land cover classification and change analysis of the twin cities (minnesota) metropolitan area by multitemporal landsat http://doi.org/10.14710/geoplanning.5.2.229-236 http://doi.org/10.14710/geoplanning.5.2.229-236 https://doi.org/10.3390/ijerph15040737 https://doi.org/10.1177/030913339602000403 https://doi.org/10.9790/2402-1004010110 https://doi.org/10.5539/jgg.v3n1p104 https://doi.org/10.13057/biodiv/d150108 https://doi.org/10.1016/j.ecolind.2017.06.032 https://doi.org/10.1007/s10980-010-9454-5 https://doi.org/10.2737/pnw-gtr-351 https://doi.org/10.1371/journal.pone.0196940 https://doi.org/10.1016/j.proenv.2010.10.119 https://doi.org/10.1007/978-3-319-05906-8 https://doi.org/10.1002/ece3.2477 https://doi.org/10.3368/lj.27.1.41 http://dx.doi.org/10.1016/j.landurbplan.2005.10.002 che’man & salihin/ geoplanning: journal of geomatics and planning, vol 5, no 2, 2018, 229-236 doi: 10.14710/geoplanning.5.2.229-236 236 | remote sensing. remote sensing of environment, 98(2–3), 317–328. [crossref] http://doi.org/10.14710/geoplanning.5.2.229-236 https://doi.org/10.1016/j.rse.2005.08.006 99 zgeoplanning journal of geomatics and planning vol. 11, no. 1, 2024 original research beyond park boundaries: exploring the effect of surrounding land use on sound levels of parks josephine siaw ling lee1, nafisa hosni1*; noradila rusli2, nabila abdul ghani1 1. department of urban and regional planning, faculty of built environment and surveying, universiti teknologi malaysia, johor bahru, malaysia 2. centre for innovative planning & development (cipd), faculty of built environment and surveying, universiti teknologi malaysia, johor bahru, malaysia doi: 10.14710/geoplanning.11.1.99-120 abstract urban parks in big cities can help reduce noise while providing spaces for recreation and rest, but their size, location and surroundings can limit their environmental benefits. this article will discuss how surrounding land use affects noise levels in a particular park, as well as how park landscaping can limit noise exposure. four study areas were selected from kuala lumpur and putrajaya to highlight a range of land uses, locations and park sizes. the sound levels were measured twice for each site-morning and evening-using measurement points along the park path and the sl-5868p sound level meter. the results showed that the study area exceeded the recommended noise limit of 55dba as stipulated by malaysian noise limit and world health organization guidelines. in addition, there was a pattern of influence on the measured noise levels based on land use and landscape around the park. parks located in dense land use have higher noise levels, but have lower variation in noise levels within the park due to higher surrounding noise levels, compared to parks with more than 87% tree cover. the klcc park, with 76% tree cover, has an overall higher noise level of more than 60dba, indicating that the tree cover serves as a noise barrier for the park. therefore, park planning should be tailored to its location and environment, while landscaping can be used to reduce noise levels and keep them within noise limits. in the future, the soundscape idea may be taken into account to enhance malaysia's park environment. copyright © 2024 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction in the context of rapid urbanization and high-density developments, the increased noise levels contributed to an increasing sense of environmental unpleasantness. developing nations including china, india and vietnam experience increasing traffic noise pollution (ma et al., 2006). this includes malaysia where there is a constant increase in road transportation network to support the country’s development process resulting in higher noise levels which degrades the quality of the environment. high density developments introduce elevated levels of human activities, higher vehicular traffic and industrial operations which increase noise pollution (tong & kang, 2020; yuan et al., 2019). meanwhile, the increase in urban population density amplifies the impact of noise pollution which negatively impacts the wellbeing of the residents. these concerns contributed to the recognition of urban noise pollution as a threat to environmental health. to protect the urban community from this urban noise pollutions, the maximum permissible noise level at suburban and residential areas should not exceed 55 decibels (dba) based on the environmental limit set by the malaysian department of environment (department of environment, 2019). similarly, the world health e-issn: 2355-6544 received: 20 december 2023; accepted: 29 february 2024; published: 08 march 2024. keywords: sound level, park, gis, land use, ndvi *corresponding author(s) email: nafisa@utm.my https://doi.org/10.14710/geoplanning.11.1.99-120 mailto:nafisa@utm.my lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 100 organization (who) guidelines on noise level limit are fixed at 55dba during the day and 45dba at night. research into these issues is necessary to secure the health and wellbeing of urban residents, as well as to ensure that planning decisions are based on evidence that considers the potential health and environmental consequences of development. unwanted or disturbing sounds (noise) may not only be a harmful pollutant to human health as defined by who and european centre for environment and health (european environmental agency, 2020) but they may also become a global and growing matter of concern that threatens the preservation of natural areas (lynch et al., 2011). consequently, public spaces are increasingly viewed as a potential setting for urban regeneration strategies. the proximity of these developments to urban green areas results in heightened ambient noise levels within these green spaces which underscores the significance of mitigating noise infiltrations to parks to ensure the role of parks in enhancing the quality of life in the urban environment. margaritis and kang (2017) reviewed role of green areas to reduce noise levels in urban environment with geospatial analysis. another study by tashakor et al. (2023) developed a combined gis-artificial neural network model to predict the spatio-temporal contribution of parks to mitigate noise pollutions in iran, demonstrating a spatial relationship between land use, landscape, and noise level of parks. public spaces are a vital asset of a city. urban parks are considered as one of the public spaces. the visual experience of a visitor is always considered to be the determining factor in why people visit the park . it is recognized that a good public space, especially urban parks with natural elements would benefit people’s psychological and physical health and contributes to the increase of quality of life. understanding how urban parks attract more visitors has increased the significance of urban park soundscape knowledge in terms of providing comfortable acoustic experience in the park. tse and kwan (2013) highlighted the complex relationship among sound, environment, and individuals in investigating the soundscape quality of parks. a good acoustic environment is no longer simply the reduction of noise levels (aletta et al., 2016; hong et al., 2017) but the perception of the people of the acoustic environment as per the soundscape concept in iso12913-1(international standardization organization, 2014). soundscape perception of a park can be measured through a subjective aspect of non-acoustic factor by looking at people’s perception of the landscape elements while the objective aspects relate to landscape characteristics, such as accessibility (votsi et al., 2012), vegetation coverage (dzhambov et al., 2018) landscape spatial pattern (liu et al., 2014) and biodiversity (gunnarsson et al., 2017). effect of surrounding land use on sound levels the surrounding land use of a park can affect its sound level. land use categories are commonly used in national environmental noise polices to determine exposure limits implying the relationship between different land uses and sound level (lechner et al., 2022; department of environment, 2019). according to ajayi & adeleke (2022), parks’ surrounding with mixed-use land use including living and other activities records higher noise levels as compared to parks surrounded by purely residential areas. margaritis et al. (2020) in their investigation of land use with sounds in urban environments, discovered a correlation between the urban form and distribution of activities and sound sources in the urban environment. wang and kang (2011) found significant differences in the distribution of noise level between highand low-density cities. dense urban areas with numerous heavy structures tend to have higher noise levels (sakieh et al., 2017). findings of their study suggested there is a distance-dependent relationship between green areas and noise levels. thus, landscape ecology plays an effective role in planning a greener and calmer city by exploring how noise propagation and built-up areas interrelate. at a local scale, the surrounding land use of the park can affect the physical activity at the park and mediate the observed sound level within the park. a study investigated whether parks adjacent to neighborhoods with high land use diversity had higher levels of physical activity and interactions with the number of facilities in the park and found that parks with low surrounding land use diversity records higher physical activities in the park https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 101 (huang et al., 2020; kaczynski et al., 2010). however, parks located in commercial areas with busy streets may deter the public use of the park (fry et al., 2021; kaczynski et al., 2010). landscape as noise level barriers noises in parks may be influenced also by the design of the park itself, such as placement of sound barriers and natural sound sources such as water features and vegetation (di et al., 2021; jaszczak et al., 2021; sun et al., 2022). in terms of aural-visual interactions, numerous studies have suggested a close relationship between soundscape and landscape perception (pheasant et al., 2010; vijay et al., 2018). votsi et al. (2012) mentioned a link between tranquility, environment quality and human health correlating with landscape structure. in addition, research has demonstrated that landscape features such as the normalized difference vegetation index (ndvi) and landscape shape index (lsi) can significantly influence the perception of certain sounds (liu et al., 2013). ndvi is commonly used to estimate vegetation density and cover, the reflectance of vegetation and thus the ndvi values are influenced by a number of factors including canopy type, type of land use and seasonality (liniger et al., 2016). densely vegetated areas are typically ecologically favorable habitats for organisms such as birds and insects and as a result they are often rich with biological sounds. when planned alongside a road, the dense vegetation of a park’s landscape could also act as barriers, thereby affecting sound propagation and perception. according to a study by liu et al. (2013), dense vegetation could reduce the perception of human sound, mechanical sound and geophysical sound. a study used spatio-statistical approach to model associations between noise pollution metrics of land categories including green covers and found that green covers were negatively associated with noise pollution levels (sakieh et al., 2017). similarly, natural features such as trees and shrubs, as well as man-made barriers can effectively hinder the propagation of noise (uebel et al., 2022). to date, few studies have examined how sound levels within an urban park vary according to its surrounding land uses and landscape. studies on the influence of land use on sound levels have mainly focused on parks surrounded by residential land uses (sun et al., 2022; yuan et al., 2019). however, this study aims to focus on the impact of different types of land use such as commercial, institutional, and other public facilities on parks by prioritizing urban parks instead of neighborhood parks. this is because the location of urban parks in cities are crucial as a place of relaxation and the impact of sound levels on the restoration of the park users are more prominent in urban parks (buxton et al., 2021; fang et al., 2021). the aim of the study is to compare noise levels in the different parks and the characteristics of the landscapes. therefore, this paper aims to extend the knowledge of the impact of the park’s surroundings and the park’s landscape on the sound level of the park. with this, the paper addresses the two (2) of question, (1) how the surrounding of the park influences the sound levels within the park and (2) does the landscape index of the park influence the sound levels in the park. this paper will examine the influence of a park’s surroundings on the perception and experience of the park users. this research will contribute to our comprehension of the environmental function of urban parks in dense cities by characterizing the urban park environment. the findings will also be important in identifying the implications for urban park planning and design, particularly with regard to how urban livability can be improved. 2. data and methods 2.1. area size and locational characteristics of the parks this study focused on the sound levels of the parks in kuala lumpur and putrajaya (figure 1), the nation's capital, and malaysia's national federal administrative capital, to reflect an urban setting. a park’s location by major roads may expose visitors to traffic pollution, and the size of the parks may influence the space for noise to attenuate (lam et al., 2005). three (3) types of parks were included in this study. the park type is determined by the gpp for the provision of public space in malaysia (department of town and country planning, 2010) where the size of the parks corresponds to the number of inhabitants and functional recreational elements accorded to the site. city parks and district parks are recreational areas aimed to cater to the urban population https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 102 while metropolitan park offers opportunities for informal education facilities. the parks were selected based on their geographical attributes in terms of their surrounding land uses, location and size of the park which may have a profound implication on the park environmental quality. table 1 details the geographical characteristics of the selected study area. source: google maps, 2019 figure 1. study location of selected parks in kuala lumpur and putrajaya table 1. characteristic of the parks no. no park type of park description size (ac) 1 klcc park, kuala lumpur city park smaller hardscape and landscaped spaces for a highly intensified urban environment in the city center, provide breathing spaces for people to gather, socialize, rest and relax 50 2 taman tasik permaisuri, kuala lumpur district park densely surrounded by several residential areas and is integrated with other sports and recreational facilities in the neighborhood 122 3 bukit jalil recreational park, kuala lumpur district park located on hilly terrain and surrounded by ongoing developments in the district, commercial buildings and residential areas and a golf resort. also integrated with other sports and recreational facilities in the neighborhood 80 4 putrajaya botanical garden, putrajaya metropolitan park located in the putrajaya, often referred as "city in the garden", the park is adjacent to the largest man-made pond and a neighboring park 230 2.2. data collection 2.2.1. sound level measurements this study employed measurements of sound levels in the parks based on objective acoustic environment by equivalent continuous sound pressure level (shao et al., 2022). like many other studies on acoustic environment in parks (di et al., 2021; evensen et al., 2016; sudarsono et al., 2016; sun et al., 2022), the acoustic measurements were measured based on the on-site soundwalk method where sound level measurements were taken along the park routes. different observation sites were sampled due to the different sizes of these urban parks, with 20 points in klcc park, 27 points in bukit jalil recreational park, 24 points in taman tasik permaisuri and 34 points in putrajaya botanical garden, respectively (figure 1). the sl-5868p sound level meter, held at a height of 1.5 meters above ground and at least 3.5 meters away from any sound-reflecting walls, buildings, or other structures, is used to measure the sound pressure level. the sl-5868p was chosen in this study for its measuring features, which included the laeq (equivalent continuous a-weighted sound level). laeq is the standard weighting method for outdoor measurements represent the loudness of sound perceived by human ears for a real human reaction to the level of intensity and discomfort (guo, 2019). the sound levels were measured in dba values along the park routes. the distance between the measurement location and the ground was 1.2-1.5m (mookiah & ramasamy, 2018). it. one https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 103 measurement was taken every 10 seconds. each location’s data was recorded for 5 minutes. short duration samples can be justified in light of previous research and documentations (axelsson et al., 2010; oldoni et al., 2015). spatial interpolation methods were then calculated using arcgis pro with geostatistical methods (kalisa et al., 2022), to fit a particular model to the data and allows the prediction of the sound levels at unsampled locations in the park. 2.2.2. material and software the data for this study was collected from primary data sources which includes land use map and satellite imagery for the year 2021 and aerial photography to verify the current land use of the site surrounding (table 2). table 2. dataset used in the study dataset source url license sentinel-2 landsat 8 satellite imagery (2021) eos land viewer https://eos.com/landviewer/ public domain aerial photography (2021) google earth pro https://earth.google.com/web/ public domain existing land use (2021) kuala lumpur & putrajaya thinkcity https://maps.thinkcity.com.my/think-city/maps/95345/downtownkuala-lumpur open data license iplanplanmalaysia https://iplan.planmalaysia.gov.my/public/geoportal?view=semasa source: analysis, 2023 2.3. method of analysis 2.3.1. sound level data analysis using microsoft office excel 2016, the maximum noise level (lmax) and the minimum noise level (lmin) were calculated from the collected data. the park’s equivalent noise level (laeq) was calculated with equation 1, in the unit dba. 𝐿𝐴𝑒𝑞 = 10 log ∑ (10) 𝐿𝑖 10(𝑡𝑖) 𝑖=𝑛 𝑖−𝑡 ………(eq.1) where n is the total number of samples taken, li is the noise level in dba of the ith sample, and ti is the fraction of the total sample time. according to the planning and guideline for environmental noise limit and control (department of environment, 2019), the noise level is considered to be in compliance if the laeq value does not exceed the existing guideline for maximum laeq by the receiving land use for planning and new development. evaluations of l10 and l90 were performed in microsoft excel 2016 using [= percentile (array, k)], with k = 0.90 for l_10 evaluations and k=0.10 for l_90 evaluations. l_10 represents noise levels exceeding 10% of the measurements, while l_90 represents noise levels exceeding 90% of the measurements, known as background sounds in the area (ismail et al., 2015; napi et al., 2021). analysis of variance (anova) test was then used to determine whether there was a statistically significant difference in the sound levels between each study area. using arcgis pro, sound maps were then created for the spatial analysis of the sound levels. 2.3.2. spatial analysis several spatial analyses were carried out to identify the characteristics of the selected parks and their relationship to the soundscape of the parks including park surrounding and park landscape mapping. figure 2 details the steps undertaken for the study. all spatial analysis were computed using the arcgis pro version 3.0.2 (e.s.r..i inc. canada). https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 104 source: analysis, 2022 figure 2. flowchart of research analysis 2.3.3. spatial mapping secondary data is commonly used in green space and recreation spatial analysis to look at the characteristics of neighborhood parks based on the aspect of the park location and accessibility (malik et al., 2018). this study uses the secondary data obtained from the interactive web map by think city and iplan geoportal (table 2) to extract the land uses of the plots surrounding the study area in kuala lumpur, the landuse masterplan for putrajaya from putrajaya corporation’s website and google earth pro’s satellite image dataset (figure 3a). source: thinkcity, 2023 source: iplan geoportal, 2022 (a) (b) source: google map, 2022 source: analysis, 2023 (c) (d) figure 3. spatial mapping method for surrounding land use (a) land use map from think city’s interactive map; (b) land use map from iplan geoportal; (c) satellite view of park surrounding from google earth pro; (d) land use of park’s surroundings (post-verification?) the satellite image utilized in the study is acquired during same period of data collection in november 2021 with the most accurate cloud-free imagery for the studied area (figure 3b). the satellite image was mainly https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 105 used to produce the urban form maps of the parks’ surroundings to make sure that the data collected were accurate and precise for analysis (figure 3c). a buffer of 200m and 500m from the park boundary was considered as the minimum impact of close proximity an infrastructure project such as roadway have onto developed areas in guidelines of noise (department of environment, 2019). the land uses of the park surroundings were validated with an on-site observation during the data collection process from november 2021 to august 2022 to update the land uses according to the current status of the building plots (figure 3d). 2.3.4. kriging interpolation analysis different spatial interpolation strategies can be used to produce sound maps (aumond et al., 2018). kriging interpolation has a solid statistical theory basis and can estimate an error point by point (zuo et al., 2016), which is suitable for the present study. although there are other interpolation methods that can be used to map sound levels, such as inverse distance weighting (idw) and multiquadric interpolation (harman et al., 2016), kriging served as the best option in this study as there is at least moderate spatial autocorrelation among the sampled data points. kriging interpolation is used in this study to estimate the sound level of the park from the points of measurements taken within the study area. the dataset of sound level measurements in the park is used as the input dataset, semi variogram model and configuration of the type of kriging to generate the best linear unbiased estimate at each location. cross validation was used to assess the semi-variogram model, for the prediction accuracy of the interpolated sound levels in the park. the weights are determined from a spherical variogram based on the spatial structure of the data and applied to the sample points according to the formula in equation 2, (esri, 2012): ζ (𝜒𝜊 − μ ) = ∑ λi𝑛 𝑖=1 ζ(𝜒𝑖) − μ( 𝜒𝑜)………(eq.2) where μ is a known stationary mean, assumed to be constant over the whole domain and calculated as the average of the data; the parameter λi is kriging weight, 𝑛 is the number of sampled points for the estimation depending on the search window; and μ( 𝜒𝑜) is the mean of the samples within the search window. 2.3.5. ndvi analysis while there are several landscape spatial indices such as landscape shape index (lsi), largest patch index (lpi), and others commonly used to measure landscape fragmentations (rutledge, 2003), ndvi (equation 3) is commonly used to measure the effect of vegetation and prominence of sound sources, especially bio phony sounds of birds from the tree canopies (leveau & isla, 2021). it is also the most commonly used objective measure of vegetation density and is used to measure greenness exposure in urban settings for environmental health studies (jimenez et al., 2022; reid et al., 2018; rhew et al., 2011). the use of ndvi provides a quantitative measure of vegetation cover, which can be used to assess the impact of vegetation on sound levels in urban parks. vegetation has been found to have significant effect on noise reduction, emphasizing on the impact of vegetation canopy and canopy density (caprio, 2005; guo et al., 2020). the dataset for vegetation greenness based on the area of interest (aoi) is extracted from a cloud-based gis platform approach, which is built upon dede & widiawaty’s (2020). research findings that the eos platform can be used as an effective and efficient satellite image processing for vegetation greeneries. all vegetation greenness processing uses the eos platform accessed from google chrome (64 bit) browser. the ndvi dataset in this study was collected from the sentinel-2 satellite in the eos platform (cloudbased gis vendor) using the land viewer, eos processing and eos storage. the analysis began by uploading aoi to the land viewer which then directs the map to the study area. then, the sensor types and instruments, observation time, type of scene or mosaic data is entered into the platform. the ndvi workflow can then be selected for further analysis. cloud coverage is limited to 0-10 during the acquisition of satellite images, data acquisition of satellite image is set to be within the study frame of november 2021 to may 2022. the satellite https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 106 image with the least cloud coverage during the period of november 2021 to may 2022 is used for the ndvi analysis. ndvi values are calculated in relation with the following equation and for each pixel by using satellite images (greenhill et al., 2003; jiang et al., 2008; jung et al., 2005). 𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅[𝐵𝑎𝑛𝑑 8]−𝑅𝐸𝐷 𝐵𝑎𝑛𝑑 4 𝑁𝐼𝑅[𝐵𝑎𝑛𝑑 8]+𝑅𝐸𝐷 𝐵𝑎𝑛𝑑 4} ………(eq.3) where nir band 8 and red band 4 represent the near-infrared and red band of sentinel-2a image product, respectively. 3. results and discussion 3.1. sound levels of the parks the sound levels of the four parks obtained in this study is higher than the permissible sound level limit of 55dba (figure 4) for the parks designated by the doe guideline and the who guidelines causing discomfort to some of the users, especially those who are more sensitive to noises (department of environment, 2019). this may cause interference with speech communication, disturbing individuals who want to converse or relax in the park. according to an anova test, the sound levels were significantly different among the locations (f value=38.328, p <.01) suggesting that sound levels in parks could vary considerably between the different park types and locations. the park with a significantly higher sound level was klcc park, with laeq of 67.1dba (morning) and 62.4dba (evening) while the sound level in putrajaya botanical garden is significantly lower than the other parks in the study with 61.6dba (morning) and 57.4dba (evening) (figure 4). this suggests that the sound level of a park is dependent on the park’s characteristics and the use of the park. sound level for all parks slightly decreases in the evening because of the pattern of park usage with majority of the park users in kuala lumpur prefer going to the park for fresh air (sreetheran, 2017). this is excluding taman tasik permaisuri which demonstrated a higher sound level in the parks during the evenings (60.2dba) as compared to the 58.7dba during the mornings, suggesting that the park has a higher volume of activities early in the morning than in the evenings, which might be because it is situated in the midst of a residential area, thus promoting the use of parks for children’s leisure in the evenings. this situation demonstrates how the decrease in the sound level of the park is linked to certain behaviors of the park users including their use of the park, and the influence on the park’s surroundings on the sound level of the parks. source: analysis, 2023 figure 4. sound levels of the selected parks at different times of the day https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 107 noise levels were higher in klcc park and bukit jalil recreational park, surrounded by blocks of developments and heavy traffic (figure 5a and 5d). another reason of the high sound level in the park may be attributed to the large groups of people gathering by the park, evidently in klcc park where there is a large space for sitting by the musical fountain and in bukit jalil recreational park where there are areas designated for picnics and group activities in the park. the mean sound level of the putrajaya botanical garden (57.4dba during the evenings of a weekday) is the only park which is closer to the sound level limit for recreational areas. this can be justified by the surroundings of the park where putrajaya botanical garden is located in precinct 1 of putrajaya with only administrative offices and a large man-made pond bordering of the park (figure 5e and 5f). klcc park presented a great difference in the sound levels of the parks during the mornings and evenings from 67.1dba to 62.5dba. this difference suggests that klcc park during the morning may be more influenced by the disturbance effect of noise events in its surroundings, such as the traffic noises in the city center. the background noises in the urban area contribute to the higher overall sound level of the klcc park (figure 5a). putrajaya botanical garden also recorded a difference approximately 4dba during the morning (61.6dba) and evening (57.4dba), which can be attributed to variations in traffic volume related to land use, background institutional noise, and pedestrian activity. the park’s large size may also reflect its variability where only certain areas of the park such as the area nearer to the park entrance have more activities while other sections of the park is quiet as the park is big and people may not travel through the entire park such as in figure 5e which shows the lake broadways facing the calm lake. source: photographs during site visit, 2022 figure 5. (a) klcc park surrounded by high rise buildings; (b) taman tasik permaisuri with an ongoing development of mixed-use building neighboring the park; (c) one of the main sources of biophonic sounds in taman tasik permaisuri; (d) bukit jalil recreational park also overseeing high rise apartments; (e) putrajaya botanical garden surrounded by a man-made lake and calm surrounding; (f) surroundings of putrajaya botanical garden king et al. (2012) analysis the spatial and temporal variation in environmental noise with respect to land use. their research concluded that there is a smaller noise variation in mixed use developments as compared to the noise levels in residential neighbourhoods. similarly, findings of this research demonstrate the same effect (a) (b) (c) (d) (e) (f) https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 108 where the noise level variation in klcc park, an area with mixed-use developments although has a higher mean sound level, but a lower variation as compared to the variation in sound levels of putrajaya botanical garden with majority administrative land uses and taman tasik permaisuri majorly surrounded by residential developments. however, findings of this study are not consistent with napi et al. (2021) who investigated noise pollution in residential areas and commercial areas and found that although the noise level in terengganu both exceeded the permitted limit, noise level in residential areas are higher than in commercial areas due to the traffic volume and noise from nearby activities. in this case, the sound level of the parks surrounded by mixed-use land use and commercial land use (klcc park and bukit jalil recreational park) recorded a higher sound level as compared to the ones surrounded by residential area (taman tasik permaisuri). the sound levels in taman tasik permaisuri during the time of measurement are mainly caused by the surrounding development of mixed development building, and the tree in the middle of the lake which attracts birds to the area (figure 5b and 5c). the sound level of the park measured in the interior of the selected parks made it possible to study the pattern of the sound levels and its relationship to the surroundings of the parks. this can be justified by the landscape coverage of the parks and the involved sound sources which may decrease the sound pressure level with the progressive increase in distance from the noise source (carvalho & cleto, 2012). 3.2. surrounding land use of the parks figure 6a to figure 6d illustrates the levels of sound measurement in the parks during peak hours of mornings and evenings. the results demonstrated a pattern in which higher sound levels were found at areas with high commercial land uses, for example in klcc park (figure 6a) which is mainly sur-rounded by dense commercial high rises, and in figure 6c where the west section of the park neighbouring a high-rise commercial building records a higher sound level in the park. taman tasik permaisuri (figure 6b) which is surrounded by higher density residential apartment to the west of the park also recorded a higher sound level, together with the northern section of the park which is bordering the highway. putrajaya botanical garden in figure 6d illustrates a low sound level (within the 55dba noise level limit of parks) throughout most sections of the park due to its large size, fronting a large man-made lake along the park and institutional land uses surrounding the park which houses many government offices, resulting in a rather calm environment. these findings suggest a pattern in which areas with high density commercial areas such as in klcc central records a high volume of background sounds. as contrast, small commercial lots in a neighbourhood do not influence the surrounding sound levels of the park as much. this can be explained in terms of the activities large commercial areas have to offer and the higher pedestrian volume in the area. these results support the influence of human activities on the increase of noise level, which is in line with findings from kalisa et al. (2022) who revealed a high risk of noise sensitivity level in city centres and areas with high volume of human activities such as the areas with a concentration of small businesses. residential developments on the other hand, do not impact the sound levels of the park as much as commercial developments. although there appears to be a pattern where high-rise residential developments do result in a higher sound level in the areas of park bordering such residential development. reasons of the high volume could be attributed to the traffic flow of the area such as in taman tasik permaisuri where the west side of the park is surrounded by apartments and the residents tend to park their vehicles by the roadside resulting in massive jams in the area. another evident finding is that sound levels of all the parks appear to be higher at sections of bordering highways and busy roads as a result of the high background traffic sounds. commercial land use generates more noise pollution than open space with hard pavements or land used for residential purposes (yuan et al., 2019) explaining the lower sound level in taman tasik permaisuri surrounded by residential land uses and institutional areas (figure 6b). on the other hand, bukit jalil recreational park is subjected to higher sound level due to the close proximity to the bukit jalil national stadium and other commercial establishments. this can be explained by the volume of pedestrian activities and the commercial activities of the areas surrounding the park. similarly, klcc park which is surrounded by the petronas twin towers, suria klcc shopping mall and various office buildings, is likely to experience higher https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 109 sound levels due to the commercial activities in the area. similar to the area fronting the lake in klcc park (figure 6a), commercial areas have higher noise level because of the number of people lingering in the area and the use of loudspeakers to attract clients to shop and music playing. that is why commercial land uses, which often include many streets for pedestrians and large retail areas, frequently result in a high-noise environment due to crowds of people and loud entertainments (meng & kang, 2015; oyedepo & saadu, 2009). (a) (b) (c) source: analysis, 2023 (d) figure 6. sound levels extracted from the interpolations of the measurements in the park (a) klcc park; (b) taman tasik permaisuri; (c) bukit jalil recreational park; (d) putrajaya botanical garden. comparing putrajaya botanical garden and the other three parks in the study, putrajaya botanical garden has the lowest sound level due to its surroundings where the only type of buildings within 200m of the park boundary is institutional and public facilities which are located further from the park’s border. although the park is surrounded by some commercial land uses, it is comparatively isolated from major commercial activities, which may result in a lower sound level of the park. these types of land uses do not produce many noises; thus, the sound level of the park is not affected by its surroundings and is only slightly over the permitted noise level by the department of environment and the who noise limit of 55db. the only type of sound source which would significantly influence the sounds heard within the park is the traffic noise from the surrounding highway https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 110 into the park. the lower sound level in putrajaya botanical garden can also be explained by the presence of trees functioning as acoustic barriers and/or by the sound attenuation due to the increase of the distance from traffic road. results of the research is supported by king et al. (2012) who highlighted that business zones document elevated levels of noise pollution, and noise levels in mixed land use areas are greater than in single land use areas. relationship between park’s surrounding and sounds levels within the park the primary reason that land use types significantly affect sound levels in urban environment is associated with the varying levels of human activity, traffic and noise-generating sources. this study’s spatial analysis of the park’s surrounding and the park’s sound level revealed that parks are louder when surrounded by residential developments and in central business districts, but quieter in areas with a high proportion of green space and institutional land uses. the high sound level of klcc park, which is located in the city centre is reflective of its location in the central business district. this is in line with findings from baloye and palamuleni (2015) who compared the noise pollution levels in urban centres of nigeria and discovered that noise disturbance is significant in areas with high population density, and has a negative impact on people’s daily life, sleep, work and study. it also agrees with the findings by margaritis and kang (2017) revealing strong correlations were identified between 60% to 79% that lower noise levels were detected in the cluster with higher green space coverage. the sounds generated within the park are reflective of its surroundings, and the allocation of activities within and surrounding the park. commercial areas have higher sound levels due to the high human activity, traffic and presence of businesses that generate noise such as restaurants, shops, and entertainment venues. this is similar to klcc park neighbouring suria klcc twin tower, which is fronting the restaurants and entertainment shops of klcc, attracting a large number of visitors to the area at all hours of the day. noise levels associated with urban land use have been found to be higher in commercial sites (70.0dba) as compared to other land use types (kalisa et al., 2022). in contrast, residential areas have lower sound levels because they consist of residences and living spaces, with the noise levels influenced by the population density of the residential area and the commercial or industrial activities (king et al., 2012). mixed use areas, where commercial and residential land uses coexist, may experience elevated noise levels because of the combination of various noise sources and human activities (lechner & kirisits, 2022). areas of the park near major construction sites and major roads have higher sound level measurements. traffic conditions such as congestion and vehicle types also influence the noise level experienced within the park, which can be observed during the peak hours of traffic in bukit jalil recreational park and putrajaya botanical garden. this shows that the size of the roadway and traffic conditions in the vicinity of the park can have an impact on the sound levels within the park. it is consistent with findings from papafotiou et al., (2010) and papafotiou et al., (2004) indicating that larger roadways with higher traffic volumes produce more noise which penetrates the interior of the park. land uses associated with transportation and commercial activities in developing country cities contribute to the increase in noise pollution, as high noise levels in cities are attributable to traffic congestions resulting in honking and noise generated during the movement of vehicles (aditya & chowdary, 2020; vijay et al., 2018). thus, it is implied here that land use type such as transportation and commercial land uses affects noise pollution and the sound levels of the nearby parks. margaritis et al. (2020) also found that areas of recreational and residential area showed dominance of natural sounds and human sounds; as compared to areas with mixed-use land uses such as commercial, industrial, institutional and residential with traffic sounds dominating the soundscape; and areas with commercial and recreational only having dominant human sounds and is less likely to be affected by traffic sounds. however, natural sounds were found to be almost imperceptible in these areas. denser urban environments also typically have a higher noise level due to the increased traffic and human activity (wickramathilaka et al., 2022). tall commercial buildings are often found in central business districts, such as in klcc park. building height reflects and scatters sound waves, which either increase or decrease the https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 111 noise experienced within the park depending on the specific urban configuration (yildirim & arefi, 2023). in other words, taller buildings create noise barriers that prevent noise from entering the park, whereas shorter buildings may allow more noise to penetrate the interior of the park. factors such as building heights, distance from park, density of the area, size of roadway and traffic conditions all play a role in determining the noise levels experienced within the park (counts & newman, 2019). the sound levels of the parks in malaysia exceeded the permissible limit for both day-time and night-time, indicating that the parks in malaysia are exposed to high noise levels from surrounding land uses, and that the acoustic environment in the parks may not be quiet enough for a good restoration. similarly in an urban park in madrid, park visitors closer to a major road reported lower levels of perceived restrictiveness and tranquillity compared to those further away (matsinos et al., 2008). therefore, noise pollution from traffic or other sources can disrupt the tranquillity and sense of escape that people seek in natural environments 3.3. effect of landscape spatial pattern on sound levels spatial patterns of local landscapes could affect soundscape perception through landscape composition and landscape configuration (benocci et al., 2022; liu et al., 2013). the sound levels experienced within the park can have an impact on its calm soundscape. the landscapes of the parks, especially at the border of the parks, allow for the reduction of the sound levels, as a noise barrier. the landscape composition of the park was analysed through the ndvi values of the park’s spatial landscape characteristics, as illustrated in figure 7 and further break down in table 3. according to the u.s. geological survey (2018), ndvi values ranges from 0.1 – 0.2 are barren rocks and open soils, while ndvi of 0.2 – 0.5 are sparse and moderate vegetations such as grasslands and shrubs. higher ndvi values of 0.4 – 0.9 include dense vegetation such as trees canopy. table 3. ndvi values of the selected parks klcc park bukit jalil recreational park taman tasik permaisuri putrajaya botanical garden size (ha) 20.23 32.37 49.37 93.08 land cover (ha (%)) tree canopy 15.80 (78.1) 29.17 (90.1) 43.05 (87.2) 88.15 (94.7) shrubs 0.99 (4.90) 1.92 (5.92) 2.29 (4.64) 3.26 (3.5) land 1.56 (7.73) 0.87 (2.69) 1.12 (2.27) 1.05 (1.13) water & artificial surfaces 1.88 (9.28) 0.42 (1.29) 2.89 (5.86) 0.59 (0.63) total percentage (ha (%)) 20.23 (100.0) 32.97 (100.0) 49.37 (100.0) 93.08 (100.0) source: eosda landviewer, 2022 ndvi value is higher in putrajaya botanical garden with its surroundings surrounded by areas with vegetations and water surfaces (figure 6d). there is a clear pattern in which darker greens were observed at the borders of the park to reduce the noise levels from the close proximity to the highway into putrajaya. this is in agreement that the existence of walls in the perimeter of the urban parks’ functions, even partially, as noise barriers (carvalho & cleto, 2012). the big area of water surfaces (red) at the border of the park, which is the man-made pond in putrajaya, also contributes to the lower sound levels measured within the park. the center of putrajaya botanical garden recorded high percentage of tree canopies with dense vegetations (figure 7d), suggesting that the presence of bio phony sounds such as animals and birds in the area would be higher than in other sections of the park explaining the higher sound level in the area (figure 6d). this is also in line with findings from leveau & isla (2021), that areas with ndvi index of higher than 0.3 has a higher presence of bird sounds, which increases the diversity of bio phony sounds in the area. taman tasik permaisuri showed similar high ndvi tree coverage in the southern sections of the park, where people described it as a ‘forest-like’ area (figure 5b). interestingly, the sound levels in this section of the park are lower than other parts of the park (figure 6b), which is different from the comparison of sound level and ndvi in putrajaya botanical garden (figure 6d). this may be because while putrajaya botanical garden is https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 112 relatively quiet due to the low volume of visitors and the large size of the park, the sound levels observed in the park is mainly contributed by the sounds of nature in the park; while taman tasik permaisuri which is located in a residential area, have a higher volume of people using the park, resulting in a higher sound level at the northern area of the park where people gather for social activities. figure 6b and 6d also demonstrated the lower sound levels of the two parks, justified by the higher tree coverages in the two parks acting as a noise barrier to the surrounding noises from outside the park. this may be a part of the reason putrajaya botanical garden and taman permaisuri are described similarly to the park visitors as an area for relaxation and recreational purposes. source: analysis, 2023 figure 7. ndvi values of the parks (a) klcc park; (b) taman tasik permaisuri; (c) bukit jalil recreational park; (d) putrajaya botanical garden it is also clear from the ndvi figures of klcc that the park’s surroundings are high with artificial surfaces, indicating a limited area of green spaces within close vicinity to the park (figure 7a). it is almost similar to the bukit jalil recreational park where the surroundings of the park are commercial and residential development, except for the section towards the north of the park surrounding, where it is a golf and country resort. from figure 6a, klcc park showed a higher overall sound level and a smaller noise level variation as compared to taman tasik permaisuri and putrajaya botanical garden. this can be explained by the location of the park highlighted in the previous section, as well as the lesser tree coverage in both parks. in other words, the location of klcc park in the central business district, as well as the lower vegetation density in the park, increases the background sounds within the park, explaining the higher sound levels heard from within the park. in this case, noise events such as noises from children playing or conversations would be less noticeable in klcc park as compared to other parks. the percentage of tree coverage in klcc park is the lowest at 78.1% compared to the other parks which are well over 87.0%. this may contribute to the higher mean sound level of the park and higher percentages of sounds from human activities (anthrophonic activities) as there are lesser biophonic sounds in the park. the findings here agree with dzhambov et al., (2018) that green space with higher ndvi percentage which signifies greater tree cover are mediated by the lower sound level of the parks and reflects lower noise annoyance. according to benocci et al. (2022) different urban environments and natural sound abundance contribute to the different soundscape scenario. the heavier tree coverages are located by the border of the park as a barrier to the noises outside the parks and increases the biophonic sounds heard from within the park. https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 113 impact of park landscapes on the sound levels ndvi index has been used to monitor changes in land use patterns surrounding urban areas (ehsan & kazem, 2013) and the abundance of vegetations around people’s home (larkin & hystad, 2019). evidently in figure 7a which reflects a park located in a dense urban environment, the ndvi of the park’s surroundings are reds (<0.2), suggesting very limited vegetation coverage in the surroundings while showing greens (>0.6) within the boundaries of the park. places with low ndvi value showed low level vegetations scattered around in small bits (teeuwen et al., 2024). this suggests that the park environment of klcc park offers great contrast from its dense urban surroundings. likewise, in singapore, the ndvi of urban areas ranges from 0.4 – 0.6, indicating a high vegetation coverage across the city (gaw et al., 2021). therefore, in comparison, the role of parks in malaysia are more evident as a space for getaway of the urban environment and demonstrates a higher significance on the reductions of urban sound levels in the parks. the result showed that sound levels are higher across all four parks at areas with lower ndvi values, indicating less tree coverage in the area. this agrees with the analysis of de oliveira et al. (2022) that planting varied vegetation typologies such as trees and shrubs in high density efficiently contributes to noise reduction. this is also similar to the finding that areas of parks with trees surrounded can be perceived as the ‘quietest’ while paths along areas of grass can be perceived as the ‘loudest’ (guo, 2019). it suggests that vegetation coverage may contribute to a part of the observed result of sound levels within the park, because of the biophonic activity driver. certain sound categories may also be influenced by the varieties of land cover and their spatial characteristics. liu and shen (2014) mentioned in his study of city parks that human sound perception showed close relationship towards water and building land cover. their research also highlighted the correlation between soundscape diversity and that water areas were perceived as positive, suggesting that adding water features to parks could increase their appeal to parkgoers. the use of vegetation in urban planning and park design to reduce noise pollution is becoming increasingly common. the increase of vegetation cover in the form of forest and grassland is recommended to help reduce urban noise (akay & önder, 2022; han et al., 2018; ow & ghosh, 2017) where green buffering zones can be installed to minimize the impact of noise on surrounding land uses (yuan et al., 2019). forest, trees and shrubs are effective for managing noise pollution, other types of land cover can also be useful considering the seasonal variation in attenuation across diverse land covers of urban environment. thick-branched and densely covered trees and bushes can act as natural sound barriers and lowers noise levels (papafotiou et al., 2010). jaszczak et al. (2021) agreed that park design elements such as the arrangement of vegetation can also influence the park’s ability to reduce noise levels. another study by akay and onder (2022) suggests that plant groups and the distance between the noise source can help with traffic sound mitigation. while anthropogenic noise is increasing globally due to population growth, increased transportation and resource extraction, land cover can influence noise attenuation (gaudon et al., 2022). in a study conducted in shenyang, china (yang et al., 2019), the impact of high-density urban traffic noise on acoustic environment of urban parks were analyzed, revealing that parks were clustered, and laeq of the traffic sound simulation on the roadways adjacent to the parks ranged from 59.0-70.9dba, with a specific pattern based on time and day. both tasik permaisuri and bukit jalil recreational park have a high wall surrounding the park and multiple natural features to hinder the transmission of noise inside the park. the areas where such landscape exist in bukit jalil recreational park, neighbouring the busy highway adjacent to the park, showed that traffic sounds were lower due to sound absorption effect. this is similar in lu xun park where areas affected with traffic sounds were designed with multiple natural landscapes for sound absorption (yang et al., 2019). the study also revealed that as one moves deeper into the interior of the park, the sound levels and perception of traffic noises decreases. this is influenced by the spatial characteristics, landscape characteristics and sound composition in the parks. sounds were more likely to be reflected with paved grounds, making it not conducive to the attenuation of traffic noises. https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 114 landscape planning of a park is also carried out as a method of redesigning places most exposed to noise to maximise the soundscape perception in three parks in olstyn, poland (jaszczak et al., 2021). the study then proposes two design activities to address noise reduction through the reduction of undesirable sounds and the introduction of desirable sounds to the park. similar to the parks in malaysia, areas which are most exposed to noises are often located at the park boundaries along the main access roads and park entrances. therefore, similar measures of sound intervention can be considered in the planning phase of urban parks to minimize the impact of surrounding sounds and increase the beneficial influence of natural sounds for restoration. 3.4. implications from the effect of land use and landscape on sound levels of parks this research focuses primarily on the influence between landscape and sound level, hence ndvi is used to examine the effect of vegetation density on sound level. in such context, it can be applied during the planning stage, to determine the suitable location for urban parks, as well as to propose plants with dense tree coverage especially in borders of parks at dense urban areas to minimize the impact of surrounding noises on the acoustic environment of the parks. this is essential to ensure and maximize the park’s function as a space of leisure, social activities, and relaxation. similar to a study in hong kong (lam et al., 2005), which emphasized the need of urban parks as a place of social functions rather than environmental functions, urban parks in malaysia should be designed to provide greenery and social space for the urban community to relax and interact with one another to provide greenery and the social space for urban inhabitants to interact with one another. research findings in this study demonstrated the pattern of land-uses and the landscape (vegetation coverage) in influencing the sound levels of the parks in malaysia. this is crucial so that in the future planning of a park, the surrounding land uses, and its existing sound levels should be taken into consideration to minimize the impact of noise on the park’s environment. in the context of an existing urban environment, land use that is proposed to be in an area with a generally intolerable noise level may be permitted if the impacts and benefits of the proposed land use in that location are weighed. according to mennitt et al. (2014), maps can be generated to represent and predict the consequences of sound level variation on landscape in different scenarios. in such cases, mitigation actions involving the use of landscape as a strong noise barrier should be considered. this is reflective in the city of san diego general plan (city of san diego, 2008) which states that parks should be in calm locations whenever feasible and that noise exposure levels should be considered during the planning and design process. place the most noise-sensitive uses, such as children's playgrounds and picnic tables, in the site's calmer areas when the parks are in livelier areas. these mitigation actions help to enhance the park’s environment for a better restoration and relaxation purpose as well as increasing the health benefits of the urban parks. as margaritis and kang (2017) highlighted, noise pollution is significantly influenced by urban design, urban density, urban morphology, street distribution, street environment, and urban land use. gerolymatou et al. (2019) also noted that the design of public spaces and activities hosted within the neighbourhood buildings can have a significant acoustic impact on the sound comfort experienced by the residents. the noisiest park among the four-study areas is klcc park, the smallest park in the study located in the most central area of kuala lumpur city. the sound level within the park records decibels above 60db(a), dominated by sounds from the surrounding land uses of high-density commercial areas and road traffic. the least noisy park in the study area reflects the biggest park among the study area located in putrajaya. also reflective of its location, the park’s surrounding is rather peaceful with a large portion of the park bordering the man-made lake of putrajaya while the other sections border the highway into putrajaya. tranquility perceived in different environments is based on the visual and acoustic characteristics. however, the dense tree coverage as seen from the ndvi analysis illustrates the significant function of the shady trees as noise barriers to the park. the role of landscape coverage in influencing the sound levels of the parks is evident in taman tasik permaisuri’s lower sound levels at the southern sections of the park, which is high in its ndvi level, signifying a dense tree coverage. https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al./ geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 115 the findings of this study suggest that there is a pattern in the influence of parks’ surrounding land use and landscape of the parks on the sound levels measured in the park. these results confirm that especially in urban environments with high densities and busy settings, the sound levels measured within the parks located in such environment to be higher that the permissible noise level limit of outdoor spaces (55dba), which may cause possible interference in speech intelligibility and some inconvenience to visitors who wish to communicate or relax in these spaces. these analyses suggest that the ability of urban parks to improve the sound quality is limited. 4. conclusion the study contributes to the understanding of the role of parks from the influence of the surrounding sound level and the importance of vegetation coverage in parks to mitigate the urban sounds, especially in urban areas. this study demonstrates how surrounding land use provides opportunities for vibrant activities and pedestrian flow will increase the sound levels of the park. it also shows how landscape of the park in terms of the vegetation density can be used to reduce the impact of surrounding sounds for a calmer park experience. even though ndvi indices can be used as an indicator of the how vegetation coverage reduces surrounding sounds into the park, it would be beneficial if the perception of the park visitors were taken into consideration as well to provide a deeper insight of how perceived sound sources and volume have an impact on visitors’ experience. for example, further studies can be carried out to measure how and why dense vegetation coverage leads to higher biophonic activities and the effect of biophonic sounds on park experiences. therefore, a limitation to this study is that the influence of landscape and land-use of the parks’ surroundings in this study were only measured by the objective measurements of the sound levels. while the perceived sound level of the park visitors may differ according to their tolerance of noise levels, it is likely that the influence of sound level on the visitors’ perception may differ according to the personal preferences of the park visitors. hence further study should investigate the relationship between people’s perception of the sound levels of the parks. the study also uses ndvi to measure the landscape spatial pattern on sound level. however, ndvi only measures the amount of vegetation in an area and does not provide information on the type or quality of the vegetation. although ndvi index on its own can be used to measure the relationship of vegetation density with sound level, it is not sufficient to differentiate between the sound component of the parks due to the acoustic complexity of the area and to identify the effect on the soundscape perception of the park visitors. this suggests that other types of landscape indices can be considered in investigating the relationship between landscape spatial pattern and sound levels for a thorough understanding of landscape effect on acoustic perceptions. this study is also limited to the sound levels measured from within the park boundaries thus will benefit from the comparison of the sound level at the exterior boundary of the park and the internal boundary of the park. future studies would benefit to include that aspect as to validate and examine the relationship between the influence of tree canopies as effective noise barriers from the surrounding noises. as parks play a role in promoting recreation, park planning and design should consider including soundscape interventions, ndvi and land use analysis to see which areas may be prone to noise pollution so that actions on mitigation can be planned. the purpose of a park is for recreation and relaxation from busy urban settings; therefore, its environment should always be conducive for recreation purposes. conflicts of park use due to its inconducive environment should always be minimized to the very least to maximize the benefits of parks in cities. therefore, further emphasis should be undertaken on the concept of soundscape in parks to enhance the park environment in malaysia. 5. acknowledgements nhad acknowledges the dostphilippine council for agriculture, aquatic and natural resources research and development (pcaarrd) for the balik scientist grant. kjar and jeld acknowledges deutscher https://doi.org/10.14710/geoplanning.11.1.99-120 lee et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 99-120 doi: 10.14710/geoplanning.11.1.99-120 116 akademischer austauschdienst (daad) german academic exchange service for the in country/in region scholarship. pasm and aop would like to thank the dost – accelerated science and technology human resource development program (asthrdp) for the scholarship. the authors declare no conflicts of interest. 6. references aditya, k., & chowdary, v. 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[crossref] https://doi.org/10.14710/geoplanning.11.1.99-120 https://doi.org/10.1007/s40726-023-00250-1 https://doi.org/10.3390/s16101692 refleksi 5 tahun paska erupsi gunung merapi 2010: menaksir kerugian ekologis di kawasan taman nasional gunung merapi | 73 geoplanning vol 6, no. 1, 2019, 73-80 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.6.1.73-80 spatial distribution of potential area for community forest development in grindulu watershed a. miardinia, p. d. susantia a balai penelitian dan pengembangan teknologi pengelolaan das, indonesia abstract: the effect of deforestation on the environmental degradation shifted the orientation of forest management into carrying capacity of the watershed. based on law no. 41/1999 on forestry, mandates adequacy forest area defined minimum of 30% of the watershed area which fulfilled by public forest and private forest. state forest area has limitations, so development of community forests is needs for optimal forest area in a watershed is required. the purpose of this study was to determine spatial distribution of potential area for community forests development in grindulu watershed. the potential of community forest was examined through an interpretation of landsat 8 of 2016 path/row 119/668 for land availability and the transformation of ndvi (normalized difference vegetation index) as the density classifier. the classification of forest density was: low density class of 5148.12 hectares or 7.20% (ndvi = 0 to 0.356), moderate density class of 12076.39 hectares or 16.88% (ndvi = 0.356 to 0.590), and high-density class of 54294.04 ha or 75.92% (ndvi = 0.590 to 0.841). the land available for prioritised community forest development was 37774.40 hectares (52.82%) in the form of dryfields, shrubs, grasses, farms, which were located outside the protected areas and production forest. based on the assessment of field surveys which were conducted proportionally at 89 sample, known good accuracy results by 0.84. potential area for community forest development was 31281.54 ha (43.74%) including in pacitan (9 districts) of 29111.98 hectares, ponorogo (5 districts) of 263.29 hectares, and wonogiri (2 districts) of 1906.27 hectares. copyright © 2019 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): miardini, a., & susanti, p.d. (2019). spatial distribution of potential area for community forest development in grindulu watershed. geoplanning: journal of geomatics and planning, 6(1), 73-80. doi:10.14710/geoplanning.6.1.73-80. 1. introduction forest is a strategic natural resources (subekti, 2016) and important for the socio-economic development of societies (asante et al., 2017). indonesia is one of the southeast asian countries that are experiencing deforestation. during 2000–2010, sumatra, kalimantan, sulawesi, moluccas, and papua lost 14.7 mha of forests in total (abood et al., 2014). deforestation leads to degradation and erosion that have an impact on sedimentation on water bodies, thus, forest cover has a crucial function in the socioeconomic development and the ecological balance (siddiqui et al, 2004). in indonesia, minimum area of forest cover regulated on the law no. 41/1999 which mandates the government to determine and maintain forest area extent adequacy and forest coverage for each watershed and or island, to optimise environmental, social and economic benefits for local community at least 30% (thirty percent) of watershed and or area extent at proportional distribution deforestation resulting in the depletion of forest cover. nowadays, the needs for forest cover are fulfilled by public forest and private forest. public forest is a forest area that grows on land not encumbered property, whereas private forest or community forests are forests growing on land subject to property rights. the extent of state public forest is limited based on forest designation; hence the improvement of community forests to fulfill of optimal forest area in watershed is required. community forest management has been identified as a win-win option for reducing deforestation while improving the welfare of rural communities in developing countries (santika et al., 2017). according to the regulation issued by minister of forestry p.03/menhut-v/2004, community forests are forests growing on landsubject to property rights article info: received: 24 may 2018 in revised form: 5 february 2019 accepted: 11 july 2019 available online: 30 august 2019 keywords: spatial distribution, ndvi, potential community forest, grindulu watershed corresponding author: arina miardini balai penelitian dan pengembangan teknologi pengelolaan das email: arinamiardini@gmail.com open access http://doi.org/10.14710/geoplanning.6.1.73-80 mailto:arinamiardini@gmail.com miardini and susanti/ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 73-80 doi: 10.14710/geoplanning.6.1.73-80 74 | or other rights to the provision of a minimum area of 0.25 ha with canopy closure of perennial woody plants and other crops over 50%. the goals of community forests development are: yards, embankments and critical land based on soil and water conservation (ritohardoyo, 1999). community forest development requires mapping to find out the potential location of community forests. acharya, (2002) said that forest boundary surveying and mapping is an important tool to support community forest. the uses of gis based technology should be cost effective. the unit analysis used in this study is the watershed. basically, the orientation of forest management should be targeted on the entire potential of forest resources including to enhance the function and carrying capacity of the watershed. the development of community forests in pacitan as the dominant district in grindulu watershed involved the spatial pattern plan of cultivation area for community forest. development activities in the grindulu watershed, both upstream and downstream, are quite intensive and the population pressures are quite high. pacitan and wonogiri districts have decreased the ability of land to absorb water, and protect the soil from erosion, which in turn leads to high surface runoff and erosion. the development of community forests in pacitan as the dominant district in grindulu watershed involved the spatial pattern plan of cultivation area for community forest. criteria for the designation of community forests are forests growing on land subject to property, dominated by annual crops, and area that can be utilised for settlement, agriculture, plantation, community forests, and other silviculture activities (regional government of pacitan regency, 2011). at present there has been no mapping of the potential of community forests in the grindulu watershed, so that by conducting this research, it can be seen that the potential of community forests can be developed. the purpose of this study was to determine spatial distribution of potential area for community forests development in grindulu watershed. 2. data and methods 2.1. time and study area this study was conducted in grindulu watershed in 2016. grindulu watershed has an area of 71518.54 hectares. administratively, grindulu watershed consist of three districts, which is dominated by pacitan district of 64708.48 ha (90.48%) covering 9 subdistricts and 97 villages; ponorogo districtof 2715.46 ha (3.80%) comprising of 5 subdistricts and 10 villages; and wonogiri districtof 4094.60 ha (5.73%) covering 3 subdistricts and 7 villages. grindulu watershed is one of the prioritised watersheds, ranging from the upstream of gunung sewu, mount lawu, and wonogiri karsts to the downstream in pacitan regency as the outlet. the study area is presented in figure 1. figure 1.study area of grindulu watershed (data analysis, 2017) http://doi.org/10.14710/geoplanning.6.1.73-80 miardini and susanti/ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 73-80 doi: 10.14710/geoplanning.6.1.73-80 | 75 the climate of grindulu watershed is classified as type c based on schmidt and ferguson. the rainfall in grindulu watershed is 2165 mm/yr with an average temperature of 27.4%. the average solar radiation is 10.8 hours/day, the average wind velocity is 1.4 m/sec, and the average evapotranspiration is 4.33 mm/day. evaporation is amounted to 1584 mm/yr and precipitation is 1938 mm/yr. the topography of grindulu watershed is dominated by hilly to mountainous with an extent of 32.93% and 30.25% situated at a slope of 15-25% and >25%, respectively. there is also an undulating topography with a slope of 8-15% of 22.36% and a flat topography with a slope of 0-8% of 14.46%. geologically, grindulu watershed is divided into three zones, namely: a) miocene sedimentary facies (alluvial plains), which covers the upstream (north) in nawangan district and bandar district to the southern coast of the eastern part of the watershed. the material consists of lithosol and complex red lathosol; b) structural hills (andesite). the structural hills are part of the line overgrown by quater nary volcanoes. in grindulu watershed, structural hills spread around pacitan sub district and arjosari sub district. materials of structural hills are basaltic andesite and dacite that constituted arjosari formation. the results of weathering of andesite and dacite are complex reddish brown lathosoland volcanic lithosol; and c) gunung sewu thousand mountains (limestone). in grindulu watershed, pringkuku, tulakan, and kebon agung sub district are dominated by limestone. it is generally made up of mediterranean soil and association of lithosol and reddish-brown mediterranean soil. 2.2. material and tools the materials required in this study included map of grindulu watershed, rbi map scale 1: 25000, landsat 8 of 2016 path/row 119/66, map of protected forest and production forest, the spatial planning document of pacitan in 2009-2028. furthermore, gps, asus notebook core i3 capacity of 6 gb ram, and 500gb hdd, arcgis 10.2 software, were employed tools. 2.3. data processing and analysis this study modified the results of community forest analysis carried out previously by bpkh xi and mfp ii 2009. the potential of community forests in the area of grindulu watershed was analysed by performing landsat 8 imagery interpretation for land use analysis to determine the availability area for community forest and transformation of ndvi (normalized difference vegetation index) for density classification. the initial image processing was done to obtain the radiometric calibrated image, hence the value of surface reflectance was generated. land use was obtainedby updating rbi scale 1: 25.000 with the 432 image composite. land use map was made to identify the potential area for community forests. vegetation detection was carried out based on the transformation of the ndvi vegetation index. ndvi is a measure of vegetative cover based on remotely sensed data (bluffstone et al., 2018). vegetation index is a spectral transformation applied on the multi-bandimages to highlight the density aspect, e.g., biomass, leaf area index (lai), chlorophyll concentration, and so forth. vegetation index as mathematical transformation involves multiple bands simultaneously to produce a new image that is more representative in providing the aspects related to vegetation (danoedoro, 2012). basically, formula for calculating ndvi is as follows: ndvi = where: nir : infrared band (band 5). red : red band (band 4). the results of the formula range from –1 to +1 where the value of –1 indicates that the red band has the maximum reflectance value and the infrared band has the minimum reflection. it demonstrates the non-vegetation area. vice versa, the value of +1 indicates the maximum reflection occurs in the infrared band and the minimum reflectance in the red band, showing vegetated area with a high density. furthermore, based on ndvi values and the result of field study, density classification was defined into low, moderate, and high. community forest assessment was done with restrictions: 1) the class density was high-density forest (the vegetation cover>50%), 2) the high-density forest with land available for community forest was selected, 3) the extent of minimum community forest (>0.25 ha) was determined. http://doi.org/10.14710/geoplanning.6.1.73-80 miardini and susanti/ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 73-80 doi: 10.14710/geoplanning.6.1.73-80 76 | 3. results and discussion based on ndvi analysis, a range of -0.319 to 0.084 was generated. the values of -0.319 to 0 were eliminated since they indicated non-vegetated areas or open area. tovar, (2011) ndvi values below 0.1 correspond to bodies of water and bare ground, while higher values are indicators of high photosynthetic activity linked to scrub land, temperate forest, rain forest and agricultural activity. ndvi values remained within the 0.8 to 0.9 range for the native forests (cristiano et al., 2014). ndvi classes were classified into three classes, namely low, moderate, and high. sample coordinates were 89 points that represented each density class proportionately. in accordance to the estimation of field surveys, the accuracy result was 0.84. grindulu watershed was dominated by high-density classamounted to 54294.04 ha (75.92%). this class was dominated by monoculture and mixed forest without any association with other land uses. ndvi classification and density classes are illustrated in table 1 and sample location in figure 2. table 1. ndvi classification and density classes. no density class ndvi values area (ha) percentage (%) description 1 low 0 – 0.356 5148.12 7.20 community forest mixed with settlement. 2 moderate 0.356 –0.590 12076.39 16.88 community forest mixed with wet/dryland agriculture and settlement 3 high 0.590 – 0.841 54294.04 75.92 community forest of fulltrees. total 71518.54 100.00 source: data processing, 2017. figure 2. sample location and ndvi classification and vegetation density (data analysis, 2017) potential areaof community forest development in grindulu watershed was estimated based on the high-density class with ndvi values from 0.590 to 0.841 on available area for the development of http://doi.org/10.14710/geoplanning.6.1.73-80 miardini and susanti/ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 73-80 doi: 10.14710/geoplanning.6.1.73-80 | 77 community forests with an area of >0.25 ha. potential area for the prioritised community forest development is 37774.40 ha (52.82%) in the form of dryland agriculture, shrubs, grasses, plantation, and situated outside the protected and production forests. the extensive areas available for community forest are particularly includedin tegal ombo sub-district of 10014.42 ha and arjosari sub-district of 8736.60 ha. figure 3 demonstrates the potential area for community forest and the forest area extent. the extent of potential community forest area in grindulu watershed as presented in table 2 was estimatedin accordance with the high-density class of potential area, which is 31281.54 ha (43.74%). the most extensive potential areas for the community forest development are located in tegal ombo sub-district of 8017.45 ha and arjosari sub-district of 8017.45 ha. table 2. the extents of potential community forest area of each sub-district in grindulu watershed. no district/sub district available area (ha) potential community forest area (ha) a pacitan 34732.51 29111.98 1 arjosari 8736.60 7878.34 2 bandar 3888.54 3132.57 3 kebonagung 2246.06 1835.86 4 nawangan 2853.06 2245.10 5 pacitan 2327.02 2123.79 6 pringkuku 800.03 736.79 7 punung 1497.23 1445.13 8 tegalombo 10014.42 8017.45 9 tulakan 2369.55 1696.94 b ponorogo 875.15 263.29 1 badegan 2.47 0.01 2 balong 22.77 15.63 3 jambon 48.70 27.80 4 ngrayun 323.18 18.51 5 slahung 478.04 201.35 c wonogiri 2166.73 1906.27 1 karangtengah 2126.86 1876.53 2 kismantoro 39.87 29.74 total 37774.40 31281.54 source: data processing, 2017. http://doi.org/10.14710/geoplanning.6.1.73-80 miardini and susanti/ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 73-80 doi: 10.14710/geoplanning.6.1.73-80 78 | figure 3. spatial distribution of potential area for community forest (data analysis, 2017) community forest types based on the results of field survey was dominated by monoculture and mixed forest without any association with other land uses. in addition, the most common plants consisted of sengon (paraserianthes falcataria), mahogany (swietenia macrophylla), teak (tectona grandis), acacia (acacia mangium) and jabon (anthocephalus cadamba). in mixed community forest, there were 16 species planted between the lines of staple crops. the associations of the crops in the community forest in grindulu watershed are kelapa (cocos nucifera), pisang (musa paradisiaca), kopi (coffee arabica), ketela pohon (manihot esculenta), coklat (theobroma cacao), kacang tanah (arachis hypogaea), kacang panjang (vigna cylindrica), jagung (zea mays), cabai (capsicum frutescens), bamboo (bambusa sp), ubi (ipomoea batatas), padi gogo (oryza sativa), papaya (carica papaya), lengkuas (alpinia galangal), porang (amorphophallus muelleri) dan janggelan (mesona palustris). species selected in the development of community forests should have criteria of adaptive to the ecosystem, fast-growing, high commercial value, uncomplicated procurement of high-quality seeds and seedlings, and market demand-oriented. in addition, they should have economic viability and can produce commodities such as fruits, fodder, and others in a short term. fast-growing species are opted due to their high commercial value, uncomplicated seedling nursery and high-quality seeds, and market demandoriented. another advantage of community forest development in addition to degraded land and environmental rehabilitation is the economic benefits. the target of community forest development is prioritized on land identified as critical land. based on data obtained from (solo watershed and protection forest management center, 2011), the critical condition levels of grindulu watershed consisted of potential critical of 7132.95 ha (9.97%), slightly critical of 48248.72 ha (67.5%), critical of 15333.96 ha (21.44%) and very critical of 20.42 ha (0.02%). in appropriate improvement and rehabilitation effort would worsen the critical degree and reduce watershed functionality and carrying capacity. one of the attempts to address the critical area is the development program of community forest as suggested by (suherdi et al., 2015). another advantage of community forest development in addition to degraded land and environmental rehabilitation is the economic benefits. (waluyo et al., 2010) reported the increased timber utilisation of community forest program to meet the market demand for timber. species selected in the development of community forests should have criteria of adaptive to the ecosystem, fast-growing, high commercial value, uncomplicated procurement of highhttp://doi.org/10.14710/geoplanning.6.1.73-80 miardini and susanti/ geoplanning: journal of geomatics and planning, vol 6, no 1, 2019, 73-80 doi: 10.14710/geoplanning.6.1.73-80 | 79 quality seeds and seedlings, and market demand-oriented. in addition, they should have economic viability and can produce commodities such as fruits, fodder, and others in a short term. community forest with agroforestry concept can be applied to the rehabilitation of the region. agroforestry is land use system that combines woody plants (trees, shrubs, bamboo, rattan, etc.) and non woody plants or grasses, or components of livestock or other animals (bees, fish) to form ecological and economical interaction between woody plants with other components (mcadam, 2000). in this study only limited physical data on land use and cover, but did not consider aspects of information from the community regarding land ownership. even though information about local communities is very useful in the accuracy of the data produced. natural resource management must take community-based approach to succeed by considering conservation and development goals. the concept of this region must be designed with map based on local knowledge resources based how the community perceives its resources can be managed (etongo & glover, 2012). based on research peters-guarin and (peters-guarin & mccall, 2010) the contribution of local communities in the acquisition of data useful for activities related to forest management and carbon sequestration. utilization of gis technology for mapping community forests should be cost effective. it is recommended to explore the potentiality of combining existing surveying system and gis based techniques in community forest mapping surveying to improve accuracy and valid information. 4. conclusion community forest mapping is an important tool to support community forest development. utilization of ndvi data combined with field data survey can be used as an alternative in distribution mapping of community forests. based on the assessment of field surveys which were conducted proportionally at 89 sample, known good accuracy results by 0.84. the spatial distribution of potential area for community forest development in grindulu watershed was 31281.54 ha (43.74%) that was dispersed in pacitan district (9 sub-districts) of 29111.98 ha, ponorogo district (5 sub-districts) of 263.29 ha, and wonogiri district (2 sub-districts) of 1906.27 ha. to determine the location of community forests, it is necessary more discussion and interpretation to add a combination of participatory mapping methods to represent the spatial knowledge of local communities. 5. acknowledgments the authors are grateful to balai pengelolaan daerah aliran sungai dan hutan lindung solo (bpdashl) for their data support so this paper can be arranged. 6. references abood, s. a., lee, j. s. h., burivalova, z., garcia-ulloa, j., & koh, l. p. 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[crossref] http://doi.org/10.14710/geoplanning.6.1.73-80 https://doi.org/10.1155/2012/871068 https://doi.org/10.1017/s0014479700261085 https://doi.org/10.1016/j.gloenvcha.2017.08.002 https://doi.org/10.1016/s0273-1177(03)00469-1 https://doi.org/10.22146/jkn.12547 https://doi.org/10.25015/penyuluhan.v10i1.9916 https://doi.org/10.20886/jphka.2010.7.3.271-280 | 147 geoplanning vol 3, no. 2, 2016, 147-160 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.3.2.147-160 mitigation scenarios for residential fires in densely populated urban settlements in sukahaji village, bandung city s. a. h. sagala a, p. adhitama b, d. g. sianturi c, u. al-faruq b a bandung institute of technology, indonesia b resilience development initiative, bandung, indonesia c perumnas, indonesia abstract: residential fires are a form of disaster that often occurs in urban areas especially in densely populated settlements. this study looks at possible mitigation scenarios for this kind of disaster. a case study was conducted in babakan ciparay subdistrict in bandung city, among the densely populated settlements, and was focused especially on sukahaji village, a sub-unit of babakan ciparay, which is the most densely populated village in bandung city with up to 234.14 people/ha. there have been six structural fires recorded from 2007 until 2010 occurring in sukahaji. this study applied stratified random sampling as the preferred sampling technique and data collection method from a total population of 3,227 buildings. the data was then examined using risk analysis. the results have led to two intervention measures suggested as mitigation scenarios for residential fires that can be applied within the sukahaji village. the study concludes that mitigation measures through strengthening community capacity can be the principal option in reducing risk to fires in densely populated urban settlements. copyright © 2016 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): sagala, s. a. h., et al. (2016). mitigation scenarios for residential fires in densely populated urban settlements in sukahaji village, bandung city. geoplanning: journal of geomatics and planning, 3(2), 147-160. doi:10.14710/geoplanning.3.2.147-160 1. introduction fires in urban areas often break out in densely populated residential areas. the source of the fire hazard is often caused by careless residents during daily activities such as smoking, cooking, use of electronic equipment, playing with live fire, and gas leaks. besides, fires can as well be caused by natural incidents such as lightning, earthquakes (rupturing gas pipes), volcanic eruptions, and drought (indonesian government, 2007). sagala et al. (2014) observed that community's behavior related to residential fires is unsafe. it involves fire related activities, which are often conducted along with other activities. according to the ifrc (2010), densely populated residential areas are vulnerable to disasters, especially fires. bandung is a city with high population density with up to 16,008.53 people/km2 or roughly 160.0853 people/ha (bandung central bureau of statistics, 2009). in the recent study by tarigan et al. (2016), bandung has grown as one of the important metropolitan areas in indonesia. huang (2009) argued that some of the most loss-inflicting fires are the ones that occurred in urban/residential areas. in addition to that, xin and huang (2013) studied the model reporting the relation between losses of lives, per m2 annually. the inappropriate set up of billboards on exterior walls on the building could potentially trigger fires and accelerate fire spreading (zhou, 2013). therefore, the general relationship between urban planning and the incident of urban fire exist. as stated by the fire prevention and mitigation agency of bandung, urban fires occur most often in areas with a high population density. in general, the data from local disaster management agency (bpbd) show that during ten years (20002010), 1,624 fires broke out with around 773 incidents (48%) taking place in residential areas. the average article info: received: 26 february 2016 in revised form: 10 september 2016 accepted: 01 october 2016 available online: 31 october 2016 keywords: fires, densely populated areas, urban risk, mitigation scenario corresponding author: saut aritua hasiholan sagala bandung institute of technology, indonesia email: saut.sagala@gmail.com open access http://dx.doi.org/10.14710/geoplanning.3.2.147-160 http://dx.doi.org/10.14710/geoplanning.3.2.147-160 mailto:saut.sagala@gmail.com sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 148 | number of fire incidents in bandung sums up to 162 incidents per year with the material loss going up to idr 21,137,813,636 each year (bpbd bandung, 2016). table 1 shows the high rate of urban fires and the huge loss-inflicting on bandung. table 1. total loss and fire incidents in bandung city (bpbd bandung, 2016) year total no. of incidents fatalities non-fatal casualties approximate loss (rupiah-idr) 2000 180 6 5 18,874,700,000 2001 167 2 10 74,557,150,000 2002 207 4 22 20,464,050,000 2003 157 4 9 10,883,600,000 2004 173 4 3 13,880,300,000 2005 134 10 12 17,771,000,000 2006 123 3 3 11,041,750,000 2007 160 3 10 36,521,500,000 2008 141 1 8 12,235,700,000 2009 121 4 9 9,801,200,000 2010 61 0 2 6,485,000,000 2011 124 na na na 2012 136 na na na 2013 131 na na na 2014 162 na na na 2015 177 na na na total 2,354 41 93 232,515,950,000 table 1 provides data on the total number of incidents and also the approximate loss bandung sustained due to residential fires during 2000-2010. the economic loss hit the highest number in 2001 with damages amounting up to idr 74,557,150,000. out of all these occurrences, the majority of them took place in residential buildings. this information is presented in figure 1. figure 1. fire incidents in bandung city years 2000-2010 (bpbd bandung, 2016) http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 | 149 babakan ciparay (figure 2) stands out as one of bandung’s sub-districts with the highest number of fire incidents from 2007-2010, with 34 occurrences within that timeframe. it is exacerbated by the fact that babakan ciparay sub-district has the highest population than any other districts in bandung with 144,892 residents (bandung central bureau of statistics, 2009). spreading up to 745 ha, babakan ciparay is the densest populated district; approximately 19,448.59 people within each square kilometer, making it a vulnerable target to fires. in total, six villages make up babakan ciparay sub-district, where sukahaji village proves to be the densest populated measuring up 234.14 people/ha. from recorded incidents, it is known that six of them occurred within the houses of sukahaji in 20072010. based on this fact, sukahaji is used as a case study in this work. the study will focus on sukahaji’s neighborhood unit (rw) 0104 to be more detailed and rigorous. this study aims to develop proper mitigation scenarios feasible to be applied in sukahaji village. fire mitigation scenarios are required by the indonesian law no. 24/2007 – a law on disaster mitigation – as a form of disaster risk reduction measure. furthermore, the integration between development programs and risk reduction is also mandated by the law. figure 2. map of the study area (authors, 2016) some of studies on residential fires in densely populated settlements have been previously conducted. prathama (2011) and dwijayanti (2008) carried out research of urban fires within bandung city but omitted geographical information systems (gis) as a tool of analysis. the use of gis in disaster studies is expected to boost the accuracy in which the study is conducted and also the mitigation measures produced by the study. wahyudi (2004), on the other hand, utilized gis but the area of study was too large and resulted in homogenous risk analysis for each type of land use. thus, it was not able to create any mitigation measures for a smaller area of study. this study aims to fill in the gap where gis is used to analyze fire risk in detail and a smaller area of study. this article will conduct a more detailed discussion using individual buildings from neighborhood sub-units (rt) as a basis variable for analysis (focused on neighborhood unit 01-04 of sukahaji village). the result will be a more detailed analysis of the area of study thus producing more area of study bandung city babakan ciparay sub-district http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 150 | specific mitigation measures. this study on densely populated settlements may be replicated, or at least referred to, by other residential areas. in more concrete applications, the results of this study could be implemented in the designing of detailed spatial plans (rdtr) and building and environmental plans (rtbl) as additional material. eventually, future spatial plans will take into account the disaster aspect of the built environment, especially residential fires and other fire disasters. at the outset, this article will examine urban and residential fires in bandung city, particularly within sukahaji village, and the facts surrounding the incidents. the next part will present a literature review based on other studies that have been conducted before discussing fires and fire mitigation in urban areas. the third part will explain the methodology used in this study, followed by the fourth part providing possible mitigation scenarios to be carried out based on previous risk analyses of sukahaji village. the fifth and final part will present the conclusion and also recommendations which will expectantly serve as input for decision makers in fire risk reduction efforts in sukahaji village. 2. data and methods this section consists of two parts. the first explains the components of a fire hazard that will be used in the analysis. the second part discusses data surrounding vulnerability. in general, this study calculates and analyzes all parts of the study area. a total of 3,227 building units were analyzed through a sample of 882 units taken using stratified random sampling. the units analyzed include houses, shops, as well as vacant lots. hazard components were analyzed based on the density of the buildings and activities involving fires where each building has the potential to be the cause of a fire for using fires while cooking, doing household activities, or conducting industrial processes. 2.1 hazard in the study area, the hazard component consists of liquefied petroleum gas/gas tank (lpg) warehouses, household and industrial activities, and building density. according to suprapto (2008), fires are flames ignited and spread without deliberation. fires break out when objects catched by fire go out of control and pose a threat to people and properties. a fire incident usually goes through a certain process before being put out. mantra (2005) explains that the process of developing a fire is as follows: a) ignition this is marked by a small fire caught by an object in a certain space/room caused by heat energy. b) growth fire grows and spreads depending on how much fuel or flammable materials are nearby—a fuelcontrolled fire. at this point, evacuation is highly recommended during 3-5 minutes before flashover. c) flashover a transition period where the whole room is ignited before engulfed in flames. this phase happens rapidly with temperatures going from 300 °c up to 600 °c. d) fully developed when it is no longer ‘a fire in a room’ rather ‘a room on fire’, the whole room is on fire and the room is fully involved. the temperature may reach 1200 °c. e) decay stage fire starts to decay when materials serving as fuel have all been burned out decreasing temperature and slowing the rate of the fire. sources of fire hazards in residential areas are usually neglecting activities such as smoking, cooking, use of electronics, playing with fire, gas leaks, etc. (huang, 2009). fire development is significantly affected by the availability of fuel and combustible materials in the course of the fire. in residential areas, building materials usually become sources of fire; the more materials there are the bigger the fire there will be. to mitigate fire hazards, the time of ignition and size of fire needs to be identified in order to subdue the fire http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 | 151 before it consumes all the combustible materials in its path. during a developing fire, it is optimal to extinguish it before the flashover phase (mantra, 2005). according to barnwell et al. (2005), fire hazards are often connected to fire exposure. in fire science terminology, fire hazards can explain the potential intensity of fires. the term shows the differing intensities of fires, and there needs to be a classification of sources of hazards based on potential intensities that will affect the size of the fire. fire risk is the probability of a fire igniting that has the potential to injure persons and damage private property. the bigger the fire the more damage it causes. huang (2009) argued that the number of fire incidents in residential areas including its population and building characteristics are connected. the majority of incidents can be linked to human behavior and personal routines. this is supported by kai huang’s research in 2009 that stated that human behavior is the number one cause of residential fires. 2.2 fire mitigation in urban areas according to cova (1999) disaster mitigation forms a part of disaster mitigation. mitigation is an effort to reduce or eliminate the possibility and/or consequences of hazards. mitigation is carried out to treat hazards in such a way that its effects on society is importantly reduced. coburn et al. (1994) explain that the protection from disaster hazards can be achieved by eliminating the causes of said hazards (reducing the risk) or by reducing the effects of hazards when they appear (reducing vulnerability or increasing the potential capacity of elements at risk). moga (2002) describe that mitigation planning is the developing of strategies to reduce the impacts of disaster on communities, facilities, rural and urban areas, or countries. mitigation planning can be categorized into many groups, most commonly into structural mitigation and non-structural mitigation (moga, 2002). five types of basic measures that can be used in mitigation planning programs are as follow: engineering and construction measures, physical planning measures, economic measures, institutional and management measures, and community action (coburn et al., 1994). mitigation measures aim to not only prevent loss of human lives and lessen financial loss but also decrease adverse effects on socio-economic activities caused by disasters. if sources of mitigation are limited, mitigation measures can be targeted towards the most effective element that significantly influences community activity. vulnerability assessments are an important aspect of an effective mitigation planning. vulnerability covers, but is not limited to, risk of physical damage, economic loss, and the lack of resources to recover from disasters. moreover, coburn et al. (1994) stated that disaster mitigation measures can be grouped into either passive or active mitigations. passive mitigation measures are carried out through control or penalties to prevent undesired actions such as land use control, mandatory insurance, etc. active mitigation measures are taken to promote desired measures through incentives such as building material subsidies, education and training, etc. there are countless of ways to carry out fire mitigation measures in order to increase house safety, especially from combustible materials, installing smoke detectors (duncanson et al., 2002). installing smoke detectors is an easy and effective method that serves as a warning system in the case of a fire (duncanson et al., 2002). community participation is an important component in reducing the number of fire incidents. educating the community is extremely important strategy to prepare them to take appropriate measures in the event of a fire (duncanson et al., 2002). 2.2.1 building density table 2 shows that rw 03 has far more people living in it than others. building density can be calculated by comparing the area of each building with each parcel/block of land. the analysis showed that the study area has a building density of about 55.24%-85.31%, which falls under the category of medium to high density (rianta, 2007). http://dx.doi.org/10.14710/geoplanning.3.2.147-160 file:///c:/users/hp/downloads/10305-23476-1-rv.docx%23_enref_3 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 152 | table 2. demographic characteristics in sukahaji village (sukahaji village, 2011) rw population 01 4,127 02 4,271 03 5,649 04 3,240 2.2.2 distribution of lpg warehouses and tofu industries lpg warehouses and tofu industries/factories are apparent sources of fire hazards in sukahaji village. other sources include residential buildings that are too close to each other and made of flammable materials. the table 3 below showcases the fact that rw 03 poses the greatest risk having two tofu factories than other rws. table 3. distribution of lpg warehouses and tofu industries (sukahaji village, 2011) no building rt rw unit(s) 1 lpg warehouse 02 02 3 2 tofu factory 02 03 1 03 03 1 04 03 2 05 03 2 05 04 1 2.2.3 vulnerability in this study, analyses were carried out in numerous phases. general data such as population data, distribution of lpg warehouses, roof materials, wall materials, road width, and sources of water were used to give a broad picture of sukahaji village. these elements are considered as vulnerabilities for the residents include roof and wall materials in the buildings as well as road width and sources of water for better access for firefighters. a. roof and wall materials roof and wall materials are components used to conduct a fire risk analysis in sukahaji village. based on observations done in the field, there are a number of materials used by residents in the study area including asbestos, plastic, rattan webbings, zinc, clay tiles, wood, cement, and a combination of bricks and cement. there were also buildings that utilized more than one type of materials for its roofing such as a combination of clay tiles and plastic, clay tiles with asbestos, clay tiles and zinc, and other combinations. table 4. roof materials (sukahaji village, 2011) classification roof type number of buildings using it fireproof cement, zinc, clay tiles and zinc, clay tiles 665 easily combustible asbestos, wood, plastic 175 not easily combustible plastic and zinc, bricks and cement, rattan webbings and zinc, clay tiles and asbestos, clay tiles and plastic, plastic and zinc 42 http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 | 153 wall materials used by the locals moderately vary including rocks, cement, wood, rattan webbings, plywood, zinc sheets, plastic sheets, concrete, and glass. houses using a combination of these materials equally existed. tables 4 and 5 present in detail the material used by sukahaji locals for their roofing and walls. table 5. wall materials (sukahaji village, 2011) classification type of wall buildings using it fireproof bricks; bricks and cement; bricks, cement, and ceramics; bricks, cement, and zinc; concrete; glass; ceramics; cement; zinc 661 not easily combustible bricks; cement, bamboo; bricks, cement, and rattan webbings; bricks, cement, and wood; bricks, cement, and zinc sheets; bricks cement, and plywood; bricks, cement, and plastic; wood and zinc 48 easily combustible rattan webbings; wood; wood and rattan webbings 157 b. road width and sources of water road width in the study area importantly vary ranging from local roads measuring 0.6 m up to secondary roads with 4 m. road width surrounding the area of study affect the accessibility of fire trucks in the event of a fire (figure 3). the 3.5 m width for roads is minimum required for fire trucks to be able to pass through (figure 4). there are only four roads (babakan ciparay road, terusan pasir koja road, and two local roads) in the study area having the required width of more than 3.5 m. these roads are already well paved made out of asphalt and are well maintained and can be used without obstruction by four-wheeled vehicles or fire trucks. the area surrounding these wide roads are within access of fire trucks, while as areas outside of the roads are not within reach of fire department services if any fire incident breaks out. figure 3. wall materials in the sukahaji (authors, 2016) http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 154 | figure 4. road width in the sukahaji (authors, 2016) sources of water within the study area include wells, the river, waters depots, and local pools/ponds (figure 5). these sources will serve well in the process of extinguishing fires. thirty-one wells with a total water volume of 118,338.8 l in the dry season is accessible. on average, a well has a volume of 4,000 l which has the same capacity as one fire truck. it can be used for fire extinguishing to control the flames before firefighters arrive. in the area, a total of 10 water depots/water reservoirs with a volume 12,480 l is available. since the primary source of water is the local water company (pdam), the necessary amount of water for extinguishing fires is fulfilled. this particular source has been used to extinguish fire incidents in rt12, rw02 to compensate the fact that firefighters have very little access to the site of the fire. it is clear that water depots have been proved to be an effective alternative in extinguishing the fire. figure 5. river ponds/pools as a source of water (authors, 2016) http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 | 155 there are 8 ponds with a total volume of 148,000 l, averaging 18,500 l each pond. this source of water can be effective in extinguishing fires as it can serve as a reservoir for fire trucks. there is a river that flows through the study area with a flow rate of 80 l/s or equals to 24,000 /m. this source of water can be used to supply water to fire trucks and help in fire extinguishing efforts. areas that have sources of water and can access them are said to be areas provided with a water source, while areas without a source of water are areas that are not provided with sources of water. c. risks of fires in sukahaji village based on statistical data of sukahaji village, a fire risk map was created using gis focusing on rws 0104. an illustration of the spatial aspects of the risks is provided in figure 6. to determine the level of fire risk in sukahaji village, a classification was made based on fire risk analysis. this risk classification is relative to the area of study, meaning that it may not be suitable for other areas prone to fires. nevertheless, this classification relative to the area can be used as a reference for deciding fire mitigation measures. figure 6. map of fire risks in sukahaji village (analysis, 2016) http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 156 | buildings counted to have a high risk of catching fire indicate its proximity to a source of hazard (lpg warehouse and tofu factory) and are made of not-easily-combustible building materials. buildings classified as having a medium risk of catching fire indicates its proximity (not as close as before) to a source of hazard and are made of easily-combustible building materials. the last category classifies buildings with a low risk as buildings that are not in proximity to a source of hazard and are made of fireproof materials. it can also mean that these are open lots. from the risk analysis, it has been found out that most of the study area is classified under medium risk, which is 61.17% of the total buildings (1,974 building units). the other 1,193 units are classified as having a high risk, i.e. 36.97% of the total buildings. unfortunately, only 60 buildings can be classified as low risk counting 1.86% of the total buildings. 3. results and discussion based on the results of sukahaji’s risk analysis, numerous mitigation scenarios were produced. a comparison showing the different results of spatial risk calculations from the produced scenarios is presented in figure 7. in general, two fire mitigation scenarios were made each one with a different intervention than the other. the first scenario will intervene by including capacity (c) variables in calculating fire risk. these variables are sources of water and road width adequate for fire trucks to pass through. from the risk analysis using capacity variables, it is discovered that the number of buildings categorized as having a high risk is 262 units (8.12 %). the number of buildings having a medium risk sums up to be 417 units (12.92 %), whereas buildings with a low risk goes up to 2548 units (78.92 %). the second scenario is to intervene at the hazard (h) component of the risk analysis. this hazard component consists of tofu factories and lpg warehouses as well as population density. based on the analysis, it is known that this mitigation measure has a significant impact on the study area. this impact can be observed by the decline of risk in the area that will affect the degree and the amount of damage and loss of private property as well as life. the risk analysis shows that a number of 805 units (23.94 %) of buildings having high risk, up to 2362 buildings having medium risk (73.19 %), and 805 units (24.95 %) having low risk. this section will discuss the findings resulted from the analysis. the figures 7-8 show the three scenarios in which different interventions are taken. mitigation scenarios can be carried out through approaches on capacity (c) and hazards (h). the first scenario can be done by intervening risk and taking into account the capacity variable. it can be accomplished by supplying and installing a water container constantly available for firefighters during fire extinguishing efforts. improving road conditions is another way to facilitate maneuverability of fire trucks in the event of a fire. as sources of water in the location, there should be water containers (for example 2 panel tanks with a capacity of 520 l each) equipped with wheels to help the locals transport the tanks to the location of the fire. these tanks should always be filled with water to be used in the case of a fire. furthermore, identification markers for each tank are necessary to facilitate locals recognizing them as a source of water. with these efforts, sources of water are expected to be easily accessed within sukahaji. the roads reparation in the study area is suggested in order to facilitate fire trucks in the case of a fire. moreover, certain important roads need to be expanded to increase maneuverability of the fire trucks. total expansion of road is not possible due to the high building density in some areas. these rebuilding efforts should not cause damage to the surrounding villagers. the second mitigation scenario can be carried out by alleviating the hazard (h) variable through engineering and construction. this measure will separate or relocate sources of danger from populated areas. household activities are part of these areas and cannot be moved. instead, sources of danger, such as lpg warehouses and tofu factories, should be relocated. these sources of danger can be moved to their proper land uses. form of law or regulation should be enforced to delineate the prohibition of industrial activities and storing flammable materials within residential areas. http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 | 157 figure 7. comparing risks of numerous fire mitigation interventions (analysis, 2016) no intervention intervention 1 intervention 2 http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 158 | effort to decrease the density of buildings within sukahaji would be nearly impossible as there would be mass evictions and demolitions. there would also be issues of who would be evicted and whose house would be demolished. from the risk analysis and mitigation scenarios in the previous part, type of mitigation would have the most significant impact for number of units with a high risk. a comparison between numbers of buildings with a high risk after each mitigation scenario is illustrated in figure 8. from the graph below, it can be concluded that if the building materials are replaced with fire resistant ones (the first mitigation scenario), it will dramatically decrease the number of buildings with a high risk. it is proven to be more effective than relocating lpg warehouses and tofu factories (the second intervention). even then, optimal effort can be achieved if a combination of the first and second scenario can be executed in sukahaji. figure 8. number of buildings with a high risk after application of each mitigation scenario (analysis, 2016) this study seeks to explore fire mitigation scenarios in densely populated settlements and also learn the lesson to be potentially and further applied elsewhere in indonesia. the area of study, rws 01-04, mostly consists of buildings with a medium risk of catching fire. this classification is relative towards the study area, meaning that risk classification within sukahaji cannot be compared with other areas as they will have specific sources of hazards and vulnerabilities. based on the analysis it is known that mitigation through optimizing local human capacity can be a primary alternative in handling fire risks within the area of study. overall, sukahaji is considered to have medium-high population density (rianta, 2007), which means that optimizing local capacity can be a primary choice in handling fire risks within medium-high density populated settlements. in case of capacity variable (c), optimizing capacity can be realized by providing more water hydrants or other sources of water available during the event of a fire. furthermore, widening of roads is correspondingly essential for fire trucks accessibility to incident locations since densely populated settlements usually have narrow roads. in case of fire, it hampers fire extinguishing efforts and delays the safety of the surrounding environment. coburn et al. (1994) stated that five types of basic measures can be utilized in mitigation planning programs such as engineering and construction measures, physical planning measures, economic measures, institutional and management measures, and community action. from the previous mitigation scenarios, only engineering (relocating sources of hazards, road widening, etc.) and construction measures (replacing combustible materials with fire resistant ones) were applied. community participation and early warning systems are other interventions that can be done. community participation is an important http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 | 159 component that should be utilized in reducing fire risk. in order for communities to actively participate in risk reduction, providing adequate information including its dissemination is imperative. community based mitigation efforts should be integrated with all levels of society (schools, government officials, and other groups) (duncanson et al., 2002). this has to do so that the community understands important things (to do list) during an emergency. training, counseling, as well as evacuation simulations during a fictitious fire are several important activities in community-based mitigation efforts. in addition, sukahaji can additionally install a fire alarm as a fire mitigation effort. installing smokedetecting fire alarms is an easy and effective method that can serve as an early warning system (duncanson et al., 2002). installing fire alarms in fire prone areas can increase community alertness and preparedness. if the warning systems go off, the locals can act quickly and subdue the fire before it spreads even further. physical planning measures are a form of mitigation planning that can be applied in disaster prone areas. in the context of the indonesian urban fires, detailed plans (rdtr) and building layouts (rtbl) should be referred as documents governing these kinds of physical planning. as it has been discussed before, a primary alternative for mitigation is optimizing capacity (supplying sources of water and widening roads); thus this needs to be included in rdtrs and rtbls. this can be done by supplying more water hydrants and other sources of water. evacuation routes can also fit as additional elements in rdtrs/rtbls as community-based mitigation efforts. 4. conclusion this study explores fire mitigation scenarios in densely populated settlements and has contributed to the limited literature on fire mitigation in indonesia, especially that of urban fires. the results produced some mitigation scenarios such as intervention on the risk component as it can reduce the overall risk within sukahaji, bandung, potentially applied elsewhere in indonesia. optimizing capacity as a mitigation measure can be a primary alternative in handling fire hazards in areas with medium-high population density. furthermore, an early warning system is discovered as an important factor in mitigation efforts. therefore, it should be taken into account to optimize the risk reduction efforts in fire-prone areas. it promotes the involvement of community-based approach utilizing the local and existing resources within the community. furthermore, this study can be integrated with the programs for fire mitigation by local governmental agencies, particularly the fire department (dinas pemadaman kebakaran), local disaster management agency (badan penanggulangan bencana daerah; bpbd), and agencies related to the approval of building permits. fire mitigation can be carried out at the earliest stage if the building density is taken into consideration. 5. acknowledgments the authors are thankful for assistance and comments provided by resilience development initiative (rdi) members, elisabeth rianawati, and ramanditya wimbardana. 6. references bandung central bureau of statistics. (2009). bandung in figures 2009. bandung. barnwell, c., et al. (2005). urban wildfire exposure modeling in the municipality of anchorage, alaska. in esri user conference. coburn, a. w., et al. (1994). mitigasi bencana 2nd ed. program pelatihan manajemen bencana, undp. cova, t. j. (1999). gis in emergency management. geographical information systems, 2, 845–858. duncanson, m., et al. (2002). socioeconomic deprivation and fatal unintentional domestic fire incidents in new zealand 1993-1998. fire safety journal, 37(2), 165–179. http://doi.org/10.1016/s03797112(01)00033-9 dwijayanti, f. (2008). mitigasi bencana kebakaran di permukiman padat kecamatan bojongloa kaler (studi kasus : kelurahan babakan asih dan kelurahan jamika). institut teknologi bandung. retrieved from http://digilib.itb.ac.id/files/disk1/673/jbptitbpp-gdl-fajaresthy-33603-1-2008ta-r.pdf huang, k. (2009). population and building factors that impact residential fire rates in large us cities. ifrc. (2010). world disaster report. retrieved from http://www.ifrc.org/global/publications/disasters/ wdr/wdr2010-full.pdf http://dx.doi.org/10.14710/geoplanning.3.2.147-160 sagala et al. / geoplanning: journal of geomatics and planning, vol 3, no. 2, 2016, 147-160 doi: 10.14710/geoplanning.3.2.147-160 160 | indonesian government. (2007). law no 24/2007 disaster management. mantra, i. b. g. w. (2005). kajian penanggulangan bahaya kebakaran pada perumahan (suatu kajian pendahuluan di perumahan sarijadi bandung). jurnal permukiman natah, 3(1), 24–33. moga, j. (2002). disaster mitigation planning: the growth of local partnerships for disaster reduction. in regional workshop on best practices in disaster mitigation--lessons learned from the asian urban disaster mitigation program and other initiatives (pp. 24–26). prathama, f. p. (2011). persepsi risiko dan kesiapsiagaan penduduk dalam menghadapi bahaya kebakaran di permukiman padat (studi kasus: kelurahan sukahaji, kota bandung). institut teknologi bandung. rianta, e. (2007). pemetaan risiko bermacam bahaya lingkungan di kelurahan kampung melayu, cipinang besar utara dan penjaringan provinsi dki jakarta. jakarta. sagala, s., et al. (2014). perilaku dan kesiapsiagaan terkait kebakaran pada penghuni permukiman padat kota bandung. forum geografi, 28(1), 1–20. suprapto. (2008). tinjauan eksistensi standar-standar proteksi kebakaran dan penerapannya dalam mendukung implementasi peraturan keselamatan bangunan. bandung. tarigan, a. k. m., et al. (2016). bandung city, indonesia. cities, 50, 100–110. wahyudi, a. (2004). identifikasi tingkat risiko kebakaran menggunakan sig (studi kasus: kota bandung). institut teknologi bandung. xin, j., & huang, c. (2013). fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management. fire safety journal, 62, 72–78. zhou, b. (2013). analysis of fire hazards of billboards on exterior walls of buildings and fire control safety countermeasures. procedia engineering, 52, 693–696. http://dx.doi.org/10.14710/geoplanning.3.2.147-160 1 geoplanning journal of geomatics and planning vol. 11, no. 1, 2024 original research three decades of river bank erosion and accretion appraisal along bank line shifting trend in a transboundary river, teesta floodplain of bangladesh masud parvej1, kazi mohammad masum1*, md. sahinur islam fahim1, mohammad redowan1,2 1. department of forestry and environmental science, shahjalal university of science & technology, sylhet-3114, bangladesh 2. school of education and the arts, queensland university, rockhampton, qld 4701, australia doi: 10.14710/geoplanning.11.1.1-16 abstract as the world's largest delta, bangladesh possesses distinctive geomorphology dominated by transboundary rivers, making it vulnerable to climatic hazards such as river erosion that causes severe loss of land and other resources. using four landsat imageries of 1991, 2001, 2011 and 2021 the current study analyzed the amount and trend of river erosion and accretion on the teesta floodplain of bangladesh for three decades. findings indicate that the teesta river experiences severe bank erosion and accretion regularly, causing bank line shifting and thus significant affecting the land-use/land-cover (lulc) change of the area. between 1991 and 2021, approximately 194 square kilometers of land were eroded, while an equivalent area of land was accreted. approximately 1072 km2 of agricultural land was converted into other categories, with the settlement area gradually increasing. this trend of changes shows that agricultural land and water-bodies will reduce in the next two decades while barren land and settlement areas will increase. the agricultural lands and barren lands have a greater chance of being occupied by settlement areas. at the same time, crop production patterns will move to those crops that require less water due to the reduction of water-bodies. reduced flow during the dry season and massive discharge during the monsoon from india's gajoldoba barrage caused massive siltation and erosion. comprehensive river management and restoration with an intergovernmental treaty or understanding between india and bangladesh is required to resolve this crisis in the long run. copyright © 2024 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction bangladesh is one of the largest active deltas in the world with unique landscape features and biodiversity (zevenbergen et al., 2018; hasan et al.,2020; masum et al., 2023). the entire country of bangladesh is made up of a generic hilly terrain, a small amount of high land, and a large expanse of plain land inundated by river water (rasul et al., 2004). the bangladesh delta is a highly dynamic region that is vulnerable to natural disasters and climate change due to unique geographical location, landscape features, large number of rivers, and monsoon climate (mutahara et al. 2018; sharma et al. 2010). the majority of bangladesh's lands are represented by quaternary deltaic deposits (zevenbergen et al., 2018). bangladesh's natural location lies between the himalayas and the bay of bengal, with a tropical monsoon climate that is prevalent throughout the country (rasul et al., 2004). the catchment area of the main rivers is around 1.65 million square kilometers, of which only 7.5 percent lies inside bangladesh's periphery, spawning 1200 km of run-off yearly, of which only 10% is generated within bangladesh (afroz & rahman, 2013). these e-issn: 2355-6544 received: 27 april 2023; accepted: 27 december 2023; published: 08 march 2024. keywords: teesta floodplain, spatiotemporal lulc change, remote sensing application, transboundary river. *corresponding author(s) email: kmmasum@gmail.com https://doi.org/10.14710/geoplanning.11.1.1-16 mailto:kmmasum@gmail.com parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 2 rivers carry around 1.1 billion tons of silt every year in addition to massive amounts of water, and are responsible for flooding and shoreline erosion in bangladesh (agaton et al., 2016). the combination of large ejections and heavy sediment masses with high water substances from the annual wet monsoon, a low grade of compaction, and a massive amount of runoff materials results in vastly variable and dynamic channel morphologies to adjust their bed configurations (alam, 2017; bandyopadhyay, 2007). in any season, the river channel might alter by more than 300 meters (akhter et al., 2019). bangladesh has almost 2,400 kilometers of bank line along 700 rivers (including tributaries and distributaries). the process of bank erosion is strongly influenced by river dynamics. the river's dynamic feature produces riverbank erosion, a terrible natural hazard in bangladesh (cegis.,2015). according to satellite pictures, the rivers consume over 6,700 hectares of agricultural land each year, affecting approximately 8,00,000 people (dmb, 2017; cegis, 2015). the use of satellite remote sensing to examine fluvial channel dynamics over a vast area is particularly successful (leigh, et. al., 2004). although this technique has been frequently utilized to study fluvial channel movement and discover paleo-braided channels on terraces surfaces, it has not been generally employed to study fluvial channel migration (leigh, et. al., 2004). several researches have used geospatial tools to explore basic channel alteration, such as overlaying a set of historical channel maps in various types of river systems (akhter et al., 2019). the teesta is bangladesh’s one of the most dynamic river and the country’s fourth largest river system (akhter et al., 2019). the teesta floodplain area is one of bangladesh’s major geomorphic units, including fourteen districts in the country’s north (raihan et al., 2018). the teesta is one of the 54 transboundary rivers crisscrossed across the bangladesh periphery. it is the most disputable river, over which bangladesh and india wrangled over for the 50 years (afroz & rahman, 2013). india constructed gajoldoba barrage in upper course of teesta without considering the situation in bangladesh. sudden discharge from gajoldoba barrage creates enormous havoc in downstream with massive flooding and excessive river erosion (islam, 2016). the erosion and shifting of rivers in bangladesh have long been a dominant environmental problem (ferdous &mallick, 2019) with negative impact on riverside dwelerssecurity and shelter, along with their means of subsistence (brouwer et al., 2007). this study is intended to estimate the extent of riverbank erosion and accretion in the teesta floodplain along bank line shifting during 1991 to 2021.during the last few decades, the teesta river has changed its plan form from braided to straight through meandering and back (akhter et al., 2019). this process includes changes in width, braiding intensity and extent of annual bank erosion. the objective is to understand better the erosion and accretion mechanisms of the teesta river by estimating the spatial extent and patterns of bank line alterations and island area by remote sensing. no specific research has explored the bank erosion and accretion along bank line shifting trend in this transboundary river, teesta floodplain of bangladesh. akhter et al. (2019) has worked with the spatiotemporal changes in five districtsin the reach of teesta river. several other researchers have explored basic channel alteration in otherbig river systems like padma and meghna (hasan et al., 2017, ophra et al., 2018, hossain et al., 2013). therefore, this paper aims to assess the extent of riverbank erossion and accretion as well as channel dynamics of the teesta river. the outcome of this study will contribute to understand the prediction of the morphological behavior (channel sifting, erosion and deposition) of the teesta river. then a land use/land cover (lulc) has developed, and finally, based on the trend of land use changes, a prediction has been given which predicts the land pattern in the next two decades if any steps are not undertaken.moreover different government and nongovernment organizations related with river can develop different planning and create models for mitigating the bank line erosion. the predicted map will help policy makers to take the necessary steps in the future for sustainable development activities, conservation of recourse. finally it will assist to implement ‘teesta river comprehensive management and restoration project’ which bangladesh government planned to execute with the collaboration of chinese government. https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 3 2. data and methods this chapter explains how to validate the use of specific processes and techniques to discover, select, and evaluate data in order to better understand the study problem. several literature reviews were used to guide the design of this study. this chapter also discusses the challenges of putting methods into practice. because the study aims to determine the trend of riverbank shifting, erosion, and deposition of the teesta river in chosen regions, the research methodologies used in this study are analytical in nature. this study is both quantitative and qualitative in character. to conduct this research, the necessary data were mainly collected from satellite image analysis. 2.1. study area the teesta floodplain area is bangladesh’s largest geomorphological unit, covering a considerable portion of northern bangladesh. the teesta is bangladesh’s most active river and the country’s fourth largest river system. the teesta river originates in india’s sikkim, from the pauhunri glacier in the eastern himalayas. the teesta river flows across bangladesh’s northern area (figure 1). this river is recognized as the lifeline of bangladesh’s northern territory. around 21 million in bangladesh are directly and indirectly depend on the river, which covers nearly 14 percent of bangladesh’s total agricultural area and offers 7.3 percent of the country’s livelihood prospects (statistics, 2015). the teesta floodplain is located between 25.30° and 26.18° n latitudes and 88.52° and 89.45° e longitudes, and it flows through five districts in bangladesh’s rangpur division (nilphamari, kurigram, lalmonirhat, gaibandha and rangpur districts). as shown in the 2011 population census, the population is predicted to be 10.42 million (statistics, 2015). the teesta river basin is around 2,000 sq.km and is made up of fine to medium – grained typical of an alluvial floodplain. the shallow depressions and valleys of defunct river channels impacted by the monsoon climate, which formed long morphological alterations in the teesta river’s reach. figure 1. location of the study area (teesta floodplain) flooding is a risk in the research area. each year, flash floods occur, with the biggest flooding occurring during the monsoon season in that area due to a sudden surge of water from india’s gajoldoba barrage (islam 2016). the teesta river is a vital source of water in the northern drought-prone region, and millions of people rely on it for their livelihoods. the study area is in a sub-tropical monsoon climatic region where rainfall occurs https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 4 only during monsoon months (june to september), with the rest of the year being dry (islam, 2016). despite the fact that the northern region remains dry during the postand pre-monsoon seasons, the area receives more than 1900 mm of yearly rainfall. summer and winter mean temperatures in the teesta river basin are around 35 °c and 15 °c, respectively (rahman et al., 2011). 2.2. research design research design is the conceptual frame work within which the research is conducted; it is the blueprint of the research. the method and techniques that are used to conduct this research are analytical in nature. overall research design is showed in figure 2. figure 2. research design 2.3. satellite image analysis freeware landsat satellite data were collected from archive of united states geological survey using earth explorer (http://earthexplorer.usgs.gov), a user friendly online dynamic data visualization and procurement tool from usgs. as the gajoldoba barrage in the upper steam were constructed in 1985, for clear understanding of the long term (three decades) effect, availability of cloudless images for the same month at every ten years interval and finally for facilitating trend analysis4 satellite images of 1901,2001,2011 and 2021 were using for assessment (table 1). other images with different seasonal variation also investigated for ensuring the perfection of the study. images from landsat-7 were avoided because of its scanline error.arcgis version: 10.5, erdas imagine 2014, google earth; version: pro were used to complete the analysis. table 1. collected satellite image details acquisition date satellite id sensor id path/row spatial resolution 08-03-2021 landsat 8 oli &tirs 138/42 30 meters 13-03-2011 landsat 5 tm 138/42 30 meters 17-03-2001 landsat 5 tm 138/42 30 meters 06-03-1991 landsat 5 tm 138/42 30 meters pre-processing operations were used to correct for data distortions caused by sensor and platform-specific radiometric and geometric errors. all preprocessing methods namely geometric correction, radiometric correction and atmospheric correction were done with due care. the dn values of landsat tm were converted to radiance data using the following eq. 1: https://doi.org/10.14710/geoplanning.11.1.1-16 http://earthexplorer/ parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 5 𝐿𝜆 = 𝐿𝑀𝐴𝑋𝜆 −𝐿𝑀𝐼𝑁𝜆 𝑄𝐶𝐴𝐿𝑀𝐴𝑋−𝑄𝐶𝐴𝐿𝑀𝐼𝑁 × (𝑄𝐶𝐴𝐿─𝑄𝐶𝐴𝐿𝑀𝐼𝑁) + 𝐿𝑀𝐼𝑁𝜆……...(eq.1) where, lλ = is the cell value as radiance, qcal= digital number, lminλ= spectral radiance scales to qcalmin, lmaxλ = spectral radiance scales to qcalmax, qcalmin = the minimum quantized calibrated pixel value (typically = 1), qcalmax = the maximum quantized calibrated pixel value (typically = 255). then, the radiance data was converted into at sensor reflectance or toa reflectance using the following eq. 2, 𝜌𝜆 = 𝜋×𝐿𝜆×𝑑2 𝐸𝑆𝑈𝑁𝜆×cos 𝜃𝑠 ……...(eq.2) where, ρλ = unitless planetary reflectance, lλ= spectral radiance (from earlier step), d= earth-sun distance in astronomical units, esunλ= mean solar exa-atmospheric irradiances, θs = solar zenith angle. enhancements were used to make it easier for visual interpretation and understanding of imagery. image classification uses the reflectance statistics for individual pixels. the images were analyzed through histogram equalization, supervised, unsupervised and ndvi classification. the quality of the downloaded cloud-free landsat images was improved using a histogram equalization procedure. the study region was classified as supervised, unsupervised, and ndvi to distinguish between water and land features, or to put it another way, to demarcate the river line/bank from the water. the unsupervised classification of the research area was chosen to assess the changes since it provided an explicit scene of the water and land feature among the three categories of supervised, unsupervised, and ndvi. there were made 20 to 25 classes of the study area and then these classes were reclassified into 4 major classes as, agriculture, barren, settlement and waterbody (table 2). table 2. lulc classification scheme used in this study class name description agriculture crop fields, farmlands and sparsely vegetated area barren land areas of exposed soil and barren area influenced by human impact settlement residential, commercial, industrial, transportation, roads, mixed urban land, homestead tree and playground waterbody river, open water, lakes, ponds and reservoirs 2.3.1. change detection with lulc trajectories the lulc trajectory matrices were used to perform a change detection analysis (ahmed, 2006; chen et al., 2005; sejati et al., 2023; zaki et al., 2022). processing time series data from the research area from 1991 to 2021 yields lulc trajectories (banskota et al., 2014; mosammam et al., 2017). in this work, supervised classification has been done by selecting training zones. as a supervised image classifier, a maximum likelihood classifier (mlc) was used arcgis 10.8 was used to transfer the classified images to the gis layer for quantification of eroded and deposited land covers. boundary of river areas in 1991,2001,2011 and 2021were digitized through visual interpretation of the converted layers of the classified images in 1991,2001,2011 and 2021 respectively. 2.3.2. quantification of erosion and deposition the river boundary was then superimposed, followed by converted layers of classified images. the converted layers from classified images from 1991, 2001, 2011, and 2021 were clipped based on digitized river boundary layers from 1991, 2001, 2011, and 2021. the clipped layers in 1991, 2001, 2011, and 2021 correspond to the river areas in 1991, 2001, 2011, and 2021, respectively. the teesta’s eroded and deposited areas were calculated by superimposing and comparing river layers from 1991 to 2001, 2001 to 2011, and 2011 to 2021. the river area in 1991 was subtracted from the river area in 2001, and the converted layer of the classified image https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 6 of 1991 was clipped on the basis of this subtracted layer. this clipped layer depicts the eroded areas between 1991 and 2001. the same method was used to sort out the eroded areas from 2001 to 2011 and 2011 to 2021. the attribute tables of these clipped layers were summarized to quantify the eroded land covers from 1991 to 2001, 2001 to 2011, and 2011 to 2021. when deposition was used, the river area in 2001 was subtracted from the river area in 1991, and the converted classified image in 2001 was clipped based on this subtracted layer. this clipped layer indicates the deposited land cover from 1991-2001. the same process was also followed to sort out the deposited land covers from 2001-2011 and 2011-2021. the attribute tables of these clipped layers are summarized for calculation of the deposition from 1991-2001, 2001-2011 and 2011-2021. 2.3.3. movement of channels due to erosion and deposition of the banks of the teesta, the river channels are changing. to visualize the movement of channels, the river channels were selected using the attribute tables from the river areas in1991, 2001,2011 and 2021. the river channels were visualized and compared with the river area. 2.3.4. accuracy assessment in arc map 10.8, the accuracy of each map was assessed by taking around 260 random points, known as reference points. a valid accuracy assessment map must have a minimum of 30 points for each class and a total score of more than 250 (congalton and green, 1999). with the use of these reference locations and the categorized map, a combine table was built. using the pivot table tool box, a confusion matrix table was created from this combine table. the relationship between the categorized map and the reference data is summarized in an error matrix (jensen, 2005) and used to calculate the controller and explainer’s dependability (gerard et al., 2010). this matrix table exported in ms excel was used to calculate omission percent, commission percent, producer accuracy, and user accuracy. with the use of this derived data, the overall accuracy and kappa coefficient were tested. the kappa coefficient is always in the range of 0 to 1 (appendix 1). 2.4. secondary data collection secondary data were gathered from various sources in accordance with the study’s requirements. data such as the total area of the study sites, population of the area, cultivable land of the site, past data on flooding and river erosion, and so on were gathered from the union parishad office, bwdb (bangladesh water development board), and so on. furthermore, supporting data and materials were gathered from a variety of sources, such as the internet, previous studies, and survey reports. the data collected for this study was subjected to statistical and cartographic processing in preparation for further analysis and synthesis. 2.5. result evaluation and report writing the overall scenario of riverbank erosion was evaluated with satisfactory precision. after successful evaluation of all findings this report finds a way to be alive. knowledge, findings and recommendation from previous relevant study was taken in count wisely during the completion of this report. 2.6. forecasting of lulc for the next two decades the values for lulc have forecasted for 2031 and 2041 (ten years intervals) based on the current trend of changes. this forecasting is based on the assumption that the current trend of changes will continue at the average rate, which is found between 19912021. these trend lines have been drawn by the excel forecasting function, which is the simple statistical relationship between the dependent variable, y (area) the independent variable, x (area), were used. the linear equation is as follows (eq. 3): 𝑦 = 𝑎 + 𝑏𝑥…. (eq.3) where, 𝑎 = �̅� − 𝑏�̅� and 𝑏 = ∑ (𝑥−�̅�)(𝑦−�̅�) (𝑥−�̅�)2 , �̅� = 𝑀𝑒𝑎𝑛 𝑜𝑓 𝑌𝑒𝑎𝑟, �̅� = 𝑀𝑒𝑎𝑛 𝑜𝑓𝐴𝑟𝑒𝑎 https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 7 3. results and discussion 3.1. teesta river course teesta is one of the most dynamic rivers of bangladesh. it changes its course very frequently. to investigate morphological changes of the teesta river, the four satellite images have been analyzed in this study. river courses were extracted with a series of methods using arcgis 10.8. in this study images from 1991, 2001, 2011 and 2021 have been taken to show the dynamic shifting of the teesta river. the following figure 3 shows how the course of teesta was in different years. figure 3. course of teesta river in different years most rivers in humid and sub-humid areas complete their processes in three stages: young, mature, and older. the river flows in a meandering course due to the gradual slope in the first of these three stages. as a result, the river basin has seen lateral erosion and channel shifting. widening of river teesta was very conspicuous in 2011 and in 2021 like all big rivers (hasan et al., 2017, ophra et al., 2018) which was mainly for the formation of island. bank failure (the separation and entrainment of bank materials in the form of grains, aggregates, or blocks due to fluvial, subaerial, and geotectonic processes) is a common occurrence in the lower reaches of all rivers (bandopadhyay, 2007). 3.2. shifting nature of the teesta river channel the changing nature of the teestariver in our study area is a common fluvio-geomorphic phenomenon that can be seen in any part of the river. this shifting nature is like the pendulum of a wall clock, and it occurs along the river’s left and right banks. to demonstrate the shifting nature, a map of the teesta channel’s position has been created based on maps from 1991, 2001, 2011, and 2021, respectively (figure 4). from figure 4 it is seen that, the river had rightward movement in upper portion and leftward movement in lower portion during 1991-2001 period. in 2011 river have divided into two braided channels in the middle portion. it shows overall rightward movement. the river course of 2011 and 2021 shows the split in channel. according to akhter et al. (2019) sudden surges of water from gajoldoba barrage have causes lateral shifting of teesta river channel. channel shifting of teesta river placed within 2 km to 8.5 km in the period of last 30 years (figure 4). however, the common river channel from 1991 to 2021 showed in figure 5 proves the lateral shifting of teesta river course. channel migration is evident in study area however it doesn’t follow any predictable manner. this https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 8 unpredictability happened due to the gajoldoba barrage constructed in upstream of the river. they haven’t follow any rules and regulations towards transboundary rivers (sharma & goyal, 2020). this unpredictable channel shifting causes enormous river erosion and thus accretion (afroz & rahman, 2013). figure 4. shifting nature of the teesta river channel in 1991-2021 figure 5. unchanged river course since 1991 3.3. assessment of riverbank erosion and accretion riverbank erosion and accretion assessment has been done using remote sensing (rs) and geographic information system (gis) approach. eroded and deposited areas of the teesta between the years are calculated by superimposition and pairwise comparison of river layers in 1991-2001, 2001-2011 and 2011-2021.figure 6 represents the teesta river erosion and accretion for different periods. the erosion of the river teesta was calculated in this study during three ten-year intervals, from 1991 to 2021 (figure 7). the river teesta is degraded and accreted simultaneously. on the one hand, the riverbank is eroding, while on the other, new chars are appearing like other big river systems (hasan et al., 2017, ophra et al., 2018). figure 6. riverbank erosion and accretion in different periods https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 9 figure 7. riverbank erosion and accretion during 1991-2021 river erosion occurred over a 62square-kilometer area between 1991 and 2001. new chars have sprouted up in an area of 61 square kilometers at this time. in a 53square-kilometer stretch, the river has remained unchanged. the river’s erosion and accretion were nearly equal (57 square-kilometer) from 2001 and 2021. and the analogous location remained the same. in 2011, river erosion damaged 75 square kilometers, with sedimentation concealing 56 square kilometers. the foregoing finding (akhter et al., 2019) show that the river teesta is being eroded and accreted at the same time. the vulnerable people who live along the teesta river’s bank are the ones who suffer the most from the erosion and accretion (brouwer et al., 2007). the teesta is a mighty and flashy river with a long history. river erosion has historically been, which was normal and people were accustomed to it. but in the nineties, the indian government was trying to control the flow of the teesta by constructing the gajoldoba barrage on the upstream of the river teesta (afroz & rahman, 2013). 3.4. lulc changes in teesta floodplain the study area was characterized and mapped into four major land use/land cover (lulc) classes, and the spatiotemporal patterns of these lulc dynamics were demonstrated. these included; agriculture, barren, settlement and waterbody. lulc map 05 1991,2001,2011 and 2021 are shown in figure 8. figure 8. classified lulc map of teesta floodplain for different years https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 10 analysis revealed a significant change in the proportions of the various lulc types of the study area in different years (table 3 and table 4). the results of the image classification showed that the total land area of tf is 3280 square kilometers. individual class area and change statistics are summarized in table 4. table 3. area statistics and percentage of the lulcc units during 1991-2021 lulcc classes 1991 2001 2011 2021 area (km2) area (%) area (km2) area (%) area (km2) area(%) area (km2) area(%) agriculture 2060 63% 1758 54% 1543 47% 1544 47% barren 181 6% 371 11% 301 9% 263 8% settlement 763 23% 934 28% 1260 38% 1362 42% waterbody 276 8% 217 7% 175 5% 110 3% table 4. change of land use class from 1991-2021 (‘+’ indicates increase and ‘‘indicates decrease) lulc change in extent in km2 1991-2021 agriculture -516 barren +82 settlement +599 waterbody -165 the percentage area of each class in different years showed that agriculture had the largest share in 1991 representing 63 % (2060 sq km) of the total lulc categories assigned. this class faced a decreasing shift and it was reduced to 47 % (1544 sq km). but this decreasing rate is not reflected in the period of 2011 to 2021. it is happened because of different factors like, technological innovation i.e., development of drought resistant varieties of food crops. because of that new varieties people are now able to cultivate the harsh land like char areas. they converted lot of bare land into agricultural land. the government provide incentives to the farmers, and took several initiatives to develop the crop production of the study area (ferdous & mallick, 2019). the covid 19 pandemic situation also shows a positive notion of crop production in the study area (economic review 2020). the other class which faced decline during the study period was waterbody. the area of this class in 1991 was 8% (276 sq km) of the total area and in 2021 it was reduced to 3% (110 sq km)). the major increment was faced by settlement area. its share was increased from 23% (763sq km) in 1991 to 42% (1362 sq km) in 2012.this study revealed that there was about more than 179 % increase of settlement area i.e., from 1991 to 2021. this figure represented the dramatic change in land cover in the built-up surface category, putting tremendous pressure on non-built-up surfaces, particularly agricultural fields. 3.5. trend analysis figure 9 shows forecasting using excel forecasting function that if the current rate of change continues at the average rate seen between 1991to 2021, the agricultural land will be 1285.5 km2 and 1109.2 km2 in 2031 and 2041, respectively. in contrast settlement area will be 1610.5 km2 by 2031 and 1822.8 km2 by 2041. along with decreasing rate of agricultural land, the water bodies were at a decreasing rate. the trend showed that the area of water bodies will be 59.5 km2 by 2031 and 5.5 km2 by 2041. figure 10 showed that the transition from agricultural lands to settlement areas was higher than any other transitions. it indicates that though agricultural lands were in a stable condition from 2011-2021 due to technological innovation but the huge increasing rate of settlement area will create a huge pressure on agricultural lands, which may force the agricultural lands to decrease in the next two decades due to conversion from agricultural lands to settlement areas. due to the declining rate of water bodies, the available lands are used for producing crops that require lower amounts of water, such as wheat and maize. these wheat and maize are gradually taking up more land although the lands used for rice production were in static condition (mahmud et al. 2021). if the current trend https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 11 of decreasing waterbodies continues as per the prediction, there is a chance of complete conversion of rice production to wheat and maize production. according to the changing pattern of barren lands, it will be 323 km2 in 2031 and 340.6 km2 in 2041 (figure 9). from 1991-2021, most of the barren areas were converted to settlement areas, then some of the barren lands were converted to agricultural lands and the minimum amount was converted to waterbodies (figure 10). so, as the trend analysis of barren lands are showing an increasing rate in the next two decades, there is a greater chance to increase settlement areas by occupying the barren lands as per the previous records, and the trend pattern of settlement areas also supports this. figure 9. projected net change between 1987 and 2037 for each of the major land use. 3.6. accuracy of classified images to determine the accuracy of a classified map, an error/confusion matrix is constructed (appendix 1). this is the most frequent method for determining per-pixel classification (lu & weng, 2007). for each classed map, the kappa statistics/index was calculated to assess the accuracy of the results. the accuracy of the resulting classification of land use/cover maps from 1991, 2001, 2011, and 2021 was 83%, 82%, 79%, and 81%, respectively. for the years 1991, 2001, 2011, and 2021, the kappa coefficient was 0.76, 0.75, 0.71, and 0.73, respectively. for a dynamic area like teesta floodplain, this level of overall accuracy is acceptable for subsequent analysis and change (lei & zhu, 2018). 3.7. transitions between lulcc classes there is an enormous transition between lulcc classes in the teesta floodplain because it is such a dynamic place. figure 10 illustrates the temporal transitions between different lulcc classes in the different period from 1991 to 2021. trajectory matrices were formed with data table of these change maps. using the trajectory matrix, this study investigates the transformation between different lulc classes. these trajectory matrices are summarized in table 5. there is a significant number of spatial changes among the land use classes in the study period. the majority of agricultural lands in the teesta floodplain are being changed into other land classifications. as a result, agricultural land is disappearing at an alarming rate similar to padma basin (ophra et al., 2018). between 1991 and 2001, approximately 681 sq km of agricultural land was turned into settlements, and 139 square kilometers have been converted into barren land, with the remaining 90 square kilometers becoming waterbodies. between 2001 and 2011, around 554 sq km of agricultural land was converted to settlement, and between 2011 and 2021, roughly 364 sq km of agricultural land was converted to habitation. in the 30 years between 1991 and 2021, around 920 sq km of agricultural land was transformed into settlement. the study found that about 400 sq km area has been converted from settlement area to agricultural land between 1991 and 2021. however, since the teesta floodplain is a dynamic region, all kinds of transitions are possible there. https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 12 a large area of land was lost in the river as a result of river erosion; nevertheless, a large amount of land was discovered in the aftermath of the char, and the hardworking people of north bengal began cultivating it again, resulting in the conversion of the settlement area into agricultural land. people have also begun farming in the surroundings of their houses in order to suit the requirements of an expanding population (akhter et al., 2019). as a result, settlement could also be converted into agricultural land. approximately 158 sq km of land was converted from water to agricultural land between 1999 and 2001. the river teesta dries out in the winter due to a lack of water, and many huge regions along the river become suitable for agriculture. furthermore, due to the rise of chars in the river, people are farming in those areas, resulting in a changeover (brouwer et al., 2017). figure 10. transition between lulc classes (ag=agriculture, st=settlement, bn= barren and wb=waterbody) table 5. transition between different classes from class to class 1991-2001 2001-2011 2011-2021 1991-2021 area (km) area (km) area (km) area (km) agriculture barren 139 42 39 97 settlement 681 554 364 920 waterbody 90 88 27 55 barren agriculture 24 85 40 38 settlement 24 119 93 59 waterbody 44 15 30 15 settlement agriculture 426 314 320 400 barren 109 39 65 67 waterbody 59 40 23 25 waterbody agriculture 158 69 70 117 barren 33 68 21 30 settlement 61 46 53 113 https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 13 3.8. seasonal variation of teesta river flow the flow of the river teesta has fluctuated dramatically with seasonal variation. in this study we have tried to find out the change in the flow of the river teesta by remote sensing method. during the dry season and monsoon of 2011 and 2021, the flow of teesta river has been taken out using satellite image (figure 11). as can be seen from the figure 12, the flow of the river teesta in the rainy season in 2011 was 238 sq km, while in 2021 the flow was 236 sq km. and the dry season the river flow moved from an area of 44 sq km in 2011 to 31 sq km in 2030. during the monsoon season, the flow of water in the river teesta does not change greatly, but during the dry season, the flow of water in the river teesta decreases day by day. figure 12. seasonal variation of waterflow seasonal variations in the teesta’s water flow are more man-made than natural. the gajoldoba barrage, which was built on the upper upstream of the teesta river, caused unusual abnormalities in the changes of water flow. during the dry season, when water is needed for agriculture in north bengal, bangladesh, almost all the gates of gajoldoba barrage remain closed. when the teestariver overflows during the monsoon season, they opened all the gates. the area was thereafter flooded as a result of the water onslaught, and river erosion became rampant (khan & islam, 2015; hassan et al., 2016). the river teesta eroded even before the construction of the gajoldoba barrage, but the residents of the area were able to adapt. however, the people of the area are now unable to cope with the unpredictable erosion induced by the gajoldoba barrage’s sudden and unexpected release of water. it causes plenty of severe catastrophes for the inhabitants of the region (rahman, 2013). the socioeconomic impact of teesta river erosion is discussed in the next section of this paper (brouwer et al., 2017). figure 11. seasonal variations in river flow https://doi.org/10.14710/geoplanning.11.1.1-16 parvej et al. / geoplanning: journal of geomatics and planning, vol 11, no 1, 2024, 1-16 doi: 10.14710/geoplanning.11.1.1-16 14 4. conclusion bangladesh is a riparian country with 57 transboundary rivers, including the teesta, which is bangladesh’s fourth largest river after the ganges, the brahmaputra and the meghna. the majority of transboundary rivers entering the country from india encounter one or more upstream diversions basically in drought period. as a result, bangladesh has year-round water-related socio-economic and environmental issues due to excessive water during the monsoon and scarcity during the non-monsoon months. because bangladesh has a lengthy dry season, which lasts around 7 to 8 months each year, these transnational rivers are vital to bangladesh’s agricultural production, navigation, underground water supply, and fishery resources. throughout the research period, the teesta river has been found undergoing severe erosion and siltation during 2011 to 2021, widening of rivers forming island inside, rightward movement in upper portion and leftward movement in lower portion and finally splitting in channel. the rapid release of surplus water from the gajoldoba barrage during dry season is the main reason for causing significant erosion and flooding, resulting in massive losses for the population. reduced flow during the dry season, as well as the rapid release of water, might have far-reaching social and environmental consequences for bangladesh by transforming riverside agriculture to temporary settlement and damage of agri-crop resulting from rapid discharge consecutively. the disappointing situation has been forecasted in trend analysis that the massive decrease of waterbody and agriculture with the significant increase of barren area by the year 2041. the findings of this study would provide researchers and decision makers with elementary but essential information for resilient and comprehensive management of the teesta floodplain in lower riparian bangladesh, as well as assist top management in bangladesh and india in understanding the situation associated with upstream transboundary river water withdrawal. because our country is heavily populated and the majority of its citizens are directly or indirectly dependent on agriculture, a comprehensive riverbank erosion management measures should be developed urgently on a national scale. on the basis of our research findings, the recommendations have proposed that a firm agreement with indian government for the distribution of water is crucial. moreover, successful implementation of teesta river comprehensive management and restoration will help to protect the river and the people from the erosion and drought. finally, afforestation programs should be motivated and proper steps for relief and rehabilitation for the victims should be taken including the setting embankments in areas prone to severe erosion. 5. acknowledgments we express sincere gratitude to the ministry of science and technology, for supporting this research under the national science and technology fellowship for first author. we also acknowledge the use of landsat images from nasa’s land processes distributed active archive center (lpdaac) at usgs/eros. 6. references afroz, r., & rahman, a. 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[crossref] https://doi.org/10.14710/geoplanning.11.1.1-16 https://doi.org/10.1061/(asce)he.1943-5584.0000299 https://doi.org/10.3329/sja.v15i2.35163 https://doi.org/10.1016/j.apgeog.2004.03.004 https://doi.org/10.5937/gp27-40927 https://doi.org/10.1016/j.atmosres.2019.104670 https://doi.org/10.1016/j.ejrs.2022.03.002 https://doi.org/10.1080/15715124.2018.1433185 159 geoplanning journal of geomatics and planning geoplanning: journal of geomatics and planning, vol. 12, no. 2, 2025, 159 – 172 original research classification and monitoring of kahayan river riparian zone settlement expansion utilizing satellite imagery to prevent environmental damage herwin sutrisno1*, theresia susi1, singgih hartanto1, petrisly perkasa1, benong supriadi2, handri mantana3 1. master program in urban and regional planning, university of palangka raya, central kalimantan, indonesia 2. public works and public housing service, central kalimantan province, katingan regency, central kalimantan, indonesia 3. the national land agency's regional office in central kalimantan province, palangka raya, central kalimantan, indonesia doi: 10.14710/geoplanning.12.2.159-172 abstract as the population residing along the kahayan river increased, many tall trees were cut down. this rapid growth of settlements negatively impacted environmental quality and accelerated soil erosion. human activities such as tree logging and mining further aggravated erosion along the riverbanks, increasing the risk of flooding and damaging ecosystems. settlements on the riverbanks became vulnerable to flooding, especially during heavy rains, which could destroy buildings and cause significant financial losses. this study aims to understand the relationship between settlement development and forest loss in the riparian zone of the kahayan river in palangka raya city. to achieve this goal, high-resolution imagery and geographic information system (gis) were used in conjunction with periodic satellite image classification methods. the main findings of the study show a drastic landscape transformation. during the study period, settlement areas expanded exponentially by 412%, increasing from 47.44 hectares to 243.07 hectares. this trend inversely correlated with a significant 57% reduction in riparian forest cover, decreasing from 390.08 hectares to 166.66 hectares. these findings have dual implications. institutionally, the data provide an urgent empirical basis for local governments to formulate stricter and more effective spatial planning policies. theoretically, this study strengthens understanding of the cause-and-effect relationship between urbanization processes and the degradation of sensitive riparian ecosystems. this quantitative evidence underscores the need to integrate urban planning and environmental conservation to achieve sustainable development. copyright © 2025 by authors, published by universitas diponegoro publishing group. this open access article is distributed under a creative commons attribution 4.0 international license 1. introduction the urbanization of river basins has been a topic of discussion in the fields of spatial planning and the environment over the past three years (sejati et al., 2025; sejati et al., 2024). several problems arise when areas that should be protected are instead used for expansion of built-up areas as a result of uncontrolled housing development (buchori & tanjung, 2014). this is an important issue worldwide because watersheds should be protected as part of their protective function and should not be used for built-up land. e-issn: 2355-6544 received: 08 may 2025; revised: 02 october 2025; accepted: 25 october 2025; available online: 31 october 2025; published: 31 october 2025. keywords: image classification, river zones, settlements, environmental damage, land cover *corresponding author(s) email: herwin.sutrisno@arch.upr.ac.id https://doi.org/10.14710/geoplanning.12.2.159-172 mailto:herwin.sutrisno@arch.upr.ac.id sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 160 one of the fastest growing areas and a center of global attention is kalimantan (borneo). the issues of deforestation and land conversion around rivers are major problems and threaten sustainability in kalimantan. furthermore, this condition is also experienced by palangka raya city, the capital of central kalimantan province, which has the kahayan river, one of the longest rivers in indonesia (600 km in length, 500 meters in width, and 7 meters in depth). for the dayak ngaju ethnic group, the largest tribe in central kalimantan, this river is the center of life, upon which they rely for various needs, including logging, forest product collection, fisheries, and traditional agriculture (sutrisno et al., 2019; suwito et al., 2020; wainarisi & tumbol, 2022). historically, the riverbanks have functioned as focal points for human habitation, with villages situated along its edges (heriyanto et al., 2022; siburian et al., 2025). the kahayan river plays a crucial role in the ecological functioning of palangka raya city, yet rapid settlement expansion has caused significant environmental alterations and degradation (segah et al., 2023). the dynamic urban growth and organic spread of settlements have triggered a shift in settlement patterns away from the river, a transition that contradicts the river’s original role as a guiding element for the dayak ngaju community (usop et al., 2022). previous studies on environmental degradation, particularly around rivers, have focused only on the impact of activities around rivers on land quality. such as this environmental degradation is manifested in the silting of the river, erosion that increases water turbidity, and accumulation of waste (dirun et al., 2021; marlina & novrianti, 2018). elevated erosion negatively impacts fish populations and raises the flood risk in surrounding settlements (srivastava & tripathi, 2022). the problems of plastic waste and inadequate sanitation further exacerbate the situation, with domestic waste often being directly discharged into the river (jovanović, 2017; martani et al., 2022; riani & cordova, 2024). moreover, environmental damage is aggravated by the loss of trees that function as the land cover due to settlement expansion (cantera et al., 2023). another interesting study also focused on indigenous customary forest ownership has been converted for palm oil plantations and mining activities, which not only pollute river flows but also shift community livelihoods from the primary sector (forestry) to secondary and tertiary sectors (bose-o’reilly et al., 2016; fikri et al., 2023; jayanti et al., 2025; usop et al., 2022). the land cover changes caused by human activities necessitate in-depth analysis to facilitate sustainable land use planning and ecosystem conservation, and the analysis of riparian zone changes can be effectively conducted using satellite imaging technology (chapa et al., 2019). previous research has not focused on time series monitoring and measures for environmental policy-making in areas surrounding rivers. given this gap, this study offers a method for rapid assessment as a first step in policy-making for spatial utilization control in riverine areas. given the urgency of this issue, this study aims to present the results of satellite imagery processed using google earth pro from 2014 to 2023 for an integrated monitoring system for riverine areas. this research complements previous studies, particularly in terms of contributing knowledge on monitoring areas around rivers, which not only focuses on existing conditions but is also capable of predicting future conditions. 2. data and methods 2.1. study area the designated geographical region is commonly referred to as pahandut seberang. situated in the city of palangka raya in the province of central kalimantan, indonesia. the research area encompasses the riparian area of the kahayan river, with a total area of 702.57 hectares (figure 1). pahandut seberang is situated on the opposite bank of the river from the central area of palangka raya, with distinct geographical and infrastructural characteristics. both locations can be accessed either by crossing a bridge or by traveling on water. the topographical features exert a significant influence on people's preference for river transportation, particularly motorized boats, as a means of travel and accessing different destinations. this motorized boat serves as a dependable means of transportation for accessing the forests surrounding the kahayan river in order to gather forest products. additionally, it is occasionally used to repair peat infrastructure in order to prevent forest fires (perkasa et al., 2024). https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 161 the number of houses in this region correlates with the expansion of palangka raya city. land use can be altered as a result of population growth and the demand for housing. this can involve deforestation and altering the ecological dynamics of the region, such as watershed hydrology and increased flood vulnerability (pumo et al., 2017; segah et al., 2023). the income and livelihood of the pahandut seberang community are dependent on fishing, agriculture, small-scale enterprises, and offering transportation services to community members via motorboats. such community enterprises typically exert an influence on the surrounding environment, manifesting in activities such as pollution or alteration of land use (dirun et al., 2021). the process of urbanization in pahandut seberang poses a significant threat to the environment, as it leads to the destruction of natural habitats, exacerbates pollution, and disrupts the functioning of the local ecosystem (pumo et al., 2017). in order to comprehend and resolve this issue, it is crucial to undertake study on the repercussions of an increased population residing in the vicinity. pahandut seberang is undergoing rapid transformation as the city of palangka raya progresses (wijaya & herlambang, 2022). changes in land usage, environmental implications, and social and economic expansion present both challenges and opportunities. source: authors, 2024 figure 1. research area located in the riparian area of pahandut village, adjacent to the seberang kahayan river in palangka raya 2.2. research design a quantitative descriptive technique to studying land cover change involves collecting and analyzing numerical data to demonstrate how land usage or cover varies over time. this quantitative descriptive methodology facilitates the identification of patterns and trends in alterations in land cover, enabling the classification and monitoring of the spread of human settlements in the riparian zone of the kahayan river (donatien et al., 2024). this approach also facilitates the organization and coordination of tasks more effectively. the research concept will utilize satellite photos captured at various time intervals to gather data on land cover (mengist et al., 2020). this utilization of multi-temporal data represents a fundamental approach in river environment studies, both for tracking physical changes such as riverbank erosion (khan et al., 2017) and for quantifying changes in land use functions (vaggela et al., 2022). field data obtain their information from surveys or records collected directly in the field. image processing methods are employed in land cover classification to categorize satellite imagery into specific land cover types, such as settlement expansion. specifically, the supervised classification method is a reliable technique for this purpose (vaggela et al., 2022). images from different periods are compared using temporal analysis to identify changes in land cover (setiabudi & kusumaningrum, 2021). this method enables the observation of changes over time and the identification of emerging trends. https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 162 descriptive statistical data analysis is employed to draw definitive and precise conclusions on land cover. this encompasses understanding the dimensions of each land cover category, the rate at which it undergoes change over time, and its spatial distribution (burrough, 2001). mapping and visualization involve the creation of thematic maps or other visual representations that illustrate the temporal and spatial changes in land cover. the graphic displays a transition from one sort of cover to another. data interpretation offers a comprehensive understanding of the mechanisms and reasons behind alterations in land cover. this include factors that induce alteration, such as the proliferation of human settlements, deforestation, or modifications in the availability of open land (nitze et al., 2015). 2.2 data analysis data analysis employs periodic satellite image classification to detect changes in land cover by delineating the polygon area of interest (aoi) based on the boundaries of the target region, specifically the riparian zone of the kahayan pahandut river in palangka raya city (figure 2). import polygon data into google earth pro by uploading a file in either kml or kmz format. once the aoi polygon has been appropriately aligned with the image, proceed with executing the download command. the subsequent step involves georeferencing the image by assigning geographic coordinates, enabling accurate placement of the image on a map or other gis data within the software. source: google, 2024 figure 2. the process of creating an aoi and downloading picture data in google earth pro is captured in layers four placemarks are strategically positioned at the corners of the viewer to serve as geographic reference points. these placemarks are then recorded in kml format. software is utilized for image preprocessing and for land cover analysis. the conversion process involves transforming each jpg file into a geotiff raster format using kml points as ground control points (gcp) for georeferencing. ultimately, the regions of interest are trimmed to their corresponding limits. exporting photos from google earth pro at the eye level consistently yields a pixel resolution of 0.2 meters. these values correspond to the necessity of differentiating specific urban characteristics, such as small areas of greenery or cars. lowering the eye height can result in equivalent or improved pixel quality (tonyalouglu et al., 2021). perform image-based land cover classification using supervised classification, which requires manual determination of sample classes, to identify and categorize different types of land cover. aoi is categorized into five primary land cover classes, namely land, shrubs, settlement, forest, and water body (padmini et al., 2023). osm files contain exported representations of roads and rivers from openstreetmap (jaroszewicz et al., 2023). the data is transformed into a shapefile. the road's width is established by converting the lines into polygons. https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 163 3. result and discussion google earth pro allows for the capture of image data in the years 2014, 2016, 2018, 2020, and 2023. this satellite image has a great level of detail, since each pixel in the image can accurately represent a small section of the earth's surface, with a resolution of approximately 30 cm per pixel (wang et al., 2023). this feature enables users to perceive relatively small entities, such as trees, vehicles, or buildings. google earth pro sources its satellite images from multiple commercial and public vendors, including digitalglobe (formerly maxar technologies) and cnes/airbus. satellite photos are often updated; however, the updates are not synchronized globally. locations of higher popularity or significance may undergo more frequent updates compared to less important locations (warnasuriya et al., 2020). (a) (b) (c) figure 3. assessment of alterations in the land cover of the riparian zone along the kahayan river in the year 2014. (a) photograph taken in 2014. (b) map depicting the distribution of land cover in the year 2014. (c) the land cover area in 2014 the findings of the regular analysis of satellite images to detect alterations in the land surface of the study area in 2014 are displayed in figure 3. the analysis was conducted in 2014 using periodic satellite image classification. the results showed that there were 101.94 hectares of land, 55.91 hectares of shrubs, 47.44 hectares of settlement, 390.08 hectares of forest, and 107.21 hectares of water bodies. this indicates that the region has notable hydrographic features (nugraha et al., 2022). the presence of several water bodies might be indicative of the existence of various aquatic ecosystems, such as swamps or river deltas, which provide a habitat for a wide range of aquatic flora and fauna (de rosari et al., 2020). the riparian forest surrounding the research site remains thick and expansive, serving a crucial function in preserving ecological equilibrium along waterways, fostering biodiversity, and offering essential ecosystem services for both humans and the environment (jamaludin et al., 2020). the human population is relatively small and resides in uncomplicated conditions, relying heavily on rivers as a vital source of sustenance. the estimated population of palangka raya in 2014 was between 240,000 to 250,000 individuals (bps palangka raya city, 2023). https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 164 the findings of the regular analysis of satellite images to detect alterations in land cover at the study site in 2016 are displayed in figure 4. the analysis was conducted in 2016 using periodic satellite image categorization. the results showed that the land area was 136.97 hectares, shrubs covered an area of 28.37 hectares, settlements occupied 61.10 hectares, forests spanned 365.63 hectares, and there was a water body covering 110.50 hectares. the projected population of palangka raya in 2016 ranged from approximately 265,000 to 275,000 individuals. the growth of the city is fueled by urbanization, migration from rural areas, and advancements in the economic and educational sectors, which consistently draw in new citizens (bps palangka raya city, 2023). in 2016, palangka raya underwent ongoing infrastructure development, encompassing enhancements to its road network, bridges, and many public amenities. the construction of this infrastructure is crucial for facilitating urban expansion, enhancing mobility, and enhancing the citizens' standard of living. the forest's state is deteriorating as towns continue to expand. (c) figure 4. assessment of alterations in the land cover of the riparian zone along the kahayan river in the year 2016. (a) photograph taken in 2016. (b) map depicting the distribution of land cover in the year 2016. (c) the land cover area in 2016 the findings of the regular analysis of satellite images to detect alterations in land cover at the study site in 2016 are displayed in figure 4. the analysis was conducted in 2016 using periodic satellite image categorization. the results showed that the land area was 136.97 hectares, shrubs covered an area of 28.37 hectares, settlements occupied 61.10 hectares, forests spanned 365.63 hectares, and there was a water body covering 110.50 hectares. the projected population of palangka raya in 2016 ranged from approximately 265,000 to 275,000 individuals. the growth of the city is fueled by urbanization, migration from rural areas, and advancements in the economic and educational sectors, which consistently draw in new citizens (bps palangka raya city, 2023). in (a) (b) https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 165 2016, palangka raya underwent ongoing infrastructure development, encompassing enhancements to its road network, bridges, and many public amenities. the construction of this infrastructure is crucial for facilitating urban expansion, enhancing mobility, and enhancing the citizens' standard of living. the forest's state is deteriorating as towns continue to expand. the findings of the regular analysis of satellite images to detect alterations in the land's surface in the specified research area over the year 2018 are displayed in figure 5. the analysis was conducted in 2016 using periodic satellite image classification. the results showed that there were 106.86 hectares of land, 33.52 hectares of shrubs, 191.92 hectares of settlement, 273.70 hectares of forest, and 96.57 hectares of water bodies. the estimated population of palangka raya in 2018 exceeds 260,000 individuals. this rise is consistent with the pattern of urbanization and migration from rural regions to urban areas, propelled by economic prospects and improved amenities in palangka raya (bps palangka raya city, 2023). (c) figure 5. assessment of alterations in the land cover of the riparian zone along the kahayan river in the year 2018. (a) photograph taken in 2018. (b) map depicting the distribution of land cover in the year 2018. (c) the land cover area in 2018 an intriguing matter in 2018 revolved on the preliminary deliberation on the feasibility of relocating the indonesian capital from jakarta to kalimantan. palangka raya was frequently cited as a prospective contender because of its advantageous position in the heart of indonesia. however, no definitive decision had been reached by the end of that year (hantoro, 2018). palangka raya is currently undergoing population growth and urbanization, as a significant number of immigrants are choosing to establish residence in the city. there is a noticeable upward trajectory in land cover analysis, which is driving the need for housing, infrastructure, and other public services. urbanization leads to the spread of settlements into suburban regions, necessitating the provision of essential services and infrastructure. (a) (b) https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 166 the outcomes of the regular analysis of satellite images to detect alterations in the land cover of the study area in 2020 are displayed in figure 6. the analysis was conducted in 2020 using periodic satellite image classification. the results showed that there were 120.41 hectares of land, 67.31 hectares of shrubs, 223.88 hectares of settlement, 190.20 hectares of forest, and 100.76 hectares of water bodies. the projected population of palangka raya in 2020 is expected to range between 290,000 and 300,000 individuals. the city center and well-established residential areas, such as pahandut and jekan raya subdistricts, have the highest population density. these places serve as hubs for commercial, governmental, and educational activities (bps palangka raya city, 2023). the unemployment rate is rising due to companies downsizing or shutting down. this leads to an escalation in poverty rates, necessitating the government to offer social support to impacted residents. the proliferation of settlements in riverfront locations has persisted during the covid 19 pandemic. in general, the number of communities along the banks of the kahayan river has grown due to their practicality and adherence to the economic, cultural, and environmental requirements of the local population. rivers serve as vital sources of resources and also hold significant significance in the social and cultural fabric of communities (selly et al., 2021 ). (c) figure 6. assessment of alterations in the land cover of the riparian zone along the kahayan river in the year 2020. (a) photograph taken in 2020. (b) map depicting the distribution of land cover in the year 2020. (c) the land cover area in 2020 the outcomes of the periodic analysis of satellite images to detect alterations in land cover at the specified research site in 2023 are displayed in figure 7. the analysis was conducted in 2023 using periodic satellite image categorization. the findings of the analysis showed that the land area was 116.04 hectares, shrubs covered an area of 58.24 hectares, settlements occupied 243.07 hectares, forests spanned 166.66 hectares, and there was a water body measuring 118.56 hectares. the population in 2023 fluctuated between 310,000 and 320,000 individuals. the population growth demonstrates an ongoing pattern of urbanization, resulting from both (a) (b) https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 167 internal migration and natural increase through births. these locations serve as the focal points for commercial, governmental, and educational activities (bps palangka raya city, 2023). (c) figure 7. assessment of alterations in the land cover of the riparian zone along the kahayan river in the year 2023. (a) photograph taken in 2023. (b) map depicting the distribution of land cover in the year 2023. (c) the land cover area in 2023 in palangka raya, the year 2023 is characterized by a multitude of transformations and obstacles, primarily centered around recuperating from the repercussions of the epidemic and advancing infrastructural development. observations indicate that the rate of human habitation has surpassed the extent of forest cover in the riparian zone of the kahayan river in pahandut seberang palangka raya city. monitoring the spread of settlements also has a significant impact on the occurrence of environmental damage. monitoring is a crucial procedure for gathering precise data on environmental conditions and the effects of damage. early detection of environmental issues is crucial for effective monitoring (budiyanti et al., 2020). various sites within the riparian zone of the kahayan river are utilized as monitoring sites for studying waste accumulation, erosion along the river banks, and logging activities. these monitoring sites are depicted in figure 8. surveillance of plastic garbage in the kahayan river reveals a rise in the buildup of plastic debris during the rainy season, as it is transported by the river's currents. this data can motivate local governments to enhance waste management programs in rivers located upstream and raise public consciousness regarding the consequences of plastic garbage on ecosystems in river basins. an effective solution in this context is the implementation of a waste management system that is based on the local community. plastic can be transformed into recycled plastic ore through the process of recycling (samadikun et al., 2020). to effectively address erosion on the banks of the kahayan river, a cost-effective and efficient method involves rehabilitating the area with riparian vegetation. this entails planting trees and plants that are wellsuited to the river bank environment, hence enhancing the stability and strength of the river bank. the plant's roots serve to anchor the soil and inhibit erosion (lafage et al., 2019). an effective strategy for the long term is to provide education to individuals regarding the significance of safeguarding river banks and the detrimental (a) (b) https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 168 effects of human actions, such as deforestation and unauthorized construction, which can intensify erosion. engage local people in reforestation and riparian vegetation restoration initiatives to enhance communal consciousness and accountability (oprasmani et al., 2020). (a) (b) (c) (d) (e) figure 8. the documentation of monitoring the spread of settlements also has a significant influence on the extent of environmental harm in 2024. (a) the buildup of plastic debris in the riparian zone of the kahayan river. (b) the kahayan tributary was closed because of erosion and the construction of a damaged bridge. (c) harvesting timber for the purpose of constructing materials. (d) increase in the size of human settlements in the riparian zone of the kahayan river. (e) riparian zones that continue to retain their sustainability settlement expansion can take place either gradually or swiftly, depending on the rate of population growth and the policies implemented for development. in certain regions, the rate of development can surpass the environment's ability to sustain it. the increase of settlements and the loss of trees in the riparian zone of the kahayan river provide a serious challenge to sustainable development and environmental conservation. expansion of settlements frequently results in the direct alteration of existing forests and vegetation, resulting in the loss of trees and degradation of ecosystems. the comparison is illustrated in figure 9 below. deforestation also leads to the depletion of vital resources for communities, including firewood, fruit, and traditional remedies. in addition, communities reliant on trees for their sustenance may face the loss of their means of living (meilani et al., 2021). trees have a crucial role in preserving soil moisture and mitigating erosion. deforestation leads to soil deterioration, diminished fertility, and heightened susceptibility to landslides. robust zoning restrictions, stringent building standards, and effective urban planning are necessary to safeguard the environment from potential harm caused by residential expansion (putraditama et al., 2019). multi-temporal analysis of land cover in the riparian zone of the kahayan river from 2014 to 2023 reveals a significant and rapid landscape transformation. the main findings of this study confirm a reciprocal relationship between the expansion of settlements and the reduction of riparian forests, driven by urbanization and population growth. quantitative results from satellite image classification indicate a strikingly contrasting trend between the two primary land cover classes. over the nine-year period (2014–2023), settlement areas increased exponentially from 47.44 ha to 243.07 ha, representing a surge of 412%. conversely, forest cover in the same zone experienced a drastic decline from 390.08 ha to only 166.66 ha, amounting to a 57% loss of the total forest area in 2014. these opposing trends are clearly visualized in figure 9, which illustrates how the settlement growth curve rises consistently while the forest cover curve sharply declines. these findings directly address the research gap identified in the introduction by providing previously unavailable multi-temporal https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 169 quantitative data on the rate and scale of changes within this specific study area. this rate of change, therefore, indicates intense development pressure on the riparian ecosystem. (a) (b) figure 9. an analysis of the expansion of human settlements and the loss of forested areas (a) the growth of settlements from 2014 to 2023. (b) the extent of deforestation between 2014 and 2023 this massive expansion of settlements strongly correlates with the population growth of palangka raya city, which increased from approximately 240,000 inhabitants in 2014 to more than 310,000 in 2023. this growth has been driven by urbanization and migration, accelerated by infrastructure development. the observed pattern of settlement expansion is organic in nature and tends to follow accessibility along the riverbanks—a pattern consistent with studies on slum settlements in other river basins, that also highlight unplanned urban growth. the loss of more than half of the riparian forest cover within a short period has serious ecological implications, as documented in figure 8. this degradation is no longer a mere threat but an observable reality in the field, manifested in the accumulation of plastic waste, active riverbank erosion, and tree felling for construction materials. the loss of vegetation directly reduces the function of the riparian zone as a buffer zone, which should stabilize the soil and prevent erosion. consequently, this has led to increased turbidity and sedimentation—phenomena that have also been reported in previous studies of the kahayan river and other river basins. furthermore, these findings are consistent with the study by cantera et al. (2023), which directly links deforestation to functional changes in river ecosystems, including the decline of fish populations. accordingly, the quantitative data from this research provide strong evidence that each hectare of riparian forest converted into settlement areas contributes directly to a broader chain of environmental degradation—ranging from erosion to the loss of biodiversity. therefore, firm and sustainable zoning policies and urban planning are needed to manage settlement growth in ways that do not harm the environment. however, implementing such policies must take into account the complexity of local ecosystems. studies on policy interventions in riparian zones (majumdar & avishek, 2025) indicate that conventional approaches using fixed-width buffer zones often fail to address the complex interactions between buffer effectiveness and site-specific characteristics. in contrast, context-sensitive adaptive frameworks—where the minimum buffer width is adjusted based on ecological objectives (for example, 10–30 m https://doi.org/10.14710/geoplanning.12.2.159-172 sutrisno et al. / geoplanning: journal of geomatics and planning, vol 12, no 2, 2025, 159 172 doi: 10.14710/geoplanning.12.2.159-172 170 for erosion control and 30–100 m for nutrient retention) and local biophysical conditions—have proven more effective. this approach enables the formulation of evidence-based policies that balance ecological protection with socio-economic considerations, while linking local environmental management to global sustainable development goals (sdgs). such policies must be accompanied by education and active community participation in greening and riparian vegetation restoration programs to enhance awareness and collective responsibility. the integration of social and environmental aspects into development planning is expected to create a balance between economic growth and natural resource conservation, thereby supporting ecosystem sustainability and improving the quality of life for the people of palangka raya. 4. conclusion this study confirms the occurrence of massive land cover changes in the riparian zone of the kahayan river during the 2014–2023 period. the novelty of these findings lies in the quantitative evidence demonstrating a 412% expansion of settlements, which contrasts sharply with a 57% reduction in riparian forest cover. these findings directly address the previously identified research gap concerning the availability of multi-temporal data capable of measuring the rate and scale of land-use change within the study area. the implications of these findings are highly significant for both institutional development and theoretical advancement. institutionally, the quantitative data provide a strong foundation for local governments to formulate sustainable land-use planning and more assertive spatial policies. theoretically, this study reinforces the understanding of the causal relationship between urbanization processes and environmental degradation, particularly within the sensitive riparian ecosystem. based on these findings, two main recommendations are proposed for future research. first, spatial policy scenario modeling should be conducted to predict its effectiveness in curbing the rate of deforestation. second, a more in-depth analysis is needed to explore the socio-economic factors that drive communities to settle within river buffer zones, so that policy interventions can be designed to be more practical and contextually relevant 5. acknowledgments the authors thank the postgraduate program of the university of palangka raya for financial support. this support, provided through the dipa (budget implementation list) scheme for the 2024 fiscal year, was essential in enabling this research to be carried out and published. 6. references bps palangka raya city. 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[crossref] https://doi.org/10.14710/geoplanning.12.2.159-172 https://doi.org/10.59032/jpsi.v1i1.5541 https://doi.org/10.1080/17477891.2024.2341720 https://doi.org/10.1007/s11069-023-06298-y https://doi.org/10.24843/jal.2021.v07.i01.p15 https://doi.org/10.20886/jakk.2021.18.1.17-29 https://doi.org/10.1111/apv.12448 https://doi.org/10.1007/978-3-030-93897-0_6 https://doi.org/10.26811/peuradeun.v7i3.279 https://doi.org/10.1088/1755-1315/451/1/012097 https://doi.org/10.32328/turkjforsci.741030 https://doi.org/10.24259/fs.v6i1.13472 https://doi.org/10.14710/geoplanning.9.1.47-60 https://doi.org/10.38091/man_raf.v9i1.273 https://doi.org/10.1111/gcb.16833 https://doi.org/10.1080/01490419.2020.1822478 https://doi.org/10.24912/stupa.v3i2.12384 | 1 geoplanning vol 7, no 1, 2020, 1-16 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.7.1.1-16 spatial analysis for fire risk reduction in kampung ampel cultural heritage area, surabaya f. hudantia, t. okubob , p. n. indradjatic* a east java provincial government inspectorate, indonesia b department of civil engineering, college of science and engineering, ritsumeikan university, japan c school of architecture, planning, and policy development, bandung institute of technology, indonesia abstract: the objective of the research is to improve fire risk reduction in kampung ampel surabaya through [1] identifying the current firefighting system in surabaya; [2] identifying the characteristics and conditions of kampung ampel; [3] identifying the structure of the problem; [4] proposing strategies for fire risk reduction in kampung ampel. the analysis will focus on determining the risks and resources of kampung ampel to fire hazards using geographic information system (gis) analysis. risk and resources are combined to find out the areas that have the highest risk of fire hazard. the results of the analyses consist of challenges and possible solutions. the challenges can be concluded as follow: [1] resources for firefighting cannot cover the entire area of kampung ampel; [2] resources for evacuation cannot accommodate all the population and visitors. the proposed solutions for those challenges are [1] reactivation of inactive fire wells; [2] utilization of source of water in ampel mosque; [3] proposing wider road to connect roads which are wider than 3.5 meters but are blocked by narrower roads; [4] the purchase of adapters to connect different types of fire hoses; [5] adding the number of fire hoses brought to the site; [6] remodeling the vulnerable buildings using inflammable materials with keeping the value of cultural landscape; [7] keeping portable fire pump in ampel mosque to facilitate the fire handling by residents; and [8] preparing evacuation route to the closest open space areas. the application of those solutions can reduce the high-risk area from 26.6% to 0.2%. copyright © 2020 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to cite (apa 6th style): hudanti, f., okubo, t., & indradjati, p. (2020). spatial analysis for fire risk reduction in kampung ampel cultural heritage area, surabaya. geoplanning: journal of geomatics and planning, 7(1), 1-16. doi: 10.14710/geoplanning.7.1.1-16 1. introduction fire can be defined as a thermo-chemical reaction caused by three factors: oxygen, fuel, and heat, which will lead to fires that generate heat, flames, smoke, and gas. a fire incident is the existence of an unwanted fire. fire events begin with burning, then the fire is out of control and threatens life and property (mantra, 2005; suprapto, 2008). a fire event has several processes until the fire is extinguished. the developmental process has several stage, i.e., (1) ignition/ explosion phase: this stage is characterized by the emergence of fire caused by the heat energy of the material in space; (2) fire growth phase: fire has begun to develop by the quantity of fuel available. this phase is the best stage for evacuation. in this phase, fire sensors and extinguishers must have started working; (3) flashover phase: a phase transition from the growth phase to the full combustion phase. this stage is high-speed, with the temperature usually ranges between 300º c and 600º c; (4) full combustion phase: at this stage, the release's heat is the greatest because the fire has spread to the entire space, the temperature can reach 1200ºc; (5) receding phase: at this point, all material was burned, and the temperature has begun to fall, and the firing rate also declined (mantra, 2005). a disaster occurs when a hazard strikes a vulnerable community. thus it is a result of the interaction between hazards and vulnerability (setiawan & wiguna, 2012). however, vulnerability, a community, will article info: received: 4 april 2018 in revised form: january 2019 accepted: january 2020 available online: 7 july 2020 keywords: ampel, fire risk reduction, gis-based spatial analysis *corresponding author: petrus n. indradjati school of architecture, planning, and policy development, bandung institute of technology, indonesia email: natalivan@sappk.itb.ac.id open access http://ejournal.undip.ac.id/index.php/geoplanning https://doi.org/10.14710/geoplanning.7.1.1-16 hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 2 | also have capacities or strengths that help reduce the impact of the hazard. therefore, in every disaster prevention effort, the three factors of hazard, vulnerability, and capacity are the assessment's main points. in terms of fire hazards, the factors that influence fire vulnerability are building density, population density, population activities, building material, number of stories, and building condition. meanwhile, the factors that influence the capacity of an area include the availability of fire stations, firefighting infrastructure including water resources, road width, and availability of open space (rijanto, 2010; miadinar, 2009; rusli, 2011; sujatmiko, 2012; adi et al., 2013; latifah & pamungkas, 2013; rahman et al., 2015). mitigation measures aim to save the life of the human and reduce the loss of property and reduce the adverse consequences of economic and social activities. if mitigation sources are limited, mitigation actions can be targeted to the most effective elements that greatly impact their community activities. vulnerability assessment is an important aspect of effective mitigation planning. indirect vulnerabilities include vulnerability to physical damage, economic damage, and lack of resources for recovery from disasters (sagala et al., 2013) protection against disaster threats can be achieved by eliminating the causes of the threat (reducing hazards) or by reducing the effects of threats if threats emerge. in other words, mitigation can be prepared by reducing vulnerability or increasing risky elements' capacity potential. mitigation planning is a strategy developed to reduce disasters' impact on communities, facilities, regions, cities, or countries (coburn et al., 1994; moga, 2002). in terms of fire mitigation, regarding the amount of water that should be available on-site, there are three firefighting phases (okubo, 2003): [1] the first phase is that of a small fire, handled by citizens with small amounts of water. water accessibility is most important in this phase; [2] the second phase is deemed a standard house scale fire, fought by professional firemen. the amount of water must be sufficient for professional use; [3] the third phase is block scale fire, grappled with by various support teams for fire fighting, usually from other cities. continuous and ample amounts of water are needed, particularly in this last phase. previous research discusses fire risk reduction in densely populated areas such as cities (price & bradstock, 2014), industrial areas (azad et al., 2018), residential and commercial areas (sivakumar et al., 2018), low-income and informal settlements (twigg et al., 2017). however, there are still a few studies that take case studies in cultural heritage areas. furthermore, this study fills that gap by selecting study areas that have distinctive cultures. the objective of the study is to improve fire risk reduction in kampung ampel surabaya. the research will be conducted to determine the study area's spatial characteristics, including the current firefighting systems and cultural heritage buildings' characteristics. to address the objective, several steps will be conducted as follow: (1) to identify the current firefighting system in surabaya; (2) to identify the characteristics and condition of the cultural heritage area in kampung ampel surabaya; (3) to identify the structure of the problem based on the characteristics and conditions of kampung ampel area and the fire system of surabaya city; (4) to propose strategies for fire risk reduction in kampung ampel. this study is important because it is conducted regarding the development plan of a valuable cultural heritage area prone to a fire disaster. in kampung ampel, besides the cultural heritage buildings that have been existed since the 15th century, there are also cultural nuance and activities of the arab community that has been inherited by generations. therefore, they need to be preserved due to the historical values that can give a city character or identity. the study area covers the whole area of kampung ampel, semampir sub-district, surabaya, indonesia, with approximately 40 ha, consisting of 17 rw. as the second-largest city in indonesia after jakarta, surabaya, with a total area of 326,81 km2, is considered a highly dense area. in 2013, the population of surabaya accounted for 3,2 million, with the density reached 9,793 people/km2. due to the high density, surabaya becomes vulnerable to fire. during 2005-2014, many fires that occurred in surabaya accounted for 3,611 incidents with a loss of 280,475 million rupiahs or usd 21 million (bps, 2015). kampung ampel, located in the semampir sub-district in surabaya, is a part of kota lama surabaya (old city of surabaya), an important cultural heritage area. kampung ampel is famous as a kampung inhabited by many arab ethnicities for generations. during the era of walisongo –the nine saints, known as the propagator of islam hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 3 in java island in the 15th century– kampung ampel was known as the center for the spread of islam in java (silas et al., 2012). in kampung ampel, several cultural heritage buildings are assigned by the surabaya city government, such as the great mosque and tomb of sunan ampel, tomb of habib muhammad bin idrus alhabsyi, etc. (bappeko 2012). therefore, up to now, kampung ampel attracts thousands of visitors from within and outside surabaya. however, according to surabaya spatial plan 2014-2034, kampung ampel is an area prone to fire. during the last ten years, several fire incidents occurred. these incidents potentially harm cultural heritage buildings in the kampung ampel region. 2. data and methods this research will use descriptive analysis and gis-based analysis to deal with data related to the physical condition of the study objects and the area. the gis-based analysis will also provide some suggestions on how to develop the existing firefighting measures. the research will be started by conducting problem identification. after that, the literature review will be carried out to determine the aspects that need to be considered in developing the area regarding fire prevention. data collection, including both spatial and non-spatial data, will be done afterward. finally, spatial analysis using gis will be conducted to provide development strategies for fire mitigation in the study area. the spatial analysis will be conducted according to figure 1. figure 1. spatial analysis to be conducted a disaster occurs when a hazard strikes a vulnerable community. however, vulnerability, a community, will also have capacities or strengths that help reduce the impact of the hazard. therefore, in every disaster prevention effort, the three factors of hazard, vulnerability, and capacity are the assessment's main points. in this case, the hazard to be mitigated is urban fire. furthermore, vulnerability and capacity factors to fire hazards need to be examined. risk management is all the efforts to understand and deal with possible negative impacts on the objectives. it includes identifying, analyzing, and prioritizing/evaluating risk (pedersoli jr & michalski, 2016). risk identification is to verify hazard factors from the cultural heritages and historical surrounding buildings strategies for fire risk reduction hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 4 | to take responses and protective measures. to evaluate potential disaster risk in cultural heritage and historical buildings, there are six key principles, i.e., (1) assess not only the main and visible factors but also potential hazard factors; (2) consider the hazards resulting from the facility interior factors and the environment surrounding factors; (3) establish the intent relationship between mitigation, preparedness, response, and recovery stage; (4) establish an advanced risk management and assessment program to protect the value of cultural heritages; (5) use traditional knowledge, engineering, and methods to achieve the purpose of mitigation; (6) connect disaster risk management and preservation maintenance plan tightly in every stage (jigyasu & arora, 2014). the objective of undertaking risk assessment of cultural heritage sites is to prioritize risk reduction strategies and decisions on mitigation. risk identification and analysis may be undertaken at [1] heritage site level, [2] individual heritage building level, and [3] urban level. in this case, risk identification of kampung ampel will be carried out at the heritage site level. risk identification and analysis includes the following aspects, i.e., (1) establishing the values and significance of the site (heritage value assessment); (2) listing all the natural and human-induced hazards that could potentially have an adverse impact on cultural heritage; (3) when combined with potential hazards, identifying the issues could cause a disaster risk to the site. these may be issues of site management, physical conditions of the site and/ or buildings and movable objects, underlying social and economic issues, etc; (4) analyzing the cause-effect relationships between various primary hazards and underlying risk factors increases the vulnerability and exposes it to disaster risk (jigyasu & arora, 2014). the major planning framework for risk-preparedness for cultural heritage properties (stovel, 1998) consists of three major phases, preparedness, response, and recovery. preparedness phase includes reducing risk at source, reinforcing the ability of a property to resist or contain the consequences of the disaster, providing adequate warning of impending disaster, developing emergency response plans. response phase includes ensuring the availability of the response plan and mobilizing the conservation team. recovery phase consists of efforts to mitigate the negative consequences of the disaster, efforts to rebuild the physical components of the property and the social structure of using the property and its community, efforts to reinstate and enhance preparedness measures. to determine the most important location and the most vulnerable to fire, fire vulnerability factors will be studied. the factors that mostly affect the vulnerability to the fire include: [1] building density; [2] population density; [3] population activities/ building activity; [4] number of stories; [5] building construction/ construction material type/ percentage of the non-permanent building; and [6] building condition/ building quality (adi et al., 2013; latifah & pamungkas, 2013; miadinar, 2009; rijanto, 2010; sujatmiko, 2012; rahman et al., 2015). in addition to the vulnerability factor, to conduct a disaster risk assessment, it is also necessary to understand the capacity factors. the capacity factor reflects the ability to overcome or prevent the occurrence of hazards. simply put, capacity factors can be defined as the positive aspects of the situation. in fire hazards, capacity factors include [1] availability of fire station; [2] firefighting infrastructure including water resource/ water supply/ hydrant; [3] road width/ road network/ accessibility; [4] fire prevention facilities including vehicles, personnel, equipment; and [5] availability of open space (adi et al., 2013; rusli, 2011; latifah & pamungkas, 2013; rijanto, 2010; sujatmiko, 2012; rahman et al., 2015). from the abovementioned variables, most of the variables are suitable for the study area and used in the analysis process. also, because kampung ampel is a cultural heritage area often visited by tourists, tourist arrivals and cultural heritage buildings should also be considered. because both cultural heritage buildings and tourists are objects that need to be protected during a fire event, then both fall into the category of vulnerability factor. after obtaining variables to be used in the analyses, it is necessary to specify each variable's parameters. these parameters will then be used to score each map as a basis for further analysis. consequently, the variables and parameters that will be used in the analysis are displayed in table 1. hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 5 table 1. variables and parameters to be used in the analyses factor variable parameter basic medium high very high v u ln er ab ili ty population density <150 people/ha 151-200 people /ha 201-400 people /ha > 400 people/ha building density 11-22 building/ha 23-45 building/ha > 45 building/ha flammable material <20% 20-30% 31-40% cultural heritage value outside the buffer inside the buffer building importance housing public facilities, commercial, restaurant, shop house distribution of visitors outside the buffer inside the buffer c ap ac it y accessibility areas within the range of fire hoses areas that are not within the range of fire hoses fire infrastructure areas covered by water resources areas that are not covered by water resources availability of open space 3. results and discussion 3.1. kampung ampel cultural heritage area in 2017, the total population in the area accounted for 21,766 persons (kelurahan ampel 2017). this kampung is dominated by arab ethnicities (60%), and the density reached 577 people/ha. due to the high density and high building density, kampung ampel is considered an area vulnerable to fire. table 2 shows the incidents of fires in kampung ampel from 2008-2016. kampung ampel is a cultural heritage area that exists since the 15th century. thus, in this region, several cultural heritage objects have many cultural and historical values that can give surabaya character or identity, particularly for the kampung ampel itself. the cultural heritage objects in kampung ampel are; (1) great mosque and tomb of sunan ampel. ampel mosque is an ancient mosque built in 1421 by sunan ampel. the area accounts for 120x180 square meters. ampel mosque also has a 50-meter high minaret. the tomb of sunan ampel is also located in the vicinity. it is a cemetery complex of sunan ampel, his wife, five of his relatives, his students, and 182 other muslims who died during pilgrimage to mecca (is, 2014; mappaturi, 2015). towards the area of great mosque and tomb of sunan ampel, there are five gapura/ gates which symbolize the five pillars of islam such as (a) gapura paneksen this gate symbolizes the first pillar of islam, shahada, or the declaration of faith. when deciding to be a muslim, the first thing one is obliged to do is to recite the shahada as a declaration of faith. (b) gapura madhep symbolizes the second pillar of islam, salat, or five daily prayers. after declaring the faith, a muslim is obliged to perform five daily prayers. (c) gapura ngamal symbolizes the third pillar of islam, zakat, which means compulsory charitable giving according to each person's amount of property and income. (d) gapura poso symbolizes the fourth pillar of islam, fasting in the month of ramadhan. during the ramadhan month, muslims are obliged to do fasting, increase worship, and good hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 6 | deeds. (e) gapura munggah symbolizes the fifth pillar of islam, namely the hajj. hajj is a pilgrimage to mecca, the holy city for muslims located in saudi arabia. furthermore, the other several symbols illustrate the heritage such as (1) tomb of habib muhammad bin idrus alhabsyi. tomb of habib muhammad bin idrus alhabsyi is a family cemetery complex built in the 18th century. habib muhammad bin idrus alhabsyi settled in surabaya in the mid of 20th century. he was a great scholar and died in surabaya in 1917 (sulistiowati, 2000). (2) al-irsyad hospital. al-irsyad hospital used to be a residential house of 2,600 m2 built by the baswedan family. in 1973, most of the building was donated to al-irsyad foundation for future development activities in north surabaya. since 2002, expansion is done by gradually increasing the building floor, but the main building parts are still maintained and preserved as a cultural heritage building (surabaya.go.id, 2015; al-irsyad, 2016). (3) house of oesman nabhan family. the building, built-in 1915, was owned by the dutch and was functioned as elementary school for the arab community. the size of this building is 40x30m. during the japanese colonial period (1942-1945), it functioned as a military brigade headquarters of the army/8 brawijaya regional military command. bought by the family in 1974, the building currently belongs to oesman nabhan family (akasah, 2011). (4) kemajuan hotel. kemajuan hotel was built in 1928. the two-story building is owned by al-irsyad foundation surabaya. one of the objectives of this hotel's development is to fund a school run by al-irsyad foundation. from the construction until today, the building is relatively unchanged. this hotel's area accounts for around 740 m2 (realita.co., 2014; surabaya.go.id, 2015). table 2. fire incidents in kampung ampel in 2008-2016 (surabaya fire department, 2017) date time location victims/ lost description 14 july 2008 09:30 jl. ampel sawahan gg. ii no. 17 warehouse causes: short circuit effort: 4 units of fire truck were deployed to the scene 13 nov 2012 07:55 jl. nyamplungan no. 95 household appliances causes: burning mattress effort: 1 unit fire truck was deployed to the scene 6 sept 2014 08:15 pegirian rt 04 rw 13 6 houses causes: a gas stove explosion effort: 17 units of fire trucks were deployed to the scene 7 nov 2015 19:20 jl. pertukangan b (baru) no. 22e 1 bedroom causes: short circuit effort: 1 unit fire truck was deployed to the scene 4 jan 2016 04:26 jl.nyamplungan gg.vii 1 big tree (d=50cm; h=3m) owned by local government causes: open flame1 effort: 1 unit fire truck was deployed to the scene 19 agt 2016 09.50 jl. nyamplungan ix/49 rice stall (3x3=9m2) causes: open flame effort: 1 unit fire truck was deployed to the scene 1 according to surabaya firefighting department, open flame includes fire caused by a cigarette butt, people who burn garbage (usually in the field/ open space), and lpg explosion hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 7 3.2. disaster risk assessment of kampung ampel before beginning the spatial analysis discussion, it is necessary to identify general disaster risk in the study site. identifying all the risks that threaten the heritage building, monument, or site is necessary to propose effective risk reduction strategies and decisions. the spatial analysis will be conducted afterward to present evidence for the disaster risk assessment entries. to begin with, the following hazards, vulnerability, and capacity are factors that necessary to be considered. vulnerability factors affect the vulnerability to fire hazards and determine whether the fire hazard will cause greater damage or not. on the other hand, capacity refers to all the strengths, qualities, and resources available within a community or society to manage and reduce disaster risks (jigyasu & arora, 2014). the disaster risk assessment will be identified using these three tools of hazard, vulnerability, and capacity (table 3). after conducting spatial analyses on risks and resources in kampung ampel, there are several highlights as presented in table 4. table 3. disaster risk assessment of kampung ampel hazard vulnerability capacity fire high population density availability of fire station near kampung ampel high building density narrow passages the existence of cultural heritage objects availability of active fire wells a large number of buildings are utilized as souvenir shops which sell flammable goods: clothing, snacks, accessories, books located nearby pegirian river restaurants play a role in increasing the vulnerability of the area the high number of visitors, up to 20,000 in the peak season lack of open space area allows for evacuation building material (the usage of timber/ flammable materials) no firefighting infrastructures such as hydrant or fire alarm the aforementioned proposed solution can be illustrated as follow: 1) reactivation of inactive fire wells. out of seven fire wells located in the vicinity of kampung ampel, four of them are inactive fire wells. there are several reasons why fire wells are inactive (see table 4). if possible for reactivation, the coverage area of firefighting resources will be wider, thus increasing the safety in the area (see figure 2). 2) utilization of source of water from ampel mosque. ampel mosque, located right in the middle of kampung ampel, has a water source that is believed to be the holy water that brings goodness to the drinker. the existence of that water source can be an alternative water resource to help secure the area (see figure 3). the utilization of this water source can be an effective solution since the ampel mosque is one area that falls into the high-risk category (rijanto, 2010; miadinar, 2009; rusli, 2011; sujatmiko, 2012; adi et al., 2013; latifah & pamungkas, 2013; rahman et al., 2015). however, since surabaya city fire department does not own the water source, the water hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 8 | resources volume in this mosque is unknown. also, it should be investigated whether it is possible to be utilized in the case of an emergency (see figure 4). table 4. condition, challenges and possible solutions for fire risk reduction in kampung ampel no. condition and challenges possible solutions 1 resources for firefighting cannot cover the entire area of kampung ampel reactivation of inactive fire wells utilization on the source of water in ampel mosque firefighting resources do not reach the buildings located in the middle of the ampel region propose a wider road to connect roads which are wider than 3.5 meters but are blocked by narrower roads the purchase of adapters to connect different types of fire hoses adding the number of fire hoses brought to the site can be an alternative strategy vulnerable buildings can be remodeling to make the building stronger using inflammable materials with keeping the value of the cultural landscape 2 resources for evacuation cannot accommodate all the population preparing evacuation route to the closest open space areas the open space buffer does not cover the area on the north side open space inside kampung ampel cannot accommodate all the population figure 2. comparison of fire resources buffer before and after reactivation of inactive fire wells hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 9 figure 3. source of water inside ampel mosque area figure 4. comparison of fire resources buffer before and after the added source of water from ampel mosque and reactivation of inactive fire wells 1) propose a wider road to connect roads wider than 3.5 meters but are blocked by narrower roads. on the west side of kampung ampel, two roads' lines actually have a width of more than 3.5 meters (jl. petukangan utara and jl. petukangan tengah i). still, they have not added buffers because smaller roads block the roads' entrance access from the main street. therefore, if it is possible to propose a wider road, the buffer areas will be bigger, thus covering wider areas (rijanto, 2010; miadinar, 2009; rusli, 2011; sujatmiko, 2012; adi et al., 2013; latifah & pamungkas, 2013; rahman et al., 2015). the illustration of the buffer area after road widening is displayed in figure 5 (the target of road widening is inside the black circles). hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 10 | figure 5. comparison on fire hose range buffer before and after road widening afterward, to see the comparison of risks and resources between before and after the application of proposed solutions above (figure 6): figure 6. comparison of the map of risks and resources before and after proposed solutions before: high-risk area 26.6% after: high-risk area 16.6% hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 11 the above map was obtained by combining the risk of fire spreading with several recommendations: [1] reactivation of inactive fire wells; [2] utilization of water source from ampel mosque; [3] road widening to connect blocked road with the main road. the map shows that after some suggestions are applied to the study site, the areas that can be covered by firefighting resources are wider. therefore, the high-risk area is decreasing from 26.6% to 16.6%. 2) the purchase of adapters to connect different types of fire hoses according to surabaya city fire department, in each fire fighting action, each fire truck carries 6 outdoor hoses sized 2.5 inches and six indoor hoses sized 1.5 inches, each of which has a length of 20 meters. to connect both types of fire hoses, the purchase of adapters can be an effective solution. after adapters connect both types of fire hoses, the calculation on the fire hose range will be as follow: fire hose range = length of fire hose = 240m √2 √2 = 169.70m = 169m thus, a buffer of 169m was created along the roads with a width of 3.5 meters and more. the comparison of fire hose range buffer before and after installing fire hose adapters shows a big difference in the coverage area, as shown in figure 7. because of the increased radius of fire hose range due to the installation of fire hose adapters, the same buffer will be applied to fire infrastructure elements, namely river and fire wells, including the inactive fire wells, with the assumption that the inactive fire wells will be reactivated. the map illustrates a significant difference between the before and after coverage areas (see figure 8). figure 7. comparison of fire hose range buffer before and after the installation of the fire hose adapter then, both maps of fire hose range buffer and fire infrastructure above will be overlaid with a map of risk of fire spreading to see the comparison of risks and resources between before and after installing fire hose adapters. the map shows that after some suggestions are applied, the high-risk area decreases from 26.6% to 3.4%. (see figure 9). furthermore, to treat the remaining 3.4% high-risk area, the possible solution is to utilize the water source from ampel mosque. when the water source is utilized using fire hoses after hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 12 | installing adapters (a buffer of 169 meters will be added), the high-risk area decreases to 0.2% (see figure 10). figure 8. comparison of fire resources buffer before and after reactivation of inactive fire wells and installation of the fire hose adapter figure 9. comparison on the map of risks and resources before and after reactivation of inactive fire wells and installation of the fire hose adapter before: high-risk area 26.6% after: high-risk area 3.4% hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 13 figure 10. comparison on the map of risks and resources before and after reactivation of inactive fire wells, installation of fire hose adapter, and utilization of water source from ampel mosque 3) adding the number of fire hoses brought to the site can be an alternative strategy. as can be seen from the recommendation on the installation of the fire hose adapter above, a buffer of 169 meters was added. the result of this proposed solution shows that a large area categorized as high risk has decreased to lower levels. only a few areas remain in the high-risk category. the total number of fire hoses brought by the surabaya city fire department to the fire scene is 12 units. therefore, to cover the entire area with firefighting resources, providing additional fire hoses can be an effective solution. however, to cover a wider area than 169 meters, the number of fire hoses must be above 12. 4) preparing the evacuation route to the closest open space area. evacuation route is obtained from the most visited tourist area to the nearest open space. in this case, the most visited tourist area is the mosque and tomb of sunan ampel. there are four possible routes as follows (see figure 11): a. from mosque and tomb of sunan ampel to open space 2 with a length of 415 meters (yellow line); b. from mosque and tomb of sunan ampel to open space 2 with a length of 423 meters (purple line); c. from mosque and tomb of sunan ampel to open space 3 with a length of 452 meters (red line); d. from mosque and tomb of sunan ampel to open space 4 with a length of 1068 meters (green line). the preparation of these evacuation routes illustrates which road is important for facilitation and maintenance to accelerate evacuation, especially for tourists. facilitation can be done by providing signage, clearance path of illegal or non-permanent stalls, or route socialization. before: high-risk area 26.6% after: high-risk area 0.2% hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 14 | figure 11. possible evacuation routes 5) keeping a portable fire pump in ampel mosque for a faster fire handling in surabaya, the fire pump is carried to the location of fire only by the firefighters. therefore, as ampel mosque is the most visited cultural heritage in kampung ampel, keeping the fire pump in ampel mosque can speed up the residents' fire handling before the firefighters reach the site considering that there are no major access routes to ampel mosque. the research conducted by rahmawati et al. (2016) produces a fire risk map by taking into account three variables, namely fire hazard, vulnerability and capacity. this research not only produces fire risk maps but also fire disaster risk reduction scenarios that can be carried out to preserve cultural heritage. 4. conclusion like the way we preserve our ancestors' treasure, cultural heritage has such sanctity and needs protection. it is essential to mitigate the risks of disasters, resulting in the loss of irreplaceable artistic and cultural assets. kampung ampel, as a cultural heritage area, has several cultural heritage objects that need to be preserved. the high number of fire incidents in the area becomes a challenge in preservation means. therefore, spatial analyses to assess the risks and resources of the area to fire hazards need to be performed. the results of the analyses consist of challenges and possible solutions. the condition and challenges can be concluded as follow resources for firefighting cannot cover the entire area of kampung ampel. resources for evacuation cannot accommodate all the population, including the visitors. reactivation of inactive fire wells owned by surabaya city fire department is an effective solution for fire risk reduction because out of seven fire wells located in the vicinity of the ampel area, three are active, while four are inactive. the existence of holy water inside the ampel mosque can be an alternative water resource to secure the area. however, since the fire department does not own it, the water resource volume in this mosque is unknown. besides, it should also be investigated whether it is possible to be utilized in an emergency. proposing wider road to connect roads which are wider than 3.5 meters but are blocked by narrower roads facilitates the accessibility of fire engines to get deeper into the site, thus hudanti, okubo, and indradjati / geoplanning: journal of geomatics and planning, vol 7, no 1, 2020, 1-16 doi: 10.14710/geoplanning.7.1.1-16 | 15 expanding the coverage area of firefighting resources. the purchase of adapters to connect different types of fire hoses can be a very effective solution because it can expand the firefighting resources' coverage area. previously, the number of fire hoses is only six units, but the number can be doubled to twelve units with the installation of adapters. adding the number of fire hoses brought to the site can be an alternative strategy. similar to the installation of fire adapters, an addition to the number of fire hose will definitely expand the coverage area of firefighting resources. to cover a wider area than installing adapters, the number of fire hoses ideally more than twelve units. vulnerable buildings can be remodeling to make the building stronger using inflammable materials by keeping the cultural landscape's value. preparing evacuation routes to the closest open space areas will illustrate which road is important for facilitation and maintenance to accelerate evacuation, especially for tourists. facilitation can be done by providing signage, clearance path of illegal or non-permanent stalls, or route socialization. the application of reactivation of inactive fire wells, utilization of water source from ampel mosque, and proposing wider roads for blocked roads can reduce the high-risk area from 26.6% (before application) to 16.6% (after application). the installation of a fire hose adapter and reactivation of inactive fire wells can reduce the high-risk area from 26.6% (before application) to 3.4% (after application). the installation of a fire hose adapter, reactivation of inactive fire wells, and water source utilization from ampel mosque can reduce the high-risk area from 26.6% (before application) to 0.2% (after application). keeping a portable fire pump in ampel mosque can speed up the fire handling, which can be done by the residents. 5. acknowledgments the author (s) would like to express the gratitude, especially to the state ministry of national development planning/ national development planning agency (bappenas), for supporting this research. 6. references adi, wahyu, t. j., & mirnayani. 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[crossref] https://doi.org/10.1016/j.landurbplan.2014.06.013 https://doi.org/10.1016/j.ijdrr.2015.07.003 https://doi.org/10.1016/j.sbspro.2016.06.091 https://doi.org/10.2478/ace-2018-0022 https://doi.org/10.3390/ijerph14020139 85 geoplanning journal of geomatics and planning vol. 8, no. 2, 2021 original research trends in the adoption of new geospatial technologies for spatial planning and land management in 2021 walter t. de vries 1* 1. technical university of munich, germany doi: 10.14710/geoplanning.8.2.85-98 abstract changes in spatial planning and land management practices, regulations and operations have frequently relied on the uptake of innovations in geospatial technologies. this article reviews which ones the spatial planning and land management domains has effectively adopted and which new ones might potentially disrupt the domain in the near future of 2021 and beyond. based on an extensive concept-centric trends synthesis and meta-review, the analysis demonstrates that whilst geospatial technologies are clearly gaining wider societal recognition and while private companies are indeed developing promising applications, its adoption in office work of public officials and public decision makers remains almost as limited as before. the potentially most disruptive technologies for the domain are however bim, block chain and machine learning. copyright © 2021 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license 1. introduction although geospatial technologies have changed continuously in the past 30 years, the uptake, adoption and integration of these in spatial planning and land management practices, regulations and agencies have not always been effective and lasting. since the emergence of geographic information systems (gis) and the uptake of remote sensing technologies in spatial planning and land management literature, one can only conclude that some technological advancements and conceptualisation artefacts have been more persuasive than others have. this has partly to do with natural evolution and adoption (or the lack thereof) of technologies in general, but also with the specific nature and demands of spatial planning and land management practices, regulations and agencies at large. this article poses three questions: which geospatial technologies do spatial planning and land management practices, regulations and agencies currently (in 2021) effectively employ and integrate?; what are the geospatial technology trends of 2021 which have the potential to change (or even disrupt) spatial planning and land management practices, regulations and agencies significantly?; which evidence, artefacts and manifestations exists that spatial planning and land management practices, regulations and agencies are fundamentally changing because of these technologies? this article first describes the boundaries of the conceptualisations of geospatial technologies on the one hand and spatial planning and land management practices, regulations and agencies on the other. it then explains how this research is addressing each of the questions within the scope of this paper. e-issn: 2355-6544 received: 5 august 2021; accepted: 1 december 2021; published: 30 december 2021. keywords: geospatial technologies; spatial planning; land management *corresponding author(s) email: wt.de-vries@tum.de https://doi.org/10.14710/geoplanning.8.2.85-98 mailto:wt.de-vries@tum.de de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 86 1.1. conceptualising geospatial technologies for spatial planning and land management the term geospatial technologies is concrete and ambiguous at the same time. this paper distinguishes the following functional categories of geospatial technologies that are relevant for spatial planning and land management. integrative and analytical technologies. these include geographic information systems (gis), in the form of proprietary systems, or through web based or open source based systems. goals of these technologies have always been to detect and deduct locations, spatial patterns and spatial clusters on the one hand, and to register, record, allocate, adjudicate and assign properties to spatially distributed artefacts and objects on the other hand. data acquisition and data processing technologies. these include all surveying, photogrammetry and remote sensing technologies at large. increasingly these technologies converge, especially with more cloud and point based systems. goals of these type of technologies have always included establishing reliable and accurate georeferenced foundation data and associated geodetic networks, geometric descriptions and classifications of objects and changes in objects, and to acquire systematically and dynamically georeferences during or for navigation purposes. smart and artificial intelligence technologies. this overarching category refers to technologies, which generate new results and scenarios and also can independently and autonomously derive and execute decisions. in addition to the more conventional spatial decision support systems (sdss) and planning support systems (pss), these include autonomous sensor and surveillance technologies, machine learning and artificial intelligence. visualisation, representation and simulation technologies. these type of technologies are both constructing data models and converting these into graphic static and dynamic images and other types of representations, which provide a more comprehensive perspective on a particular matter, or a set of phenomena. besides the conventional cartographic visualisation technologies to display objects and processes in 2d, 3d, or 4d, these include virtual, augmented, immersive and mixed reality, and new types of hardware such as decision support tables, hologram tables and head mounted displays in order to visualise, feel, touch, hear and perceive dynamic simulated environments. data management technologies. these technologies structure and store data in such a manner that their inter-relations can be easily accessed, queried and analysed. traditionally these referred to relational or sqlbased (geo) databases, but recently also non-relational or nosql data management technologies have advanced. these include graph stores, column stores, key value stores and document stores. additionally, data architectures and access technologies have evolved, culminating in for example decentralised blockchain architectures. spatial planning and land management practices, regulations and agencies encompass all activities, decisions, government and non-government organisations, guided and unguided behaviour which have the aim to intervene in socio-spatial and bio-physical artefacts, constructions and relations which are needed to benefit from the land, housing and shelter. (de vries, 2018a) would refer to these encompassing people-to-land/space interventions as fundamental changes, which are both functions of and dependent relations of the respective changes in governance, law, social-spatial relations, economic opportunities and dependencies, perceptions and beliefs and behaviour. these chances are also visible in the manner in which functions of spatial planning and land management are currently carried out. there are various types of functions and aims of spatial land interventions, and the execution of interventions typically takes place in both a consecutive, iterative and integrated manner (giz, 2012; metternicht, 2018). table 1 provides an overview of such functions (including spatial structure and design, spatial monitoring, administration of land and properties and compliance and coercion) a number of article references which highlight ongoing or recent changes in how these functions are carried out. https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 87 table 1. functions and aims of spatial land interventions spatial planning and/or land management function examples recently described in -amongst others spatial structure and design (city) master planning (li et al., 2021) physical planning (bakır et al., 2018) spatial localization (pokonieczny, 2016) land use zoning (pu et al., 2013) land consolidation (demetriou, 2018) land redistribution (hentze and menz, 2015) urban development boundaries (liu et al., 2017) urban form and shape (williams, 2017) spatial monitoring and assessment land use change detection (wang et al., 2020) urban growth (setyono et al., 2016) urban greening (heckert and rosan, 2018) urban blight (mireku, 2020) vacancy of houses / unused land (zou and wang, 2020) informal settlements growth (estoque and murayama, 2015) risks and impact assessments (buchori et al., 2018) land encroachment (thapa and bahuguna, 2021) administration of land/properties land registration (budiman, 2020) land recordation (chipofya et al., 2021) land valuation and pricing (elmanisa et al., 2017) spatial / land restrictions (kitsakis and dimopoulou, 2017) communal, customary tenure (chigbu et al., 2021) land grabbing (petrescu et al., 2020) conservation of cultural heritage (pepe et al., 2020) compliance and coercion housing permit compliance (offei et al., 2018) sanctions and penalties (boodhoo, 2021) evictions and relocations (desai et al., 2018) participation and mobilisation stakeholder needs analysis (giuffrida et al., 2019) handling of complaints (dhini et al., 2017) collaborative design (jankowski et al., 2021) community participation (kusmiarto et al., 2020) a few comments to explain and describe the content of the referred articles and associated changes in functions in table 1. spatial structure and design encompasses both finding the right location for new structures as well as the spatial allocations of land (use) rights, restrictions or responsibilities. geospatial technologies can typically support these activities by querying and modelling spatial phenomena with the purpose to create a rational design in desired or anticipated land use outputs or spatial forms. spatial monitoring is an evaluation and assessment type of activity, which is usually needed to measure the degree of progress of a spatial policy intervention. geospatial technologies typically support the measuring and clustering of variations in spatial phenomena. land and property administration is a branch of spatial planning and land management which records and registers relations between subjects and objects, in terms of rights, restrictions, responsibilities, values, development activities. often this sector relies on robust relational (geo) databases and domain models. compliance and coercion functions refer to the policing and regulatory actions leading to an intervention by force or by penalties. participation and mobilisation is a typical activity of both spatial planning and land management, which connects political and societal goals and needs to spatial planning and land interventions. typically, those geospatial technologies, which are available, accessible and operable for all citizens at all levels and registers of society, could support this activity. https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 88 2. data and methods in order to reveal which technologies have become mainstream in spatial planning and land management practices, regulations, for the analysis of where and how the new geospatial technologies may disrupt spatial planning and land management practices and agencies and in order to describe, highlight and synthesize current 2021 trends in geospatial technologies we have relied on: a concept-centric summary of cited evidence and referrals from scientific literature (journals) and geospatial conferences. the selection of journals was based on the listed gis and rs journals by (biljecki, 2016), added with the list of https://3d.bk.tudelft.nl/journals/ on the one hand, and an internal list of land management journals maintained by the chair of land management at tum; a selection from relevant conferences, grey literature and strategic (national) policy documents. these include the land related and geospatial technology conferences (such as fig, isprs, plpr, eald, rsa, agile); and a synthesis of various systematic government information sites (e.g. noaa), review papers, geospatial magazines (e.g. gim international, geospatial world), yearly or regular trend watcher blogs and opinion pieces in relation to geospatial technologies. the aim was to select manuscripts and electronic sources published in 2014 or later, which connect geospatial methods to specific functions of land management and spatial planning. 3. result and discussion 3.1. currently employed geospatial technologies in spatial planning and land management practices, regulations and agencies table 2 shows the synthesis of historical references and review papers, describing what sort of geospatial technologies and algorithms have been used for in relation to spatial planning and land management activities and models. table 2 is by no means complete or fully inclusive. the emphasis in the selection of examples has been to display the variety and broadness in both the technologies and the applications. as such, the references, which represent specific studies connecting the technologies to specific applications, are also exemplary. still, however the table 2 provides a summary of which technologies have become mainstream in spatial planning and land management practices, regulations and agencies. what is obvious is that the combination of (open) gis and the embedding of different kinds of data models has become conventional and fully accommodated in different phases and functions of spatial planning and land management processes. this enables the development of geoweb applications with tools such as the javascript openlayer apis, geoext, leaflet, and with openlayer api as the development environment for geo web 2.0 software applications. gis servers such as geoserver, mapserver, and degree are supporting the distribution of spatial data into various web services formats such as web mapping services (wms). postgresql with an extension of postgis provides the open source object relational database system. finally, models such as (city) gml and ladm are addressing the problems of geospatial conventional data model standards, such as the disconnect between different geometric representations for the same objects and processes. some words of caution and reflexivity are nevertheless necessary for the adoption of open source technologies in combination of big data. vast amounts of geospatial literature tends to remain focused on the technical modelling and simulation aspects of the physical spatial environment and not so much on the political, discretionary and behavioural aspects of the social spatial environment which are also crucial for spatial planning and land management. an exception to this are the agent-based modelling (abm) techniques, model and evaluate dynamic behaviour. in essence, abm simulates complex systems through detailed assumptions in behaviour and interactions of people, animals or vehicles (kieu et al., 2020), and it has therefore been applied in for example urban traffic simulation, disaster responses and evacuations. in combination with data assimilation techniques, which provide continuous updates with real-time data, real-time forecasts and predictions improve. https://doi.org/10.14710/geoplanning.8.2.85-98 https://3d.bk.tudelft.nl/journals/ de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 89 table 2. mainstream geospatial technologies in spatial planning and land management type of function examples of technologies, algorithms type of applications references integrative and analytical (open) gis city master planning (gong et al., 2014) digital surface model (dsm) land use change detection (asokan and anitha, 2019) object based nearest neighbour land cover change detection (aslami and ghorbani, 2018) rational polynomial coefficients (rpcs) urban built-up expansion (prakash and bharath, 2020) discrete mathematics, migrating bird algorithms land redistribution (tongur et al., 2020) data acquisition multispectral image processing land monitoring, land conservation (radočaj et al., 2020) gnss auto correlation function (acf) change detection method detecting human settlements (kleynhans et al., 2015) smart and artificial cellular automata urban flood modelling (ahmed et al., 2018) agent-based modelling (mustafa et al., 2017) visualisation and simulation urban sim modelling urban transportation expansion (di zio et al., 2010) 3d digital photogrammetry city modelling and visualisation (litwin et al., 2017) geometric modelling urban expansion (purevtseren et al., 2018) virtual reality participatory planning (meenar and kitson, 2020) data models and management relational data domain models land administration (pržulj et al., 2019) open source databases (e.g. postgresql/postgis) (teja et al., 2020) (city) gml, ladm city management (beil and kolbe, 2017) 3.2. potentially disruptive geospatial technology trends of 2021 various research review papers, blogs and opinion pieces summarize the 2021 trends and developments in the geospatial technologies landscape and refer to a distinct selection of technologies as being disruptive. we define ‘disruptive’ here as drivers and changes, originating from technological innovations which displace and replace existing socio-organizational structures and workflows, interpersonal and inter-institutional relations, utilization of technologies, and societal situations (de vries et al., 2020). this implies that not every technology is disruptive, but only those, which result in fundamental, and lasting changes. the trend watchers are particularly interested in those technologies, because they also provide new market shares and revenues (abdullah, 2021a; richardson, 2017; geoctrl, 2021). table 3 provides a synthesis of the potentially disruptive geospatial technologies. with regard to the integrative and analytical technologies one can argue that the technologies such as building information modelling (bim) and opensource gis , and the emergence of big and linked data are not new, as they have been existing within separate technological domains. however, the volume of the uptake and the persuasive embedding of these technologies are starting to disrupt and fundamentally change the processes and structures in which they are used. one of such disruptions concerns the adoption of cloud computing solutions, in the form of cloud computing saas (software as a service), in particular for geospatial applications. gis as saas provides cloud based mapping tools, open data platforms, ai integration, geospatial data editing and sharing and helps handling big data. current geospatial cloud services provide ready-to-use geospatial datasets and images whereby users can conduct different types of analyses at a variety of geographic scales. companies like esri, google maps (google), bing maps (microsoft), super map, zondy crber, geostar, hexagon geospatial, carto and gis cloud are participating in gis cloud computing technology. companies such as amazon (aws), google (google earth), and microsoft (bing maps) are already providing these https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 90 information architectures for the past 10 years, but the google earth and bing maps mapping tools are not suitable for large enterprise-wide gis applications. instead, a geospatial cloud, providing gis as saas is able to give many analytic and visualisation capabilities and ready to use map or imagery layers. integrated with ai and machine learning, the gis cloud can automate techniques like classification, change detection, clustering etc. the extensions of saas are paas and iaas, i.e.‘ platform as a service‘ and ‘infrastructure as a service‘. saas delivers applications without downloading or installation (e.g. google apps, dropbox and concur). paas provides a framework for developers. it is built on virtualization technology ( e.g windows azure, google app engine). iaas gives infrastructure to organisations. in iaas resources are available as a service (e.g. microsoft azure, amazon aws, digital ocean etc.). table 3. functional categories of potentially disruptive geospatial technologies type of function examples references integrative and analytical bim connected to gis (kaden et al., 2020; goyal et al., 2020) geospatial analytics (lin et al., 2020) big and linked geospatial data (werner and chiang, 2021) data acquisition (open) lidar (ye et al., 2020) drone technologies (yunus and azmi, 2020) miniaturized sensors smart and artificial machine and deep learning (muhammad et al., 2021) pattern recognition bayesian network modelling (marcot and penman, 2019) visualisation, representation and simulation digital twins (ketzler et al., 2020) mapping as service (abdullah, 2021b) citygml3.0 (kutzner et al., 2020) extended, immersive and mixed reality (çöltekin et al., 2020) data management blockchain (verheye, 2020) nosql (bennett et al., 2019) cloud computing graph databases (zheng et al., 2017) data warehousing the branch of geospatial analytics extend the application of gis functionalities. in addition to relying on traditional maps and georeferenced objects, geospatial analytics uses data from all kinds of technology, including location sensors, social media, mobile devices, satellite imagery. the main purpose of geospatial analytics is to build data visualizations for understanding phenomena and finding trends in complex relationships between people and places, in order to make predictions on socio-spatial and bio-physical spatial changes easier and more accurate. examples of where geospatial analytics may become useful include making more informed choices about building or expanding facilities, speeding up logistics by running routing scenarios, finding patterns of criminal activity within a region, or minimizing risks from hazardous location-based events like powerful storms (usc (university of southern california), 2021). specifically for the domains of spatial planning and land management the role of bim connected to gis is crucial. the open geospatial consortium (ogc), supported by buildingsmart international (bsi) are now preparing an initiative to explore geospatial and bim data integration based on meaningful real-world use cases. so far, the two communities rely on different data modeling approaches with respect to fundamental concepts, semantics, access, level-of-detail, and several other aspects. the next step is however to verify how to connect and integrate the geospatial open standards such as citygml, landinfra/infragml, indoorgml, and imdf with the bim open standards such as ifc (industry foundation classes), iso19650, and the opencde api portfolio, such that digital models for the built environment can be interchanged. https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 91 of particular interest in the emerging data acquisition technologies is light detection and ranging (lidar) technology. compared to using traditional stereophotogrammetry relying on 2d aerial photos or images images to generate a 3d digital terrain model, lidar creates such a digital terrain model using a large amount of points collected by a laser, a scanner, and a specialized gps receiver. although the raw data are discrete-return, classified point-cloud data provided in las format, lidar data products are often created and stored in a gridded or raster data format. the raster format can be easier for many people to work with and also is supported by many different commonly used software packages. originally designed for 3d terrain mapping, the range of applications relevant for spatial planning and land management is growing fast, including coastal floodplain mapping, forest and green area mapping, hydrological assessments, landscape ecology, urban planning, survey assessments, volumetric calculations of buildings and constructions, and design and evaluation of coastal engineering structures (noaa, 2021). point-cloud data acquisition, such as lidar, and the variety of drones have significantly altered and extended the data acquisition techniques. abdullah (2021b) describes an increasing uptake of lidar due to improvements in lidar data density, quality and accuracy. pauschinger and klauser (2020) list both public users of drones (such as emergency services, police, archaeology and urban planning), and private ones (such as filmmaking, security, land surveying, infrastructure development and agriculture). for the development of smart cities and regions, the machine learning community applies artificial neural networks. deep learning is a type of machine learning, which is a subset of artificial intelligence. deep learning can analyze images, videos, and unstructured data in ways machine learning can’t easily do. muhammad et al. (2021) provide a taxonomy of currently available deep learning methods applied in smart city development and discovered that generally the use of convolutional neural networks are highly popular in deep learning based smart city applications. the applications of deep learning algorithms are especially in the domains of road, transportation and mobility management, but also emerging in monitoring of air pollution and real estate management. the major disruption related to smart and artificial technologies is the fact that and increasing number of people are ‘plugged in’ as compared to ever before. this allows for smart tech solutions which are more effectively targeting spatial planning and land management issues in real time, due to the vast amount of active and passive data generation, which is stored and analysed by interconnected systems (brode 2021). in the field of visualisation, representation and simulation technologies one can observe many changes and improvements, such as digital twins, mapping as service citygml3.0 and extended, mixed and immersive reality. digital twins are the digital surrogate, replica or representation of a physical object, process or service. these can include specific objects, such as buildings or wind mills, but also represent larger and abstract objects, such as projects sites or entire cities. representing these objects and phenomena in a digital environment, connected with digital programs, models and algorithms enables predictions and simulations of how changes or interventions play out (without an actual intervention or disturbance). kutzner et al. (2020) describe how the citygml version 3.0 has extended its core modules with the new modules construction, versioning, and dynamizer, as well as the revised building and transportation modules. common in all the new representation techniques is that one can more easily than before simulate, experiment, test and visualise expansions, risks, movements and behavioural scenarios. the concepts related to extended realities, referred to as xr, is an umbrella term for the virtual, augmented, and mixed reality (vr, ar, mr) refer to technologies and conceptual propositions (çöltekin et al., 2020). the technologies do not only help to envision alternative scenarios, especially relevant when planning cities or landscapes, but can even change people’s realities, as most of these systems are interactive and with cognitive effects and impacts. the changes in data handling and management technologies particularly address the limitations of relational databases. blockchain technologies are particularly well equipped to address transparency, access and accountability problems, which are often tied to centralised relational databases. this type of technology is particularly suitable for applications whereby regular transactions take place and whereby these transactions need to be accurate and systematically monitored. as such, the field of land administration, highly dependent on reliable transactions and mutations, is a very suitable application field. blockchain would also be applicable for https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 92 setting which depend on participatory processes. therefore there is also a high potential of blockchain technologies for participatory processes, needed in spatial planning in general (muth et al., 2019). in a similar vein as blockchain, graph databases ironically address the problems of finding relations and correlations between data, which in relational databases are only possible by constructing the appropriate queries. as such, graph data structures are better capable dealing with unknown and hidden patterns and are more flexible in constructing and analysing relations. 3.3. current evidence of changes and disruptions in spatial planning and land management practices, regulations and agencies due to innovations in geospatial technologies there a clear difference between where which technologies have already a major impact and those technological advancements where the impact is still limited or being disputed. clearly, advancing and integrating for the domain are the visualisation and modelling technologies. the connection of bim with (open) gis, and the citygml models are not only fostering more accurate and up-to-date representations of the building environment, but also fostering a connection between architectural, planning and land management processes and professionals. this trend is visible through the increasing professional and scientific publications on 3d cadastres, making use of the connection of bim and gis (sun et al., 2019), and in the combination of housing permit or land use compliance activities (altıntaş and ilal, 2021). furthermore. insurance and construction companies are increasingly investing in bim in combination with gis as this combination can make both an assessment of the volumes and shapes of property assets, which are underlying the property value and possible loss assessments, as well as cater for possible evacuation routes. hence, fire disaster plans can rely on these technologies. nevertheless, in practice the legal adoption of 3d cadastres using these technologies is still limited worldwide. paasch and paulsson (2021) argue a clear and unambiguous legal definition of 3d property remains difficult. the extended reality technologies are equally disruptive, as they provide entirely new cognitive experiences, real-life-like alternative scenarios for stakeholders in the spatial planning process. datta (2019) predicts especially an uptake of these technologies in tourism, architecture and construction, but also foresees a realistic adoption in retails management and safety management. the holocity example (lock et al., 2019) shows how planners virtually wander through the city of sydney and explore possible re-design alternatives interactively. similarly, a virtual walk through a never built project of a century ago based on 92-year-old drawings interpreted and digitally recreated in halle shows how one can experience alternative and timeless realities (fuhrmann, 2021). the alternative modelling and data processing technologies such as the use of graph technologies and blockchain-based data handling are also on the rise, and seem to be especially relevant for areas where there is a high need for large-volume and reliable and transparent transactions. this applies in particular for the land registration and land recordation functions (ameyaw and de vries, 2020; bennett et al., 2020), even though there are not many operational examples of where administrations truly rely on blockchain. the role of big data and big data analytics, combined with artificial intelligence and machine-learning algorithms is furthermore growing, especially in the activities of (automated) land use mapping, automated monitoring and spatial (change, risk) assessments, collaborative planning and community participation (de vries, 2018b). currently still disputed for one or more reasons are the embedding of digital twins in planning processes, the use of artificial intelligence for compliance and enforcement, and the veracity of big data. regarding digital twins marcucci et al. (2020) argue that in a planning process there must be an active role of planners and decision makers, which should at least be familiar with the basic tenets, functionalities benefits and limitations of the technologies. as long as this is not the case, a full adoption in participatory planning and decision-making phases is still hampered. this corresponds to the critique of tomko and winter (2019) among others, who argue that the metaphor of a digital twin seems to neglect a fundamental aspect in the digital environment, namely people and the cyber-social ecosystem connected to the cyber-physical ecosystem. people can influence and alter both ecosystems, whilst being a passive or active change agent of it. https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 93 there are several discourses about the use of artificial intelligence in the context of compliance and enforcement. whilst some applaud its use, for example for the managing and enforcement of conservation and maintaining public spatial restrictions (fang et al., 2019) , others warn for certain types of misuse (maas, 2019; hoffmann-riem, 2020) and the need for more ethical considerations (georgiadou et al., 2020). despite the fact that geospatial data are now directly uploaded through mobile platforms and active sensors, the mere existence of data does not necessarily produce a direct benefit. it still requires complex methodologies, continuous accuracy and validity feedback loops and some form of accountability checks to make these data meaningful, especially in a spatial governance context. veracity and reliability are therefore still crucial issues, as well as informational privacy and human dignity. the compound word (geo) privacy suggests that the location of an individual does not only relate to traditional geographic coordinates, but can be inferred from people’s connotations, expressed interests, activities, and sociodemographic profiles. finally, the actual adoption of the technologies in spatial planning and land management processes is still largely in an experimentation and testing phase. for example, the bavarian survey authority (bayerische vermessungsverwaltung) do apply lidar measurements, and shifted from relying on stereoscopic pictures. additionally, they apply their own bim integrated gis system achieving higher levels of details for their digital maps and storages, and apply machine-learning algorithms in identifying newly built houses and constructions comparing two consecutive taken images of the same area. nevertheless, final decisions on land use zoning, compliance and administration are still made by the human staff members. this also includes big data analytics. 4. conclusion the synthesis of documented evidence demonstrates that a broad range of geospatial methodologies, instruments and technologies exist, which the fields of spatial planning and land management are currently already employing. the uptake of gis-based and image processing algorithms are especially evident for the functions of spatial monitoring and assessment and the administration of land and properties, but also for the functions of spatial structuring and design, coercion and compliance and participation and mobilisation for example agent-based modelling and the use of cellular automata are effectively used. despite the significant advancements in planning and management capabilities, most of these technologies still have a number of problems. they are too rigid, too inflexible and lack capabilities of capturing and finding non-standard models, relations and uncertainties. in spatial planning and land management, and especially when dealing with dynamic stakes, interests and behaviour of people on the one hand, and complex ecological systems on the other, handling such dynamic uncertainties is crucial. the novel technologies, which are most likely to affect and possibly disrupt current functions and processes of spatial planning and land management, include machine-learning, lidar, bim in connection with gis, blockchain, big data analytics, extended, immersive and mixed reality, different types of operational research and digital twins. these technologies are better able to handle dynamic uncertainties and provide alternative access authorities. the prime advantages are faster and more accurate mining and analysis possibilities, decreased dependence on centralised storage of data, easier and more democratised access to analytical functions and algorithms and more automated integration of technologies and services. it must also be noted that despite its advantages, blockchain technology for example must never be a goal in itself for innovating land registration. downside of this technology is also higher ecological footprint connected to its decentralised data storage, data processing and data volumes, and continued steep learning curves for practitioners. there is increasing evidence that spatial planning and land management practices, regulations and agencies are fundamentally changing because of the disruptive technologies. active stakeholders such as construction companies, insurance companies, developers, building owners, municipalities, and professionals increasingly invest in bim in connection with gis for their 3d models, assessments, plans and developments https://doi.org/10.14710/geoplanning.8.2.85-98 de vries / geoplanning: journal of geomatics and planning, vol 8, no 2, 2021, 85-98 doi: 10.14710/geoplanning.8.2.85-98 94 and hence increasingly rely on bim with gis for their business, private and/or public financial and economic decisions. also, deep learning algorithms find a broadening set of application domains. whilst technologies keep on developing, there is an increasing need to reflect on the ethical dilemmas related to the technologies. whereas technical professionals could previously always rely on relatively value-neutral technologies and technological products, issues such as uncontrolled automated judgments, surveillance, deep fake and (geo) privacy infringements are more at stake than ever. the legal and societal impacts are yet still relatively underrepresented in current research. 5. acknowledgements no funding was available for this research. 6. references abdullah, q. 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(2020). individual vacant house detection in very-high-resolution remote sensing images. annals of the american association of geographers 110 (2):449-461. https://doi.org/10.14710/geoplanning.8.2.85-98 | 245 geoplanning vol 4, no 2, 2017, 245-256 journal of geomatics and planning e-issn: 2355-6544 http://ejournal.undip.ac.id/index.php/geoplanning doi: 10.14710/geoplanning.4.2.245-256 challenging potency of jayengan: new opportunity for development of sustainable jewelry creative industrial kampung-based tourism in surakarta w. astuti a, q. qomarun b, a. febela b, r. a. putri a, d. w. astuti b auniversity of sebelas maret surakarta, indonesia buniversity of muhammadiyah surakarta, indonesia abstract: local-based tourism becomes one of economic development strategies of the area based on local potency. sustainable tourism can be defined as ‘tourism, which takes into account of its current and future economic, social and environmental impacts’, addressing the demands of visitors, the environment, industry and local communities as the host of development. kampung jayengan surakarta is the traditional settlement located in the downtown, which spontaneously developed by banjar community, that arrived in surakarta in 1746 as jewelry traders. right now, the existence and the identity of kampung jayengan as kampung of jewelry has been lost its attraction, constrained by development of modern public facilities and services in the city center. this study analyzed the challenge faced by kampung jayengan to develop its potencies as jewelry industrial kampung-based tourism becoming a part of tourist destination in surakarta as a creative city. the research type was predictive research by using mixed methods. several analyses have been conducted from identification of the potencies of kampung. it consisted of analysis conformity of the area to the spatial structure general plan policy; analysis of demographic; analysis of economy, analysis of availability of public infrastructure; analysis of building and environment and analysis of land use suitability. results of analysis shows that the area has a great challenge for jewelry industrial kampung-based tourism development, which will have multiplier effect on increasing economic development of the area as well as economic development and welfare of the local community. copyright © 2017 gjgp-undip this open access article is distributed under a creative commons attribution (cc-by-nc-sa) 4.0 international license. how to site (apa 6th style): astuti, w. et al., (2017). challenging potency of jayengan: new opportunity for development of sustainable jewelry creative industrial kampung-based tourism in surakarta geoplanning: journal of geomatics and planning, 4(2), 245-256. doi:10.14710/geoplanning.4.2.245-256. 1. introduction development of primary tourism product is very important related to tangible and intangible products, as a main pull factors, which motivates tourists to visit. they expect for experiencing and exploring the special product of the destination area. this will be continued by development of diversification of the product as many elements are associated with the product itself (benur & bramwell, 2015). local-based tourism becomes one of strategies of economic development of the area based on local potency and special characteristics of the area. sustainable tourism can be defined as ‘tourism, which takes into account of its current and future economic, social and environmental impacts, addressing the demands of visitors, the environment, the industry and local communities as the host of development’. therefore development of general spatial plan will has a role for directing of future development, maximizing all the potency and generating opportunity for the area for regional economic development based on the locality (jamal & getz, 1995). the attraction of tourist destination depends on most potency of physical characteristics, socio cultural characteristics as well as economic potencies as a basic product of the destination special interest tourism is a trip undertaken by tourists which visits a particular place due to their special interest to certain objects or activities that can be found or carried out at the location of the tourism destination (hall, 1992). the products of special interest tourism can be local uniqueness potentials and attractions including natural physical characteristics, socio-cultural, and building architectures. development of primary tourism product article info: received:13 january 2017 in revised form: 15 february 2017 accepted: 20 august 2017 available online: 30 october 2017 keywords: jayengan, creative industrial, land use suitability, tourism development corresponding author: winny astuti universitas sebelas maret, indonesia email: winnyast64@gmail.com open access http://doi.org/10.14710/geoplanning.4.2.245-256 http://doi.org/10.14710/geoplanning.4.2.245-256 mailto:winnyast64@gmail.com astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 246 | is very important related to tangible and intangible products. therefore it is important to develop the diversification of the product to be interested by tourists to visit. with the complexity of tourism development and the spread of activity and actors involved, new concept in planning for tourism has to be based on integrated planning concept. according to baud-bovy (1982) any stage in tourism development plan should be integrated into the national socio-economic and political policies, into the natural and the built environment, into the socio and cultural circumstance, into the related sectors of economy, public budget and into the international tourism market. kampung is a traditional settlement in indonesia, which is usually has special characteristics of ethnics and locality. on government of indonesia encourage “one village one product” in order to encourage kampung to increase the economic development based on their own resources. most of the resources is based on special characteristic of creative industry, such as ‘batik-traditional clothes of indonesia’, ‘blankon-traditional hat of java’ and jewelry. kampung jayengan surakarta is the traditional settlements located in the downtown, which spontaneously developed by banjar community, that arrived in surakarta in 1746 as jewelry traders. right now, the existence and the identity of kampung jayengan as kampung of jewelry has been lost attraction constrained by development of modern public facility and services in the city center. this study aims to explore the challenges of kampung jayengan as jewelry industrial kampungbased tourism, which is a part of tourist destination in surakarta as a creative city. results from this research shows that the challenges of development of jayengan kampung as jewelry industrial kampung tourisms are mainly by strengthened political policies and institutions supporting development of the area; collaboration among stakeholders; integration of the primary product of tourist destination and other socio-cultural associated potencies of the area; and integration of potencies with integration of potencies with related sectors of the economy. this will sustain the area, generate multiplier effect on economic development and welfare improvement of the area as well as of the local community. integration of all the potencies with all aspects of development: nation’s political policy, natural and the build environment, socio–cultural of the community, all related sectors of economy will challenge and sustain the further development of jayengan jewelry kampung as new tourist destination area in surakarta based on creative industry. 2. data and methods 2.1. jayengan as a part of surakarta as creative city the concept of the creative economy expands from creativity in the whole of the economy, including socio economic processes and the organization of creative labor (moore, 2014). urbanization creates a high density of population, which is pulled by attractiveness of the city, especially in javanese cities. creative economic activities of the urban people become a new source of economic activities in the highly economic competition of the city. creative industries in urban rejuvenation, place art and creative industries as a significant role for rebranding the city. in indonesia, the development of the creative industries has been started since 2007 by the department of commerce through research and publications regarding the role of creative industries in indonesia and it was continued by development roadmap. although this concerned about the creative industry, it has been moved to the ministry of tourism and creative economy. based on creative industries mapping study based department of commerce republic of indonesia, the definition of creative industries in indonesia is derived from benefitting creativity and inventiveness of the individual (departemen perdagangan republik indonesia, 2007 in (wiryono et al., 2015)) some cities have been declared as creative city such as bandung, surabaya, jogjakarta as well as surakarta. kampungjayengan is located in the center of the city of surakarta, which is split into two areasby gatotsubroto street, with the darussalam mosque as the center. having administrative area of 29.30 ha, the most land usage in jayengan is dominated by residential development, covered of 13.51 ha. it consists of 9 rws (neighborhood unit) and 30 rts (community unit), with number of households was 1,424 and population of 5,771 persons in 2015. jayengan also has very strategic location in between kampunglaweyan and kampungkauman as batik kampung, which was previously existed as kampung tourist destination based on creative industry. according to general spatial plan of surakarta, jayengan is located in bwk i (city development area i) (figure 1), which is planned for the main tourist destination together with kasunanan palace, mangkunegaran palace, sriwedari park and batik museum. in spite of this, policy direction of potential sector in jayengan is strengthening creative industry and tourism to encourage regional economic http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 | 247 development of surakarta city in national levels, in directed of the center for development of creative industry. formulation of forum jayengan jewelry kampung in july 2015 has been widen opportunity for developing networking among government, private sector, ngos and other institutions. figure 1. the map of kampung jayengan surakarta (source : astuti et al. (2015)) 2.2. research methodology this research includes in a predictive research, where exploring the potencies and constraints of existing area for future area development as tourism destination based on creative industry of jewelry. this research used qualitative approach with mixed of both qualitative and quantitative data (figure 2). several focused group discussions, interviews to related local government and field survey were organized. the field observation was conducted in order to obtain special characteristics of the area using the spatial mapping survey method for the following aspects (table 1): http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 248 | figure 2. process of analysis table 1. survey design (sources : astuti et al. ( 2015)) no variables/ aspects purpose of analysis technique 1 political policies and institutions supporting development of the area • analysis of comformity and supporting policies in national, province as well as local area for development the area as kampung tourism based on jewelry creative industry in jayengan • content analysis of government document • interview • fgd 2 collaboration among stakeholders • analysis of stakeholders • analysis of organizational networks • analysis of roles of stakeholders • secondary data • interview with related government units and stakeholders • fgd 3 integration of the primary product of tourist destination and other socio-cultural associated potencies of the area • analysis of basic sector • analysis of primary, secondary and tertiary economic activity of the area • field observation • interview with related government units and stakeholders • fgd 4 integration of potencies with related sectors of the economy • analysis of availability of related sectors of economy • field observation • interview with related government units and stakeholders • fgd 3. result and discussion 3.1. challenging potency of jayengan as jewelry industrial kampung-based tourism as discussed earlier that the challenges of potencies of jayengan have been indicated from several aspects of political policies and institutions supporting development of the area; collaboration among stakeholders; integration of the primary product of tourist destination and other socio-cultural associated potencies of the area; and integration of potencies with related sectors of the economy. all the aspects are interellated to each other supporting and challenging the development of jayengan jewelry kampung. 3.1.1. political policies and institutions supporting development of the area integration of the development plan of the area into the nation’s policies as well as city development policy is one consideration for tourism development. this partly because the attention of community-based tourism and domestic recreation is mostly supported by public financing and the consideration of inter regional tourism development (baud-bovy, 1982). development of kampung jayengan as jewelry industrial kampung-based tourist has been supported by general spatial plan of surakarta. this is stated that jayengan is located in bwk i (city development area i), which is planned for the main tourist destination together with kasunanan palace, mangkunegaran palace, sriwedari park and batik museum. the policy direction is addressed for strengthening creative industry and tourism to encourage regional economic development of surakarta city (general spatial plan of surakarta, 2011-2031). in national policy, bappenas has introduced the program of local economic development (led), which is applied in several local government in indonesia. the local government of surakarta city, through http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 | 249 bappeda has developed blue print creative economic development 2015-2025 and establishing roadmap and development program of creative economy. the forum of jayengan jewelry kampung has been included as one of 31 economic cluster in surakarta. it means that the government of surakarta has supported the development of jkp as new tourist destination based on economic creative industry. 3.1.2. collaboration among stakeholders collaboration is "a process of joint decision making among key stakeholders of a problem domain about the future of that domain" (gray, 1989). while, collaboration for community-based tourism planning is a process of joint decision making among autonomous, key stakeholders of an inter-organizational, community tourism domain to resolve planning problems of the domain and or to manage issues related to the planning and development of the domain. collaboration is necessary where in the process of development the problems are getting complex and require an interor multi-organizational response, as they are beyond the capability of any single individual or group to solve single-handedly (trist, 1983). therefore, this needs effective organizational structure and network for developing tourism management and for sustaining the area as well as the socio-economic of the community. achieving coordination among the government institutions, between the public and the private sector, and among private enterprises is challenging. this needs mechanisms and processes for incorporating the diverse element in the tourism system. in jayengan kampung permata (jkp), internal institution in charge in this process of development of jayengan as jewelry tourism kampung is forum jkp, which leaded by local people in charge in economic activity as jewelry trader. development of jayengan jewelry kampung recently is in early stage of development where the perception of communities in charge in the jewel’s economic activities and related supporting goods vary. most of the members of jkp forum is individual business actor as jewel trader, who has not seen this process of development of tourist destination as benefit to them, which increase the competitiveness among them and in turn, could affect the economic viability of the tourism industry in the community. some community is perceived that development of jkp is only for individual benefit. even though external support multi-organization especially from the government to development of the area is proved, the internal organization has faced a complex institutional relationship problem. darussalam foundation, which was first institutionally established in jayengan as religion-based institution for managing presence of community service organization of darussalam elementary school and cultural resources under the darussalam mosque becomes dominant institution actor in the development of jayengan jewels kampung. the institution which should become collaboration partner with forum jkp, has therefore encouraged the forum of jkp and tend to be more powerful and authorized. this limits involvement of jayengan community in the process of development due to exclusiveness of darussalam foundation in jayengan as banjarness community. 3.1.3. integration of the primary product of tourist destination and other socio-cultural associated potencies of the area development of tourism destination based on the local potency of community should encourage integration between tourism product and local socio-economic condition of the area. visitors always have expectation for experiencing and exploring the specific environment and socio–cultural–economic of the destination. therefore, development of primary products in tourist destination is very important and becomes the identity of the area, as “one village one product”. however, usually many elements associated with the main product (smith, 1994 in benur & bramwell, 2015). in kampung jayengan, the special product of tourist destination area is jewelry art. it also has special potencies related to the existence of wonderful heritage artefact, named darussalam mosque (figure 3), which was originally built by banjar community who came to surakarta in 1910. recently, darussalam mosque becomes a center for moslem activities such as education and moslem tradition. education activities are occurred in darussalam moslem elementary school and pesantren (moslem boarding school) of darussalam. one of moslem cultural activities can be found in the field yard of darussalam mosque every ramadhan, which is called bubur banjar tradition (banjar porridge) (figure 4), distributed to all moslem community surrounding areas that come to the mosque in the evening. this tradition becomes interesting attraction in kampung jayengan every year, which attracts tourists and information media such as national and local television studios and journalists to report. there are also some moslem traditional attractions such as hadrah (moslem dance) and haul tradition, which attracts people surrounding to visit. http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 250 | figure 3. darussalam mosque as heritage building figure 4. tradition of bubur banjar in jayengan in jayengan as tourist kampung, the primary product of jewelry art has associated with building and environment characteristics of javaness-banjarnese traditional buildings found in the area (figure 5), which has opportunity to attract the tourists. in spite of this it has kartopuran square for development support to jayengan jewelry kampung in conducting events for tourist attractions figure 5. heritage building in jayengan 3.1.4. integration of potencies with related sectors of the economy tourism destination based on creative industry is dependent for its development with many other sectors of the economy such as transportation, industry and handicraft, urban development, telecommunication, information, which are influenced by the policy in national, regional and municipal authority. therefore it has to be planned accordingly, it has to be integrated into comprehensive planning at national, regional and local levels. tourism product and attraction in destination area is very important in giving experience and impression to visitors. primary tourism product will be more interesting, eventhough it is a complext due to many elements associated with this product, such as input of services, hospitality, choice of tourism, tourist involvement in delivery services, and tourist experience (smith, 1994 in benur & bramwell, 2015). jayengan (figure 6) becomes a new tourism destination area in surakarta, which concentrates on primary product of jewelry, with its diversification such as gold, silver and stone. product concentration in one area will have advantage on competitiveness and sustainability of the area (table 2). from data of pdrb (domestic regional product) of serengan district, manufacture industry is the second position in contributing to 31% of regional product. the urban facilities grow and expand, such as hotels, restaurants, and trading, which are the main infrastructure for development of tourist destination, are really important. this is also supported by national economic policy of one village one product, which has been strongly found in this area by special product of jewel art with most of banjar community living there in charge in jewel production activity as they are originally come from martapura banjarmasin kalimantan island, which http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 | 251 is famous as jewelry community (figure 7). the primary product of jewelry is also supported by banjar culinary in emergence the areas such as banjar soto, wadai wadai, srabi notosuman, bakmi ketoprak dan center of surakarta souvenir, which have been previously exist. figure 6. map of location of economic activities in jayengan kampung figure 7. jewelry art and stones as the main creative industry in jayengan (left). the mayor of surakarta took attention to jewellery collection in yusuf’s jewelry in the kampung launching, 18 october 2015 (right) http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 252 | table 2.the challenges potency of jayengan for development of sustainable jewelry creative industrial kampung-based tourism (sources: analysis astuti et al. (2015)) no aspects the challenges 1 political policies and institutions supporting development of the area • national political and policies and institutions supporting development of jayengaan jewelry kampung • general spatial plan of surakarta and sectoral plan related to development of jayengan jewelry kampung • blue print of development of creative economy 2 collaboration among stakeholders • the challenges of human resources individually as well as as organization in managing the tourist destination area and collaborating with other institutions and parties 3 integration of the primary product of tourist destination and other sociocultural associated potencies of the area • the challenges of jewelry product as primary product of the area • the associated potencies for development of jkp; cultural-religion potencies, architectural potencies, moslem-education potencies 4 integration of potencies with related sectors of the economy • availability of diversification of product such as gold, silver and stone • availability of urban facilities growth and expansion, such as hotels, restaurants, and trading • availability of main infrastructure for development of tourist destination • availability of related sectors of economy banjar culinary 3.2. concept, skenario of development of jayengan jewelry kampung-based tourism concept of tourism planning development has to consider the interdependency between development of tourism and the overal socio-economic development of the area as well as development of the country; the interdepency between tourism sector itself, between the resources, various related markets and touristm industry, and the interests of particiapnts in the developmet of tourism (baud-bovy, 1982). based on the analysis of special characteristics of the area and multisectoral analysis, the concept of the area is formulated as jayengan jewellery kampung as ‘integrated creative-industrial tourism’ based on locality. industrial district is networks, formulated from synergizing all local industries, which tends to be agglomerated in order to benefit one and another as industrial cluster (kuncoro, 2002). according to madecor, in purwaningsih (2012, in (setiyani, 2014)), there are 3 main components of industrial cluster. the first is primary product of industries, which is the main industrial clusters, it has great impact in development of cluster. in jayengan jewelry kampung, the main industrial cluster is found to be jewelry activity production, which is going to be diversification of product of experiencing attraction in the area. the second is diversification of product of industries as supplier industries which supports the development of the core industry. in this case, there are production industry of jewelry stones (diamond) and production industry of jewelry frame, which is usually made from gold or silver. and the third is related to socioeconomic cultural sectors of industries which has contributions to and get benefit from the core industry such as raw material, technology, human resources and marketing distributors. in jayengan jewelry kampung, the government of surakarta in kelurahan levels as well as in the city levels takes a part in development of kampung. in spite of this, there is community forum of jayengan jewelry, which has an important role for networking and negotiating to other institutions and other parties, which are supporting the development of kampungjayengan as jewelry kampung based on locality (figure 8) http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 | 253 figure 8. concept of jayengan jewelry kampung as ‘integrated creative-industrial tourism’ based on locality (sources : astuti et al. (2015) developed from cluster model of madecor (2001) in (setiyani, 2014)) this research developed grand design of jayengan jewelry kampung, which has been planned for 20 years (2015-2035). in the first five years, kampung development is planned for strengthening internal institution and initiation of government networks with the capacity building strategies in the form of forum jayengan jewelry kampung; developing of block area i as the center of the planned area; strengthening networks with other institutions and related parties; developing of jewelry industrial cluster institution and cooperative institution; developing of related events such as bazar, expo; and developing of information and technology for marketing kampung as tourist destination based on creative industry. in the second five years, kampung development is focused on strengthening networks to external institutions (government, private, community and ngos), with the strategies of networking to related industrial clusters such as batik, blangkon, fashion, etc together with conducting physical construction of the area including signage, pedestrian, street furniture and marketing facilities in block area i. according to low and altman 1992 in wang and chen 2015, place satisfaction is an important aspect of sense of place that deals with how a place meets to fill preconceived expectations and therefore needs standard of quality of life. physical construction of the area will generate image and identity of a place for association between people (tourist) and place. in the third five years development, this will be focused on physical development of block area ii, which is the development of creative industry production as an experienced tourist attraction for visitor and strengthened production chain and development of tourist infrastructure and accessibility. in the fourth five years (after 2030) the kampung will sustain the area. 3.3. new opportunity for development of jayengan jewelry kampung according to the potencies explored above, jayengan can be developed as jewelry kampung-based tourism with the future development plan 2035 (table 3), which the future block plan as described in figure 9 below: conditional factors human resources, physical resources, knowledge resources, infrastructure, capital resources diversification of product of industries production industry of jewelrystone and production indutry of gold and silver frame market market of jewelry industry, market of tourist destination of creative industry primary tourism product of industry main activity of production activity of jewelry (finishing good) related socio-economic cultural sectors community forum of jkp, government, annual events, marketing, culinary industries, etc strategy & competitiveness http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 254 | figure 9. block plan of jayengan jewelry kampung 2015-2035 table 3: future development plan based on block area characteristics development block characteristics of area future development plan (2015-2035) block area i existence of office of head of kelurahan jayengan ; darussalam mosque and center of information of jayengan jewelry kampung as the office of forum jkp existence of annual event of banjar porridge ( bubur banjar), hadrah traditional moslem dance and other banjarjava traditional cultural activities. kartopuran square as the center public space of the area existence of some jewelry shops, tourist facilities such as hotel, restaurant physical condition of the area has not determined as tourist destination space identity directed for block of primary product development integrated with socio-economic-cultural associated potencies block area i will be developed as center of development of the jayengan jewellery kampung; center of local government office; center of information; darussalam mosque will be developed as center of socio-cultural-education activities. katopuran square will be created as place for interaction among communities and tourist attraction and events creation. center of production market of the kampung block area ii existence of residential area of jewelry workers generates opportunity for tourist product development physical condition of the area has not determined as tourist destination space identity directed for block of diversification of primary product of jewelry for tourist unforgetable experience,. block area iii existence of culinary economic activities along the mainroad , which has not been physically good condition occupation of darussalam mosque owned land by several economic activities especially culinary vendors potency for development of darussalam mosque as a center of jayengan jewelry kampung integrated with center of local culinary directed for block of diversification of primary product and integration with socio-economic-cultural associated potencies preservation of darussalam mosque as heritage artifact and development it’s area surrounding as a center of jayengan jewelry kampung integrated with center of local culinary block area iv existence of hotel matahari and other hotels and tourist facilities potential for development of tourist facilities directed for development of jkp byintegration of potencies with related sectors of the economy center for development of tourist facilities of jayengan jewelry kampung such as hotel, travel agent, guest house, souvenir shops etc. block area v residential area planned for adequate residential area http://doi.org/10.14710/geoplanning.4.2.245-256 astuti et al./ geoplanning: journal of geomatics and planning, vol 4, no 2, 2017, 245-256 doi: 10.14710/geoplanning.4.2.244-256 | 255 4. conclusion development of creative industrial kampung-based tourism is challenged by firstly, political policies and institutions supporting development of the area. development plan jayengan jewelry kampung should be integrated with and conformed to nation’s policies as well as city development policies as tourist destination development based on local potencies, which is mostly supported by public financing and consideration of interegional tourism development. secondly, collaboration among stakeholders is obviously needed, where in the process of development is getting complex and need inter-organizational responce, which are beyond the capacity of individual or group to solve. development of jayengan jewelry kampung is challenged by cohesiveness of internal community forum (jkp), coordination among stakeholders, mechanisms and processes for incorporating the diverse elements in the tourism system, which constraint the sustainability of the development. thirdly, integration of the primary product of tourist destination and other socio-cultural associated potencies of the area. as development of primary product in tourist destination becomes identity of the area as one vilage one product, which attract tourist to visit, integration with specific local socio-economic cultural condition will attract tourist to visit for experiencing and exploring the area. the challenges are how the destinantion area develop spesific product attracting people to visit. development of jewelry as primary tourism product based on creative industry in jayengan kampung becomes spesific in surakarta, which cannot be found in other areas, except in martapura as the origin of jewels industry. fourthly, integration of potencies with related sectors of the economy is challenging, where tourist destination based on creative industry is associated with related sectors of economy such as transportation, industry and handicraft, urban development, telecommunication, information. how the local policies can support development of its related sectors and limit the impact of development. for sustaining the development needs long term development plan based on the concept of jayengan jewelry kampung as ‘integrated creative-industrial tourism’ based on locality as instrument for direction and integration of any parties, responding opportunities and challengesfor development of jayengan jewelry kampung. 5. acknowledgments the authors would like to thank ministry of research, technology and higher education for the research grant supported this research and board of research and community services universitas sebelas maret. 6. references astuti, w., febela, a., qomarun, q., andisetyana r (2015). grand design pengembangan kampung wisata industri jayengan sebagai upaya percepatan ekonomi kawasan berbasis lokalitas. directoral general of higher education, ministry of research, technology and higher education. baud-bovy, m. 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