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 sub- district, 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 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 sub- district 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. (2006). Dapatkah pengelolaan kolaboratif menyelamatkan Taman Nasional Danau Sentarum?. CIFOR: Forest and Governance Program No 7/2006 Beier, P., & Noss, R. F. (1998). Do habitat corridors provide connectivity? Conservation Biology, 12(6), 1241– 1252. Retrieved from http://www.jstor.org/stable/2989843 Douglas-Hamilton, I., Krink, T., & Vollrath, F. (2005). Movements and corridors of African elephants in relation to protected areas. Naturwissenschaften, 92(4), 158–163. http://doi.org/10.1007/s00114- 004-0606-9 Indrawan, M., Primack, R. B., & Supriatna, J. (2012). Biologi Konservasi: Biologi Konservasi. Jakarta: Yayasan Obor Indonesia. Retrieved from https://books.google.co.id/books?id=FYfkdv4VGQgC INFRONT. (2008). Analisa Kemiskinan Partisipatif di Kecamatan Selo. Landcare International. Non-published IUCN. (2005). Benefits Beyond Boundaries. Proceedings of the Vth IUCN World Parks Congress : Durban, South Africa 8-17 September 2003. IUCN--The World Conservation Union. Island Press: Washington DC. Joshi, A., et. al. (2013). Connectivity of tiger (Panthera tigris) populations in the human-Influenced forest mosaic of Central India. PLoS ONE, 8(11), 1–11. http://doi.org/10.1371/journal.pone.0077980 Joshi, P. K., Yadav, K., & Sinha, V. S. P. (2011). Assessing impact of forest landscape dynamics on migratory corridors: a case study of two protected areas in Himalayan foothills. Biodiversity and Conservation, 20(14), 3393–3411. http://doi.org/10.1007/s10531-011-0123-z Kushwaha, S. P. S., & Hazarika, R. (2004). Assessment of habitat loss in Kameng and Sonitpur Elephant Reserves. Current Science, 87(10), 1447–1453. 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. http://doi.org/10.14710/geoplanning.2.2.69-81 Marhaento, H. (2007). Landslide hazard analysis using heuristic-statistic method in combination with multi temporal landslide data A Case study in Loano sub district, Purworejo district, Central Java, Indonesia. Master Thesis. Magister Geo-Informasi Manajemen Bencana UGM Universitas Gadjah Mada. Yogyakarta Marhaento, H., & Sudibyakto, H. (2007). Landslide hazard analysis using heuristic-statistic method in combination with multi temporal landslide data: A Case study in Loano sub district, Purworejo district, Central Java, Indonesia. Universitas Gadjah Mada. McRae, B. H., et. al. (2012). Where to restore ecological connectivity? detecting barriers and quantifying restoration benefits. PLoS ONE, 7(12), 1–12. http://doi.org/10.1371/journal.pone.0052604 Nirwansyah, A. W., Utami, M., Suwarno, S., & Hidayatullah, T. (2015). Analisis pola sebaran kejadian longsorlahan di Kecamatan Somagede dengan Sistem Informasi Geografis. Geoplanning: Journal of Geomatics and Planning, 2(1), 1-9. Pouzols, F. M., & Moilanen, A. (2014). A method for building corridors in spatial conservation prioritization. Landscape Ecology, 29(5), 789–801. http://doi.org/10.1007/s10980-014-0031-1 Priyono, K. D. (2008). Analisis morfometri dan morfostruktur lereng kejadian longsor di Kecamatan Banjarmangu Kabupaten Banjarnegara. Forum Geografi, 22(1), 72–84. Putri, T. D. (2008). Analisis Neraca Air di Lereng Gunung Merbabu, Boyolali. Bachelor Thesis. Universitas Gadjah Mada. Qutni, D. C. (2004). Taman Nasional Gunung Leuser. Yogyakarta: Mitra Gama Widya. Silveira, L., et. al. (2014). The potential for large-scale wildlife corridors between protected areas in Brazil using the jaguar as a model species. Landscape Ecology, 29(7), 1213–1223. http://doi.org/10.1007/s10980-014-0057-4 Subekti, A. B., & Hadmoko, D. S. (2013). Tingkat kerawanan longsorlahan dengan metode weight of evidence di Sub das Secang Kabupaten Kulonprogo. Jurnal Bumi Indonesia, 1(3). Retrieved from http://lib.geo.ugm.ac.id/ojs/index.php/jbi/article/view/121 Thornthwaite, C. W., & Mather, S. (1957). Instruction and tables for computing potential evapotranspiration and the water balance. Publication Climatologi, x(3). Wangchuk, S. (2007). Maintaining ecological resilience by linking protected areas through biological corridors in Bhutan. Tropical Ecology, 48(2), 177–187.