









































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 dry-
fields, 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 demand-

oriented. 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 high-

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 
                                                              

 | 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. (2014). Relative Contributions of the 
Logging, Fiber, Oil Palm, and Mining Industries to Forest Loss in Indonesia. Conservation Letters, 8(1), 
58–67. [Crossref] 

Acharya, K. P. (2002). Use of GIS based technology for community forest Boundary surveying in Nepal. 
Asian Conference on Remote Sensing. Http://Www. Gisdevelopment. Net/Aars/Acrs/2002/for/248. 
Pdf. 

Asante, E. A., Ababio, S., & Boadu, K. B. (2017). The Use of Indigenous Cultural Practices by the Ashantis for 
the Conservation of Forests in Ghana. {SAGE} Open, 7(1), 215824401668761. [Crossref] 

Bluffstone, R. A., Somanathan, E., Jha, P., Luintel, H., Bista, R., Toman, M., … Adhikari, B. (2018). Does 
Collective Action Sequester Carbon? Evidence from the Nepal Community Forestry Program. World 
Development, 101, 133–141. [Crossref] 

Cristiano, P., Madanes, N., Campanello, P., di Francescantonio, D., Rodr\’\iguez, S., Zhang, Y.-J., … Goldstein, 
G. (2014). High NDVI and Potential Canopy Photosynthesis of South American Subtropical Forests 
despite Seasonal Changes in Leaf Area Index and Air Temperature. Forests, 5(2), 287–308. [Crossref] 

Danoedoro, P. (2012). Pengantar penginderaan jauh digital. Yogyakarta: Andi. 
Etongo, D. B., & Glover, E. K. (2012). Participatory Resource Mapping for Livelihood Values Derived from the 

http://doi.org/10.14710/geoplanning.6.1.73-80
https://doi.org/10.1111/conl.12103
https://doi.org/10.1177/2158244016687611
https://doi.org/10.1016/j.worlddev.2017.07.030
https://doi.org/10.3390/f5020287


 
Miardini and Susanti/ Geoplanning: Journal of Geomatics and Planning, Vol 6, No 1, 2019, 73-80 
doi: 10.14710/geoplanning.6.1.73-80 

80 | 
 

Forest in Ekondo-Titi Subregion, Cameroon: A Gender Analysis. International Journal of Forestry 
Research, 2012, 1–9. [Crossref] 

McAdam, J. H. (2000). Tropical Agroforestry. By P. Huxley. Oxford: Blackwell Science (1999), pp. 371, 69.50. 
ISBN 0-632-04047-5. Experimental Agriculture, 36(4), 519. [Crossref] 

Peters-Guarin, G., & McCall, M. K. (2010). Community Carbon Forestry (CCF) for REDD: Using CyberTracker 
for Mapping and Visualising of Community Forest Management in the Context of REDD. Kyoto: Think 
Global, Act Local (K: TGAL) Report. 

Regional Government of Pacitan Regency. (2011). Regional Regulation of Pacitan Regency No. 3 of 2010 
concerning Regional Spatial Planning for Pacitan Regency in 2009-2028. Regional Government of 
Pacitan Regency. 

Republic of Indonesia. (2004). Law Number 41 of 1999 concerning Forestry. State Secretariat, Jakarta. 
Ritohardoyo. S. (1999). The Effect of Use of Community Forest Land on Farmers’ Welfare in the Special 

Region of Yogyakarta. Journal of Geography Forum, 13(2), 126–146. 
Santika, T., Meijaard, E., Budiharta, S., Law, E. A., Kusworo, A., Hutabarat, J. A., … Wilson, K. A. (2017). 

Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic 
and climate complexities. Global Environmental Change, 46, 60–71. [Crossref] 

Siddiqui, M. N., Jamil, Z., & Afsar, J. (2004). Monitoring changes in riverine forests of Sindh-Pakistan using 
remote sensing and GIS techniques. Advances in Space Research, 33(3), 333–337. [Crossref] 

Solo Watershed and Protection Forest Management Center. (2011). Integrated Watershed Management 
Plan SWP Grindulu. Directorate General of Solo Watershed and Protected Forest Management Center. 

Subekti, I. (2016). MANAJEMEN KOPERASI DALAM RANGKA PENGELOLAAN HUTAN RAKYAT DAN 
PENGARUHNYA TERHADAP KETAHANAN EKONOMI MASYARAKAT (Studi Pada Koperasi Wana Lestari 
Menoreh di Kabupaten Kulon Progo DIY). Jurnal Ketahanan Nasional, 22(2), 158. [Crossref] 

Suherdi, S., Amanah, S., & Muljono, P. (2015). Motivasi Petani dalam Pengelolaan Usaha Hutan Rakyat Desa 
Cingambul, Kecamatan Cingambul, Majalengka. Jurnal Penyuluhan, 10(1). [Crossref] 

Tovar. M.C.L. (2011). NDVI as indicator of degradation. Unasylva 238, 62(2), 39–46. 
Waluyo, E. A., Ulya, N. A., & Martin, E. (2010). Perencanaan sosial dalam rangka pengembangan hutan 

rakyat di sumatera selatan. Jurnal Penelitian Hutan Dan Konservasi Alam, 7(3), 271–280. [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

