Spatial Model of Forest Cover Change using Moisture Index in Kolaka Regency

Authors

DOI:

https://doi.org/10.70561/geocelebes.v10i2.50316

Keywords:

Forest Cover, Kolaka Regency, Normalized Difference Moisture Index, Remote Sensing

Abstract

Kolaka Regency plays a crucial role, both ecologically and economically. This study aims to identify long-term changes in forest cover using the Moisture Index, using a collection of Landsat satellite imagery. Forest cover is defined by a threshold: values >0.7 are considered forest cover, while values <0.7 are considered non-forest areas. Forest cover in Kolaka Regency fluctuates. The 2000-2005 period saw the sharpest decline, reaching an unmitigated loss of 404.00 km2. Although forest cover recovered in subsequent years, it accounted for only a third of the total forest loss, reaching 462.31 km². Extractive mining activities and land conversion drive forest loss in this region. A comparison of protected forest areas and the rate of forest loss indicates that legal protection does not automatically guarantee the presence of vegetation cover in the field. Therefore, forest management in Kolaka must shift from an administrative approach to a spatially based, real-time monitoring approach.

Author Biographies

  • Septianto Aldiansyah, Haluoleo University, University of Indonesia

    1Department of Geography Education, Faculty of Teacher Training and Education, Universitas Halu Oleo, Kendari 93232, Indonesia.

    2Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia.

  • Amniar Ati

    Department of Geography Education, Faculty of Teacher Training and Education, Universitas Halu Oleo, Kendari 93232, Indonesia.

  • Fitriyani Saudi

    Department of Geography Education, Faculty of Teacher Training and Education, Universitas Halu Oleo, Kendari 93232, Indonesia.

References

Aldiansyah, S., & Saputra, R. A. (2023). Comparison of Machine Learning Algorithms for Land Use and Land Cover Analysis Using Google Earth Engine (Case Study: Wanggu Watershed). International Journal of Remote Sensing and Earth Sciences (IJReSES), 19(2), 197–210. https://ejournal.brin.go.id/ijreses/article/view/13716

Badan Pusat Statistik. (2025a). Penduduk, Laju Pertumbuhan Penduduk, Distribusi Persentase Penduduk, Kepadatan Penduduk, Rasio Jenis Kelamin Penduduk Menurut Provinsi, 2025. Badan Pusat Statistik. https://www.bps.go.id/id/statistics-table/3/V1ZSbFRUY3lTbFpEYTNsVWNGcDZjek53YkhsNFFUMDkjMyMwMDAw/jumlah-penduduk--laju-pertumbuhan-penduduk--distribusi-persentase-penduduk--kepadatan-penduduk--rasio-jenis-kelamin-penduduk-menurut-provinsi.html?year=2025

Badan Pusat Statistik. (2025b). Luas Kawasan Hutan dan Konservasi Perairan Menurut Kabupaten/Kota (ha) di Provinsi Sulawesi Tenggara, 2023. Badan Pusat Statistik. https://sultra.bps.go.id/id/statistics-table/1/NDY4MyMx/luas-kawasan-hutan-dan-konservasi-perairan1-menurut-kabupaten-kota-ha-di-provinsi-sulawesi-tenggara-2023.html

Berenschot, W., & Dhiaulhaq, A. (2025). The production of rightlessness: palm oil companies and land dispossession in Indonesia. Globalizations, 22(8), 1377–1395. https://doi.org/10.1080/14747731.2023.2253657

Bose, P. (2023). Equitable land-use policy? Indigenous peoples’ resistance to mining-induced deforestation. Land Use Policy, 129, 106648. https://doi.org/10.1016/j.landusepol.2023.106648

Choi, W.-I., Kim, E.-S., Yun, S.-J., Lim, J.-H., & Kim, Y.-E. (2021). Quantification of One-Year Gypsy Moth Defoliation Extent in Wonju, Korea, Using Landsat Satellite Images. Forests, 12(5), 545. https://doi.org/10.3390/f12050545

D’Alisa, G., & Demaria, F. (2024). Accumulation by contamination: Worldwide cost-shifting strategies of capital in waste management. World Development, 184, 106725. https://doi.org/10.1016/j.worlddev.2024.106725

de Souza, J. C., Mendes, T. S. G., Bignotto, R. B., de Alcântara, E. H., & Massi, K. G. (2025). Land use dynamics in a tropical protected area buffer zone: is the management plan helping? Journal of Environmental Studies and Sciences, 15(1), 156–166. https://doi.org/10.1007/s13412-024-00905-5

Demarquet, Q., Rapinel, S., Dufour, S., & Hubert-Moy, L. (2023). Long-Term Wetland Monitoring Using the Landsat Archive: A Review. Remote Sensing, 15(3), 820. https://doi.org/10.3390/rs15030820

Donatien, L. M. B., Clobite, B. B., & Midel, M. L. M. (2024). Land-use/land-cover change detection using multi-temporal Landsat imagery in the North of the Congo Republic: a case study in the Sangha region. Geocarto International, 39(1), 2425184. https://doi.org/10.1080/10106049.2024.2425184

Dragomir, L. O., Popescu, C. A., Herbei, M. V., Popescu, G., Herbei, R. C., Salagean, T., Bruma, S., Sabou, C., & Sestras, P. (2025). Enhancing Conventional Land Surveying for Cadastral Documentation in Romania with UAV Photogrammetry and SLAM. Remote Sensing, 17(13), 2113. https://doi.org/10.3390/rs17132113

Escobar-López, A., Castillo-Santiago, M. Á., Mas, J. F., Hernández-Stefanoni, J. L. H., & López-Martínez, J. O. (2024). Identification of coffee agroforestry systems using remote sensing data: a review of methods and sensor data. Geocarto International, 39(1), 2297555. https://doi.org/10.1080/10106049.2023.2297555

Foody, G. M. (2024). Ground Truth in Classification Accuracy Assessment: Myth and Reality. Geomatics, 4(1), 81–90. https://doi.org/10.3390/geomatics4010005

Gaveau, D. L. A., Santos, L., Locatelli, B., Salim, M. A., Husnayaen, H., Meijaard, E., Heatubun, C., & Sheil, D. (2021). Forest loss in Indonesian New Guinea (2001–2019): Trends, drivers and outlook. Biological Conservation, 261, 109225. https://doi.org/10.1016/j.biocon.2021.109225

Giljum, S., Maus, V., Kuschnig, N., Luckeneder, S., Tost, M., Sonter, L. J., & Bebbington, A. J. (2022). A pantropical assessment of deforestation caused by industrial mining. Proceedings of the National Academy of Sciences, 119(38), e2118273119. https://doi.org/10.1073/pnas.2118273119

Gunawan, H., Mulyanto, B., Suharti, S., Subarudi, S., Ekawati, S., Karlina, E., Pratiwi, P., Yeny, I., Nurlia, A., Effendi, R., Widarti, A., Martin, E., Kalima, T., Desmiwati, D., Takandjandji, M., Heriyanto, N. M., Garsetiasih, R., Sawitri, R., Rianti, A., Kwatrina, R. T., Sihombing, V. S., Fahmi, S., & Marsandi, F. (2024). Forest land redistribution and its relevance to biodiversity conservation and climate change issues in Indonesia. Forest Science and Technology, 20(2), 213–228. https://doi.org/10.1080/21580103.2024.2347902

Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., & Townshend, J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693

Jiang, H., Wang, S., Cao, X., Yang, C., Zhang, Z., & Wang, X. (2019). A shadow-eliminated vegetation index (SEVI) for removal of self and cast shadow effects on vegetation in rugged terrains. International Journal of Digital Earth, 12(9), 1013–1029. https://doi.org/10.1080/17538947.2018.1495770

Li, J., Bhatti, U. A., Nawaz, S. A., Huang, M., Ahmad, R. M., & Ghadi, Y. Y. (2024). Remote-Sensing Image Classification: A Comprehensive Review and Applications. In Deep Learning for Multimedia Processing Applications (1st Ed., p. 30). CRC Press. https://doi.org/10.1201/9781032646268-2

Lo, M. G. Y., Morgans, C. L., Santika, T., Mumbunan, S., Winarni, N., Supriatna, J., Voigt, M., Davies, Z. G., & Struebig, M. J. (2024). Nickel mining reduced forest cover in Indonesia but had mixed outcomes for well-being. One Earth, 7(11), 2019–2033. https://doi.org/10.1016/j.oneear.2024.10.010

Nguyen, A., Kovyazin, V., & Pham, C. (2025). Application of Remote Sensing and GIS in Monitoring Forest Cover Changes in Vietnam Based on Natural Zoning. Land, 14(5), 1037. https://doi.org/10.3390/land14051037

Oo, T. Z., Humphries, U. W., & Mone, M. K. (2026). Remote sensing–derived land use land cover for hydrological modelling of flood hydrographs in the Mun River Basin, Thailand. Geocarto International, 41(1), 2620189. https://doi.org/10.1080/10106049.2026.2620189

Ordway, E. M., Asner, G. P., & Lambin, E. F. (2017). Deforestation risk due to commodity crop expansion in sub-Saharan Africa. Environmental Research Letters, 12(4), 044015. https://doi.org/10.1088/1748-9326/aa6509

Pain, A., Marquardt, K., Lindh, A., & Hasselquist, N. J. (2021). What is secondary about secondary tropical forest? Rethinking forest landscapes. Human Ecology, 49(3), 239–247. https://doi.org/10.1007/s10745-020-00203-y

Piragnolo, M., Pirotti, F., Zanrosso, C., Lingua, E., & Grigolato, S. (2021). Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS. Remote Sensing, 13(8), 1541. https://doi.org/10.3390/rs13081541

Rakatama, A., & Pandit, R. (2020). Reviewing social forestry schemes in Indonesia: Opportunities and challenges. Forest policy and economics, 111, 102052. https://doi.org/10.1016/j.forpol.2019.102052

Rawat, P., Bagri, D. S., & Kumar, S. (2026). Decadal assessment and future prediction of land use land cover changes in the Upper Yamuna basin using CA-ANN modeling. Discover Geoscience, 4(1), 47. https://doi.org/10.1007/s44288-026-00426-4

Santoro, A., Piras, F., & Yu, Q. (2025). Spatial analysis of deforestation in Indonesia in the period 1950–2017 and the role of protected areas. Biodiversity and Conservation, 34(9), 3119–3145. https://doi.org/10.1007/s10531-023-02679-8

Schultz, M., Shapiro, A., Clevers, J. G. P. W., Beech, C., & Herold, M. (2018). Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor. Remote Sensing, 10(11), 1850. https://doi.org/10.3390/rs10111850

Shimizu, K., Ota, T., & Mizoue, N. (2019). Detecting Forest Changes Using Dense Landsat 8 and Sentinel-1 Time Series Data in Tropical Seasonal Forests. Remote Sensing, 11(16), 1899. https://doi.org/10.3390/rs11161899

Singh, R., Pal, M., & Biswas, M. (2025). Cloud Detection Methods for Optical Satellite Imagery: A Comprehensive Review. Geomatics, 5(3), 27. https://doi.org/10.3390/geomatics5030027

Supriatna, J., Shekelle, M., Fuad, H. A. H., Winarni, N. L., Dwiyahreni, A. A., Farid, M., Mariati, S., Margules, C., Prakoso, B., & Zakaria, Z. (2020). Deforestation on the Indonesian island of Sulawesi and the loss of primate habitat. Global Ecology and Conservation, 24, e01205. https://doi.org/10.1016/j.gecco.2020.e01205

Tariq, A., Jiango, Y., Li, Q., Gao, J., Lu, L., Soufan, W., Almutairi, K. F., & Habib-ur-Rahman, M. (2023). Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon, 9(2), e13212. https://doi.org/10.1016/j.heliyon.2023.e13212

Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., Huntington, J. L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J.-F., Strobl, P., & Cook, B. D. (2022). Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 113195. https://doi.org/10.1016/j.rse.2022.113195

Downloads

Published

2026-10-02

Issue

Section

Articles

How to Cite

Spatial Model of Forest Cover Change using Moisture Index in Kolaka Regency. (2026). JURNAL GEOCELEBES, 10(2), 241–257. https://doi.org/10.70561/geocelebes.v10i2.50316