Informasi Spasial Perubahan Tutupan Lahan untuk Pengelolaan Wilayah di Kecamatan Mataraman
DOI:
https://doi.org/10.31599/pkv7s916Keywords:
perubahan tutupan lahan, penginderaan jauh, Random Forest, Google Earth Engine, pengelolaan wilayahAbstract
Land cover change is an important indicator of environmental dynamics because it is closely associated with ecological function alteration, ecosystem degradation, and the sustainability of land resource utilization. Mataraman District, Banjar Regency, South Kalimantan, has experienced rapid expansion of oil palm plantations and mining activities that potentially affect environmental conditions and landscape structure. However, spatial information regarding the location, extent, and direction of land cover change remains limited, highlighting the need for more detailed information to support environmental monitoring and regional management. This study aimed to develop spatial information on land cover change from 2020 to 2025 using Sentinel-2 imagery and the Random Forest algorithm within the Google Earth Engine platform. The analysis was conducted through land cover classification, change detection, and transition matrix approaches to identify changes in area, spatial distribution, and conversion patterns among land cover classes. The results showed that secondary forest decreased by 6,026.04 ha (-30.22%), while oil palm plantations and mining areas increased by 4,422.16 ha (+22.18%) and 1,606.97 ha (+8.06%), respectively. The largest land cover conversion occurred from secondary forest to oil palm plantations, covering 4,069.57 ha, followed by secondary forest to mining areas, covering 1,537.75 ha. Change status analysis indicated that approximately 8,736.12 ha, or 44% of the total area of Mataraman District, experienced land cover change during the observation period. The spatial information generated provides a detailed representation of land cover dynamics and can serve as a supporting reference for environmental monitoring, land-use evaluation, and more adaptive regional management in response to land use change.
Downloads
References
Austin, K. G., Schwantes, A., Gu, Y., & Kasibhatla, P. S. (2019). What Causes Deforestation in Indonesia? Environmental Research Letters, 14(2), 024007. https://doi.org/10.1088/1748-9326/aaf6db
Balsem, T., & Buurman, P. (1989). Guidelines for Land Unit Description. Dalam Soil Data Base Management Project Technical Report No. 13, Version 2. Center for Soil Research.
Belgiu, M., & Csillik, O. (2018). Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sensing of Environment, 204, 509–523. https://doi.org/10.1016/j.rse.2017.10.005
BPS Kabupaten Banjar. (2025). Kabupaten Banjar dalam Angka (berbagai edisi tahun 2020–2025). BPS Kabupaten Banjar. https://banjarkab.bps.go.id/
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108–1111. https://doi.org/10.1126/science.aau3445
FAO. (2020). Global Forest Resources Assessment 2020: Main Report. FAO. https://www.fao.org/forest-resources-assessment/2020
Global Forest Watch. (2024). Indonesia - Dashboard | Global Forest Watch. https://www.globalforestwatch.org/dashboards/country/IDN/
Global Forest Watch. (2025). Banjar, Kalimantan Selatan, Indonesia - Dashboard | Global Forest Watch. https://www.globalforestwatch.org/dashboards/country/IDN/13/3/?map=eyJjYW5Cb3VuZCI6dHJ1ZX0%3D
Greenpeace Indonesia. (2023). Main Api dengan Deforestasi: Analisis Risiko Kehilangan Hutan Indonesia akibat Kebijakan FOLU Net Sink 2030. Greenpeace Indonesia. https://www.greenpeace.org/indonesia
Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt, R. D., Lovejoy, T. E., Sexton, J. O., Austin, M. P., Collins, C. D., Cook, W. M., Damschen, E. I., Ewers, R. M., Foster, B. L., Jenkins, C. N., King, A. J., Laurance, W. F., Levey, D. J., Margules, C. R., … Townshend, J. R. (2015). Habitat fragmentation and its lasting impact on Earth’s ecosystems. Science Advances, 1(2). https://doi.org/10.1126/sciadv.1500052
Hansen, M. C., Potapov, P. V, Moore, R., Hancher, M., Turubanova, S. A., & Tyukavina, A. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
IPCC. (2019). Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Intergovernmental Panel on Climate Change. https://www.ipcc.ch/srccl/
IPCC. (2022). Chapter 10: Asia. In: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC Sixth Assessment Report (AR6), Working Group II. https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-10/
Kementerian ESDM. (2025). Data Izin Usaha Pertambangan (IUP) Kabupaten Banjar, Kalimantan Selatan. https://modi.esdm.go.id
Kementerian Kehutanan Republik Indonesia. (2009). Peraturan Menteri Kehutanan Republik Indonesia Nomor P.32/Menhut-II/2009 tentang Tata Cara Penyusunan Rencana Teknik Rehabilitasi Hutan dan Lahan Daerah Aliran Sungai (RTkRHL-DAS). Kementerian Kehutanan Republik Indonesia.
Lambin, E. F., & Meyfroidt, P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences of the United States of America, 108(9), 3465–3472. https://doi.org/10.1073/pnas.1100480108
Pemerintah Republik Indonesia. (2022). Enhanced Nationally Determined Contribution: Republic of Indonesia. Ministry of Environment and Forestry, Republic of Indonesia.
Pettorelli, N., Laurance, W. F., O’Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W. (2014). Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology, 51(4), 839–848. https://doi.org/10.1111/1365-2664.12261
Ramadhan, G. F., & Hidayati, I. N. (2022). Prediction and Simulation of Land Use and Land Cover Changes Using Open Source QGIS. A Case Study of Purwokerto, Central Java, Indonesia. Indonesian Journal of Geography, 54(3). https://doi.org/10.22146/ijg.68702
Sabaghy, S., Abuzar, M., Crawford, D., McAllister, A., & Sheffield, K. (2025). Remote sensing for land cover mapping across Victoria, Australia – a machine learning application. Scientific Data, 12. https://doi.org/10.1038/s41597-025-04900-5
Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109
Sonter, L. J., Ali, S. H., & Watson, J. E. M. (2018). Mining and biodiversity: key issues and research needs in conservation science. Proceedings of the Royal Society B: Biological Sciences, 285(1892), 20181926. https://doi.org/10.1098/rspb.2018.1926
Sun, J., & Ongsomwang, S. (2023). Optimal parameters of random forest for land cover classification with suitable data type and dataset on Google Earth Engine. Frontiers in Earth Science. https://doi.org/10.3389/feart.2023.1188093
Tesfaye, W., Elias, E., Warkineh, B., Tekalign, M., & Abebe, G. (2024). Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management. Environmental Systems Research. https://doi.org/10.1186/s40068-024-00366-3
Vancutsem, C., Achard, F., Pekel, J.-F., Vieilledent, G., Carboni, S., Simonetti, D., Gallego, J., Aragão, L. E. O. C., & Nasi, R. (2021). Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Science Advances, 7(10). https://doi.org/10.1126/sciadv.abe1603
World Bank, & Asian Development Bank. (2021). Climate Risk Country Profile: Indonesia. World Bank Group and Asian Development Bank. https://climateknowledgeportal.worldbank.org/
Zhang, T., Su, J., Xu, Z., Luo, Y., & Li, J. (2021). Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Applied Sciences (Switzerland), 11(2), Article 543. https://doi.org/10.3390/app11020543
