Open-data remote sensing framework for peat hydrological unit condition scoring in South Sumatra Peatlands (2018-2024)

Muaffan Alfaiz Wisaksono (Master’s Programme in Precision Agriculture, Faculty of Agribusiness and Commerce, Lincoln University. Ellesmere Junction Road, Lincoln 7647, Lincoln, New Zealand)
Abdhullah Syawalbhi Leo (School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Parkville, Victoria 3010, Australia)

Jurnal Lahan Suboptimal
Vol. 15 No. 2 (2026): JLSO

Tropical peatland degradation remains a critical environmental challenge in Southeast Asia, yet operational monitoring frameworks that align with governance boundaries remain limited. This study aimed to develop an open-data remote sensing framework for assessing peatland condition at the Peat Hydrological Unit (Kesatuan Hidrologis Gambut, KHG) scale in South Sumatra, Indonesia. Using Google Earth Engine, four satellite-derived indicators were extracted annually for 12 KHGs over seven years from 2018 to 2024: Normalized Difference Water Index from MODIS MOD09A1, Enhanced Vegetation Index from MODIS MOD13Q1, fire frequency from NASA FIRMS, and surface water extent from the JRC Global Surface Water dataset. The indicators were normalized and integrated into a weighted composite score using weights of 30% for NDWI, 25% for EVI, 30% for inverse fire frequency, and 15% for surface water extent. In 2024, seven KHGs were classified as At Risk and five as Intact/Recovering, with scores ranging from 46.5 to 76.9. Six KHGs declined between 2018 and 2024, while five improved. El Nino years reduced health scores by an average of 11.3 points, and cropland conversion was the strongest land-cover predictor of lower scores (r=−0.52). Weight sensitivity analysis showed that seven of twelve KHG classifications remained stable across five weighting scenarios. The framework was reproducible, governance-aligned, and suitable as a screening tool for peatland restoration prioritization.