<![CDATA[Tropical coastal regions are highly susceptible to acid sulfate soi formation due to ecological and hydrological changes driven by land cover dynamics and sedimentation. This study analyzes land cover changes from 1990 to 2025 and their implications for ASS development in Segara Anakan, Indonesia. Landsat imagery (Landsat 5 and Landsat 8/9 OLI) was classified using Random Forest and Gradient Boosting Tree algorithms within Google Earth Engine. Classification accuracy was assessed using overall accuracy and the Kappa coefficient. Land cover classes included mangrove, nipa palm, paddy fields, aquaculture ponds, settlements, bare land, water bodies, and forest. Results reveal substantial conversion of natural vegetation into paddy fields, bare land, and settlements, particularly in low-lying tidal areas. These changes disrupted ecological conditions that previously sustained organic matter accumulation, low-energy environments, and anaerobic waterlogging—three of the five key factors for ASS formation. Field validation confirmed soil pH < 4 in high-risk areas. This research demonstrates the effectiveness of integrating multi-temporal Landsat imagery with machine learning to detect spatio-temporal land cover dynamics and to identify areas prone to ASS formation, offering valuable insights for adaptive coastal management.]]>
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