Three-Decade Land Use Transitions in a Coastal Deltaic Region: Integrating Google Earth Engine and Random Forest Classification for Multi-Temporal Change Detection

Coastal Bangladesh represents one of the world's most dynamic and vulnerable deltaic landscapes, experiencing intensive anthropogenic pressures from agricultural expansion, aquaculture development, urbanization, and climate-related environmental changes. The southwestern coastal region, encompassing districts adjacent to the Sundarbans mangrove forest, faces particularly acute land use pressures driven by population growth, economic development, and integration into global supply chains. Despite the critical importance of this region for ecosystem services, food security, and climate resilience, comprehensive multi-decadal assessments of land use and land cover (LULC) change patterns across large spatial extents remain limited. This study quantifies LULC dynamics across 11,700 km² in southwestern coastal Bangladesh over three decades (1995, 2010, 2025), employing multitemporal satellite imagery, machine learning classification, and rigorous transition analysis.​ We processed cloud-free Landsat imagery from multiple sensors (Landsat 5 Thematic Mapper, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2) in Google Earth Engine, applying strict quality filters (cloud cover