Long-Term Mangrove Dynamics and Environmental Associations in the Sundarbans, Bangladesh: Integrating Remote Sensing, Machine Learning, and Explainable AI

Mangrove ecosystems are increasingly threatened by anthropogenic disturbances and climate change, despite providing ecological services, including biodiversity conservation, coastal protection, and blue carbon storage. Long-term evaluations of mangrove dynamics and associated environmental conditions remain limited, particularly in the Sundarbans, the largest mangrove forest in the world. This study examined the spatiotemporal dynamics of the Sundarbans mangrove ecosystem, Bangladesh, using multi-temporal Landsat imagery (1990–2025), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP). Model performance was evaluated using the Global Mangrove Watch (GMW) dataset and spatially blocked holdout validation. Classification accuracy was high (94.56–100%) under internal and spatially blocked validation (93.5–99.8%), whereas comparison with GMW yielded lower accuracies (66.4–95.7%). Change detection showed that mangrove area expanded from 2096.66 km2 in 1990 to 3875.95 km2 in 2025, a net gain of almost 1779.29 km2, with substantial inter-period variability and a pronounced mapped decline between 2000 and 2005. SHAP analysis indicated that the relative contributions of model predictors changed over time, with NDVI contributing more strongly to model predictions in recent years than NDWI and NDMI. Mangrove gain, loss, and net change were statistically associated with cyclone exposure and elevation, while precipitation and river discharge showed less consistent relationships. This study highlights a practical method for monitoring spectral dynamics in vulnerable coastal ecosystems and supports mangrove conservation.