Remote Sensing‐Based Identification of Habitat Loss for Herbivorous Waterbirds Driven by Wetland Inundation Alterations

Altered inundation regimes are reshaping floodplain wetlands and degrading wintering habitats for herbivorous waterbirds, yet long‐term, spatially explicit assessments that link hydrological change to habitat dynamics remain limited. Here, we developed a remote sensing‐based deep learning framework to reconstruct wintering habitat dynamics of herbivorous waterbirds in Dongting Lake from 2000 to 2021. The framework integrates multi‐source environmental predictors, receptive‐field optimization for spatial–context recognition, and Shapley Additive Explanations (SHAP) analysis for ecological interpretation. Results revealed marked habitat decline over the study period, with total habitat area decreasing by nearly half. Habitat loss was highly uneven, with the most severe declines occurring in South (−77.3%) and West (−89.2%) Dongting Lake, while East Dongting Lake remained comparatively stable. Landscape metrics further indicated progressive habitat contraction, increased isolation, and structural simplification, reflecting weakened spatial connectivity and reduced ecological resilience. Among all predictors, water recession timing emerged as the most influential predictor of habitat suitability, with recession in mid‐to‐late October associated with the highest habitat probability. A comparison with the NDVI time series showed that this recession window coincided with higher vegetation productivity. These findings demonstrate that wintering habitat dynamics are strongly associated with hydrological timing, particularly water recession phenology, rather than static water extent alone. The proposed framework provides a transferable approach for identifying hydrological controls on waterbird habitats and offers a scientific basis for adaptive wetland management along migratory flyways.