An integrated remote sensing framework reveals zone-specific ecological response trajectories to open-pit mining on the Qinghai-Tibet plateau

Rapid mining expansion on the Qinghai-Tibet Plateau threatens its fragile alpine and arid ecosystems, with complex and spatially heterogeneous impacts, underscoring the need for long-term quantitative monitoring to support effective management. To address gaps in prior work that either focused on individual mining sites or relied on coarse assessments, we developed an integrated remote sensing framework that combines fractional vegetation cover (FVC), seasonal-trend decomposition of normalized difference vegetation index (NDVI), functional principal component analysis (FPCA), landscape metrics, and the Remote Sensing Ecological Index (RSEI). The framework was applied to 30 mines distributed across four ecological zones during 2000–2020 to characterize mining-induced ecological changes. The results showed that (1) the mining footprint expanded by more than eightfold, accompanied by significant vegetation degradation and increased landscape fragmentation, while the RSEI declined markedly faster within mines than in climate-matched buffers; (2) mining was the predominant local driver, producing ecozone-specific disturbance radii that were temporally consistent with mining histories. The disturbance radius was smallest in the temperate desert vegetation zone (TDV) and largest in the alpine vegetation zone of the Qinghai-Tibet Plateau (Alpine), with impacts typically concentrated within 2–8, km; and (3) ecological responses were highly heterogeneous, exhibiting three trajectories: sustained high-load degradation in the subtropical evergreen broadleaf forest zone (SEBF); degradation followed by partial recovery in Alpine and TDV; and high-fragility volatility in the temperate desert zone (TDD). The framework is scalable and interpretable, enabling ecosystem-sensitive tracking of mining impacts and providing a transferable basis for restoration planning, regulatory enforcement, and adaptive management in vulnerable environments.