Against the backdrop of growing global resource demand and increasing ecological constraints, mining activities have become a key driver of global environmental change, significantly affecting land surface morphology, hydrological processes, and atmospheric-ecological system structures. However, a systematic framework for understanding mining-related ecological and environmental impacts remains insufficient, particularly in multi-source data integration, cross-scale disturbance mechanisms, and full-process risk governance. To address these gaps, this study develops a global-scale framework for assessing mining ecological and environmental impacts. Results show that assessment methods have evolved from knowledge-driven and process-based models toward data-driven intelligent approaches, forming a global monitoring system based on multi-source remote sensing data fusion. Remote sensing has become a core technique for identifying mining activities and ecological disturbances, providing critical support for revealing their spatiotemporal patterns and dynamics. Moreover, mining disturbances exhibit pronounced spatial heterogeneity across the land-hydrology-atmosphere system, reflecting combined influences of natural and socio-economic factors. In addition, a PDCA-based governance model enables a shift from reactive remediation to proactive full-process regulation. Overall, this study establishes a unified framework integrating monitoring, understanding, mechanism, and governance, providing methodological support for global mining impact assessment and promoting the transition toward environmentally responsible mining development. 

Global mining,Ecological environment,Multi-source remote sensing,Deep Learnin,Ecological risk assessment,PDCA cycle