Journal of Environmental and Sustainability Science
Research Article
• Open Access
Remote Sensing and Integrated Assessment Frameworks for Mining-Related Environmental Monitoring: A Systematic Review
View PDFAbstract
Mining activities exert profound and lasting impacts on terrestrial ecosystems, manifesting across multiple spatial and temporal scales throughout the mining lifecycle—from exploration and extraction to post-mining reclamation. This systematic review synthesizes current applications of remote sensing technologies and integrated assessment frameworks for monitoring mining-related environmental disturbances. Drawing upon an analysis of peer-reviewed literature, technical reports, and case studies from diverse geographical contexts—including Kazakhstan's limestone mining regions, Indonesia's quarrying operations, Ukraine's construction sector, and tailings storage facilities across Europe—the paper examines the utility of satellite-based Earth observation, unmanned aerial vehicles, and multi-criteria decision analysis in environmental impact assessment. The review demonstrates that remote sensing indicators—particularly the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Land Use/Land Cover (LU/LC) change detection—provide reliable measurements of vegetation degradation, thermal anomalies, and landscape transformation associated with mining operations. Findings indicate that limestone mining commonly leads to 40-60% reduction in vegetation density and LST increases of 1.5-3.0°C in disturbed areas. The integration of the DPSIR (Driving Forces-Pressures-State-Impacts-Responses) framework with geospatial technologies offers a comprehensive approach to understanding the causal relationships between mining activities and environmental degradation. The review further identifies that tailings storage facility monitoring through integrated Life Cycle Assessment and Environmental Risk Assessment frameworks can quantify risk reduction potential, with smart monitoring solutions reducing failure probability from 18.9% to 13.6% and achieving measurable life cycle improvements. Challenges persist regarding data quality, model transferability, and the characterization of complex subsurface disturbance mechanisms. The paper concludes by proposing an integrated framework that combines multi-resolution remote sensing data, surface-subsurface observation coordination, and explainable artificial intelligence to support evidence-based environmental governance in mining regions.
Keywords
remote sensing, mining environmental monitoring, NDVI, LST, InSAR, UAV, DPSIR framework, tailings storage facilitiesReferences
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