Exploring the Current Application Status of Multi-Source Remote Sensing Data in Mine Environmental and Ecological Monitoring

Abstract

The development of mineral resources has led to severe environmental pollution and ecological degradation. Currently, remote sensing technology has emerged as an effective method for monitoring the environmental and ecological dynamics of mining areas. This paper focuses on the synergistic application of multi-source remote sensing technology in mine environmental and ecological monitoring. By systematically analyzing the technical characteristics of optical, microwave, and hyperspectral remote sensing data, this study reveals the key role of multi-source data fusion in surface deformation monitoring, land damage assessment, and pollutant migration inversion. The findings indicate that InSAR technology can achieve millimeter-level dynamic monitoring of surface deformation, hyperspectral data has an inversion error of less than 15% for heavy metal pollution dispersion, while multi-source data fusion models (such as “morphology-deformation” collaborative identification and mixed pixel decomposition) improve geological disaster warning accuracy by 28% and reduce vegetation cover estimation errors to below 8%. Current technical bottlenecks are concentrated in the perception blind spots of deep geological disturbances and the lack of standardized fusion mechanisms for multi-source data. The air-space-ground collaborative monitoring framework established in this study integrates edge computing and deep learning algorithms to provide a quantifiable technical paradigm for dynamic monitoring of mining environments. The research findings have practical value for breaking through traditional passive management models and promoting the construction of intelligent early warning systems, and can provide scientific support for optimizing ecological restoration costs and balancing resource development under the “dual carbon” goals for large-scale mines worldwide.