Analysis of Vegetation Ecological Anomaly Response in the Xiangshan Uranium Mining Area Based on Multi-Source Remote Sensing Data Fusion

The Xiangshan Uranium Mining Area in Jiangxi Province is a pivotal uranium extraction site crucial for China’s nuclear sector. However, vegetation ecology research in this region remains scarce, particularly studies grounded in multi-source remote sensing data. To overcome these challenges, this paper introduces a methodology that combines multi-source remote sensing data with the random forest machine learning algorithm to invert vegetation canopy structure parameters in the Xiangshan Uranium Mining Area. This approach is complemented by the integration of multiple vegetation indices for a comprehensive evaluation. To guarantee the dependability of the inversion results, this study employs Sentinel-1/2 imagery and ICESat-2 spaceborne LiDAR data, which furnish abundant optical information, terrain data, and vertical vegetation structure insights. The experimental findings reveal that the overall vegetation ecology in the Xiangshan Uranium Mining Area is in a satisfactory state, yet the low Radar Vegetation Index (RVI) hints at potential soil degradation concerns within the mining area. Furthermore, notable disparities in vegetation canopy structure between the mining area and the comparison zone underscore that the presence of mining deposits indeed exerts a potential influence on vegetation canopy structure. This study bridges the research gap and offers scientific support for mineral exploration and sustainable mining development.