Three-Dimensional Spatial Distribution of Elemental Lead (Pb) Stress in Vegetation within the Yueliangbao Gold Mining Area

Pollution control in tailing ponds represents a critical environmental challenge in mining operations. In the Yueliangbao gold mining district, the prolonged disposal of metallurgical waste has induced multilevel stress from heavy metals on local vegetation. This study integrated airborne hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) datasets with field sampling across five pollution gradient zones (20 sampling points) to investigate the spatial distribution of lead (Pb)-stressed vegetation. Methodologically, heavy metal concentrations in vegetation and soil samples were quantified using inductively coupled plasma mass spectrometry (ICP-MS). The ReliefF algorithm was employed to identify stress-sensitive spectral features, and vegetation indices (VIs) correlated with Pb content were selected to develop predictive regression models. A hierarchical fusion framework combining hyperspectral reflectance and LiDAR-derived vertical vegetation structure parameters enabled three-dimensional spatial pattern analysis. Results revealed severe Pb contamination in vegetation (0.18–84.73 mg/kg) and soil (11.59–800.94 mg/kg), exceeding national standards. The Red Edge Chlorophyll Index (RECI) and Modified Chlorophyll Absorption Ratio Index (MCARI) exhibited strong correlations with Pb levels (R²=0.305). The fused HSI-LiDAR data effectively delineated vertical Pb distribution, showing peak concentrations at 411–416 m elevation with diffusion trends toward lower and higher elevations. This multimodal approach provides a novel perspective for monitoring potentially toxic element (PTE) pollution in mining ecosystems.