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<p>Pollution control in tailings ponds remains a major environmental challenge in mining regions. In the Yueliangbao gold mining district, long-term disposal of metallurgical waste has imposed chronic heavy metal stress on local vegetation. This study integrates airborne hyperspectral imaging (HSI), LiDAR data, and field surveys across five pollution gradients (20 sampling points) to map the spatial distribution of lead (Pb) contamination. Vegetation and soil Pb concentrations were quantified via inductively coupled plasma mass spectrometry (ICP-MS), revealing severe enrichment (vegetation: 0.18–84.73&#xa0;mg/kg; soil: 11.59–800.94&#xa0;mg/kg) exceeding national limits.The ReliefF algorithm was applied to identify stress-sensitive spectral features, and key vegetation indices—including the Red Edge Chlorophyll Index (RECI) and Modified Chlorophyll Absorption Ratio Index (MCARI)—showed strong correlations with Pb levels (R<sup>2</sup> = 0.305). A hierarchical fusion of hyperspectral and LiDAR-derived structural metrics enabled three-dimensional Pb mapping, revealing distinct accumulation at 411–416&#xa0;m elevation and dispersion both upward and downslope.These findings highlight that integrating spectral and structural information offers a robust framework for quantifying and visualizing potentially toxic element (PTE) stress. The proposed multimodal approach provides an effective pathway for ecological risk assessment and precision monitoring of heavy metal pollution in mining ecosystems.</p>

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Three-dimensional spatial distribution of elemental lead (Pb) stress in vegetation within the Yueliangbao gold mining area

  • 期刊论文

  • 发表日期：

    2025年11月14日

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  • Liu Fujiang,
  • Li Bo,
  • Lin Weihua,
  • Guo yan,
  • Wang Mianzhi,
  • Tu Yiwen,
  • Shao Quansen,
  • Zhu Zhe

摘要

Pollution control in tailings ponds remains a major environmental challenge in mining regions. In the Yueliangbao gold mining district, long-term disposal of metallurgical waste has imposed chronic heavy metal stress on local vegetation. This study integrates airborne hyperspectral imaging (HSI), LiDAR data, and field surveys across five pollution gradients (20 sampling points) to map the spatial distribution of lead (Pb) contamination. Vegetation and soil Pb concentrations were quantified via inductively coupled plasma mass spectrometry (ICP-MS), revealing severe enrichment (vegetation: 0.18–84.73 mg/kg; soil: 11.59–800.94 mg/kg) exceeding national limits.The ReliefF algorithm was applied to identify stress-sensitive spectral features, and key vegetation indices—including the Red Edge Chlorophyll Index (RECI) and Modified Chlorophyll Absorption Ratio Index (MCARI)—showed strong correlations with Pb levels (R2 = 0.305). A hierarchical fusion of hyperspectral and LiDAR-derived structural metrics enabled three-dimensional Pb mapping, revealing distinct accumulation at 411–416 m elevation and dispersion both upward and downslope.These findings highlight that integrating spectral and structural information offers a robust framework for quantifying and visualizing potentially toxic element (PTE) stress. The proposed multimodal approach provides an effective pathway for ecological risk assessment and precision monitoring of heavy metal pollution in mining ecosystems.