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<p>This study demonstrates the remote sensing detection and monitoring of vegetation stress, enabling the assessment of potential metal-induced contamination across a spatiotemporal range. Specifically, the vegetation surrounding coal ash impoundments and landfills at the Belews Creek Steam Station, a coal-fired power plant in North Carolina, U.S., was monitored using multispectral imagery from the Sentinel-2 satellite between 2019 and 2023 and correlations were investigated with the groundwater monitoring well data from 2011–2019. The effectiveness of six vegetation indices (VIs) and three biophysical parameter indices (BPIs) derived from Sentinel-2 imagery were investigated in detecting vegetation stress and their correlation with leached metal concentrations near coal ash impoundments. Among BPIs, Leaf Area Index (LAI) exhibited the strongest correlation with VIs, while Canopy Chlorophyll Content (CCC) detected the highest stressed vegetation. The Chlorophyll Index Red Edge (CIRE) demonstrated the highest sensitivity to BPIs and detected the highest stressed vegetation among VIs. When stressed vegetation maps were further compared with metal concentrations, Leaf Chlorophyll Content (LCC) and the Normalized Difference Vegetation Index (NDVI) showed the strongest correlations among BPIs and VIs, respectively. Moderate positive correlations were observed for several metals, including arsenic, barium, cadmium, cobalt, lithium, radium and thallium, suggesting their contribution to vegetation stress, while molybdenum exhibited moderate negative correlation indicating its potential role in reducing stress. Additionally, higher vegetation stress levels were detected around the unlined active ash basin, suggesting increased metal leaching from this impoundment which is contributing to the observed stress on the surrounding vegetation. The proposed methodology and tools in this study can contribute to the growing body of knowledge on satellite-based remote sensing for vegetation stress detection, with potential applications in environmental monitoring and management.</p>

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Phyto-identification of leached metal contamination using satellite remote sensing: a case study on a coal-fired power plant

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Salman Karim,
  • Bahareh Nojabaei,
  • Hosein Foroutan

摘要

This study demonstrates the remote sensing detection and monitoring of vegetation stress, enabling the assessment of potential metal-induced contamination across a spatiotemporal range. Specifically, the vegetation surrounding coal ash impoundments and landfills at the Belews Creek Steam Station, a coal-fired power plant in North Carolina, U.S., was monitored using multispectral imagery from the Sentinel-2 satellite between 2019 and 2023 and correlations were investigated with the groundwater monitoring well data from 2011–2019. The effectiveness of six vegetation indices (VIs) and three biophysical parameter indices (BPIs) derived from Sentinel-2 imagery were investigated in detecting vegetation stress and their correlation with leached metal concentrations near coal ash impoundments. Among BPIs, Leaf Area Index (LAI) exhibited the strongest correlation with VIs, while Canopy Chlorophyll Content (CCC) detected the highest stressed vegetation. The Chlorophyll Index Red Edge (CIRE) demonstrated the highest sensitivity to BPIs and detected the highest stressed vegetation among VIs. When stressed vegetation maps were further compared with metal concentrations, Leaf Chlorophyll Content (LCC) and the Normalized Difference Vegetation Index (NDVI) showed the strongest correlations among BPIs and VIs, respectively. Moderate positive correlations were observed for several metals, including arsenic, barium, cadmium, cobalt, lithium, radium and thallium, suggesting their contribution to vegetation stress, while molybdenum exhibited moderate negative correlation indicating its potential role in reducing stress. Additionally, higher vegetation stress levels were detected around the unlined active ash basin, suggesting increased metal leaching from this impoundment which is contributing to the observed stress on the surrounding vegetation. The proposed methodology and tools in this study can contribute to the growing body of knowledge on satellite-based remote sensing for vegetation stress detection, with potential applications in environmental monitoring and management.