Phyto-identification of leached metal contamination using satellite remote sensing: a case study on a coal-fired power plant.
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| Title: | Phyto-identification of leached metal contamination using satellite remote sensing: a case study on a coal-fired power plant. |
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| Authors: | Karim, Salman1 (AUTHOR) salmankarim@vt.edu, Nojabaei, Bahareh1 (AUTHOR) baharehn@vt.edu, Foroutan, Hosein2 (AUTHOR) hosein@vt.edu |
| Source: | Environmental Monitoring & Assessment. Mar2026, Vol. 198 Issue 3, p1-23. 23p. |
| Subject Terms: | *Coal-fired power plants, *Groundwater monitoring, *Environmental monitoring, *Heavy metal toxicology, Remote sensing, Artificial satellites, Plant health, Plant indicators |
| Geographic Terms: | North Carolina, United States |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Environmental Monitoring & Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 192481291 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Phyto-identification of leached metal contamination using satellite remote sensing: a case study on a coal-fired power plant. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Karim%2C+Salman%22">Karim, Salman</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> salmankarim@vt.edu</i><br /><searchLink fieldCode="AR" term="%22Nojabaei%2C+Bahareh%22">Nojabaei, Bahareh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> baharehn@vt.edu</i><br /><searchLink fieldCode="AR" term="%22Foroutan%2C+Hosein%22">Foroutan, Hosein</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hosein@vt.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Monitoring+%26+Assessment%22">Environmental Monitoring & Assessment</searchLink>. Mar2026, Vol. 198 Issue 3, p1-23. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Coal-fired+power+plants%22">Coal-fired power plants</searchLink><br />*<searchLink fieldCode="DE" term="%22Groundwater+monitoring%22">Groundwater monitoring</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink><br />*<searchLink fieldCode="DE" term="%22Heavy+metal+toxicology%22">Heavy metal toxicology</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+health%22">Plant health</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+indicators%22">Plant indicators</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22North+Carolina%22">North Carolina</searchLink><br /><searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Environmental Monitoring & Assessment is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10661-026-15123-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Coal-fired power plants Type: general – SubjectFull: Groundwater monitoring Type: general – SubjectFull: Environmental monitoring Type: general – SubjectFull: Heavy metal toxicology Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Artificial satellites Type: general – SubjectFull: Plant health Type: general – SubjectFull: Plant indicators Type: general – SubjectFull: North Carolina Type: general – SubjectFull: United States Type: general Titles: – TitleFull: Phyto-identification of leached metal contamination using satellite remote sensing: a case study on a coal-fired power plant. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Karim, Salman – PersonEntity: Name: NameFull: Nojabaei, Bahareh – PersonEntity: Name: NameFull: Foroutan, Hosein IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01676369 Numbering: – Type: volume Value: 198 – Type: issue Value: 3 Titles: – TitleFull: Environmental Monitoring & Assessment Type: main |
| ResultId | 1 |
