Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

Abstract Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on...

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Autores principales:	Guillaume Lassalle, Sophie Fabre, Anthony Credoz, Rémy Hédacq, Dominique Dubucq, Arnaud Elger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0c84f0cb633243fea89a2fcef81bc830
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spelling	oai:doaj.org-article:0c84f0cb633243fea89a2fcef81bc8302021-12-02T15:08:22ZMapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices10.1038/s41598-020-79439-z2045-2322https://doaj.org/article/0c84f0cb633243fea89a2fcef81bc8302021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79439-zhttps://doaj.org/toc/2045-2322Abstract Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.Guillaume LassalleSophie FabreAnthony CredozRémy HédacqDominique DubucqArnaud ElgerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guillaume Lassalle Sophie Fabre Anthony Credoz Rémy Hédacq Dominique Dubucq Arnaud Elger Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
description	Abstract Monitoring plant metal uptake is essential for assessing the ecological risks of contaminated sites. While traditional techniques used to achieve this are destructive, Visible Near-Infrared (VNIR) reflectance spectroscopy represents a good alternative to monitor pollution remotely. Based on previous work, this study proposes a methodology for mapping the content of several metals in leaves (Cr, Cu, Ni and Zn) under realistic field conditions and from airborne imaging. For this purpose, the reflectance of Rubus fruticosus L., a pioneer species of industrial brownfields, was linked to leaf metal contents using optimized normalized vegetation indices. High correlations were found between the vegetation indices exploiting pigment-related wavelengths and leaf metal contents (r ≤ − 0.76 for Cr, Cu and Ni, and r ≥ 0.87 for Zn). This allowed predicting the metal contents with good accuracy in the field and on the image, especially Cu and Zn (r ≥ 0.84 and RPD ≥ 2.06). The same indices were applied over the entire study site to map the metal contents at very high spatial resolution. This study demonstrates the potential of remote sensing for assessing metal uptake by plants, opening perspectives of application in risk assessment and phytoextraction monitoring in the context of trace metal pollution.
format	article
author	Guillaume Lassalle Sophie Fabre Anthony Credoz Rémy Hédacq Dominique Dubucq Arnaud Elger
author_facet	Guillaume Lassalle Sophie Fabre Anthony Credoz Rémy Hédacq Dominique Dubucq Arnaud Elger
author_sort	Guillaume Lassalle
title	Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
title_short	Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
title_full	Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
title_fullStr	Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
title_full_unstemmed	Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
title_sort	mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0c84f0cb633243fea89a2fcef81bc830
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_version_	1718388168596652032

Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation indices

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