Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance

Introduction: High mercury (Hg) concentrations affect the chlorophyll in leaves, thereby modifying leaf spectra. Hyperspectra is a promising technique for the rapid, nondestructive evaluation of leaf Hg content. In this study, we investigated Hg contents and reflective hyperspectra of reed leaves (P...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Weiwei Liu, Mengjie Li, Manyin Zhang, Daan Wang, Ziliang Guo, Songyuan Long, Si Yang, Henian Wang, Wei Li, Yukun Hu, Yuanyun Wei, Hongye Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2020
Materias:	mercury pollution reed leaf hyperspectrum inversion model nondestructive monitoring Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/48a6c61dd746499580b1e613f6492072
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:48a6c61dd746499580b1e613f6492072
record_format	dspace
spelling	oai:doaj.org-article:48a6c61dd746499580b1e613f64920722021-12-02T16:25:31ZEstimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance2332-887810.1080/20964129.2020.1726211https://doaj.org/article/48a6c61dd746499580b1e613f64920722020-12-01T00:00:00Zhttp://dx.doi.org/10.1080/20964129.2020.1726211https://doaj.org/toc/2332-8878Introduction: High mercury (Hg) concentrations affect the chlorophyll in leaves, thereby modifying leaf spectra. Hyperspectra is a promising technique for the rapid, nondestructive evaluation of leaf Hg content. In this study, we investigated Hg contents and reflective hyperspectra of reed leaves (Phragmites communis) in a gold mining (Jilin province, China). Spectral parameters sensitive to Hg content were identified through basic spectral transformations, continuous wavelet transformation (CWT), and spectral indices techniques. Leaf Hg inversion models were developed using stepwise multiple linear regression, partial least squares regression, and random forest algorithms.Outcomes: The results indicated that: 1) leaf Hg content decreased with increasing distance from the mine: Jiapigou (JPG) > Erdaocha (EDC) > Laojingchang (LJC) > Erdaogou (EDG) > Lingqian (LQ) > Weishahe (WSH). 2) Hg–sensitive wavelengths were primarily in the visible region; CWT increased the correlation between hyperspectral data and leaf Hg content, and improved the regression and accuracy of inversion; 3) the continuum removal–CWT–stepwise multiple linear regression was better for estimating low leaf Hg content; while the differential spectral index–partial least squares regression was better for estimating high leaf Hg content.Conclusion: These hyperspectral inversion methods could be used for rapid, nondestructive monitoring of wetland plants.Weiwei LiuMengjie LiManyin ZhangDaan WangZiliang GuoSongyuan LongSi YangHenian WangWei LiYukun HuYuanyun WeiHongye XiaoTaylor & Francis Grouparticlemercury pollutionreed leafhyperspectruminversion modelnondestructive monitoringEcologyQH540-549.5ENEcosystem Health and Sustainability, Vol 6, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	mercury pollution reed leaf hyperspectrum inversion model nondestructive monitoring Ecology QH540-549.5
spellingShingle	mercury pollution reed leaf hyperspectrum inversion model nondestructive monitoring Ecology QH540-549.5 Weiwei Liu Mengjie Li Manyin Zhang Daan Wang Ziliang Guo Songyuan Long Si Yang Henian Wang Wei Li Yukun Hu Yuanyun Wei Hongye Xiao Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance
description	Introduction: High mercury (Hg) concentrations affect the chlorophyll in leaves, thereby modifying leaf spectra. Hyperspectra is a promising technique for the rapid, nondestructive evaluation of leaf Hg content. In this study, we investigated Hg contents and reflective hyperspectra of reed leaves (Phragmites communis) in a gold mining (Jilin province, China). Spectral parameters sensitive to Hg content were identified through basic spectral transformations, continuous wavelet transformation (CWT), and spectral indices techniques. Leaf Hg inversion models were developed using stepwise multiple linear regression, partial least squares regression, and random forest algorithms.Outcomes: The results indicated that: 1) leaf Hg content decreased with increasing distance from the mine: Jiapigou (JPG) > Erdaocha (EDC) > Laojingchang (LJC) > Erdaogou (EDG) > Lingqian (LQ) > Weishahe (WSH). 2) Hg–sensitive wavelengths were primarily in the visible region; CWT increased the correlation between hyperspectral data and leaf Hg content, and improved the regression and accuracy of inversion; 3) the continuum removal–CWT–stepwise multiple linear regression was better for estimating low leaf Hg content; while the differential spectral index–partial least squares regression was better for estimating high leaf Hg content.Conclusion: These hyperspectral inversion methods could be used for rapid, nondestructive monitoring of wetland plants.
format	article
author	Weiwei Liu Mengjie Li Manyin Zhang Daan Wang Ziliang Guo Songyuan Long Si Yang Henian Wang Wei Li Yukun Hu Yuanyun Wei Hongye Xiao
author_facet	Weiwei Liu Mengjie Li Manyin Zhang Daan Wang Ziliang Guo Songyuan Long Si Yang Henian Wang Wei Li Yukun Hu Yuanyun Wei Hongye Xiao
author_sort	Weiwei Liu
title	Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance
title_short	Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance
title_full	Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance
title_fullStr	Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance
title_full_unstemmed	Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance
title_sort	estimating leaf mercury content in phragmites australis based on leaf hyperspectral reflectance
publisher	Taylor & Francis Group
publishDate	2020
url	https://doaj.org/article/48a6c61dd746499580b1e613f6492072
work_keys_str_mv	AT weiweiliu estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT mengjieli estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT manyinzhang estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT daanwang estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT ziliangguo estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT songyuanlong estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT siyang estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT henianwang estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT weili estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT yukunhu estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT yuanyunwei estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance AT hongyexiao estimatingleafmercurycontentinphragmitesaustralisbasedonleafhyperspectralreflectance
_version_	1718384048684924928

Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance

Ejemplares similares