Introduction:Highmercury(Hg)concentrations affect the chlorophyll in leaves,therebymodifying leaf spectra.Hyperspectra is a promising technique for the rapid,nondestructive evaluation of leaf Hg content.In this study,...Introduction:Highmercury(Hg)concentrations affect the chlorophyll in leaves,therebymodifying 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 continuumremoval–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.展开更多
基金This work was supported by the Fundamental Research Funds of Central-level Nonprofit Research Institutes of China[CAFINT2014K05].
文摘Introduction:Highmercury(Hg)concentrations affect the chlorophyll in leaves,therebymodifying 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 continuumremoval–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.
