研究成果

Feasibility of estimating heavy metal concentrations in PhragmitesAustralis using laboratory-based hyperspectral data along Le'an River, China

期刊名称: International Journal of Applied Earth Observation and Geoinformation
全部作者: Yaolin Liu,Hui Chen,Guofeng Wu*
出版年份: 2010
卷       号: 12
期       号:
页       码: 166-170
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It is necessary to estimate heavy metal concentrations in plants for understanding the heavy metal contaminations and for keeping the sustainable developments of ecosystems and human health. This study, with the Le’an River and its two branches in Jiangxi Province of China as a case study, aimed to explore the feasibility of estimating concentrations of heavy metal lead (Pb), copper (Cu) and zinc (Zn) in Phragmites australis using laboratory-based hyperspectral data. 21 P. australis leaf samples were collected, and their hyperspectral data, chlorophyll concentration and Pb, Cu and Zn concentrations were measured within the laboratory. The potential relations among hyperspectral data, chlorophyll concentration and Pb, Cu and Zn concentrations were explored and employed to estimate Pb, Cu and Zn concentrations from hyperspectral data with chlorophyll concentration as a bridge. The results showed that the linear combination of normalized band depths at wavelengths 537 (green), 667 (red) and 747 (near infrared) nm could explain 82% of the variation of chlorophyll concentration; the Pb, Cu and Zn concentrations were significantly and negatively related to the chlorophyll concentration, and the chlorophyll concentration could explain around 30% of the variations of Pb, Cu and Zn concentrations, respectively; and the absolute estimation errors for more than 80% estimations of Pb, Cu and Zn concentrations were less than 30%. We conclude that the laboratory-based hyperspectral data hold potentials in estimating concentrations of heavy metal Pb, Cu and Zn in P. australis. More sampling points and spectral characteristics-based methods should be collected and employed for improving the stabilities and accuracies of estimation models.