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Spectral characteristics of the correlation between elemental arsenic and vegetation stress in the Yueliangbao gold mining

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A Correction to this article was published on 28 September 2023

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Abstract

Some soils in the Yueliangbao gold mining area have been contaminated by heavy metals, resulting in variations in vegetation. Hyperspectral remote sensing provides a new perspective for heavy metal inversion in vegetation. In this paper, we collected ground truth spectral data of three dominant vegetation species, Miscanthus floridulus, Equisetum ramosissimum and Eremochloa ciliaris, from contaminated and healthy non-mining areas of the Yueliangbao gold mining region, and determined their heavy metal contents. Firstly, we compared the spectral characteristics of vegetation in the mining and non-mining areas by removing the envelope and derivative transformation. Secondly, we extracted their characteristic identification bands using the Mahalanobis distance and PLS-DA method. Finally, we constructed the inverse model by selecting the vegetation index (such as the PRI, DCNI, MTCI, etc.) related to the characteristic band combined with the heavy metal content. Compared to previous studies, we found that the pollution level in the Yueliangbao gold mining area had greatly reduced, but arsenic metal pollution remained a serious issue. Miscanthus floridulus and Eremochloa ciliaris in the mining area exhibited obvious arsenic stress, with a large “red-edge blue shift” (9 and 6 nm). The extracted characteristic wavebands were around 550 and 680–740 nm wavelengths, and correlation analysis showed significant correlations between vegetation index and arsenic, allowing us to construct a prediction model for arsenic and realize the calculation of heavy metal content using vegetation spectra. This provides a methodological basis for monitoring vegetation pollution in other gold mining areas.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

Thanks to the State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences (Wuhan) for providing experimental support.

Funding

This independent research was Supported by the International Research Center of Big Data for Sustainable Development Goals (CBAS2022GSP05); Open Fund of State Key Laboratory of Remote Sensing Science (Grant No. 6142A01210404); Hubei Key Laboratory of Intelligent Geo-Information Processing (Grant No. KLIGIP-2022-B03); Metallogenic patterns and mineralization predictions for the Daping gold deposit in Yuanyang County, Yunnan Province (Grant No. 2022026821); Ministry of Education Industry-University Cooperation Collaborative Education Project—Remote Sensing Practical Education and Science Popularization Base Construction (Grant No. 20221008).

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Authors and Affiliations

  1. School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China

    Weihua Lin, Yiwen Tu & Fujiang Liu

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Yan Guo

  3. Piesat Information Technology Co., Ltd, Wuhan, 430070, China

    Xianbin Wang

  4. Xining Natural Resources Integrated Survey Center, China Geological Survey, Xining, 810000, China

    Junshun Su

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  1. Weihua Lin
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  4. Yan Guo
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Contributions

LW Mainly responsible for collecting field data, providing financial support for the project, and giving comments on the first draft of the paper. TY Mainly responsible for research and analysis of measurement data, research literature and methods, and writing the first draft of the paper. LF Mainly responsible for field data collection, providing ideas for thesis supervision and financial support. GY Mainly responsible for providing technical support on computer. WX Mainly responsible for providing financial support for projects and other technical support. SJ Mainly responsible for providing instrumentation and other technical support.

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Correspondence to Fujiang Liu.

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Lin, W., Tu, Y., Liu, F. et al. Spectral characteristics of the correlation between elemental arsenic and vegetation stress in the Yueliangbao gold mining. Environ Geochem Health 45, 8203–8219 (2023). https://doi.org/10.1007/s10653-023-01693-7

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