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Proceedings Paper

The spectral and image characteristics of vegetation in the presence of heavy metals in southern China
Author(s): Fengjie Yang; Na Li; Guangzhu Zhou; Cuiyu Song; Qingting Li
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Paper Abstract

The principle and methodology to monitor the heavy metal pollution using hyperspectral remote sensing are put forward based on the study areas, copper mine in De-Xing and tin ore in GeJiu, and selected plants, China Sumac, Sweet Wormwood Herb, and Nephrolepis Cordifolia. In the areas defined by former information, vegetation samples and corresponding spectral data are gathered. The samples are then analyzed in chemical lab, telling us to what extent the vegetation is polluted by heavy metal. The spectral curves are also processed, and some spectral parameters are extracted, such as reflectance, blue-shift extent, position of red-edge, vegetation index, band-depth. Then the regression model from spectral characteristic parameters to heavy metal content can be built. At last, the conclusion can be attained. In copper mine area, the vegetation is polluted by seven kinds of heavy metals. As far as China Sumac, the reflectance of red band correlates the Pb content well. The reflectance of all study plants at 1240nm and 725/675(nm) correlates heavy metal content well. The reflectance of 450nm, 550nm, 670nm, 760nm, and 1240nm can be liner combined as a parameter to monitor heavy metal pollution. Besides, some band-depth can also be combined as parameters using "Enter". In a word, as an advanced technique to monitor environmental pollution, hyperspectral remote sensing has wild perspective.

Paper Details

Date Published: 14 October 2008
PDF: 12 pages
Proc. SPIE 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 71101U (14 October 2008); doi: 10.1117/12.798575
Show Author Affiliations
Fengjie Yang, Shandong Univ. of Science and Technology (China)
Na Li, LMU Muenchen (Germany)
Guangzhu Zhou, Shandong Univ. of Science and Technology (China)
Cuiyu Song, Shandong Univ. of Science and Technology (China)
Qingting Li, Remote Sensing Application Institute (China)


Published in SPIE Proceedings Vol. 7110:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
Ulrich Michel; Daniel L. Civco; Manfred Ehlers; Hermann J. Kaufmann, Editor(s)

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