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Proceedings Paper

Heavy metal content estimation in leaf by spectrum features of plant in De-Xing copper mining area
Author(s): Fengjie Yang; Guangzhu Zhou; Yulong Pan; Hong Hu
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Paper Abstract

The estimation of heavy metal content in leaf is important to the integration of remote sensing into evaluation the ecological conditions in mining area. In this paper, correlation analysis and multivariable statistical methods were used to build hyperspectral models for the heavy metal (e.g., Cu) estimation with independent variables such as spectral reflectance, derivatives and ratio indices. Results showed that the heavy metals often display effects on plants as they changed plant moisture content, the pigment content, the leaf structure, and so on. Stepwise Multiple Regression Model predicted value and the actual value comparison showed that the model is stable, and the relative deviation about single plant mostly below2%. The first and second order differential spectrums were employed on three kinds of herbs synthesized also, the first order differential model proved better, and its relative deviation is lower than 15%.

Paper Details

Date Published: 3 February 2009
PDF: 9 pages
Proc. SPIE 7160, 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications, 71601B (3 February 2009); doi: 10.1117/12.811955
Show Author Affiliations
Fengjie Yang, Shandong Univ. of Science and Technology (China)
Guangzhu Zhou, Shandong Univ. of Science and Technology (China)
Yulong Pan, Shandong Univ. of Science and Technology (China)
Hong Hu, Shandong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7160:
2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications

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