Heavy metal content estimation in leaf by spectrum features of plantin De-Xing copper mining areal

摘要

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