Abstract
ABSTRACT If crops were polluted by heavy metals, the spectra will change. Therefore, the variation information of spectral changes has become an important basis for heavy metal pollution monitoring. Based on spectral frequency domain, we studied the spectra of maize leaves. Combined with time-frequency analysis method, we proposed DDCR-Db (Second-order Differential Continuum Removal- Daubechies) method to detect the sensitive spectral ranges of leaves. Furthermore, we proposed VCPs (Variation Characteristic Parameters), and studied correlation between VCPs and heavy metal content of leaves. And compared with CSI (Conventional Spectral Index) to explore the spectral ranges which are particularly sensitive to Cu2+ (Copper ion) and Pb2+ (Lead ion) stress. Finally, combined with the nonlinear time-frequency distribution, we constructed DDCR-Db-CW (Second-order Differential Continuum Removal-Daubechies-Choi-Williams) transform of the spectra transformation method to distinguish copper and lead pollution. The results showed that DDCR-Db can effectively extract weak spectral information of leaves under copper and lead stress. We obtained the spectral ranges which are particularly sensitive to Cu2+ and Pb2+ stress. DDCR-Db-CW transform can distinguish the spectral difference between no heavy metal stress and heavy metal stress, and intuitively distinguish the types of copper and lead stress.
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