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| Singularity diagnostic index pollution identification of corn spectral variations under copper stress |
| Received:April 16, 2018 |
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| KeyWord:copper contamination;spectral time-frequency feature analysis;singular diagnostic index;singularity |
| Author Name | Affiliation | E-mail | | LI Yan | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | YANG Ke-ming | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | ykm69@163.com | | WANG Min | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China
North China University of Science & Technology, Tangshan 063210, China | | | CHENG Feng | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | GAO Peng | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | ZHANG Chao | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | |
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| Abstract: |
| The aim of this study is to detect the stress levels of corn under different levels of Cu2+ pollution by analyzing the characteristics of hyperspectral singularity variations. By setting the potted corn experiment for stress via different concentration gradients of Cu2+ and based on the measured SVC hyperspectral data and Cu2+ concentration, corn spectral singular information was extracted with a method that combined empirical mode decomposition(EMD) and wavelet transform. The singular diagnostic index(SI) was used to indicate the corn hyperspectral singularity variations in order to screen the corn pollution level. Meanwhile, the proposed method was verified as valid compared to the conventional monitoring methods of vegetation heavy metal pollution information such as green-peak height, red edge maximum, and first derivative area of the red edge. The results showed a strong correlation between the singular diagnostic index and copper content of corn leaves. The singular diagnostic index increased with the content of Cu2+ in corn leaves. Furthermore, the correlation coefficient reached 0.972 4, which proves that the singular diagnostic index could be used to diagnose the stress levels of maize under Cu2+ pollution conditions effectively. This study provides a reference for monitoring the heavy metal pollution of crops. |
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