| 玉米叶片铜污染的EEMD-MA-FD光谱诊断模型 |
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| 引用本文： | 程凤,杨可明,王敏,李燕,高鹏,张超.玉米叶片铜污染的EEMD-MA-FD光谱诊断模型[J].农业环境科学学报,2019,38(4):779-786. |
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| 作者姓名： | 程凤 杨可明 王敏 李燕 高鹏 张超 |
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| 作者单位： | 中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083,中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083,中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083;华北理工大学, 河北 唐山 063210,中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083,中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083,中国矿业大学(北京)煤炭资源与安全开采国家重点实验室, 北京 100083 |
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| 基金项目： | 煤炭资源与安全开采国家重点实验室2017年开放基金项目（SKLCRSM17KFA09）；国家自然科学基金项目（41271436）；中央高校基本科研业务费专项资金项目（2009QD02） |
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| 摘 要： | 以铜为例探讨重金属不同胁迫浓度下玉米叶片光谱的微弱信息量差异,在2017年测定的玉米叶片光谱数据和Cu~(2+)含量的基础上,结合集成经验模态分解(Ensemble empirical mode decomposition,EEMD)、Mallat算法(MA)和分形维数(Fractal dimension,FD)构建EEMD-MA-FD光谱诊断模型来进行光谱弱信息变换监测。与红边最大值、蓝边最大值等常规重金属污染监测方法进行对比分析,验证EEMD-MA-FD模型在玉米叶片铜污染监测中的优越性,最后利用2016年采集的光谱数据作为检验数据验证模型的稳定性。结果显示,玉米叶片Cu~(2+)含量与EEMD-MA-FD模型结果存在较强的相关性,相关系数为-0.942 2,检验数据Cu~(2+)含量与模型结果相关系数为-0.993 7,与实验结果有较高的一致性。由此验证了EEMD-MA-FD诊断模型在农作物重金属铜污染监测中的可行性。
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| 关 键 词： | 铜污染 玉米叶片 集成经验模态分解 Mallat 分形维数 |
| 收稿时间： | 2018/7/3 0:00:00 |
An EEMD-MA-FD spectral diagnosis model of copper pollution in maize leaves |
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| CHENG Feng,YANG Ke-ming,WANG Min,LI Yan,GAO Peng and ZHANG Chao.An EEMD-MA-FD spectral diagnosis model of copper pollution in maize leaves[J].Journal of Agro-Environment Science( J. Agro-Environ. Sci.),2019,38(4):779-786. |
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| Authors: | CHENG Feng YANG Ke-ming WANG Min LI Yan GAO Peng and ZHANG Chao |
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| Institution: | State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology(Beijing), Beijing 100083, China,State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology(Beijing), Beijing 100083, China,State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology(Beijing), Beijing 100083, China;North China University of Science & Technology, Tangshan 063210, China,State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology(Beijing), Beijing 100083, China,State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology(Beijing), Beijing 100083, China and State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology(Beijing), Beijing 100083, China |
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| Abstract: | Copper is used to investigate the differences of weak spectral information in maize leaves under different stress gradients of heavy metals. On the basis of spectral data and Cu2+ contents in maize leaves measured in 2017, an EEMD-MA-FD spectral diagnosis model is constructed, consisting of ensemble empirical mode decomposition (EEMD), Mallat (MA), and fractal dimensions (FD), to monitor the changes of weak spectral information. In order to verify the superiority of the EEMD-MA-FD model for monitoring copper pollution in maize leaves, it was compared with conventional heavy metal pollution monitoring methods such as the maximum values of the red edge and blue edge. Finally, spectral data collected in 2016 are used as testing data to verify the stability of the model. The results showed that a strong correlation between Cu2+ contents in maize leaves and EEMD-MA-FD model results, with a correlation coefficient of -0.942 2. The correlation coefficient between Cu2+ contents of testing data and the model results was -0.993 7, which was in good agreement with the experimental results. Therefore, the feasibility of the EEMD-MA-FD diagnostic model for monitoring heavy metal copper pollution in maize was verified. |
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| Keywords: | copper pollution maize leaves ensemble empirical mode decomposition Mallat fractal dimension |
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