铜胁迫下玉米光谱变化的奇异性诊断指数与污染甄别
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摘要
通过研究不同程度铜污染胁迫下玉米光谱奇异性变化特征来诊断玉米受Cu2+污染程度。通过设置不同铜胁迫浓度下的玉米盆栽实验,根据实测的SVC高光谱数据和Cu2+含量数据,采用经验模态分解(EMD)与小波变换相结合的方法提取玉米光谱奇异信息,并构建奇异性诊断指数对玉米光谱奇异性进行定性分析,从而实现玉米铜污染程度的甄别。同时与常规的绿峰高度、红边最大值、红边一阶微分包围面积等植被重金属污染信息监测方法进行比较来验证该方法的有效性。结果显示:奇异性诊断指数(SI)与玉米叶片中Cu2+含量存在较强的相关关系,SI随叶片中Cu2+含量的增加而增大,其相关系数达到0.972 4,从而证明光谱奇异性诊断指数能有效地诊断叶片光谱的奇异性变化及其污染程度,为作物重金属污染监测提供参考依据。
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 reached0.972 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.
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 reached0.972 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.
引文
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[14]罗玉昆,罗诗途,罗飞路,等.激光超声信号去噪的经验模态分解实现及改进[J].光学精密工程,2013,21(2):479-487.LUO Yu-kun,LUO Shi-tu,LUO Fei-lu,et al.Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition[J].Optics and Precision Engineering,2013,21(2):479-487.
[15]李富香,张韡.EMD技术在机械震动故障中的诊断方法[J].舰船科学技术,2016,38(20):154-156.LI Fu-xiang,ZHANG Wei.The mechanical vibration fault diagnosis method based on EMD technology[J].Ship Science and Technology,2016,38(20):154-156.
[16]张猛,王华忠,隋志强,等.基于经验模态分解和小波变换的地震瞬时频率提取方法及应用[J].石油地球物理勘探,2016,51(3):565-571.ZHANG Meng,WANG Hua-zhong,SUI Zhi-qiang,et al.Seismic instantaneous frequency extraction based on empirical mode decomposition transform[J].Oil Geophysical Prospecting,2016,51(3):565-571.
[17]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[18]李卿.EMD时频分析方法的研究与应用[D].武汉:华中师范大学,2008.LI Qing.Research and application of time-frequency analysis method based on empirical mode decomposition[D].Wuhan:Central China Normal University,2008.
[19]龚志强,邹明玮,高新全,等.基于非线性时间序列分析经验模态分解和小波分解异同性的研究[J].物理学报,2005,54(8):3947-3957.GONG Zhi-qiang,ZOU Ming-wei,GAO Xin-quan,et al.On the difference between empirical mode decomposition and wavelet decomposition in the nonlinear time series[J].Acta Physica Senica,2005,54(8):3947-3957.
[20]张浚哲,朱文泉,郑周涛,等.高光谱数据的相似性测度对比研究[J].测绘科学,2013,38(6):33-36.ZHANG Jun-zhe,ZHU Wen-quan,ZHENG Zhou-tao,et al.Comparative study on similarity measures of hyperspectral remote sensing data[J].Science of Surveying and Mapping,2013,38(6):33-36.
[21]闻兵工,冯伍法,刘伟,等.基于光谱曲线整体相似性测度的匹配分类[J].测绘科学技术学报,2009,26(2):128-131.WEN Bing-gong,FENG Wu-fa,LIU Wei,et al.Matching and classification based on the whole comparability measure of spectral curve[J].Journal of Geomatics Science and Technology,2009,26(2):128-131
[22]张修宝,袁艳,景娟娟,等.信息散度与梯度角正切相结合的光谱区分方法[J].光谱学与光谱分析,2011,31(3):853-857.ZHANG Xiu-bao,YUAN Yan,JING Juan-juan,et al.Spectral discrimination method information divergence combined with gradient angle[J].Spectroscopy and Spectral Analysis,2011,31(3):853-857.
[2]Song B,Lei M,Chen T B,et al.Assessing the health risk of heavy metals in vegetables to the general population in Beijing,China[J].Journal of Environmental Sciences,2009,21:1702-1709.
[3]Kloiber S M,Brezonik P L,Olmanson L G,et al.A procedure for regional lake water clarity assessment using Landsat multispectral data[J].Remote Sinsing of Environment,2002,82(1):38-47.
[4]朱叶青,屈永华,刘素红,等.重金属铜污染植被光谱响应特征研究[J].遥感学报,2014,18(2):335-352.ZHU Ye-qing,QU Yong-hua,LIU Su-hong,et al.Spectral response of wheat and lettuce to copper pollution[J].Journal of Remote Sensing,2014,18(2):335-352.
[5]李庆亭.重金属污染胁迫下盐肤木的生化效应及波谱特征[J].遥感学报,2008,12(2):284-290.LI Qing-ting.Biogeochemistry responses and spectral characteristics of Rhus chinensis mill under heavy metal contamination stress[J].Journal of Remote Sensing,2008,12(2):284-290.
[6]孙彤彤,杨可明,张伟,等.玉米光谱小波奇异熵的铜污染监测研究[J].环境科学学报,2017,37(11):4360-4365.SUN Tong-tong,YANG Ke-ming,ZHANG Wei,et al.Monitoring copper pollution based on wave singular entropy of corn leaves[J].Acta Scientiae Circumstantiae,2017,37(11):4360-4365.
[7]任红艳,庄大方,潘剑君,等.重金属污染水稻的冠层反射光谱特征研究[J].光谱学与光谱分析,2010,30(2):430-434.REN Hong-yan,ZHUANG Da-fang,PAN Jian-jun,et al.Study on canopy spectral characteristics of paddy polluted by heavy metals[J].Spectroscopy and Spectral Analysis,2010,30(2):430-434.
[8]王圆圆,陈云浩,李京,等.指示冬小麦条锈病严重度的两个新的红边参数[J].遥感学报,2007,11(6):875-881.WANG Yuan-yuan,CHEN Yun-hao,LI Jing,et al.Two new red edge indices as indicators for stripe rust disease severity of winter wheat[J].Journal of Remote Sensing,2007,11(6):875-881.
[9]杜华强,金伟,葛宏立,等.用高光谱曲线分形维数分析植被健康状况[J].光谱学与光谱分析,2009,29(8):2136-2140.DU Hua-qiang,JIN Wei,GE Hong-li,et al.Using fractal dimensions of hyperspectral curves to analyze the healthy status of vegetation[J].Spectroscopy and Spectral Analysis,2009,29(8):2136-2140.
[10]Noh H,Zhang Q,Shin B,et al.A neural network model of maize crop nitrogen stress assessment for a multi-spectral imaging sensor[J].Biosystems Engineering,2006,94(4):477-485.
[11]刘美玲,刘湘南,李婷,等.水稻锌污染胁迫的光谱奇异性分析[J].农业工程学报,2010,26(3):191-197.LIU Mei-ling,LIU Xiang-nan,LI Ting,et al.Analysis of hyperspectral singularity of rice under Zn pollution stress[J].Transactions of the Chinese Society of Agricultural Engineering,2010,26(3):191-197.
[12]于雷,洪永胜,周勇,等.连续小波变换高光谱数据的土壤有机质含量反演模型构建[J].光谱学与光谱分析,2016,36(5):1428-1433.YU Lei,HONG Yong-sheng,ZHOU Yong,et al.Inversion of soil organic matter content using hyperspectral data based on continuous wavelet transformation[J].Spectroscopy and Spectral Analysis,2016,36(5):1428-1433.
[13]何汝艳,乔小军,蒋金豹,等.小波法反演条锈病胁迫下冬小麦冠层叶片全氮含量[J].农业工程学报,2015,31(2):141-146.HE Ru-yan,QIAO Xiao-jun,JIANG Jin-bao,et al.Retrieving canopy leaf total nitrogen content of winter wheat by continuous wavelet transform[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(2):141-146.
[14]罗玉昆,罗诗途,罗飞路,等.激光超声信号去噪的经验模态分解实现及改进[J].光学精密工程,2013,21(2):479-487.LUO Yu-kun,LUO Shi-tu,LUO Fei-lu,et al.Realization and improvement of laser ultrasonic signal denoising based on empirical mode decomposition[J].Optics and Precision Engineering,2013,21(2):479-487.
[15]李富香,张韡.EMD技术在机械震动故障中的诊断方法[J].舰船科学技术,2016,38(20):154-156.LI Fu-xiang,ZHANG Wei.The mechanical vibration fault diagnosis method based on EMD technology[J].Ship Science and Technology,2016,38(20):154-156.
[16]张猛,王华忠,隋志强,等.基于经验模态分解和小波变换的地震瞬时频率提取方法及应用[J].石油地球物理勘探,2016,51(3):565-571.ZHANG Meng,WANG Hua-zhong,SUI Zhi-qiang,et al.Seismic instantaneous frequency extraction based on empirical mode decomposition transform[J].Oil Geophysical Prospecting,2016,51(3):565-571.
[17]Huang N E,Shen Z,Long S R,et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J].Proceedings:Mathematical,Physical and Engineering Sciences,1998,454(1971):903-995.
[18]李卿.EMD时频分析方法的研究与应用[D].武汉:华中师范大学,2008.LI Qing.Research and application of time-frequency analysis method based on empirical mode decomposition[D].Wuhan:Central China Normal University,2008.
[19]龚志强,邹明玮,高新全,等.基于非线性时间序列分析经验模态分解和小波分解异同性的研究[J].物理学报,2005,54(8):3947-3957.GONG Zhi-qiang,ZOU Ming-wei,GAO Xin-quan,et al.On the difference between empirical mode decomposition and wavelet decomposition in the nonlinear time series[J].Acta Physica Senica,2005,54(8):3947-3957.
[20]张浚哲,朱文泉,郑周涛,等.高光谱数据的相似性测度对比研究[J].测绘科学,2013,38(6):33-36.ZHANG Jun-zhe,ZHU Wen-quan,ZHENG Zhou-tao,et al.Comparative study on similarity measures of hyperspectral remote sensing data[J].Science of Surveying and Mapping,2013,38(6):33-36.
[21]闻兵工,冯伍法,刘伟,等.基于光谱曲线整体相似性测度的匹配分类[J].测绘科学技术学报,2009,26(2):128-131.WEN Bing-gong,FENG Wu-fa,LIU Wei,et al.Matching and classification based on the whole comparability measure of spectral curve[J].Journal of Geomatics Science and Technology,2009,26(2):128-131
[22]张修宝,袁艳,景娟娟,等.信息散度与梯度角正切相结合的光谱区分方法[J].光谱学与光谱分析,2011,31(3):853-857.ZHANG Xiu-bao,YUAN Yan,JING Juan-juan,et al.Spectral discrimination method information divergence combined with gradient angle[J].Spectroscopy and Spectral Analysis,2011,31(3):853-857.
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