基于高光谱反演的复垦区土壤重金属含量经验模型优选
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摘要
以工矿复垦区为实验区域,基于ASD Field Spec 4高光谱遥感数据,结合实测的土壤重金属含量,利用回归分析与特征选择方法,开展了基于高光谱数据的土壤重金属含量反演研究与实验并进行了经验模型优选。通过对光谱曲线进行一阶微分、对数一阶微分以及对数倒数的一阶微分等数学变换有效提高了光谱数据与土壤重金属含量的相关性。在此基础上采用偏最小二乘回归(Partial least squares regression,PLSR)、随机森林回归(Random forest regression,RFR)、支持向量机回归(Support vector machine regression,SVMR) 3种回归分析模型开展土壤重金属含量反演实验,结果表明偏最小二乘回归(PLSR)对研究区内土壤中重金属含量的反演最为有效,尤其对区域内主要障碍因子镉(Cd)元素含量的反演效果最佳,验证集决定系数R2为0. 76。基于粒子群算法(Particle swarm optimization,PSO)、遗传算法(Genetic algorithm,GA)、Relief F算法3种特征选择方法对偏最小二乘回归(PLSR)模型进行优化,结果表明粒子群算法(PSO)可有效降低特征波段变量维度,进一步提高模型反演精度,使决定系数R2由0. 76提高至0. 84。综上,基于高光谱数据,采用偏最小二乘回归(PLSR)与粒子群算法(PSO)相结合的方法,可有效对工矿复垦区土壤中的重金属含量进行测度,可为复垦区土地的质量和生态指标监测提供理论方法和技术支持。
Taking industrial and mining reclamation land as the research object,based on the ASD FieldSpec 4 hyperspectral remote sensing data,combined with the field survey data of soil heavy metal attributes,using regression analysis and feature selection methods,the retrieval research and experiment of soil heavy metal content based on hyperspectral data were carried out, and the selection and comparison of empirical models were conducted. The correlation between soil heavy metal concentration and spectral data was effectively improved by the first derivative and logarithmic reciprocal etc. On this basis,three regression analysis models,including partial least squares regression( PLSR),random forest regression( RFR) and support vector machine regression( SVMR) were used to carry out the inversion experiment of heavy metal content in soil. The results showed that the partial least squares regression( PLSR) had the highest precision for the retrieval of heavy metal concentration in the reclaimed soil,especially for the cadmium( Cd) concentration,which was the main obstacle factor in the area. The determination coefficient( R2) of fit for the set was 0. 76. Particle swarm optimization( PSO),genetic algorithm( GA) and Relief F were used to optimize the partial least squares regression( PLSR) model.The results indicated that PSO can effectively reduce the dimension of characteristic band variables and further improve the model inversion. And the R2 of fit was increased from 0. 76 to 0. 84. In conclusion,based on hyperspectral data,the combination of partial least squares regression( PLSR) and particle swarm optimization( PSO) can effectively measure the concentration of heavy metals in the soil of industrial and mining reclamation area,and it can provide theoretical methods and technical support for the detection of land quality and ecological indicators in the reclamation area.
Taking industrial and mining reclamation land as the research object,based on the ASD FieldSpec 4 hyperspectral remote sensing data,combined with the field survey data of soil heavy metal attributes,using regression analysis and feature selection methods,the retrieval research and experiment of soil heavy metal content based on hyperspectral data were carried out, and the selection and comparison of empirical models were conducted. The correlation between soil heavy metal concentration and spectral data was effectively improved by the first derivative and logarithmic reciprocal etc. On this basis,three regression analysis models,including partial least squares regression( PLSR),random forest regression( RFR) and support vector machine regression( SVMR) were used to carry out the inversion experiment of heavy metal content in soil. The results showed that the partial least squares regression( PLSR) had the highest precision for the retrieval of heavy metal concentration in the reclaimed soil,especially for the cadmium( Cd) concentration,which was the main obstacle factor in the area. The determination coefficient( R2) of fit for the set was 0. 76. Particle swarm optimization( PSO),genetic algorithm( GA) and Relief F were used to optimize the partial least squares regression( PLSR) model.The results indicated that PSO can effectively reduce the dimension of characteristic band variables and further improve the model inversion. And the R2 of fit was increased from 0. 76 to 0. 84. In conclusion,based on hyperspectral data,the combination of partial least squares regression( PLSR) and particle swarm optimization( PSO) can effectively measure the concentration of heavy metals in the soil of industrial and mining reclamation area,and it can provide theoretical methods and technical support for the detection of land quality and ecological indicators in the reclamation area.
引文
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[6] ZHANG Chao,SU Jinghua. Soil mapping via diffuse reflectance spectroscopy based on variable indicators:an ordered predictor selection approach[J]. Geoderma,2018,314:146-159.
[7]张贤龙,张飞,张海威,等.基于光谱变换的高光谱指数土壤盐分反演模型优选[J].农业工程学报,2018,34(1):110-117.ZHANG Xianlong, ZHANG Fei, ZHANG Haiwei, et al. Optimization of soil salt inversion model based on spectral transformation from hyperspectral index[J]. Transactions of the CSAE,2018,34(1):110-117.(in Chinese)
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[9]马孟莉,朱艳,李文龙,等.基于分层多端元混合像元分解的水稻面积信息提取[J].农业工程学报,2012,28(2):154-159.MA Mengli,ZHU Yan,LI Wenlong,et al. Extracting area information of paddy rice based on stratified multiple endmember spectral mixture analysis[J]. Transactions of the CSAE,2012,28(2):154-159.(in Chinese)
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[13]肖科.泸州市土地整理效益分析与评价[D].雅安:四川农业大学,2009.
[14]赵安新,汤晓君,张钟华,等.优化Savitzky-Golay滤波器的参数及其在傅里叶变换红外气体光谱数据平滑预处理中的应用[J].光谱学与光谱分析,2016,36(5):1340-1344.ZHAO Anxin,Tang Xiaojun,ZHANG Zhonghua,et al. Optimizing Savitzky-Golay parameters and its smoothing pretreatment for FTIR gas spectra[J]. Spectroscopy and Spectral Analysis,2016,36(5):1340-1344.(in Chinese)
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[17] VAHID E,SAEID H,AHMAD M,Y,et al. Land cover mapping based on random forest classification of multitemporal spectral and thermal images[J]. Environmental Monitoring and Assessment,2015,187(5):167-175.
[18] GHOSH A,SHARMA R,JOSHI P K. Random forest classification of urban landscape using Landsat archive and ancillary data:combining seasonal maps with decision level fusion[J]. Applied Geography,2014,48:31-41.
[19]林楠,姜琦刚,杨佳佳,等.基于资源一号02C高分辨率数据的农业区土地利用分类[J/OL].农业机械学报,2015,46(1):278-284. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract. aspx? flag=1&file_no=20150139&journal_id=jcsam. DOI:10. 6041/j. issn. 1000-1298. 2015. 01. 039.LIN Nan,JIANG Qigang,YANG Jiajia,et al. Classifications of agricultural land use based on high-spatial ZY1-02C remote sensing images[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(1):278-284.(in Chinese)
[20]王兴玲,李占斌.基于网格搜索的支持向量机核函数参数的确定[J].中国海洋大学学报,2005,35(5):859-862.WANG Xingling,LI Zhanbin. Identifying the parameters of the kernel function in support vector machines based on the gridsearch method[J]. Periodical of Ocean University of China,2005,35(5):859-862.(in Chinese)
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