多光谱影像的陕西大西沟矿区土壤重金属含量反演  

Inversion of Heavy Metals Content in Soil Using Multispectral Remote Sensing Imagery in Daxigou Mining Area of Shaanxi

作　　者：王腾军[1,2] 赵明海 杨耘 张扬[2,4] 崔琴芳[1] 李陇同 WANG Teng-jun;ZHAO Ming-hai;YANG Yun;ZHANG Yang;CUI Qin-fang;LI Long-tong(College of Geology Engineering and Surveying,Chang’an University,Xi’an 710054,China;Key Laboratory of Degraded and Unused Land Consolidation Engineering,the Ministry of Land and Resources,Xi’an 710016,China;Shaanxi Railway Institute,Weinan 714000,China;Land Construction Group of Shaanxi,Xi’an 710075,China)

机构地区：[1]长安大学地质工程与测绘学院,陕西西安710054 [2]国土资源部退化及未利用土地整治工程重点实验室,陕西西安710016 [3]陕西铁路工程职业技术学院,陕西渭南714000 [4]陕西省土地工程建设集团,陕西西安710075

出　　处：《光谱学与光谱分析》2019年第12期3880-3887,共8页Spectroscopy and Spectral Analysis

基　　金：国土资源部退化及未利用土地整治工程重点实验室开放基金项目(SXDJ2017-3);中央高校基本科研业务费(300102269205,300102269201);NSFC-新疆联合基金项目(U1703121);国家自然科学基金项目(41301386)资助

摘　　要：传统的以“点采样+实验室分析”为主的土壤重金属含量分析技术成本高、效率低下,而基于多光谱遥感的土壤重金属高精度定量反演中存在重金属含量影响因子的优化这一难题,以陕西大西沟矿区这类山区地形条件下的金属矿区为例,利用Landsat8/OLI多光谱卫星影像、DEM数据以及外业土壤采样分析数据,开展了矿区土壤重金属含量指示因子分析及定量反演研究。首先,考虑研究区地形地貌特点,设计了沿研究区地形特征线及其两侧坡面均匀分布的样点分布方案,采集了45个样本。并对45个样本的混合样中的8种重金属含量进行了兴趣度分析,根据含量超标程度及矿的类型选取了铜、铅、砷3种元素作为分析对象。其次,根据研究区土地利用现状及地形特点,提出了以Landsat8/OLI影像B2至B7波段光谱反射率、粘土矿物比(CMR)、改进归一化水体指数(MNDWI)、差异植被指数(DVI)等八种光谱指数、以及反映研究区地形坡度和坡向三类因子作为反映土壤重金属含量空间分布特征的侯选因子。进而,对上述三类侯选因子与样本中3种金属含量进行了最小二乘相关性分析。根据分析结果,引入了基于估算误差最小准则的金属含量估算模型——基于规则的M5模型树的分段线性估算模型。以上述三大类共17个指示因子作为模型的输入,利用80%的土壤样本分析数据作为模型的训练数据,经过M5模型树的构建、平滑和树枝修剪过程,建立了3种金属的反演模型实现了研究区中土壤中3种金属含量的估算。同时,基于均方根误差(RMSE)最小准则确定了以光谱因子为主的最利于反演的最佳指示因子集。最后,用随机选取的20%的检验样本对模型进行了反演精度分析,验证了该模型对铜、铅、砷3种金属含量的反演精度比普通的线性模型分别提高了27.3%,24.6%,20.9%,同时,铜、铅元素的可信度也有所提高。利用�The problem of low efficiency and higher cost exists in the traditional method mainly on“field-work point sampling then indoor experimental analysis”.Also the problem that how to choose the optimal factors indicating the content of heavy metals in soils is difficult to solve for the quantitative inversion of high precision using multispectral remote sensing technology.Using Landsat8/OLI satellite imagery,DEM data and soil samples data,the paper performed the analysis indicators of heavy metals in soil and the quantitative inversion of the content of heavy metals in soil in order to achieve an improved accuracy,taking a study case of a mountainous and forestry mining area called Daxigou mineral of Shaanxi in China.The work was as follows:A soil sampling scheme considering terrain and geomorphology characteristics was designed and evenly sampled in both sides along main topographic feature lines in the study area and 45 soil samples were acquired.Furthermore,a mixed samples from 45 samples were analyzed in laboratory so as to choose the most interested metals(i.e.Cu,Zn,As)as our focus according to both the degree of metals content bigger than that of national authoritative statistics and the type of mineral.Secondly,the paper suggested three types of factors including six spectral reflectivity from band two to seven of Landsat8/OLI imagery,and several spectral indices such as CMR,MNDWI,DVI,EVI etc.,derived from Landsat8 image and also slope and aspect factors derived from DEM data were adopted to indicate the characteristics of the spatial distribution of the content of the three metals candidates considering land use and terrain circumstances in the study area.Subsequently,a correlation analysis of the content of three interested metals individually with six spectral reflectivity data,eight spectral indices and three terrain indicators was done using Least Squares principle.According to the consequence of the correlation analysis,the paper introduced the rule-based M5 model tree in the form of piecewise linear

关 键 词：土壤重金属 多光谱遥感影像 反演 空间分布 M5模型树 

分 类 号：X53[环境科学与工程—环境工程]