Inversion of Heavy Metals Content in Soil Using Multispectral Remote Sensing Imagery in Daxigou Mining Area of Shaanxi
WANG Teng-jun1, 2, ZHAO Ming-hai3, YANG Yun1*, ZHANG Yang2, 4, CUI Qin-fang1, LI Long-tong1
1. College of Geology Engineering and Surveying, Chang’an University, Xi’an 710054, China
2. Key Laboratory of Degraded and Unused Land Consolidation Engineering，the Ministry of Land and Resources, Xi’an 710016, China
3. Shaanxi Railway Institute, Weinan 714000, China
4. Land Construction Group of Shaanxi, Xi’an 710075, China
Abstract：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 model which was used to estimate the content of Cu, Zn, As three metals in the principle of minimizing error rate. And an inversion model for the content of the three metals was constructed through the simulation, smoothing and pruning of the model tree with an input of all three types and 17 indicators mentioned above and 80% training samples. Also, a set of optimal indicators focusing on spectrum for the inversion were determined according to the principle of minimizing RMSE. Finally, the inversion results using 20% random samples were verified, showing that our suggested method achieved a decrease of RMSE value by 27.3%, 24.6%, 20.9%, and an improvement in confidence level for Cu and As, compared to that of the three interested metals using ordinary linear regression model. Also the thematic images showing the spatial distribution were mapped using the model. Then, the comparisons between the estimated value of the content of three metals and the background value published by Chinese government in 1990 were made. Furthermore, the statistical distribution rules of the three metals were concluded and verified using field survey results.
Key words：Soil heavy metal; Multispectral remote sensing imagery; Inversion; Spatial distribution; M5 model tree
王腾军，赵明海，杨 耘，张 扬，崔琴芳，李陇同. 多光谱影像的陕西大西沟矿区土壤重金属含量反演[J]. 光谱学与光谱分析, 2019, 39(12): 3880-3887.
WANG Teng-jun, ZHAO Ming-hai, YANG Yun, ZHANG Yang, CUI Qin-fang, LI Long-tong. Inversion of Heavy Metals Content in Soil Using Multispectral Remote Sensing Imagery in Daxigou Mining Area of Shaanxi. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3880-3887.
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