Hyperspectral estimation model of plumbum concentration in soil of mining areas based on wavelet transform and random forests
Heavy metal pollution of tailings is one of the serious problems in environmental pollution, accurate estimation of soil heavy metal content is very important for the mine soil pollution monitoring.Taking Jinduicheng mine tailings in Shaanxi as the study area, soil spectral were measure with ASD spectrometer, Plumbum element content of soil samples were obtained by laboratory analysis.The wavelet transform was applied to the soil hyperspectral data for noise reduction, and the noise reduction of soil spectrum is studied by using the first derivative spectral transform and the continuum removal method.Plumbum content in the mine tailing soil were estimated by random forests, inversion results were compared with the original high spectral data and the noise reduction spectral data.The results showed that: the estimation model on the spectral data set after noise reduced by wavelet transform achieved a correlation coefficient R2 of 0.774, and the root mean square error of RMSE is 249.125, the prediction accuracy is better than the original hyperspectral data.The results provide a theoretical basis for exploring the characteristics of soil hyperspectral data extraction, and has important significance for the heavy metal pollution monitoring of tailing soil in the mining areas.
Wavelet transform Soil Plumbum Random forest Hyperspectral
J.Lv X.M.Li J.Kang
College of Geomatics, Xian University of Science and Technology, Xian, Shaanxi, China
国际会议
The 2nd International Symposium on Land Reclamation and Ecological Restoration (第二届国际土地复垦与生态修复学术研讨会)
西安
英文
223-226
2017-10-20（万方平台首次上网日期，不代表论文的发表时间）