土壤重金属铅、锌高光谱反演模型可迁移能力分析研究

doi:10.3964/j.issn.1000-0593(2018)06-1850-06

Abstract
References
Related Citation (15)

Download: PDF (2961 KB)
Export: BibTeX | EndNote (RIS)

Abstract The existing model of soil heavy metal content reversal model by hyperspectral remote sensing technology is mostly based on the limited sample points of the same study area. However, considering the practical application requirements, whether the model has a good migrate ability is an urgent question. If it is not feasible, is there any other feasible means for soil heavy metal pollution assessment? In order to answer the above-mentioned questions, this paper selects two lead-zinc mines in Chenzhou City and Hengyang City as research areas. The quantitative inversion and qualitative classification of heavy metals Pb and Zn were carried out using the sampling sites in Chenzhou area to compare the two models in Hengyang City of the migrate ability. Experiments show that: (1) Quantitative inversion model based on Partial least squares regression (PLSR) has poor migration ability. The regression model was established by four spectral preprocessing methods. It was found that the prediction accuracy of the model was very low, and it was difficult to correctly invert the contents of Pb and Zn in Hengyang research area. (2) Support vector machine (SVM) classification of qualitative inversion model has a certain ability to migrate. Based on Chenzhou area sampling data, training SVM classification model can effectively predict the Hengyang research area Pb and Zn pollution situation, the prediction accuracies are 84.78% and 86.96%, respectively. The results show that qualitative classification is a more practical way to detect soil heavy metal pollution rapidly.

Key words：Heavy metal pollution; Hyperspectral; Migration analysis; SVM classification; Partial least squares regression

Received: 2017-06-27 Accepted: 2017-11-08

ZTFLH:

O657.3

Corresponding Authors: TAO Chao E-mail: kingtaochao@126.com

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)06-1850-06 OR https://www.gpxygpfx.com/EN/Y2018/V38/I06/1850

[1] Guo K, Liu Y F, Zeng C, et al. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 2014, 64(5): 377.
[2] GONG Shao-qi, WANG Xin, SHEN Run-ping, et al(龚绍琦，王鑫，沈润平，等). Remote Sensing Technology and Application(遥感技术与应用), 2010，(2): 169.
[3] YU Xu-chun, FENG Wu-fa, YANG Guo-peng, et al(余旭初，冯伍法，杨国鹏，等). Hyperspectral Image Analysis and Application(高光谱影像分析与应用). Beijing: Science Press(北京: 科学出版社), 2013.
[4] Shi T, Chen Y, Liu Y, et al. Journal of Hazardous Materials, 2014, 265: 166.
[5] Kemper T, Sommer S. Environmental Science & Technology, 2002, 36(12): 2742.
[6] Choe E, van der Meer F, van Ruitenbeek F, et al. Remote Sensing of Environment, 2008, 112(7): 3222.
[7] DONG Lu-rui, HU Wen-you, HUANG Biao, et al(董禄睿，胡文友，黄标，等). China Environmental Science(中国环境科学)，2015，35(7)：2103.
[8] HUANG Chang-ping, LIU Bo, ZHANG Xia, et al(黄长平, 刘波, 张霞, 等). Remote Sensing Technology and Application(遥感技术与应用), 2010, 25(3): 353.
[9] FU Xin, ZHAO Yan-ling, LI Jian-hua, et al(付馨，赵艳玲，李建华，等). China Mining(中国矿业), 2013, (1): 65.
[10] SHI Zhou(史舟). Soil Ground Hyperspectral Remote Sensing Principles and Methods(土壤地面高光谱遥感原理与方法). Beijing: Science Press(北京: 科学出版社), 2014.
[11] WANG Hui-wen(王惠文). Partial Least-Squares Regression-Method and Applications(偏最小二乘回归方法及应用). Beijing: National Defense Industry Press(北京: 国防工业出版社), 1999.
[12] WANG Xiao-ling, DU Pei-jun, TAN Kun(王晓玲，杜培军，谭琨). Science of Surveying and Mapping(测绘科学), 2011，(3): 127.
[13] GB 15618—1995 Environmental Quality Standary for Soils(土壤环境质量标准)，1995.
[14] Wang J, Cui L, Gao W, et al. Geoderma, 2014, 216: 1.