云南个旧矿区土壤锌污染遥感反演研究

doi:10.11873/j.issn.1004-0323.2018.1.0088

遥感技术与应用

模型与反演

云南个旧矿区土壤锌污染遥感反演研究

宋婷婷¹，付秀丽²，陈玉³，魏永明³，王钦军³，程先锋⁴

(1.北京化工大学信息科学与技术，北京 100029；
2.北京石油化工学院信息工程学院，北京 102617；
3.中国科学院遥感与数字地球研究所数字地球实验室，北京 100094；
4.云南国土资源职业学院，云南昆明 650217)

Remote Sensing Inversion of Soil Zinc Pollution in Gejiu Mining Area of Yunnan

Song Tingting¹，Fu Xiuli²，Chen Yu³，Wei Yongming³，Wang Qinjun³，Cheng Xianfeng^4

(1.Beijing University of Chemical Technology，College of Information Science and Technology，
Beijing 100029，China;2.Beijing Institute of Petrochemical Technology，School of Information
Engineering，Beijing 102617，China；3.Institute of Remote Sensing and Digital Earth
Chinese Academy of Sciences，CAS Key Laboratory of Digital Earth Science，Beijing 100094，China；
4.Faculty of Environmental GeologyYunnan Land and Resources Vocational College，Kunming 652501，China)

全文: PDF(5515 KB)

摘要：

土壤重金属锌污染作为现代工矿业发展的产物，已逐渐入侵到人类日常的生产和生活中，危害人们的身心健康。传统的重金属监测方法在面对大规模土壤环境监测时费时费力。遥感技术由于具有宏观、快速、高效的特点已成为新时代环境监测的重要工具。以云南个旧矿区为典型区，通过野外土壤样品采集、光谱与Zn元素测量，提出了乘积变换的波段变换方法以增强Zn元素与光谱敏感波段之间的相关性，应用其建立了Zn含量最优预测模型并基于ASTER影像开展了污染制图。研究表明：①Zn元素的最大相关波段是B515波段，该波段处于闪锌矿、红锌矿、菱锌矿等含锌矿物的吸收峰附近，是反演土壤锌元素的重要波段；②光谱乘积变换在突出Zn元素敏感波段的同时，最大程度地保留了土壤原有的敏感波段信息；③研究区土壤锌含量的高光谱反演模型中，偏最小二乘法建立的模型精度最高(建模精度R=0.90，验证精度R=0.70)；4)基于ASTER影像的反演结果表明了土壤Zn元素污染与矿业活动的显著相关性(制图验证精度R=0.694)。研究结果可以为遥感定量反演重金属含量，以及大规模的环境污染监测提供研究基础与技术支持。

关键词： 土壤；锌污染；高光谱；个旧矿区；乘积变换法；偏最小二乘; ASTER

Abstract: As a product of the development of modern industry and mining industry，heavy metal Zn pollution has gradually invaded the daily production and life of human beings，which is harmful to our physical and mental health.In dealing with large-scale soil environmental monitoring.The traditional heavy metal monitoring method is time-consuming and laborious.Due to its characteristics of high speed，high speed and high efficiency，remote sensing technology has become an important tool for environmental monitoring in the new era.This study takes Yunnan Gejiu mining area as a typical area，collecting sample in field soil and measurement of soil sample spectra and Zn content.Then the band transform method based on the multiplicative transformation was proposed to enhance product conversion relationship between Zn elements and spectral sensitive bands，using the established prediction model and optimal Zn content based on ASTER images to carry out pollution mapping.Research shows that：①the maximum correlation band of Zn elements is the B515 band，close to the absorption peak of sphalerite and smithsonite zinc containing minerals，is an important band of zinc element inversion of soil;②the spectral multiplicative transformation can highlight the sensitive bands of Zn elements，and retain the most sensitive information of the original soil;③in the hypersecretion inversion model of soil zinc content in the study area，the precision of the model established by partial least squares(R=0.90)is the highest(R=0.70);④The inversion results based on ASTER images show that there is a significant correlation between soil Zn pollution and mining activities(Verification accuracy of map R=0.694).The results of this study can provide the basis and technical support for remote sensing quantitative inversion of heavy metal content and large-scale environmental pollution monitoring.

Key words: Soil Zn pollution；Hyperspectral；Gejiu mining area；Multiplicative transformation method；Partial least squares ASTER

收稿日期: 2017-01-20 出版日期: 2018-03-16

TP 79

基金资助: 国家自然科学基金项目“黄土高原浅层土壤抗剪性高光谱探测方法研究”(41601383)，国家科技支撑计划项目(2015BAB05B05-02)，云南省国土资源厅科技项目(2013-1)，中国地质调查局国土资源调查专项(1212011020000150011)，北京市属高校青年拔尖人才培育计划项目(CIT&TCD201504047)及北京市教委科研计划面上项目(KM201410017008)。

作者简介: 宋婷婷(1990-)，女，辽宁大连人，硕士研究生，主要从事遥感和图像处理方面的研究。E-mail:songtingting@ime.

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引用本文:

宋婷婷，付秀丽，陈玉，魏永明，王钦军，程先锋. 云南个旧矿区土壤锌污染遥感反演研究[J]. 遥感技术与应用, 10.11873/j.issn.1004-0323.2018.1.0088.

Song Tingting，Fu Xiuli，Chen Yu，Wei Yongming，Wang Qinjun，Cheng Xianfeng. Remote Sensing Inversion of Soil Zinc Pollution in Gejiu Mining Area of Yunnan. Remote Sensing Technology and Application, 10.11873/j.issn.1004-0323.2018.1.0088.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2018.1.0088 或 http://www.rsta.ac.cn/CN/Y2018/V33/I1/88

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