| 遥感植物地球化学方法的研究进展 |
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| 引用本文： | 崔世超,周可法,赵杰.遥感植物地球化学方法的研究进展[J].地质找矿论丛,2017,32(3):440-446. |
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| 作者姓名： | 崔世超 周可法 赵杰 |
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| 作者单位： | 1. 中国科学院新疆生态与地理研究所新疆矿产资源研究中心,乌鲁木齐830011;中国科学院大学,北京100049;2. 中国科学院新疆生态与地理研究所新疆矿产资源研究中心,乌鲁木齐,830011 |
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| 基金项目： | 国家自然科学基金，新疆维吾尔自治区国际科技合作计划项目 |
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| 摘 要： | 近些年来随着遥感技术的不断发展,在植被覆盖区的金属矿床的探查中,遥感植物地球化学方法得到了越来越广泛的应用。文章从理论、技术和应用三个方面对遥感植物地球化学方法的发展进行阐述,并指出目前存在的问题。其中使用定量化的手段提取植物地球化学信息是未来的必由之路。然而在定量化的过程中,光谱尺度效应和空间尺度效应是两个重要的影响模型精度的因素。针对这个问题,文章提出两种解决方案来减小光谱尺度效应的影响,然而这两种方法的可操作性以及可靠性还需要进一步探讨。最后,文章对遥感植物地球化学方法的发展前景进行阐述,并指出在以后定量模型的建立中可以引入支持向量机、投影追踪等机器学习的方法。
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| 关 键 词： | 遥感植物地球化学 光谱尺度 空间尺度 支持向量机 投影追踪 |
| 收稿时间： | 2016/4/15 0:00:00 |
Progress of research on remote sensing plant geochemical methods |
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| CUI Shichao,ZHOU Kefa and ZHAO Jie.Progress of research on remote sensing plant geochemical methods[J].Contributions to Geology and Mineral Resources Research,2017,32(3):440-446. |
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| Authors: | CUI Shichao ZHOU Kefa and ZHAO Jie |
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| Institution: | Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China;University of Chinese academy of sciences, Beijing 100049, China,Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China and Xinjiang Research Center for Mineral Resources, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China;University of Chinese academy of sciences, Beijing 100049, China |
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| Abstract: | With development of remote sensing technology in recent years remote sensing plant geochemical method has been widely used in the exploration of metallic deposits in the area with vegetation.In this paper is mainly expounded the development of remote sensing of plant geochemistry from three aspects of theory,technology and application and pointed out the existing problems.Quantitative extraction of plant geochemical information is the only way to take for remote sensing plant geochemistry in the future.However,in the process of quantification,the scale effect of spectrum and space are two important factors to influence accuracy of the model.To solve the problem,this paper proposes two solutions to reduce influence of the spectral scale effect while the two methods' operability and reliability need to be further explorated.Finally,this paper expounds the development prospects of the remote sensing plant geochemical method,and points out that in the future,support vector machine,projection machine,and other machine learning methods can be introduced for the quantitative model building. |
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| Keywords: | remote sensing plant geochemistry spectral scale spatial scale support vector machine projection pursuit |
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