| 基于主成分分析的植被指数与叶面积指数相关性研究 |
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| 引用本文： | 翟羽娟,张艳红,刘兆礼,刘宝江.基于主成分分析的植被指数与叶面积指数相关性研究[J].测绘与空间地理信息,2015(9):20-23. |
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| 作者姓名： | 翟羽娟 张艳红 刘兆礼 刘宝江 |
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| 作者单位： | 1. 吉林建筑大学城建学院,吉林长春,130111;2. 吉林大学地球探测科学与技术学院,吉林长春,130026;3. 中科院东北地理与农业生态研究所,吉林长春,130012 |
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| 基金项目： | 国家863计划项目(2009AA12Z136) |
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| 摘 要： | 综合分析了玉米叶面积指数与几种常见光谱植被指数相关性,确定主成分分析方法在反演叶面积指数中的作用。首先,借助MATLAB编程软件,以植被指数与玉米叶面积指数相关性最高为原则,选出遥感影像上各种植被指数,其波段组合为NDVI(752.4/701.5),RVI(752.4/701.5),MSR(752.4/701.5),SAVI(823.7/701.5),MSAVI(823.7/701.5),然后,对这5种植被指数进行主成分分析,建立LAI-VI多元逐步回归模型,并对模型精度进行验证,总体估测精度为96.237%。经实验验证,利用主成分分析方法在反演植被叶面积指数时能够起到较好的效果,具有广泛的应用前景。
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| 关 键 词： | 植被指数 主成分分析 玉米叶面积指数 |
Correction Analysis betweenL eaf Area Index and Vegetation Index of Maize Based on Principal Component |
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| Abstract: | This article gave a comprehensive analysis of correlation between leaf area index ( LAI) of corn and several common spectral vegetation indices, determined functions of principle component analysis ( PCA) in LAI inversion.Firstly, the correlation between LAI of corn and spectral vegetation index was set up as highest principle, a variety of vegetation indices was selected on remote sensing by using MATLAB programming software, and the band combination are:NDVI(752.4/701.5),RVI(752.4/701.5),MSR(752.4/701.5),SAV I(823.7/701.5), and MSAVI(823.7/701.5).Then PCA was used to analyze these five vegetation indices, LAI-VI multiple regression model was established.Meanwhile, the accuracy of the model was also verified, which was 96.237%.With the experiment, it’ s confirmed that the PCA in the inversion of vegetation LAI worked effectively, which could be widely used in the fu-ture. |
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| Keywords: | vegetation index principal component analysis maize leaf area index |
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