利用MODIS增强型植被指数(EVI)时序数据,基于中国陆地生态系统55种植被类型上的468个测试点和一个测试区进行了实验,综合比较欧氏距离、光谱信息离散度、光谱角余弦、核光谱角余弦、相关系数、光谱角余弦-欧氏距离6种距离测度方法对遥感植被指数时序数据聚类精度的影响,结果表明:相关系数方法的聚类精度最差;光谱角余弦-欧氏距离方法充分利用了植被指数时序数据的曲线幅度和形状特征,在这6种距离测度方法中表现出了最优的聚类效果;只对光谱亮度敏感的欧氏距离方法或只对曲线形状敏感的光谱角余弦方法,无论是在区分地物类型方面,还是在区域应用上,表现效果均较差;核光谱角余弦虽然在点数据测试上表现较差,但在区域应用上却有较好的表现;光谱信息离散度无论是在点数据测试上还是在区域应用上均表现出了较为适中的效果。

In order to evaluate the clustering accuracy of different distance measure methods for vegetation index time-series data, we make a comprehensive comparison among six distance measure methods (Euclidean distance, spectral information divergence, spectral angle cosine, kernel spectral angle cosine, correlation coeffi cient and spectral angle cosine-Euclidean distance) based on the MODIS Enhanced Vegetation Index (EVI) time-series data in China by selecting 468 test pixels across 55 vegetation types and a test region. The test results indicate that the correlation coeffi cient method shows the lowest clustering accuracy. However, the spectral angle cosine-Euclidean distance method which captures both the curve shape and the amplitude features of the vegetation index time-series data shows the highest clustering accuracy among the six methods. Both the Euclidean distance method which is only sensitive to the spectral brightness and the spectral angle cosine method which is only sensitive to the curve shape perform an inferior clustering accuracy not only in distinguishing different land cover types but also in the regional application. Although the kernel spectral angle cosine method does not show high clustering accuracy in the test at the point level, it shows better performance in the regional application. The spectral information divergence method has a modest performance in the test both at the point level and at the regional level.