The SPOT/VGT NDVI (S10) time series data of eastern China (1998-2005) are smoothed with two methods, the moving
average and the Savitzky-Golay filter, after they are downloaded from the official website of VITO. Then the monthly
maximal NDVI images (total 93 images) are extracted from 279 NDVI (S10) images and the Principal Component
Analysis (PCA) is applied on the 93 images. There are 3 components that each explains more than 1% of the variance, in
which the principal components 1, 2 and 3 explain respectively 93.25%, 2.77% and 1.21% of the variance in the original
93 maximum NDVI images. The principal component 1 is interpreted as the "climate" component, and principal
components 2 and 3 are interpreted as the "growth season" and "non-growth season" components respectively. Principal
components 1, 2 and 3 are composed to a 3-band color image which is classified into 7 classes (including 18 subclasses)
by ISODATA. The overall accuracy of classification in five samples is 83.6%, and the kappa index is 0.82. Finally, the
unique intra-annual NDVI curve of each vegetation class is displayed.
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