Vegetation classification in eastern China using time series NDVI images

Guifeng Han; Jianhua Xu

doi:10.1117/12.749124

14 November 2007 Vegetation classification in eastern China using time series NDVI images

Guifeng Han, Jianhua Xu

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67901N (2007) https://doi.org/10.1117/12.749124
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

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.

Citation Download Citation

Guifeng Han and Jianhua Xu "Vegetation classification in eastern China using time series NDVI images", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67901N (14 November 2007); https://doi.org/10.1117/12.749124

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KEYWORDS

Vegetation

Image classification

Principal component analysis

Climatology

Americium

Remote sensing

Climate change

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