The Change of NDVI Time Series Based on Change Vector Analysis in China, 1983-1992
1983—1992年中国陆地植被NDVI演变特征的变化矢量分析

Change vector analysis and principal components analysis in NDVI time trajectories space are powerful tools to analyse the land cover change. The length of the change vector indicates the magnitude of the change, while its direction indicates the nature of the change. This change detection method is applied to two remotely sensed indicators of land surface conditions, i.e. NDVI and spatial structure, with aims to improving the capability to detect and categorize land cover change. The magnitude and type of changes and the changes in spatial structure indicator in China from 1983 to 1992 are calculated using the above mentioned analysis methods. Through the research, the main conclusions are drawn as follows:1) The change vector magnitude of NDVI is different apparently between East and West China. Larger changes of NDVI lie to the Southeast coastal areas, Taiwan and Sichuan, Yunnan province (monsoon area of Southeast and Southwest China). The changes in Northwest area and Qinghai Xizang Plateau are little. It can conclude that the monsoon climate and the human activity impact apparently on the land cover dynamic of East area (especially the different kinds of crops).2) NDVI of land cover changes is derived from many factors (climate, hydrology, landform, soil, land use etc.). Although the basic tendency of the climate in North China is that the temperature becomes increasing and precipitation get decreasing (drying), no obvious changes are presented in large scale in this region. Landform types and vegetation types in North China are the key factors for NDVI changes.3) The spatial structure indicator is able to detect changes in the seasonal ecosystem dynamic for spatially heterogeneous landscapes. Changes of spatial structure indicators of NDVI mainly locate in South China, reflecting the high heterogeneity vegetation during the growth and decay processes. The areas composed of Tianshan Mountains, Changbai Mountains, and Xiaoxinganling Mountains reflect the impact of landform types on the spatial structure index.