In recent years, a close link between vegetation change and climate change has been established. Vegetation change can be detected with remotely sensed images, especially with normalized difference vegetation index time series records. We used change vector analysis, especially change vector magnitude (CV magnitude), as an indicator to better understand vegetation change. Twenty-one layers of CV magnitude for each 10-day period from April to October have been acquired. Maxima, range, standard deviation, mean, and minima of CV magnitude were obtained and analyzed, identifying 11 regions with different types of vegetation change during different 10-day periods. In addition, the months of maximum CV magnitude were determined to help predict future vegetation change. The following conclusions were drawn: (a) CV magnitude can serve as an indicator to compare vegetation conditions among different years; (b) 11 typical regions were identified in the study area that show vegetation changes between 1999 and 2006;
Keywords: CV magnitude; Change vector analysis (CVA); NDVI time series; Vegetation dynamic.
(c) the months with maximum CV magnitude can be used to better understand the key periods of vegetation change during the growing season from April to October.