APPLICATION OF STANDARDIZED PRINCIPAL COMPONENT ANALYSIS TO LAND-COVER CHARACTERIZATION USING MULTITEMPORAL AVHRR DATA

The concept of a vegetation vector has been developed to better visual ize and characterize land-cover at regional scales. The vegetation vec tor is derived from long time-series multitemporal normalized differen ce vegetation index (NDVI) data sets from the National Oceanic and Atm ospheric Administration (NOAA) Advanced Very High Resolution Radiomete r (AVHRR) by means of principal component analysis (PCA). The vegetati on vector can characterize vegetation cover based upon both the spatia l variation of the magnitude of NDVI and the seasonal variation of NDV I. The PCA study showed that the area under analysis must exhibit a va riety of dissimilar vegetation communities in terms of density and phe nology to successfully derive these two factors. When the PCA was appl ied to the entire state of Arizona, these two factors were derived as the first two principal components (PCs). However, when the PCA was ap plied to subset areas extracted from the entire study area by overlayi ng a vegetation map compiled through bioclimatological and ecological studies, the first two PCs did not always represent these two factors. This indicated that these two factors were not always the major cause of variation in NDVI for some vegetation communities. In this study t he vegetation vector was constructed utilizing the first two PCs deriv ed from the entire study area. Analysis of histograms of the direction of the vegetation vector for each community found in the vegetation m ap could be used to characterize most of the communities in terms of p hotosynthetic activity and phenology. A profile of the histogram could be interpreted as characteristic of the community. In addition, the r ange exhibited by the histogram could be used as a measure of the homo geneity/heterogeneity of the community based upon photosynthetic activ ity and phenology. Graphical projection of the mean vegetation vectors could be used to visualize characteristics and relationships between communities. The position of the plot represents the mean characterist ics of the community. The difference in the mean vegetation vector bet ween communities represents the similarity/dissimilarity of the charac teristic between communities. These techniques represent a simple mean s of visualizing many vegetation communities and should facilitate cha racterizing land cover at global scales. (C) Elsevier Science Inc., 19 96.