Multitemporal change analysis of multispectral imagery using principal components analysis

Early change analysis studies established the fundamental basis for applying the principal components analysis (PCA) transformation to remote sensing images acquired on two dates. There are an increasing number of studies, however, which extend this basis to longer image time series with little concern for its appropriateness. In particular, when multispectral and multitemporal data are used in the same analysis, the components may be difficult to interpret since they would contain not only temporal variation, but spectral changes as well. In this paper we seek to establish an appropriate ordination technique to condense the multispectral information from each date prior to multitemporal PCA. We find that the Normalized Difference Vegetation Index (NDVI) provides superior results because it produces annual composites with a strong physical basis.