Change vector analysis: A technique for the multispectral monitoring of land cover and condition

Abstract

Change vector analysis (CVA) is a robust approach for detecting and characterizing radiometric change in multispectral remote sensing data sets. CVA is reviewed as a useful technique to: (1) process the full dimensionality of multispectral/multi-layer data so as to ensure detection of all change present in the data; (2) extract and exploit the 'components' of change in multispectral data; and (3) facilitate composition and analysis of change images. Examples drawn from various projects are included throughout this methodological discussion, in order to illustrate the CVA approach and suggest its potential utility.