NALC Land Cover Change Detection Pilot Study: Washington D.C. Area Experiments

Abstract

As a part of North American Landscape Characterization (NALC) project, 75 change-detection techniques and variations were systematically tested and evaluated using both visual and statistical methods. These change-detection experiments were conducted in the Washington, D.C. region using a standard NALC Landsat Multispectral Scanner (MSS) data set. While more testing of the NALC data is needed in geographically diversified regions, our initial results suggest that the automated scattergram controlled regression normalized image differencing and normalized difference vegetation index differencing outperform most other change-detection techniques. These techniques are worthy of further testing and of large-scale applications for NALC data.