Comparison of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series

The once remote and inaccessible forests of Guatemala's Maya Biosphere Reserve (MBR) have recently experienced high rates of deforestation corresponding to human migration and expansion of the agricultural frontier. Given the importance of land-cover and land-use change data in conservation planning, accurate and efficient techniques to detect forest change from multi-temporal satellite imagery were desired for implementation by local conservation organizations. Three dates of Landsat Thematic Mapper imagery, each acquired two years apart, were radiometrically normalized and preprocessed to remove clouds, water, and wetlands, prior to employing the change-detection algorithm. Three change-detection methods were evaluated: normalized difference vegetation index (NDVI) image differencing, principal component analysis, and RGB-NDVI change detection. A technique to generate reference points by visual interpretation of color composite Landsat images, for Kappa-optimizing thresholding and accuracy assessment, was employed. The highest overall accuracy was achieved with the RGB-NDVI method (85 percent). This method was also preferred for its simplicity in design and ease in interpretation, which were important considerations for transferring remote sensing technology to local and international non-governmental organizations.