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Change detection is one of the most important and widely requested applications of terrestrial remote sensing. Despite a wealth oftechniques and successful studies, there is still a need for research in remote sensing science. This paper addresses two importantissues: the temporal and spatial scales of change maps. Temporal scales relate to the time interval between observations forsuccessful change detection. We compare annual change detection maps accumulated over five years against direct change detectionover that period. Spatial scales relate to the spatial resolution of remote sensing products. We compare fractions from 30m Landsatchange maps to 250m grid cells that match MODIS change products. Results suggest that change detection at annual scales betterdetect abrupt changes, in particular those that do not persist over a longer period. The analysis across spatial scales stronglyrecommends the use of an appropriate analysis technique, such as change fractions from fine spatial resolution data for comparisonwith coarse spatial resolution maps. Plotting those results in bi-dimensional error space and analyzing various criteria, the “lowestcost”, according to a user defined (here hyperbolic) cost function, was found most useful. In general, we found a poor match betweenLandsat and MODIS-based change maps which, besides obvious differences in the capabilities to detect change, is likely related tochange detection errors in both data sets

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Land cover change analysis in Mexico using 30m Landsat and 250m MODIS data