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Sustainable water resources management plans depend on reliable monitoring of land use –land cover (LULC) changes. The use of the remote sensing techniques in LULC changes detection brings consistency and reliability to the decision maker at regional scale. Three temporal data sets of images were used to obtain the land cover changes in this study: Landsat-5 Thematic Mapper (TM) acquired in 1984, and Landsat-7 enhanced Thematic Mapper acquired in 2000 and 2005 consequently. Each temporal data set consists of four Landsat scenes, which were mosaicked to cover the whole Nile Delta. Two different supervised classification algorithms were implemented to produce classification maps in thematic form. Support vector machine showed higher classification accuracies in comparison with maximum likelihood classification. The results indicated that the rapid imbalance changes occurred among three land cover classes (urban, desert, and agricultural land). These changes powered the land degradation and land fragmentation processes over the agricultural land exclusively due to urban encroachment. Slight land cover changes were detected between fish farms and surface water land cover classes.
