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This paper explored the capability of Landsat-8 Operational Land Imager (OLI) for post classification change detection analysis and\udmapping application because of its enhanced features from previous Landsat series. The OLI support vector machine (SVM)\udclassified data was successfully classified with regard to all six test classes (i.e., open land, residential land, forest, scrub land,\udreservoir water and waterway). The OLI SVM-classified data for the four seasons (i.e. winter, spring, summer and autumn seasons)\udwere used for change detection analysis of six situations; situation1: winter to spring seasonal change detection resulted reduction in\udreservoir water mapping and increases of scrub land; situation 2: winter to summer seasonal change detection resulted increase in\uddam water mapping and increase of scrub land. winter to summer which resulted reduction in dam water mapping and increase of\udvegetation; situation 3: winter to summer seasonal change detection resulted increase in increase in open land mapping; situation 4:\udspring to summer seasonal change detection resulted reduction of vegetation and shallow water and increase of open land and\udreservoir water; situation; 5: spring to autumn seasonal change detection resulted increase of reservoir water and open land; and\udSituation 6: summer to autumn seasonal change detection resulted increase of open land. OLI SVM classified data found suitable for\udpost classification change detection analysis due to its resulted higher overall accuracy and kappa coefficient

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

EVALUATE THE CAPABILITY OF LANDSAT8 OPERATIONAL LAND IMAGER FOR SHORELINE CHANGE DETECTION/INLAND WATER STUDIES