Document Type : Original Articles
Assistant Professor, Department of Natural Resources, Faculty of Agriculture, University of Ilam
MSc of Watershed Management, Faculty of Natural Resources, Yazd University
Professor, Faculty of Natural Resources, University of Tehran
The temporal and accurate change detection of earth surface features is extremely important for understanding the relationships and interaction between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for environmental change detection in recent decades. In this study, images of landsat (TM) 1988 and landsat (ETM+) 2001 were analyzed using five change detection techniques over 80,470 hectares in the region of Daresher in Ilam Province. Change detection techniques considered were image differencing, image ratioing, NDVI differencing, change vector analysis (CVA) and post-classification comparison. In this study, a statistical method was used for determining the change threshold. According to the results, the threshold level was set at ±1 standard deviation from the mean. After determining optimal threshold, areas having decreasing, increasing change and no changes was determined. Based on ground data, field visit and Google Earth, accuracy assessment of change detection techniques was carried out using overall accuracy and Kappa coefficient. According to the results, NDVI differencing with an overall accuracy of 98.5 and a Kappa coefficient of 97% showed the highest accuracy among the techniques applied while, in contrast, band ratioing with an overall accuracy of 72.5 and a Kappa coefficient of 50% had the lowest accuracy in land use/land cover change in the study area.