Study on the Trend of Range Cover Changes Using Fuzzy ARTMAP Method and GIS

The major aim of processing satellite images is to prepare topical and effective maps. The selection of appropriate classification methods plays an important role. Among various methods existing for image classification, artificial neural network method is of high accuracy. In present study, TM images of 1987, and ETM+ images of 2000 and 200 were analyzed using artificial fuzzy ARTMAP neural network within Mehrgan region, Kermanshah province, Iran, with an area of 5957 ha changes in range cover state in this basin during 3 periods of time from 1987 to 2000 and 2000 to 2006 were examined. In this study, initially, Land sat data for intended years were corrected geometrically and radiometric ally. Next, different land use classes were defined and training samples obtained via field visits. The obtained results show that, over time period of 1987-2000, the extent of low-density rangeland and farmland in study region had been increased by 89.09 and 321.08 ha, respectively, while good rangeland and fair rangeland faced a declining trend of 358.29 ha and 48.89 ha. Also, during time period of 2000-2006, the extent of poor rangeland and farmland within study region has increased by 64.98 and 727.12 ha, respectively, while good rangeland and fair rangeland faced a declining trend of 144.01 ha and 648.1 ha. Accuracy of vegetation maps resulting from satellite data classification using algorithm of artificial fuzzy ARTMAP neural network was 90.97% and 94% for TM (1987) images and ETM+ (2000,2006) respectively which indicates high accuracy of ARTMAP algorithms for classifying satellite. Therefore, this study proves high efficiency and potential of artificial fuzzy ARTMAP neural network for classification of remote sensing images.