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 effectivemaps. The selection of appropriate classification methods plays an important role. Amongvarious methods existing for image classification, artificial neural network method is ofhigh accuracy. In present study, TM images of 1987, and ETM+ images of 2000 and 2006were 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 thisbasin during 3 periods of time from 1987 to 2000 and 2000 to 2006 were examined. In thisstudy, initially, Land sat data for intended years were corrected geometrically andradiometric ally. Next, different land use classes were defined and training samplesobtained via field visits. The obtained results show that, over time period of 1987-2000, theextent of low-density rangeland and farmland in study region had been increased by 89.09and 321.08 ha, respectively, while good rangeland and fair rangeland faced a decliningtrend of 358.29 ha and 48.89 ha. Also, during time period of 2000-2006, the extent of poorrangeland 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 haand 648.1 ha. Accuracy of vegetation maps resulting from satellite data classification usingalgorithm of artificial fuzzy ARTMAP neural network was 90.97% and 94% for TM(1987) images and ETM+ (2000,2006) respectively which indicates high accuracy ofARTMAP algorithms for classifying satellite. Therefore, this study proves high efficiencyand potential of artificial fuzzy ARTMAP neural network for classification of remotesensing images.