Application of Different Methods of Decision Tree Algorithm for Mapping Rangeland Using Satellite Imagery (Case Study: Doviraj Catchment in Ilam Province)

Abstract. Using satellite imagery for the study of Earthʹs resources is attended by many researchers. In fact, the various phenomena have different spectral response in electromagnetic radiation. One major application of satellite data is the classification of land cover. In recent years, a number of classification algorithms have been developed for classification of remote sensing data. One of the most notable is the decision tree. The aim of this study was to compare three types of decision trees split algorithm for land cover classification in Doviraj catchment in Ilam province, Iran. For this, propose, first, the geometric and radiometric corrections were performed on the 2007 ETM+ data. Field data as training sites were collected in the various classes of land use. The results of image classification accuracy assessment showed that the Gini split classification. With kappa value 89.98 and the entire accuracy 91.17% was significantly higher, then categorization of branching and the branching ratio and Entropy with kappa values of 88.45 and 90.65 and the entire accuracy of 86.21 and 86.15%, respectively.