Keshtkar, H., Azarnivand, H., Arzani, H., Alavipanah, S., Mellati, F. (2012). Land Cover Classification Using IRS-1D Data and a Decision Tree
Classifier. Desert, 17(2), 137-146. doi: 10.22059/jdesert.2013.32030
H.R. Keshtkar; H. Azarnivand; H. Arzani; S.K. Alavipanah; F. Mellati. "Land Cover Classification Using IRS-1D Data and a Decision Tree
Classifier". Desert, 17, 2, 2012, 137-146. doi: 10.22059/jdesert.2013.32030
Keshtkar, H., Azarnivand, H., Arzani, H., Alavipanah, S., Mellati, F. (2012). 'Land Cover Classification Using IRS-1D Data and a Decision Tree
Classifier', Desert, 17(2), pp. 137-146. doi: 10.22059/jdesert.2013.32030
Keshtkar, H., Azarnivand, H., Arzani, H., Alavipanah, S., Mellati, F. Land Cover Classification Using IRS-1D Data and a Decision Tree
Classifier. Desert, 2012; 17(2): 137-146. doi: 10.22059/jdesert.2013.32030
Land Cover Classification Using IRS-1D Data and a Decision Tree
Classifier
2Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran
3Professor, Faculty of Geography, University of Tehran, Tehran, Iran
4Instructor, Faculty of Environment and Natural Resources, Ferdowsi University, Mashhad, Iran
Abstract
Land cover is one of basic data layers in geographic information system for physical planning and environmental monitoring. Digital image classification is generally performed to produce land cover maps from remote sensing data, particularly for large areas. In the present study the multispectral image from IRS LISS-III image along with ancillary data such as vegetation indices, principal component analysis and digital elevation layers, have been used to perform image classification using maximum likelihood classifier and decision tree method. The selected study area that is located in north-east Iran represents a wide range of physiographical and environmental phenomena. In this study, based on Land Cover Classification System (LCCS), seven land cover classes were defined. Comparison of the results using statistical techniques showed that while supervised classification (i.e. MLC) produces an overall accuracy of about 72%; the decision tree method, which improves the classification accuracy, can increase the results by about 7 percent to 79%. The results illustrated that ancillary data, especially vegetation indices and DEM, are able to improve significantly classification accuracy in arid and semi arid regions, and also the mountainous or hilly areas.
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