Land Cover Classification Using IRS-1D Data and a Decision Tree Classifier

Abstract

Land cover is one of basic data layers in geographic information system for physical planning and environmental<br />monitoring. Digital image classification is generally performed to produce land cover maps from remote sensing data,<br />particularly for large areas. In the present study the multispectral image from IRS LISS-III image along with ancillary data<br />such as vegetation indices, principal component analysis and digital elevation layers, have been used to perform image<br />classification using maximum likelihood classifier and decision tree method. The selected study area that is located in<br />north-east Iran represents a wide range of physiographical and environmental phenomena. In this study, based on Land<br />Cover Classification System (LCCS), seven land cover classes were defined. Comparison of the results using statistical<br />techniques showed that while supervised classification (i.e. MLC) produces an overall accuracy of about 72%; the<br />decision tree method, which improves the classification accuracy, can increase the results by about 7 percent to 79%. The<br />results illustrated that ancillary data, especially vegetation indices and DEM, are able to improve significantly<br />classification accuracy in arid and semi arid regions, and also the mountainous or hilly areas.