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 environmentalmonitoring. 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 datasuch as vegetation indices, principal component analysis and digital elevation layers, have been used to perform imageclassification using maximum likelihood classifier and decision tree method. The selected study area that is located innorth-east Iran represents a wide range of physiographical and environmental phenomena. In this study, based on LandCover Classification System (LCCS), seven land cover classes were defined. Comparison of the results using statisticaltechniques showed that while supervised classification (i.e. MLC) produces an overall accuracy of about 72%; thedecision tree method, which improves the classification accuracy, can increase the results by about 7 percent to 79%. Theresults illustrated that ancillary data, especially vegetation indices and DEM, are able to improve significantlyclassification accuracy in arid and semi arid regions, and also the mountainous or hilly areas.

Keywords

• land cover classification system (lccs)
• irs-1d satellite
• maximum likelihood
• ancillary data