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

Document Type: Research Paper


1 MSc. Graduate, University of Tehran, Karaj, Iran

2 Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Professor, Faculty of Geography, University of Tehran, Tehran, Iran

4 Instructor, Faculty of Environment and Natural Resources, Ferdowsi University, Mashhad, Iran


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.