Generation of land cover map using geospatial tools: A case study from Ardebil, Iran

Abstract

Remote sensing and geographical information system (GIS) have gained importance as powerful and efficient tools for land cover mapping. Digital image classification is generally performed to produce land cover maps from remote sensing data, particularly for large areas. In this study, IRS LISS III data was prepared for producing land cover map of study area, Ardebil, Iran. Digital image processing techniques were conducted for the processes of radiometric and geometric correction and classification for land cover analysis. Digital elevation model (DEM) was performed by digitizing 1/50000 scaled standard topographic map. Slope map were derived by using the DEM as layers in GIS and overlain on the classified image to delineate land cover classes including slope limits of study area for subsequent applications such as land use planning. According to results, the produced land cover map had an overall accuracy equal to 80% indicating an acceptable accuracy for this classification. Key words: Land cover, geographical information system (GIS), remote sensing, confusion matrix, image processing, supervised classification.