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The aim of this study is to analyze the urban land use changes occurred in the central part of Ulaanbaatar, the capital city of Mongolia, from 1930 to 2008 with a 10-year interval using geographical information system (GIS) and very high-resolution remote sensing (RS) data sets. As data sources, a large-scale topographic map, panchromatic and multispectral Quickbird images, and TerraSAR synthetic aperture radar (SAR) data are used. The primary urban land use database is developed using the topographic map of the study area and historical data about buildings. To extract updated land use information from the RS images, Quickbird and TerraSAR images are fused. For the fusion, ordinary and special image fusion techniques are used and the results are compared. For the final land use change analysis and RS image processing, ArcGIS and Erdas imagine systems installed in a PC environment are used. Overall, the study demonstrates that within the last few decades the central part of Ulaanbaatar city is urbanized very rapidly and became very dense.

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The aim of this study is to fuse high resolution optical and microwave images and classify urban land cover types using a refined Mahalanobis distance classifier. For the data fusion, multiplicative method, Brovey transform, intensity-huesaturation method and principal component analysis are used and the results are compared. The refined method uses spatial thresholds defined from local knowledge and the bands defined from multiple sources. The result of the refined Mahalanobis distance method is compared with the result of a standard technique and it demonstrates a higher accuracy. Overall, the research indicates that the combined use of optical and microwave images can notably improve the interpretation and classification of land cover types and the refined Mahalanobis classification is a powerful tool to increase classification accuracy.