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Land use/land cover (LULC) is one of the serious phenomena that can influence human activities. Spatial-temporal analysis of land-use/land-cover (LULC) change, as well as the monitoring and modeling of urban expansion, are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans, and policies and by power, politics, and governance in many developing countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. This paper monitored the changes of the urban suitability area, and prediction of LULC with urban growth using multi-criteria decision analysis and cellular automata (CA)-Markov chain model. The province of Orkhon is the most intensive development area in Mongolia, and a transitional zone on urban area expansion with residential, mining, and industries, where is sampled in this study. The methods are provided by the urban suitability area based on the analytic hierarchy process (AHP) with factor variables and simulation of LULC based on the CA-Markov chain model. The urban suitability area analysis is helpful to validate the prediction of an urban area by relative operating characteristic (ROC) curve. The CA-Markov chain model was calibrated with data from 1989 to 2019 and used to predict expansions for 2030 and 2040 with two datasets of explanatory variables including slope, forest, river, specially protected areas, road, railroad, and urban centers in an urban and non-urban area. All analyses were based on Landsat imageries (TM, ETM+, and OLI) that were used to derive main land use classes. The results show that the urban, agriculture and mining area were expanded intensively. Forest was decreased in the last few years caused by human influences. Besides, the simulation results were validated by ROC curves with urban suitability analysis. Finally, this kind of study results is very important and useful to land managers and urban planners.
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