Modeling Spatial and Temporal Changes in Land-Uses and Land Cover of the Urmia Lake Basin Applying Cellular Automata and Markov Chain

Land use and land cover change are critical motivations for environmental changes. It mainly arises from human activities, e.g., the expansion of urban areas, the changes in agricultural land areas, and the destruction of water area which rooted in population growth. The present research used a combination of cellular automata (CA) and the Markov chain to simulate the present land-uses in the Lake Urmia Basin using remote sensing data. First, the land-use map was produced by the maximum likelihood classification method using the Landsat satellite imagery for the years 1998, 2008, and 2018. After the integrated CA-Markov approach assessed the model, the land-use maps were predicted for the years 2028 and 2038. The trend of land-use change between 1998 and 2018 revealed that agricultural areas and urban/human-made areas have increased by 3.31 and 2.74 percent, respectively, but water areas and other uses have decreased by 6.87 and 0.71 percent, respectively. The kappa coefficient was estimated at 80% for the model, implying its high accuracy in predicting land-uses. Based on the simulation results for 2028 and 2038, agricultural land area and urban/man-made areas will expand by 40.12 and 476.36% versus those in 1998 whereas water areas and other uses will shrink by 26.67 and 5.80%, respectively. The results can greatly help policymakers and managers of natural resources to make management decisions on land uses in different regions.