Land use/cover change detection in 2025 with CA-Markov chain model (case study: Esfarayen)

Modeling and prediction of land use/cover changes is an essential need for planning sustainable use of land in country like Iran with its high level of changes in land uses and land covers. This study aimed to analyze the capability of CA Markov prediction model and Landsat satellite images for land use/cover change detection within the framework of “Iran at 1404 prospective” in Esfarayen region، North Khorasan province for year 2025. The results of CA Markov modeling showed a reduction about 5000 and 400 ha in irrigated agricultural lands and rangeland class in 2025، respectively. Furthermore، the model predicted that the poor rangeland class and Esfarayen city will be increased 30 and 450 ha in 13 years from 2009 to 2025. According to the results، if current land use continues to intensify regardless of sustainable development considerations، severe degradation in the natural resources of the study area is unavoidable in the future. Overall، the results demonstrate that satellite remote sensing data and CA Markov model can be successfully used to predict land use/ cover changes and the extracted maps can help policy makers to make better decision for the future of the region.