Land use Change Prediction using Markov Chain Compilation Model and Automated Cells (Case Study: Shirkuh)

Specifics regarding land cover and land use is an essential element of the planning process, as it can undoubtedly lead towards the debate around the present plans and patterns and the necessity to modify land use included in a regional plan. In this research, land use maps were prepared using Landsat TM (2000), (2008) and OLI (2016) satellite imaged. Land cover mapping was conducted after pre-processing and processing satellite images, creation of training samples and assessing maps accurate was done by coefficient kappa and overall accuracy. Supervised classification technique with maximum likelihood method were used to show the land use map. In this research, we use the 2000 and 2008 land cover maps to predict the 2016 land cover map and then use the 2008 and 2016 land cover maps to predict the 2024 land cover map.According to the results, with passing time the area of built-up area and mountainous increased with the passage of time while the dense poor rangeland, rich rangeland and agriculture area decreased during the period 2000-2016. The results of predicting changes in the time interval 2016-2024, showed that 55/0 of agriculture, 82% of rich rangeland, 80% of poor rangeland, 51% of built-up, and 0.97 of mountainous will remain unchanged