Detection and prediction of land use/ land cover changes using Markov chain model and Cellular Automata (CA-Markov), (Case study: Darab plain)

unprincipled changes in land use are major challenges for many countries and different regions of the world, which in turn have devastating effects on natural resources, Therefore, the study of land-use changes has a fundamental and important role for environmental studies. The purpose of this study is to detect and predicting of land use/ land cover (LULC) changes in Darab plain through the Markov chain model and Cellular Automata or CA-Markov using the Geographic Information System (GIS) and technique Remote Sensing (RS). Thus, first, using Landsat satellite images and based on the maximum likelihood method, classification maps related to three different periods, including 1989 - 1999, 1999 - 2009, and 2009 - 2019 were prepared in seven different classes. Then, using two maps of 1989 and 2009 as basic and leading maps and combining the Markov chain model and automatic cells, a forecast map related to 2019 was produced. In the next step, the projected map of  2019 with the real map of the same year was evaluated based on the amount of agreement and disagreement parameters. Finally, after ensuring the accuracy of the forecast model, the land use map related to the years 1989 and 2019 entered the model and the land use map for the next 10 years, ie 1408, was predicted. According to the produced map, the types of Landuse will have significant changes and transformations, so that from the area of agricultural lands 419.4 hectares, from the area of mountains 8529.8 hectares, from the area of pastures 4287.9 hectares and finally from The area of water resources will be reduced by 270.88 hectares. On the other hand, the area of barren lands will be increased by 11113.6 hectares, garden use by 1926/31 hectares, and residential use by 459.08 hectares.