Monitoring and forecasting of land use change progression by applying markov chain model and land change modeler (Case study of Dehloran Bartash plains, Ilam)

Nowadays modeling and anticipation of land use changes by application of satellite images can be a very useful tool for describing relations between natural environment and human activities to help planners to make decisions in complicated conditions. There are various methods for anticipation of land uses and coverage, in which the Markov chain model is one of them. In this research, land use changes in Bartash plain in Dehloran which is located in Ilam province in the area of 135244 hectares in 3 time periods (1988, 2001 and 2013) of Landsat satellite images, providing land use map in 6 classes (low density forest, medium-dense grassland, poor grassland, agricultural, alluvium sediments and non-vegetated lands) by application of Kohonens neural network and also Markov anticipation model and Land change modeler (LCM) approach was predicted for the year 2030. The classification results showed the rate of demolition and a reduction of the area of low density forests and medium grassland land uses and increase in area of other land uses. Reduction of low density forest and the medium grassland area and increasing growth of other land uses demonstrated the overall destruction in the region and replaced with poorer land uses. At the end, by application of the Markov anticipation model and LCM modeling approach, land use changes were anticipated for the year 2030. The results of changes anticipation matrix based on maps of years 2001 and 2013 showed that it is likely that in the period of 2013-2030, 45% of low density forest, 71% of medium grassland, 96% of poor grassland, 81% of agricultural lands, 93% alluvialvium sediments and 100% of non-vegetated lands remain changeless; non-vegetated lands have the most stability and low density forest have the least stability.