Land Use Change Modeling Through Markov Chain by Using of GIS and Satellite Imagery, Case Study: Ghom Province

In the last decades, urban population growth and uncontrolled urban growth in all around the world, especially in developing countries, caused many environmental issues such as diminishment of agricultural land use and natural resources as well. Land Coverage and land use change is one of the main components of the current strategy for natural resources management and environmental change monitoring. In this way, this research aimed to model the land use changes in Ghom province, through Markov chain by using of GIS and remote sensing. In this order, using time series images of Landsat satellites (4, 5 and 8) in four different dates within 30 years and performing image pre-processing (Atmospheric and topographic corrections), main land use applications such as residential and industrial areas, agricultural lands, pastures, desert and salt marsh and Salt Lake and water were extracted and studied. The 30-years map analysis showed that the area of pasture in the province decreased within 1986 to 2016, so that the area decreased from 5600 to 4848 square kilometers. The area of agricultural land on this period dropped about 100 square kilometers and with urbanization development, the extent of residential and industrial areas increased from 130 to 364 square kilometers. Finally, because the prediction of land use changes is effective in managing the natural resources and protection of agricultural lands around the cities, we used the model to predict land use changes for the next 10-year (2026) using Markov chain. The results showed an expansion of residential/industries areas, desert and salt marsh and a reduction in agricultural lands. This research revealed that the remote sensing time series data could be very effective in land use changes modeling.