Forecasting Land Use Changes and Land cover in Sirjan Plain Using Markov-Cellular mode

. Land use prediction models are essential for planning sustainable land use. This is especially needed in developing countries where activities such as deforestation, development of agricultural lands, and degradation which intensifies the phenomenon of desertification. Since land use changes occur on a large scale over time, the use of remote sensing science is essential in investigating this phenomenon. Using data such as multi-time and large coverage, this data can be used to prepare land use maps and survey them in different time periods at the lowest cost and in a short time, and from their ratio, changes can be predicted for the future. The results show that the region has a sensitive ecosystem, land use change is happening rapidly. Therefore, if the current strategy of land use in this region continues without consideration for sustainable development, severe land degradation and desertification of the region in the future is inevitable.In this research, we studied trend of land use change in Sirjan plain that located in the west of Kerman province during period (2017-1990 using Landsat satellite images (TM and OLI sensirs) . likelihood maximum method applied to classifying these images. kapa coefficient was used to evaluate the accuracy of the maps and modeling. For predicting land use changes for year 2026, we used CA_Markov automatic cell .studing land use changes trend in Sirjan plain during (1990-1990) showed that the largest area covered by bayer lands with low coverage. this level user has decreased from 88.04% in 1990 to about 84.11% in 2017. During the study period, saline, urban area and agricultural lands has been increasing. saline land increased (with 6.05%) in 1990 to about (6.91 %) in 2017. The urban area were increasing trend observed with 0.23% of the total area in 1990 to 0.85% in 2017. The trend of changes in this use in the first period (2006-1990) has increased from 291.51 hectares to about 952.99 hectares in the second period (2017-2006). The results of evaluating the accuracy of the maps produced for 1990, 2006 and 2017 were 86.7, 89.7 and 88.7, respectively, and the Kappa coefficient for these years was 0.82, 0.84 and 86, respectively. Simulation of land use changes for 2026 showed decresing trend observed in bayer lands with 2.15% about 3792.48 hectares and saline, urban area and agricultural Results increasing trend observed with 2.62%, 64.54% and 28.55 %t, and 155.74, 291.51 and 3185.32 hectares, respective.land use changing has important role in stability ecosystem and its services, and its negative effects are the reduction of the ecological and biological power of the earth, which is also known as the cause of desertification. Land use estimates showed that more than 90% this the area. Based on the prediction for 2026 showed decreasing trend in bayer lands and increasing trend in saline, urban area and agricultural lands. Therefore, if the current strategy of land use in this region continues without consideration for sustainable development until 2026, The rapid changes in land use in different parts of the world have attracted a lot of attention because it has a considerable impact on the physical and economic conditions ecosystems and communities in them. Land use change is a very important indicator for understanding the interaction between human activities and the environment. Land use prediction models are essential for planning sustainable land use. This is especially needed in developing countries where activities such as deforestation, development of agricultural lands, and degradation which intensifies the phenomenon of desertification. Since land use changes occur on a large scale over time, the use of remote sensing science is essential in investigating this phenomenon. Using data such as multi-time and large coverage, this data can be used to prepare land use maps and survey them in different time periods at the lowest cost and in a short time, and from their ratio, changes can be predicted for the future. The results show that the region has a sensitive ecosystem, land use change is happening rapidly. Therefore, if the current strategy of land use in this region continues without consideration for sustainable development, severe land degradation and desertification of the region in the future is inevitable.In this research, we studied trend of land use change in Sirjan plain that located in the west of Kerman province during period (2017-1990 using Landsat satellite images (TM and OLI sensirs) . likelihood maximum method applied to classifying these images. kapa coefficient was used to evaluate the accuracy of the maps and modeling. For predicting land use changes for year 2026, we used CA_Markov automatic cell .studing land use changes trend in Sirjan plain during (1990-1990) showed that the largest area covered by bayer lands with low coverage. this level user has decreased from 88.04% in 1990 to about 84.11% in 2017. During the study period, saline, urban area and agricultural lands has been increasing. saline land increased (with 6.05%) in 1990 to about (6.91 %) in 2017. The urban area were increasing trend observed with 0.23% of the total area in 1990 to 0.85% in 2017. The trend of changes in this use in the first period (2006-1990) has increased from 291.51 hectares to about 952.99 hectares in the second period (2017-2006). The results of evaluating the accuracy of the maps produced for 1990, 2006 and 2017 were 86.7, 89.7 and 88.7, respectively, and the Kappa coefficient for these years was 0.82, 0.84 and 86, respectively. Simulation of land use changes for 2026 showed decresing trend observed in bayer lands with 2.15% about 3792.48 hectares and saline, urban area and agricultural Results increasing trend observed with 2.62%, 64.54% and 28.55 %t, and 155.74, 291.51 and 3185.32 hectares, respective.land use changing has important role in stability ecosystem and its services, and its negative effects are the reduction of the ecological and biological power of the earth, which is also known as the cause of desertification.