In order to land cover change modeling and detect to possibility of predict the future trend of Land Change modeler (LCM) was used. VNIR Data ASTER Sensor of TERRA satellite with spatial resolution of 15m for three periods 2000, 2007 and 2016 from Gilan-e-Gharb forests of Kermanshah province were analyzed. Land cover maps of years 2000, 2007 and 2016 four categories: forest cover, pasture lands, agricultural lands and built-up area areas for each of images were extracted. The results of data analysis in the first period (2000-2007) and the second period (2007-2016) showed the greatest increase in agricultural lands and pasture lands have the greatest decrease area. Based on these changes and by taking eight independent variable, transition potential modeling of 2016 was done using Artificial Neural Network. Then by hard predict model and images were classified of first period (2000- 2007), the land cover map in 2016 using Land Change Modeler was predicted. After evaluating the model, 83.09 and 71.10 overall accuracy was obtained for the first and second periods showed the consistency between prediction map and classified map of year 2016. The land cover maps by entering the second period (2007-2016) to Land Change Modeler the land.

This study aimed at evaluating the supervised pixel-based classification of the maximum likelihood in the examination of the climactic changes of the Urmia Lake catchment area in 1-, 6-, 10-, and 14-year spans and then evaluating and modeling the changes in the gardens neighboring Lake Urmia during a 30-year span and its role in the changes at Lake Urmia water level. In this study, first the Landsat images of the years 1990, 2000, 2014, 2020, and 2021 were downloaded. Then, using the ENVI5.3 software, a classification was made based on the maximum likelihood method. Next, the IDRISSI TERRSET software and CA-MARKOV model were used to model conditions for the year 2051. This model was then analyzed in the GIS software. It was found that the classification based on the maximum likelihood method has been an appropriate one. The results of this classification showed that overall, the maximum changes from 1990 to 2021 has been related to the irrigated gardens and fields. In this period, 3495 square kilometers have been added to these lands, i.e., the number has doubled. Modeling the 2051-year conditions in the light of the transition probability matrix showed that the maximum likelihood of land use changes is in the irrigated garden and farm use. On the other hand, the modeling in this study indicated that there has been a reduction in the expanse of irrigated gardens and fields as well as salt lands in the region. Finally, the model predicted an increase in water area in 2051.

Land cover mapping is important for many planning and management activities. Today, satellite images and remote sensing techniques are extensively used in all sectors including agriculture and natural resources because they provide updated data and high analyzing abilities. In this study, in order to produce land cover map for the northern part of Hamedan province , digital satellite data IRSP6 ( Awifs time series data) were used. First, satellite image geometric correction with a mean square error of less than 0.48 pixels was applied. For image classification, the method of fuzzy classification was used. Finally, the land cover map of the study region was classified into thirteen classes. To assess the classified land cover map precision it was controlled for ground truths with a GPS. Kappa coefficient and overall classification accuracy of fuzzy classificotion were estimated 86 and 88 percent respectively. The results confirmed that the fuzzy clofifier was capable to generate land cover maps and cultivation‎ pattern‎ with high accuracy.