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Abstract
Climate change is an important environmental issue because the melting processes of glaciers and snow density are sensitive to climate change. Today, a variety of satellite sensors such as AVHRR, MODIS, GEOS, MERIS are available for snow monitoring and are widely used to investigate and investigate the fluctuations and changes in snow cover globally. Modis sensor has been considered more because of its global spatial coverage with suitable spatial accuracy and frequent temporal coverage on different scales , Therefore, in the present study, snow products of this sensor were used. In this study, after collecting statistics and data on snow-related days during the statistical period (1989-2018) in three provinces of Kermanshah, Ilam and Lorestan, they were processed using Modis snow cover data in middle Zagros as well as remote sensing techniques, Finally, the snow cover changes in the study area were studied in detail. NDSI index was used in MODIS sensor products to detect snow cover. Consequently, in order to differentiate pixels and identify different phenomena, the received images were processed in GIS environment. .  Investigation of snow cover changes in different seasons using Modis sensor images shows that most of the studied area has a significant decreasing trend, especially in the elevated areas of the study area And only in the western and southwestern regions of the study area, there is no specific decreasing trend. Also, the study of snow covered days during the study period indicates a decrease in middle Zagros snow cover and these changes have been intensified in recent years, especially in snow-covered areas of the region. Also, changes in winter and snow-capped and elevated areas were more and more severe than other seasons and other regions in the study area.           

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Expansion of urban areas has impacted on climate in local and global scale. As a result, residents’ life would be influenced accordingly. In this study, the effects of changes in land cover and land use change on climate of Shiraz and Fasa were studied in a long-term period. To ensure the normal data, Kolmogrov-Smirnoff test was employed. The minimum and maximum temperature parameters and relative humidity were evaluated using non-parametric Mann-Kendall and regression method in a 45-year period (1966-2010). The results showed a decreasing trend for the minimum temperature and humidity for Fasa and minimum and maximum relative humidity for Shiraz whereas other quantities showed an increasing trend. The study examined the effects of land use changes and land cover types in the 23-year period (1987-2010) using images of LANDSAT satellite of TM sensor on temperature pattern in two cities. During these 23 years, a reduction of 113 square kilometers for barren land of Shiraz and 5/7 square kilometers for barren land of Fasa and extending 110 kilometers residential users and 3/5 square kilometers in Shiraz and Fasa were reported.

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Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of ​​1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.