During the last years, following an increase in damages due to occurrence of landslides, human has decided to reduce such losses. Therefore, identifying regions susceptible to landslide and classifying them can partly help man to prevent from happening above phenomena. The current study aims to demonstrate factors contributing to occurrence of landslide in Candrigh Chay basin and then, flattening the basin regarding risks related to landslide event using one methods, Network Analysis Process. In this direction, as respects to basin morphology and also, findings of past researches , ten factors involved in occurring landslides in Candrigh Chay Basin were recognized and used : lithology , land use , rainfall , slope , slope aspect , road slide , sluice power index (SPI), sediment transfer index (STI).Network Analysis Process(ANP) Method was implemented to score and classify factors and  scales. Was carried out in order to measure classes of each parameter. The final map showed that the basin has 4 classes considering landslide. Findings reveal flats with low risk and flats with high risk have the least area in Candrigh Chay basin. On the other hand, flats with medium risk and high risk allocate the most area of basin for themself. Comparing slid surfaces with flats facing risks indicates regions located at high and average risk class possess most areas of slide surfaces so that a flat involving very high risk  and a flat with high risk devote 35 percent (79km²) and 32/6 (72km²) of landslides for themselves, respectively. In other words, more than 77 percent of landslides conform to flats covering very high and average risk. Moreover, petrology, slope, and, sluice slide were identified as the most effective agents in occurring land_slides. One models, Network Analysis Process (ANP) were utilized through this project                 

Studies have shown that the role of thermal temperature measurement in studying and estimating surface temperature is very important. Earth surface temperature is an important indicator in the study of equilibrium energy models on the ground at the regional and global scale. Due to the limitation of meteorological stations, remote sensing can be a good alternative to earth surface temperature estimation. The main objective of this study is to monitor the surface temperature of the Earth using satellite imagery and a relationship that can have a surface temperature with land use. For this purpose, the relevant images were first obtained and the necessary pre-processes were applied to each one. Then it was compared to modeling and classification of images. Firstly, in order to study land use change, land use classification map was extracted for each two years using a controlled classification method. Then, to study the land use change, the land use change map was extracted for a period of 28 years (1987-2015). Became finally, in order to monitor the surface temperature, the surface temperature map of Meshginshahr was extracted. The results showed that there is a strong relationship between land use and surface temperature. High-vegetation areas and low-temperature blue areas. Also, rainfed farming has the highest average temperature relative to adjacent areas, which indicates the dryness of agricultural products in the Meshginshahr city.