Among the various processes that destroy lands, soil erosion is a complex and hazardous environmental process that constitutes a serious threat to preservation of soil and water resources in Iran and has inflicted its negative effects on the vital ecosystem with the intensified utilization of nature by human beings. The present descriptive and analytic research employed the PSIAC model to study erosion and compare it with other empirical methods and models by investigating the effects of nine important and influential factors in soil erosion and sediment production in various parts of the Sefidrood Basin through giving intra-layer numerical scores to them. Erosion and landform zoning maps and also zoning maps for the nine factors were then extracted. Results indicated that 77 percent of the lands in the plain landforms faced very little erosion and 23 percent little erosion, 14 percent of the lands in the mountainside landforms were exposed to very high erosion rates, 55 percent with high erosion rates, and 31 percent with average and low erosion rates, and 17 percent of the lands in the mountain landforms experienced very high erosion rates, 58 percent high erosion rates, and 25 percent average erosion rates. In other words, the rates of erosion changed from low in the plain landforms to very high in the mountain landforms. This implies that pastures and natural resources are confronted with numerous problems. In general, considering the study carried out on the nine influential factors in the soil erosion model (the PSIAC model), we notice that the mountainside and mountainous regions are more sensitive to erosion and influential factors in erosion are more active in these regions.

The purpose of the present study is to investigate the effects of climate change on the border security of Thalassa Babajani villages. The study area consists of 11 villages of Salas Babajani city with a population of over 2500 people. 333 questionnaires were designed using Cochran formula. The research method was a descriptive-analytic and statistical questionnaire. The results showed that the impacts of climate change were firstly on food security and consequently security in the border villages of Thales Babajani so that most of the population in the study area were concerned about climate change such as flood and drought. Forced to change their livelihoods due to lack of food. The results also showed that with climate change, some former farmers have moved to their village boundaries for daily livelihoods due to climate change and food scarcity, which could in the future be the third border region and its villages. Serious challenges lead to insecurity. Because these people will face the same dangers as engaging in border guards between the two countries, Iran will result in the risk of death for those who were once farmers or ranchers and the security of the area would also be compromised.

The route optimization process is one of the analyzes that can be used when there is a constraint on resources and time, including post-earthquake conditions. In this research, this analysis has been used to solve the Travelling Salesman Problem. In this case, the goal is finding the shortest path between a set of points and the algorithm will try to minimize the transmission costs and target function. This paper due to target, is practical and developmental, due to doing method is descriptive and analytical and due to information gathering method is documental and surveying. In order to implement this problem, by considering to the strict scenario of accessing resources, two algorithms including the Ant Colony Optimization and Imperialist Competition Algorithm in the MATLAB environment with the Dijkstra algorithm in the GIS environment have been used. The view points of the model are areas that prepared to temporary post-earthquake settlement in Gorgan city and the spatial and temporal real-time distances in the urban network are used instead of Euclidian distances. The results of the implementation of the model have shown that the Ant Colony Optimization has performed the route optimization in two parameters of time and distance more effectively than the two dijkstra and Imperialist Competition Algorithm.

Information on a variation of impervious surface is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. This research attempts to detect impervious surfaces and its changes by satellite imagery in Qaemshahr. The relationship between impervious surfaces and changes in land surface temperature in the city was investigated. For this purpose, after obtaining three images in 1978, 2000, and 2017, and performing the necessary preprocessing, the reflection values of the infrared spectrum and ground surface temperature in the study area were calculated. The reflectance of this spectrum was investigated in various land uses vegetation, asphalt and building areas in two parts of the urban and the suburb.  Using the results of ANOVA and Tukey these properties compared to different land uses. By the difference between Permeable surfaces and impervious surfaces, the impervious surface index was calculated. The results of the detection and comparison of the three surveyed images showed that the impervious surfaces in Qaemshahr were significantly increased from 1978 to 2017. In the next step, by calculating the land surface temperature, it was determined that the temperature of the impervious surfaces is higher than the other parts of the study area. An increase in the population of the city followed by an increase in urban construction has led to an increase in impervious surfaces and a reduction in green space and this has caused a rise in city temperatures. The results of this study showed that increasing impervious surfaces has led to an increase of around 4 degrees in the city's temperature. Finally, any increase in the impervious surface at the city will lead to unsustainability in the urban environment, if not accompanied by proper planning.

The study of the roles and functions of cities and their developments is an important question in modern geographic studies. The unbalanced spatial distribution of populations, irregular migration to metropolises and imbalances in the distribution of economic opportunities between regions have led to focus on the idea of ​​medium-sized cities as one of the most effective approaches to planning of the territory. Therefore, the purpose of this article is to study the performance of the city of Nasimshahr as an intermediary city in the spatial development structure of Tehran province. The research method is "descriptive-analytical" and the data collection method is based on both library and field methods. Quantitative methods and models have been used to study the role of the city. Economic models show that the city's share of industrial employment is above average in terms of industrial employment. The structural change index shows that the changes in Nasimshahr were greater than those in Tehran province. The results of the share change model show that the service sector has grown faster than the province. Considering the spatial structure, whatever the role of the city, the spatial imbalance in the urban network of the province is obvious. Demographic models show that Nasimshahr has great elasticity to attract the overwhelming population of the province. Therefore, given its strengths and possibilities, it can play a key role in regional leveling as a service and industrial center in the southwest of the province.

The purpose of the present study is to assess the land surface temperature in relation to landuse for the city of Tabriz using remote sensing technology and GIS. Landsat 8 satellite image was used to map the land surface temperature for the study area. Atmospheric correction was applied to the desired image using the FLAASH method and the land surface temperature was estimated using the split-window algorithm for the study area with an accuracy of 1.51 degrees. Landuse map of Tabriz city in 6 classes was obtained using the object-based approach in eCognition software with an accuracy of 90/03. The results of studying the relationship between land surface temperature and landuse indicate that agricultural lands with a temperature of 18.22 °C have the highest land surface temperature. Also, water areas (rivers) have the lowest (10.30 °C) land surface temperature, because of their radiant power close to one. The research results also indicate that the split-window algorithm provides reliable results for land surface temperature estimation that can be used in environmental studies and earth sciences.