Due to the vast changes it brings about in the land use and land cover, the rapid expansion of cities has had many negative effects on the environmental quality at global level. Examples include air quality, increased temperature, changes in perspective, and the alteration of agricultural lands into barren lands that leads to the loss of biodiversity. Since land use changes happen at extensively, remote sensing technology is a necessary, efficient, and valuable means to monitor changes. In this study, the effect of land use changes on the temporal-spatial patterns of land surface temperature in the urban lands of Ilam city in a 30-year period (1990-2020) was examined using Landsat satellite images and simulation of changes using fuzzy ARTMAP neural network model. Landsat satellite sensors (TM, OLI, ETM) were used to investigate the longitudinal and spatial changes (LST) in Ilam city.  To provide the land use map, the pixel-based classification for all periods (1990-1995-2000-2005-2010-2015-2020) was applied using ENVI and Ecognitio software packs and then estimations were made using NDVI and LST models. The results showed that Ilam city ecosystem has moderate levels of NDVI. The average temperature of residential land use level in June is 35.8 degrees centigrade, and the temperatures of three land uses of horticulture, agriculture, and other uses (uncovered lands) were 32.22, 37.25, and 38.46, respectively, with the main lands with the minimum temperature being green highlands. In city, existence of high rise buildings and building shading, use of materials with less heat absorption such as Isogam, urban green space and furniture, and air pollution lead to lesser absorption of energy. On the other hand, the spatial distribution of NDVI was aligned with LSP values. The results regarding land use areas using fuzzy-neural network show that residential land uses has risen from 19.18 percent in 1990 to 39.35 percent in 2020, which shows the city expansion and development. On the other hand, the horticultural land use has declined from 8.64 in 1990 to 3.49 in 2020, which can be attributed to the development of urban space.

Spatial justice of uses, or in other words, fair, wise, and balanced distribution of urban uses is one of the most important issues of urban planning. The present study is research in terms of purpose and analytical in nature. The library information of this research is collected from books and articles. For the analysis, the per capita index of service users was used, which was calculated and obtained in the GIS software. The statistical population of the research is the eight districts of Qom City. This research has tried to analyze the Spatial justice of service uses in the eight districts of Qom City with the ORESTE technique, based on indicators that are service uses per capita. For this purpose, the indicators were first weighted using Shannon's entropy method, the highest weight was assigned to tourism, and the lowest weight was assigned to the park. After weighing, the ORESTE technique was performed, and Region 7 had the highest and Region 6 had the lowest amount per capita. Then, with the dispersion coefficient, the two-by-two distribution per capita of the service uses of the regions was calculated, and the results showed that regions 2 and 8 have the highest and regions 5 and 7 have the lowest spatial distribution of service uses. Also, with the Spearman correlation coefficient, the correlation between the population of each region and the per capita service users was calculated, which showed a negative correlation, which means that as the population increases, the service users per capita decreases.

The most obvious example of human activities on the land due to the increase in population and the need for development is land use changes, which lead to changes in the provision of ecosystem services. To that end, the present study was done with the aim of quantitatively evaluating the carbon storage capacity and habitat quality and showing their spatial distribution to achieve social-ecological stability, sustainable use of land resources, and develop appropriate plans and policies in Chaharmahal and Bakhtiari province. The modeling of selected ecosystem services was calculated using InVEST software models and the capacity to provide them in land use classes using Zonal Statistics analysis in a GIS software environment. The results indicated that high-value carbon storage areas are focused in areas with forest cover, which have strong carbon storage capacity and are the largest carbon sinks in ecosystems. Likewise, the highest habitat quality was also observed in these areas, and in places where the vegetation is fragmented and the surrounding environment is occupied by human threat sources, the quality of the habitat has decreased. Spatially, carbon storage and habitat quality Indicated a low-high-medium spatial distribution pattern from north to south. The maps of ecosystem service created in this study can be useful in identifying potential areas of carbon storage supply and habitat quality, and provide a scientific basis for further discussion by policymakers about future land use planning, from the perspective of minimizing climate change and increasing biodiversity.

The purpose of this research is to select temporary accommodation centers after the earthquake crisis to meet the needs of the victims. Therefore, this research has tried to identify temporary accommodation sites in District 22 of Tehran with the help of effective criteria. The required data has been obtained from the Tehran Disaster Mitigation and Management Organization and the results of the general population and housing census of 2017. The novelty of this research is to present a new combination approach to determine the effective criteria for locating temporary shelter sites. In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) with a binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels was obtained at 0.04616 and 0.0097, respectively, which indicates the high compatibility of the tri-cube kernel with effective criteria. According to the obtained maps, Chitgar Park and Azadi Sports Complex are some of the widest and most suitable areas for the construction of temporary shelter sites after the earthquake crisis in the case study. By identifying temporary shelters, relevant organizations can provide the appropriate infrastructure for these selected centers so that there is no need to spend time to provide these services in the event of an earthquake.