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.

Housing is a crucial domain for testing human interactions and serves as a physical framework where social, cultural, and economic resources intertwine. In order to achieve social progress, the realm of housing encompasses not only the residential units themselves but also their surrounding environments. The focus on housing quality indicators is essential for attaining sustainable housing, which in turn lays the foundation for sustainable urban development. However, the increasing demand for housing and the prevailing quantitative approach have led to the neglect of the fundamental issue of housing quality in its true essence. Insufficient access to suitable housing can contribute to or worsen various psychological and social challenges. Hence, this research aims to identify the main indicators and components of housing quality and prioritize them, assuming equal weighting of their influence. The research methodology employed in this study is practical in its objective and descriptive-analytical in its approach. To achieve the intended objective, the Analytic Network Process (ANP) model is utilized to determine the weight and prioritize each indicator and component of housing quality. Subsequently, after evaluating the indicators using the ANP model, the results indicate that among the examined indicators, housing facilities and amenities with  0.318, housing security with 0.218, housing comfort with 0.145, compatibility with complementary uses with 0.142, housing durability with 0.109, and housing climate adaptation with 0.065 have been assigned the highest weights, respectively.

The current research is based on the process of algorithmic design and simulation of village boundaries using the Grasshopper plugin in the Rhino software. The proposed algorithm utilizes the image simulator component and its importer in the Grasshopper plugin to input two-dimensional images taken from samples into this plugin. Before entering the data, the input images are converted to black and white to allow the system to distinguish between the background texture and the subject of analysis. Additionally, the proposed algorithm identifies the village boundary by comparing the subject and the text in the pixels of the two-dimensional image. By calculating the internal area of the identified boundary, the algorithm estimates the proportion of textures relative to the background. The main objective of this research is to examine the impact of green and dry textures as natural factors on the density of residential texture in terraced villages in Iran, across various climates. In addition to identifying the density of village textures, emphasizing the comparison of parameters will provide new insights into the texture of terraced villages. The simulation analysis tool, known as the density measurement algorithm, is developed based on aerial maps created by the researchers for this study. By using inverted aerial maps, this algorithm can determine the density of natural texture in the formation of villages and residential texture. This capability reduces the need for physical presence and enhances the accuracy of outputs in analyzing village texture. Furthermore, the identification of parameters will provide the next generation of rural housing with the density pattern of previous generations.

Drought is a natural hazard that annually causes significant economic, social, environmental, and life-threatening damage in vast areas of the Earth. The damages caused by this phenomenon are intangible but very extensive and costly.  In many circomstancs, modern remote sensing techniques can be a useful tool in monitoring drought due to high temporal accuracy, wide spectral coverage, ease of access, no need for atmospheric correction and ground referencing. In recent years, the province of Hamedan has faced many problems due to frequent droughts. Therefore, the present study focused on investigating and monitoring drought in Hamedan province using the Temperature Condition index and its impact on the vegetation cover of the province using Advanced Very High Resolution Radiometer (AVHRR) and National Oceanic and Atmospheric Administration (NOAA) remote sensing data. First, the relevant data was extracted from the Nova star database, and finally, the spatiotemporal behavior of the vegetation cover drought index was examined on 1528 pixels in Hamedan province. The spatial resolution of the data used in this study is 4 kilometers. First, the relevant data were extracted from the Navstar database and ultimately, the spatiotemporal behavior of the drought index and vegetation cover was examined. The results indicate that drought has significantly increased the vegetation cover of Hamedan province based on remote sensing data. Kendall's coefficients indicate the presence of decreasing trends in vegetation cover at a 95 Percent confidence level. Only in May, June, and December has there been a slight decrease in vegetation cover within the extent of drought in the province. The spatial behavior analysis of the drought index on vegetation cover showed that February, March, as well as April have experienced more severe droughts within Hamedan province.

Today, due to the conditions of globalization, city branding has become increasingly important due to its tourism potentials. Most regions with strategic planning in this area aim to achieve economic development and reduce deprivation through tourism. In this study, the research strategy is deductive, and its purpose is practical. The required data and information were collected from library resources and surveys (questionnaires and interviews) with experts. The meta-SWOT technique, based on an inside-out approach and a resource-based perspective, provides a framework for enhancing the competitiveness of cities and regions. The city of Kermanshah possesses significant capabilities to attract tourism, enabling it to achieve urban, regional, and global competitiveness. The findings of this study show that among the capabilities and potentials of Kermanshah, the existence of historical and cultural monuments—such as Taq-e Bostan, pillars, historic houses, museums, and its role as a center for handicrafts—as well as cultural similarities with people living in Iraq and Turkey, along with its unique culture, beliefs, and customs, serve as key tourism potentials. These attributes align with the four characteristics of the VIRO framework (Value, Rarity, Imitability, and Organization), making them the most strategic fit with macro variables affecting tourist attraction and urban competitiveness through tourism in Kermanshah. Among the major influential variables, the COVID-19 pandemic, international sanctions against Iran, climate change, natural disasters, and lack of funding have the greatest impact on the urban competitiveness of Kermanshah. Based on the analysis of resources and macro variables, a strategic fit map was developed, and appropriate solutions were proposed.

Extratropical cyclones, characterized by their frequency, duration, and intensity, serve as the primary drivers of mid- and high-latitude precipitation across the Mediterranean during the winter and autumn months. For this research, climatic variables obtained from the ECMWF network, featuring a temporal resolution of 6 hours and a spatial resolution of 0.25° × 0.25°, spanning from 1979 to 2016, were utilized. Additionally, precipitation data from four basin stations sourced from the Asfezari database for the same period were analyzed. Initially, geopotential height, temperature, humidity, and jet stream data for rainy days were extracted using MATLAB. Subsequently, a cyclonic center extraction algorithm was applied to identify cyclonic centers from the geopotential height data, based on the conditions that the geopotential height is at a minimum and the geopotential gradient is at a maximum. From the geopotential height matrix of rainy days (361×441×498), four distinct atmospheric patterns were identified through cluster analysis. The temporal and spatial frequency of these patterns, as well as the average temperature of cyclonic centers, were analyzed for the cold season months. The results indicated that the first pattern, identified as the Mediterranean trough pattern, is the most frequent, occurring 42% of the time. This pattern is characterized by the presence of a high-level system acting as a barrier, which deepens the low-level Mediterranean system and extends its axis toward the Red Sea. The interaction between low-level and high-level systems enhances instability, resulting in the highest precipitation levels among the identified patterns. Conversely, the fourth pattern, termed the western wind trough pattern, exhibits the lowest frequency at 10%. This pattern is characterized by a trough over the Caspian Sea; however, a high-level system in the southern region inhibits the entry of low-level systems, thereby confining cyclonic activity to the northern portion of the study area. Consequently, the isobars in the northern region assume a more orbital configuration, leading to a decreased influx of cyclones and, as a result, lower precipitation amounts compared to the other patterns. The analysis further revealed that cold-core cyclones accounted for 60% of occurrences in winter and 40% in autumn, while hot-core cyclones constituted 62% in winter and 38% in autumn. Notably, the frequency of hot-core cyclones increased relative to cold-core cyclones in winter, whereas an inverse trend was observed in autumn. Over the past decade, both the frequency and intensity of cyclones have diminished compared to the preceding two decades. In terms of cyclogenesis locations, the western part of the study area has consistently emerged as the most active region. Moreover, cyclogenesis activity exhibits a gradual increase from autumn to winter as the cold season progresses. These findings underscore the dynamic nature of extratropical cyclones and their significant role in shaping precipitation patterns across the Mediterranean region.
 

for the spatial analysis of precipitation in the Middle East, have been used gridded precipitation data from the World Precipitation Climatology Center (GPCC) with a monthly temporal resolution and a spatial resolution of 0.5×0.5 arc degrees. Therefore, a matrix of 80 x 160 dimensions was obtained for the Middle East region (160 longitudinal cells and 80 transverse cells). The reason for choosing network data is their proper spatial and temporal separation and their up-to-date compared to station data. The period under investigation is from 1970 to 2020 AD. Finally, the long-term maps of the Middle East precipitation were drawn on an annual and monthly basis. The results indicate that precipitation in the Middle East tends to concentrate and cluster in the spatial and temporal dimension. In other words, due to the special geographical location of the Middle East region, such as uneven topography, distance and proximity to moisture-feeding sources (Caspian Sea, Black Sea, Mediterranean Sea, Atlantic Ocean, and Indian Ocean) and the direction of unevenness, Precipitation in high altitude areas, It is concentrated in the neighborhood of seas and oceans and also in the windy slopes of the mountain range of the region. The uneven distribution of geographical conditions has caused uneven distribution of Precipitation in the Middle East. So that; The center and gravity of the Middle Eastern Precipitation is concentrated in the eastern end of the Black Sea, southern Turkey in the neighborhood of Syria and Iraq, the Ararat-Zagors belt in the west of Iran, the southern shore of the Caspian Sea, the Pamir highlands and the Bay of Bengal in India, and the Hindu Kush highlands in Pakistan. Is. However, the many parts of the Middle East, due to their proximity to large deserts (African Sahara, Lut Desert, Dasht-Kavir, Arabia's Rab-al-Khali and Afghan deserts), have less than 100 mm of Precipitation. The results showed that the maximum Precipitation of this region has been transferred to the winter season, and the summer season is still the driest period in the Middle East, and only the coasts of the Indian Ocean and the Bay of Bengal have monsoon rains