نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 131 (پاییز 1403)

The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.

  The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.

Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.

 Oil and gas pipeline projects face a wide range of safety and security risk factors globally, especially in oil and gas-producing countries that have unsafe environments and poor safety records. Inadequate information about the causes of pipeline failures and poor knowledge about safety and security hinder efforts to reduce such risks. Today, pipelines are widely used to transport oil and gas over long distances; Therefore, their risk assessment can help to identify related risks and take necessary measures to eliminate or reduce the resulting consequences. By reviewing the previous studies, it was found that the goal of the researchers was often to investigate the possibility and consequence of quantitative risk assessment in pipelines, while these parameters are based on the physical characteristics of pipelines and alone cannot cover all aspects of risk along the path of oil and gas pipelines. to cover At the same time, paying attention to the general Feild of the pipeline in an oil region and the uses that include these feild are very important from the environmental, sustainable development, and crisis management aspects. Finally, it seems that not paying attention to this aspect of the goal is one of the distinguishing features of this study compared to other studies. The main goal of this research is to implement a new approach to assess the riskiness of the overall Feild of pipelines on environmental resources around the Maroon oil region.

 In this research, first, the study model of pipeline risk (from the point of view of consequences after the event) was selected. In the next step, the factors affecting the riskiness of the pipeline (in case of various incidents such as fire, explosion, toxic gas leakage, etc.) were identified and quantified. Then, the initial risk zoning map of pipelines in the study area was modeled based on MATLAB software's Mamdani fuzzy inference system. In the final step, to validate the proposed model, the results and outputs of the pipeline risk zoning model based on the fuzzy inference system were compared with the general opinion of experts. Finally, the final risk zoning map of the region was prepared and combined with the route of existing pipelines in the area. became

 Based on the risk zoning of the oil and gas pipeline route from the point of view of facilities/structures, this route mainly passes through areas with low and medium risk that have an area of 223.32 Km2, this area is equivalent to 18.4% of the total area of the oil field. However, about 1.45% of the oil field area includes areas with high risk. These areas are located in the southern parts of the Maroon oil field. According to the land use map, the use of these areas includes main roads, bridges, and power lines. The reason for the high risk in these areas is that in case of accidents related to oil and gas transmission lines, the consequences of these risks in these areas can be significant. The results of risk zoning of the pipeline route from the perspective of environmental factors show that the route of pipelines passes through areas with low, medium, and high risk. But with the difference that, compared to the zoning map of facilities and structures, the passage route is more in areas with medium to high risk. The area of these areas is equal to 750.25 Km2 and includes 61.83% of the total area of the oil area, the use of these areas is consistent with the network of waterways and flowing sand (sand hills). The final risk map shows the path of oil and gas transmission lines, which is the result of combining two environmental and technical criteria. In this map, after zoning the area in terms of low, high, medium, and high risks, the route of the pipelines was adapted to it. In the final map, the route of the current pipelines in the Maroon oil region shows a great adaptation to the areas with low and medium risk. Most of the pipelines' route corresponds to areas with low and medium risk. Finally, the adaptation of the current pipeline route in the region and risk zoning in the northern and central parts of the field showed that these areas show a low and medium level of risk. According to the results, the area of high-risk areas in the oil field is 17.55 Km2, equal to 1.4% of the total area of the Maroon oil field. The total length of oil field pipelines is 249.14 km, the total of 7 km of this route, which is equivalent to 2.8% of the total length of the lines, passes through high-risk areas. Most of this route is located in the south and southeast part of the oil field.

 The results of this research show that the method of expert systems, artificial intelligence, and remote sensing can to a large extent overcome the shortcomings of the lack of zoning maps of general risk from the route of oil and gas pipelines in a vast oil area. The relationship between input and output information in the proposed fuzzy inference system was described as linguistic variables by applying expert opinion, which is more flexible and accurate compared to the classical model. Finally, based on the results of this study, the southern parts of the Maroon oil field are of high risk due to the existing pipeline route. This route is approximately 7 km long, i.e. 2.8% of the entire pipeline route. Finally, considering the above, it can be said that the above-mentioned method is effective in better decision-making by experts, because achieving a general view of the level of risk in different areas, in adopting strategies for prevention, evaluation, and correction of the area, is useful. has a lack of need for field studies, as well as reducing the burden of costs related to it, as well as saving time, are among the most important parameters that will make the research of researchers easier.

A flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on a convolutional neural network is introduced, and from the time series of SAR intensity and coherence to draw flood without obstruction (e.g. Flooded bare soils and short vegetation) are used. Non-cohesive areas blocked by floods (e.g., flooded vegetation) and cohesive areas with flood-blocked areas (e.g., frequently constructed flooded areas) are distinguished.This method is flexible according to the time period of the data sequences (at least one pair of pre-event and event intensities and one pair of pre-event and in-event coherence are required). The increasing number of SAR missions in orbit that have a fixed viewing scenario with a short retry time increases the chances of seeing a flood event, while also having a good pre-event scene achieved by the same sensor. This makes this method desirable for operational emergency responses.

CNN algorithm is a multilayer perceptron that is designed to identify two-dimensional information of images and includes: input layer, convolution layer, sample layer, and output layer. The CNN algorithm has two main processes: collection and sampling.The convolution process involves the use of a trainable Fx filter, deconvolution of the input image (the first step of image input, input after image convolution, is the feature of each layer called Feature Map), then by adding bx can be hand convolution of the CX layer Found. Sampling process: n pixels are collected from each neighborhood to form a pixel, then weighted with a scalar weight of Wx + 1 and a bx + 1 bias is added, then a map of The Narrow n times feature map properties are generated.Three images of Sentinel-1A VV polarization, wide width interference (IW), and mode (SLC) data were used in this study. Intensity images were pre-processed with radiometric calibration, noise reduced with a spell-filter (window size 5.5 pixels), and converted from linear units to decibels. Coherent images were obtained with a pair of consecutive images with a window of 7.28 (range _ azimuth). Validation data set due to the lack of other data in two separate sections of ground data in the urban area of GonbadKavous that have been collected to identify homes damaged by floods and terrestrial reality data from gamma image thresholds for output validation were extracted.

In this section, the results of the study are qualitatively and quantitatively analyzed. Because the simultaneous display of SAR data over time in the form of RGB compounds is widely used in the qualitative interpretation of land cover and surface dynamics, RGB compounds are used to provide evidence of flood magnitude in terms of intensity and coherence. For both cases, the results of combining intensity and coherence and intensity alone and coherence alone are quantitatively analyzed. Overall accuracy (OA), kappa correlation coefficient, false-positive rate (FPR), precision (e.g., correctly predicted positive patterns out of the total predicted patterns in a positive class), recall (e.g., a fraction of properly classified positive patterns), and an F1 score (ie the harmonic mean between precision and recall). Flood reference and ground data are mentioned and reported based on the reference.

In this paper, a method for mapping floods in urban environments based on SAR intensity and interferometry coherence was introduced. A combination of intensity and coherence extracts flood information in different types of land cover and outlet. This method was tested on the KavousGonbad flood incident obtained by various SAR sensors and the flood maps were confirmed by the flood reference resulting from thresholding and ground harvesting and satisfactory results were shown in this case study. The findings of this experiment show that the shared use of SAR intensity and coherence provides more reliable information than the use of SAR intensity and coherence alone in urban areas with different landscapes. In particular, flood detection in less cohesive / non-cohesive areas (e.g., bare soils, vegetation, vegetated areas) relies heavily on multi-temporality, while multi-temporal coherence provides more comprehensive flood information in areas Create coherence (e.g., mostly built-up areas). However, some flood-specific situations, such as flooded parking lots and flooded dense building blocks, are still challenging in terms of intensity and coherence. Also, since the proposed method is sensor and scene independent, with very frequent and regular observations of SAR missions such as Sentinel-1 and RADARSAT (RCM), there are opportunities to map global floods on a global scale, especially in small countries. Provides income.

Coastal regions, as the intersection of two distinct ecosystems, serve as one of the most active areas worldwide for the interaction and mutual communication of marine and terrestrial organisms, while providing diverse ecosystem services to humans.The macroeconomic-political approaches of nations towards coastal areas, followed by population and economic influx, have resulted in coastal cities being acknowledged as centers of population receptivity and arenas of competition among diverse groups for access to aquatic-terrestrial ecosystem services. Conflicting interests among these groups and an ineffective top-down management pattern in industrial coastal cities such as Mahshahr and Asalouyeh have exacerbated the adverse impacts of various socio-economic processes on the sustainability of coastal ecosystems, intensifying the clash between economic growth and environmental preservation. This study endeavors to quantitatively examine the associations between the governance patterns of industrial coastal cities and environmental justice within these regions. The primary objective is to develop a model that elucidates this relationship and, based on the formulated hypotheses, establish a framework for enhancing the efficiency and efficacy of participatory decision-making processes. The ultimate aim is to foster the preservation and restoration of coastal ecosystems, ensure the sustainability of ecosystem services, and mitigate environmental justice disparities during the course of economic and social development in industrial coastal cities and coastal towns.

The present study adopts a quantitative approach grounded in the established paradigm of positivism. The target population consists of residents of industrial coastal cities. The accessible population includes the resident population of Asalouyeh (Bushehr province) and Mahshahr (Khuzestan province). Data collection was conducted through questionnaires, and data analysis and modeling of the relationships between variables were performed using SPSS 26 and SMART-PLS 4 software.The study area encompasses the coastal cities of Asalouyeh and Mahshahr. Asalouyeh is located in the southernmost part of Bushehr province and serves as the center of Asalouyeh county. It has a long history of industrial, commercial, and fishing activities. The port of Mahshahr, on the other hand, is currently industrialized and serves as the center of Mahshahr County. It is situated on the transit routes of land, sea, and rail transportation, making it a significant and strategic port, along with the Imam Khomeini port complex.

The present study employed a three-section approach to assess model fit, including measurement model fit, structural model fit, and overall model fit. The measurement model fit was evaluated using factor loadings, average variance extracted, composite reliability, and two convergent and discriminant validity measures. Convergent validity was computed based on the extracted factor loadings and average variance values, while the Fornell-Larcker criterion was utilized to calculate discriminant validity.The results indicated that the factor loadings of each item exceeded 0.5, indicating satisfactory reliability of the model. Furthermore, the composite reliability, average variance extracted, and Fornell-Larcker table values surpassed the acceptable thresholds, indicating a good fit of the measurement model.The present study utilized the cross-loading validity index to assess the quality of the measurement model. The Q² values indicated that the selected tool for measuring the latent variable had an acceptable level of quality, thereby validating the measurement model of the study. The results obtained from partial least squares analysis, as presented in Figures (3) and (4) and Table (5), indicated that all path coefficients and t-values were significant, with values greater than 1.96 and p-values less than 0.05, respectively, supporting the main hypotheses based on the collected data from the study population.Furthermore, the mediator variable of social capital was found to have a moderate effect, ranging from 20% to 80%, in the relationship between desirable governance and environmental justice, indicating partial mediation.

The findings of this study demonstrate a robust and statistically significant relationship between desirable governance and environmental justice. Moreover, the study introduces social capital as a significant mediator in the relationship between desirable governance and environmental justice. The significance of the association between desirable governance and social capital has been validated in previous research.Based on these results and the substantial link between desirable governance and environmental justice, along with the mediating role of social capital, it is recommended to transition the management approach of industrial coastal cities towards desirable governance. This transition can be accomplished by implementing principles and indicators of desirable governance, such as enhancing participation, transparency, effectiveness, efficiency in decision-making and planning, and responsiveness to diverse stakeholders. These measures will establish a solid foundation for advancing environmental justice in various aspects.Furthermore, particular attention should be given to augmenting the level of social capital through well-defined and practical planning. This strategic focus will establish the necessary groundwork for leveraging social capital to enhance the effectiveness of desirable governance in industrial coastal cities, ultimately fostering environmental justice.

 The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability of different durations of precipitation in Iran over the past four decades (1977-2016). To achieve this goal, we used the third version of the Asfazari database. We defined a rainy day as a day when the precipitation is more than the average precipitation in a given place. The Markov chain method was employed to estimate the probability of precipitation duration from 1971 through 2016.

We adopted the daily data of 2188 stations under the supervision of Iran’s Meteorological Organization for the period 1971 through 2016. Accordingly, we estimated the probability of precipitation duration for 1-7 days for the entire period. We investigated the decadal changes in the probability of precipitation duration for the four study decades and compared them to the whole period under investigation. To understand the spatial features of these changes, we estimated the relationship between changes in the probability of precipitation duration for 1-7 days and spatial factors using multivariate regression models.

Our findings revealed that as the duration of rainy days increased, the area affected by precipitation decreased. Therefore, the spatial distribution of the probability of precipitation duration for more than 7 days indicated the smallest area that received precipitation. The probability duration of precipitation lasting 4 days or more throughout Iran was very small, which can be attributed to the effects of local features on precipitation formation. The probability of 1-day precipitation for most regions of Iran was higher than other durations; however, there was only a probability of 1-day precipitation in half of Iran. The highest probability of precipitation duration occurred in the Caspian region, the only region that experienced all durations of precipitation, indicating the presence of various precipitation mechanisms in this area. The greatest probability of decadal changes was observed in the 1-7 day duration in the northern part of Iran, including the northwest to the east of the Caspian Sea and in the south of Alborz Mountain range. Additionally, the most changes in the probability durations of 1-7 days of precipitation in the south have been seen in Sistan and Baluchistan. The lowest probability of decadal changes was shown in large areas of the regions from the east, southeast, and southwest. Therefore, the changes in precipitation durations in the southern half of the regions were generally low; however, in the northern half, the changes were relatively significant.In general, during the four study decades, the relationship between changes in the probability of 1-7 day precipitation durations and spatial factors, particularly latitude, was positive. Thus, decreasing latitudes resulted in an increasing probability of 1-7 day precipitation.

The most likely changes in precipitation duration were related to the western and eastern coast of the Caspian Sea and the northwestern region of Iran, as well as southern Alborz, where the probability of changes decreased. The least amount of possible changes was related to the south of Iran, where only two provinces, Sistan and Baluchistan, and Hormozgan, experienced the greatest change in the probability of one to seven days of precipitation. Thus, the possible changes in the spatial continuation of precipitation in the southern half of the country were primarily low. However, in the northern half, the possible changes in the duration of precipitation were more significant. changes in the duration of precipitation, along with changes in the intensity and frequency of precipitation, can have significant consequences in extreme events such as droughts and floods. Accurately depicting changes in precipitation duration can be helpful in addressing problems concerning precipitation.

Spatial inequalities in developing countries such as Iran are more visible due to various factors, so many Scientists (Dadashpour & Shojaei, 2022-Mosayebzadeh et al, 2021- Fotres & Fatemi Zardan, 2020- Dadashpour & Alvandipour, 2018- GhaderHajat & Hafeznia, 2018) consider the most important feature of Iran's space organization to be spatial injustice, which is the manifestation of the country's center-periphery structure at micro-local and macro-national scales. In Iran, inequality and lack of balance in the optimal distribution of facilities as a result of unprincipled past policies in industrial-service locations, growth poles, and the trend of centralization in dominant regional cities, the spatial imbalance between national, regional, district, and local levels is one of the important issues, which has emerged under the influence of mechanisms governing economic, social and political structures, this anomaly and imbalance have increased with the increase of the government's role in the economy due to the nature of its concentration and departmentalism, and more planning has been provided to the government (Faraji et al, 2019). Finally, today, the issue of inequality in many countries is mentioned as a fundamental challenge in the path of development, So it is considered one of the main obstacles in the process of national development and disruption of regional balance, Therefore, the first step in development planning is to identify the position of each region in terms of development and inequalities (Amanpour and Mohammadi, 2021); Therefore, the main goal of this research is the spatial analysis of regional inequalities in Iran during the years 2011, 2016, and 2021.

The current type of research is applied and its research method is descriptive-analytical. The collection of data in this research is in the form of a library. The statistical population of this research is 31 provinces of the country based on the last administrative and political divisions of 2021. To evaluate the state of development, 47 indicators have been used in 3 main economic-infrastructural, educational-cultural, and health-treatment dimensions. The analysis of research data has been carried out quantitatively using GIS, EXCEL, and SPSS software. In this research, to rank the provinces from the VIKOR multi-indicator decision-making model, To weight the indices using the Shannon entropy method, For data clustering using the K-Means-Cluster method, To evaluate the changes of inter-provincial inequalities using the CV statistical method, To interpolate the development of the country using the Kriging method, To evaluate the spatial correlation and the type of clustering of the development of the provinces using the Spatial Autocorrelation method (Moran's I) and Geographically weighted regression method has been used to find the relationship between development as a dependent variable and population and area as an independent variable.

The results of this research show that in 2011 due to the strong concentration of administrative, political, economic, and industrial activities in Tehran, there was a sharp divergence between Tehran province and other provinces. The growth pole theory has entered the second stage and the degree of divergence has decreased and the degree of convergence between provinces has increased. According to the results of Moran's correlation, the clustering of the country is still multipolar and there is still regional inequality in the country, so the country's border and port provinces are in a worse situation than other provinces, despite their development potentials and capacities as border corridors. The geographic weighted regression model also shows that the influence of independent variables (area and population) is greater in the northwest of the country than in the southeast of the country, This issue is estimated at 76% in 2011, 35% in 2016 and 43% in 2021.

In general, the most important cause of Iran's regional inequality should be sought in the structure of the planning system and the pattern of regional spatial development of Iran. The formation of the planning system in Iran is based on neoclassical economic theories, the growth pole and the intense concentration of activities in the center of Iran, and this issue is very influential in creating regional inequalities, and on the one hand, due to top-down planning and lack of attention to environmental potential in the country's provinces, Actually, spatial injustice is spreading in the country and this issue can act as a dangerous factor in the direction of sustainable development of the country.

The tourism industry has been able to quickly find a special place all over the world ‎due to its numerous attractions and economic benefits. So that it can be considered an ‎achievable goal in the path of sustainable development. The high importance of ‎tourism and the need to promote it has led many researchers to know more about the ‎factors influencing it, among which, undoubtedly, weather is an integral part of ‎tourism. Because tourists always seek to visit places where they feel the least ‎dissatisfaction. The close relationship between climate and tourism has led to the ‎formation of discussions about climate comfort. Temperature, radiation, precipitation, ‎wind, humidity, and fog can be mentioned among the different climatic elements that ‎have a direct relationship in creating tourist comfort. In this regard, due to the ‎vulnerability and biological sensitivities of the mangrove habitats and coastal wetlands ‎of Nayband Bay, as well as due to having landscapes with aesthetic, educational and ‎recreational values and the necessity of developing nature tourism activities in ‎national parks, it is necessary that the climatic conditions for the activity to identify ‎nature tourism in this area. Therefore, this study was carried out to investigate the comfort climate of tourism and identify the suitable months for the ‎presence of tourists, to obtain high tourist satisfaction and experience ‎on the one hand, and on the other hand, achieving correct planning for more ‎protection and least damage to the region. Based on this, the tourism climate in the ‎mangrove forests of Naiband Bay was investigated using TCI and PET indicators.‎

The climatic characteristics of the studied area show the target by emphasizing the ‎effective parameters in the application of climatic indices, therefore, in order to estimate ‎the desired indices, the characteristics of the climatic variables in Asalouye station ‎during the years 2000 to 2022 were collected and sorted. One of the indicators ‎examined in this research is the TCI index, which measures the suitability of a place's ‎climate for tourism using the variables of maximum temperature, average temperature, ‎minimum relative humidity, average relative humidity, precipitation, sunny hours, and ‎average wind speed. Another index examined in this research is the PET index. The PET index for open ‎environments is the temperature in a sample room of the heat balance of the human ‎body (the metabolic rate with light work is 80 watts based on the basic metabolic rate ‎and the value of clothing conductivity equal to 9.) with skin temperature and core ‎temperature. The body is in balance in the open environment. The PET index is based ‎on the climatic data of average air temperature in centigrade, relative humidity in ‎percent, average wind speed in meters per second, average vapor pressure in ‎hectopascals, and cloud cover in octas for Asalouye station and during the statistical ‎period from 2000 to 2022. was investigated. In this research, the energy balance model ‎or MEMI for people is used to calculate PET.In this research, to increase the accuracy of the calculations and consider ‎that the calculation of some parameters such as the average radiant temperature of the ‎environment (Tmrt) cannot be done simply by climatic data such as temperature, ‎humidity and wind speed, from the software model Ray Man, which was used by ‎Andreas Matzarakis to calculate radiation fluxes.

As the results show, the TCI index indicates the suitable conditions of the tourism climate during ‎the months of December, January and February. While the least favorability of the tourism ‎climate can be seen in the months of June, July, August, Shahrivar and Mehr. The results ‎obtained from the Riemann model also indicate that the PET index for the months of November ‎and March was without cold stress and physiological stress for tourists. While the months of ‎January, Bahman and Azar are a bit cool with little cold stress and also the physiological stress ‎level of tourists. During the first half of the year, while the temperature rises significantly, this ‎region experiences a different degree of stress and thermal sensitivity in terms of physiological ‎conditions.‎

The mangrove forests of Nayband Bay attract many tourists and nature lovers from different ‎places every year due to special climatic conditions and many recreational resources, as well as ‎being located in coastal-sea areas. On the other hand, the studied area is known as one of the ‎areas under the protection of the environmental organization with high ecological sensitivities ‎and protection prohibitions, which requires proper planning for the development of tourism and ‎the presence of visitors in the area. Based on this, in the current study, the comfort climate of ‎tourism and the determination of the appropriate time for tourism activities were investigated ‎using two indices, TCI and PET. As the results of the TCI index showed, ‎‏6‏‎ months of the year ‎‎(November, December, January, February, March and April) this region has the best conditions ‎for the development of tourism activities and the presence of visitors in the region. The results of ‎the PET index also showed that the months of November and March are without cold stress and ‎physiological stress for tourists, as well as the months of January, February‏ ‏and November‏ ‏with ‎little cold stress and the degree of physiological stress are slightly cool, which are favorable and ‎suitable conditions for the development of tourism activities.

Hydropolitics is the exploration of the role of water in the relations between countries on four scales: local, national, regional and global. With 26 border rivers and the dependence of about 30% of the country's population on the water of common watersheds, Iran is among the countries that are heavily affected by hydropolitical developments and changes in the world. Additionally, the security of the country's border areas is greatly impacted due to their peripheral location and strong reliance on water from border rivers. Hence, this research investigates the hydropolitics of Iran's border rivers, the indicators and components that influence it, and the security consequences on the border areas. The research utilizes Qualitative method and descriptive-analytic approach, employing Delphi methods, cross-matrix analysis (MICMAC), and ArcGIS software to produce maps.

Based on the purpose, this research is among the applied research and based on the method, it is among the qualitative research, with a descriptive-analytical approach and using the Delphi technique and the cross-matrix analysis method. In this research, the authors will analyze the issue by using library resources and written documents related to the topic, while describing and explaining the event, and then while studying the library resources from the questionnaire in order to identify and screen the most important Dimensions and hydropolitical indicators of Iran's border rivers, as well as the effectiveness of the indicators will be used. The statistical population of this research includes experts, custodians and elites of the country in the field of water. In this regard, due to the unlimited statistical population and the lack of official information on the number of experts and elites, it is not possible to use Cochran's formula, and the number of 20 people is considered as the statistical population in this research and they are questioned. . Due to the type of research and not knowing the full number of the statistical population, the sampling method is "targeted sampling" and the snowball sampling method. In order to analyze and analyze data and information, since this research is one of qualitative researches, in addition to the use of library sources and analysis with a descriptive-analytical approach, methods such as Delphi in order to identify dimensions and indicators, as well as the method Cross-matrix analysis will be used in future research of effective hydropolitical strategies. In this research, Arc GIS software is used for map preparation and Micmac software is used for data analysis.

The research findings reveal that more than 50 indicators affecting the hydropolitics of border rivers were identified through the use of library resources, the Delphi technique, and a questionnaire. Ultimately, 31 factors were confirmed in the second and third stages of the Delphi process. These 31 factors were categorized into five dimensions: natural factors, human factors, geopolitical factors, military factors, and geo-economic factors by forming an expert team and consulting with professors.The results of the cross-matrix analysis in MICMAC software have shown the indicators of influential, influenceable, target, independent, result indicators, and especially risk indicators in the hydropolitics of Iran's border rivers. Among these, target indicators and especially risk indicators are important strategic indicators. The indicators of the need for drinking water from the border rivers, unemployment, and migration due to water shortage in the areas of the common catchment basins, the relations of the surrounding countries affected by the catchment basins, the existence of a large population of people in the common catchment basins, the construction of dams and mines in the upstream countries, the defense and military situation of Iran's border rivers, the political and geopolitical exploitation of water by the upstream countries, and the activities of evil and terrorist groups in the upstream countries are the most important risk indicators in the hydropolitics of border rivers of Iran.

Finally, the results show that the most important security consequences of the hydropolitics of border rivers on border areas are in environmental, economic, political, social, and cultural dimensions. The most important of these include ethnic tensions on both sides of the border, smuggling of goods and drugs in the border areas, joining terrorist groups and striving for independence, migration from border areas, reduction of agriculture in border areas, growth of poverty in border areas, and as a result, the growth of crime and the increase in the cost of providing security. Other consequences include ethnic crises due to spatial and ethnic ties, conflicts over water, marginalization and increase in crime, air pollution, drying up of border wetlands, respiratory problems in border areas, the emptying of borders, and the destruction of the environment in border areas.

A problem that planners often deal with is choosing the best service distribution center in cities and rural areas. The distribution of each service in a specific area will create a pattern that can be random, dense, or scattered. In addition, the development of rural areas includes a wide range of profound changes in social and economic structures that seek to distribute income fairly, increase living standards, and provide superior services in these areas. Therefore, rural development is possible if the facilities and services that serve economically productive activities are concentrated in optimal rural centers with suitable conditions in terms of providing services. Rural service centers also have an essential role in providing the facilities and services needed by the villages under their influence because these centers are considered a base for mobility and the desire to live in rural areas. In this regard, actual development is realized when it provides the necessary conditions for all people, regardless of location, for their dynamism, growth, and material and spiritual excellence. To achieve this goal, in this article, we are looking for the optimal location for establishing rural service centers and assessing the distribution of facilities in Tashan District of Behbahan Township.

The applied study employed a descriptive-analytical research method. The data were collected via documentary studies, i.e., libraries, books, articles, databases, theses, and survey research, i.e., the statistical data of the housing foundation organization of Khuzestan Province in 2021. This research employed the Analytic Hierarchy Process (AHP) and interior point method (IPM) to have more realistic and practical results. The main focus of the hierarchical analysis process in the present study was identifying the optimal points for establishing rural service centers, and Expert Choice and Excel software were used to perform such an analysis. This work was done by completing the questionnaire by ten experts in rural affairs. Also, the IPM was used to determine the level of development in the studied rural areas. All the research maps were prepared in the ArcGIS 10.3 software and adjusted and integrated with the UTM coordinate system.

The results showed that among the selected criteria for establishing service centers, population density has the highest score of 0.167, and the topography and height criteria, access to infrastructure facilities, and access to health care services, respectively, with scores of 0.152, 0.144, and 0.128 were the most valuable and essential in the following ranks. The overlap map of the criteria illustrated that among the 49 rural points of the district, five villages are in a perfect situation with an area of 11.94 square kilometers (2.7 percent), four villages are in a good situation with an area of 36.27 square kilometers. (8.4 percent), seven villages were in a relatively suitable area with an area of 100.69 square kilometers (23.5 percent), ten villages were in an unsuitable territory with an area of 153.10 square kilometers (35.8 percent). Also, 23 villages were placed in a completely unsuitable position with an area of 124.52 square kilometers (29.1 percent). In other words, Deh Ebrahim, Sarallah, Veisi, Kalgezar, and Ab Amiri villages had the most capacity for establishing rural service centers. In the ranking obtained from the IPM, Mashhad village had the lowest value with a coefficient of 0.0081 in Si+ score, recognized as the most developed village in Tashan District. Then, Bid Boland and Piazkar villages were ranked second and third in development levels with coefficients of 0.0557 and 0.0510, respectively, in Si+ score. These villages are flat areas and are mainly in a good position compared to other villages in Tashan District regarding population density and public services to establish rural service centers.

It is necessary to design the optimal pattern of hierarchical system and stratification of villages to make easy access for small and sparsely populated villages to the facilities in the area. It should be noted that the combined application of the hierarchical process and the optimal point allows researchers to locate and evaluate maps of various criteria and help to choose the exact and optimal location for establishing rural service centers.

In the current era, following the occurrence of environmental crises, the destruction of resources and the creation of obstacles on the way to sustainable development, it is necessary to carry out programs based on the recognition and assessment of ecological potential so that exploitation in It should be fed continuously from the environment and the natural values of the environment should be preserved. Therefore, after formulating development strategies and implementing social economic programs, geographical survey of the region and recognition of its environmental capabilities and then determining the ecological potential of the land for different uses with the aim of continuous exploitation with the least Destruction and preservation of the environment is an inevitable necessity. The ecological capacity of the land allows the planner to determine the direction and solutions of the region's development based on the region's capabilities, and provide a plan that fits the conditions of the region. Evaluation of the ecological potential is an effective step and a suitable tool to direct the current activities and uses in the land towards sustainable development. FAO defines "land suitability assessment" as an assessment process that considers the performance of land when used for a specific purpose. The assessment process is directed towards optimal land use by providing important information about the opportunities and constraints in using a given land.

According to the review of various library sources as well as the conditions of the region, 10 criteria of Elevation, slope, Aspect, distance to main roads, distance to waterways, distance to fault, distance to landslide points, distance to flood plains, Normalized Difference Vegetation Index (NDVI) and land use were investigated as different criteria for capacity measurement. To study the capacity of the studied area and to prepare suitable areas for the development of Ecotourism, the following steps were carried out; A): Assessment of land suitability to check the capacity of the region for tourism development using the MCE method First step) defining the set of evaluation criteria (information layers),  The second step) standardizing the criteria, The third step) defining the weights of each criterion, Fourth step) preparation of the final map: in the last step, the obtained weights were multiplied in the required layers and then added together. The fifth step) using the final desirability layer produced at the end of the fourth step, places that have a higher value than other areas, using the location method based on suitability and area (ZLS) as suitable places. They are selected for the development of nature tourism. B): Selection of the most important socio-economic parameters and ranking of the spots: the values of these indicators were extracted for the selected spots and at the end, the rank of each spot was determined using the TOPSIS method.

The results of the AHP questionnaire show that the Criteria of slope, Elevation and NDVI are the most important Criteria affecting the development of Ecotourism in this region. The results obtained for the development of Ecotourism in the studied area show that 21,269 (8.63%) hectares of the area are without capacity, and 6634 (2.70%) hectares are of the area with very high capacity. Due to the fact that a minimum area is required for the development of concentrated Ecotourism, in this study, seven areas with areas of at least 100 hectares were selected from the very high capacity category. Based on the results obtained from the AHP method, respectively, the indicators of the diversity of the area, the amount of pristine, the distance to road, the total population within 5 kilometers of the area and the average suitable map have the most importance on the selection of the best area for the development of Ecotourism. The results of the TOPSIS method show that region 2 is the best place for the development of Ecotourism in this watershed with a score of 0.8188. Then regions 5 and 1 were selected as the second and third regions with scores of 0.7868 and 0.6506 respectively. The places with the lowest rank are regions No. 4 and 3 with a score of 0.3197 and 0.0464 respectively.

Among the different capabilities of each region, tourist attractions are one of the most important natural resources in each region, and are important factors in attracting tourists and attracting capital, and thus are the main basis for sustainable development for the region. The results of the survey show that all three selected final regions are in the central part of Noor city and region 2 has the largest area (370.759 hectares) in terms of area. Also, according to the average map, it is equal to 0.928 and in terms of access, it is 144 meters away from the main road, and its virginity is 80%. Various studies (Balist et al., 2019), (Zarei et al., 2013), (Pour Ahmed et al., 2013) in the world show the superiority of this model (the combination of AHP method and TOPSIS method) over other models confirmed in their studies.