دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 Analysis of the Roles of Environmental Factors in the Occurrence of Floods Using the Google Earth Engine System (Case Study: West of Golestan Province) تحلیل نقش عوامل محیطی در وقوع سیلابها با استفاده از سامانۀگوگل ارث انجین (مطالعۀ موردی: غرب استان گلستان) 1 18 29010 10.22108/gep.2024.142342.1659 FA سعید نگهبان دانشیار ژئومورفولوژی، گروه جغرافیا، دانشگاه شیراز، شیراز، ایران حمید گنجائیان دکتری ژئومورفولوژی، دانشگاه تهران، تهران، ایران عطرین ابراهیمی دکتری ژئومورفولوژی، دانشگاه تبریز، تبریز، ایران سید سعدی قیصریان کارشناسی منابع طبیعی مرتع و آبخیزداری، دانشگاه ملایر، ملایر، ایران Journal Article 2024 08 03 Abstract Flooding is a significant natural hazard that has increasingly threatened numerous Iranian cities, particularly in the western regions of Esan and Golestan in recent years. One notable event was the flood of April 2018, which resulted in substantial loss of life and financial damage across various western cities in Golestan Province. This research focused on monitoring the flooded areas in western Golestan and analyzing the environmental factors contributing to these events. The study utilized several key data sources, including Sentinel 1 and 2 radar images, MODIS, CHIRPS, Landsat 9 satellite imagery, and a 30-m digital elevation model. The primary research tools employed were Google Earth Engine, IDRISI, and ArcGIS. Initially, flood-affected areas were identified using Google Earth Engine followed by an analysis of their relationship with various environmental factors. Finally, flood-prone areas were delineated using the Weighted Linear Combination (WLC) model. The analysis of radar images indicated that in April 2018, significant flooding impacted urban and suburban areas, including Aqh Qhala, Siminshahr, and Gomishtappeh. The study revealed that altitude, slope, and vegetation density were the most influential environmental factors with correlation coefficients of 0.652, 0.619, and 0.543, respectively. Additionally, the WLC model identified the northern and western regions of the study area—comprising the urban and suburban areas of Gorgan, Aqh Qhala, Siminshahr, Gomishtappeh, Kordkoy, and Bandar Turkaman—as having high flood potential due to their low elevation, gentle slopes, sparse vegetation, and proximity to rivers. Keywords: Flood, Environmental Factors, Google Earth Engine, West of Golestan Province. Introduction A flood is characterized by a sudden rise in river water levels or a rapid and often destructive flow of water. Flood risk affects populations worldwide, primarily because many people reside in areas prone to flooding. This widespread natural phenomenon poses significant challenges for regions vulnerable to floods, particularly as human activities and environmental interventions have intensified. In recent years, the increasing population and human activities have heightened the likelihood of floods, exacerbating the associated risks. According to the International Hazard Database, floods, alongside earthquakes and droughts, account for the highest rates of human and financial losses. Statistics from 2010 indicate that floods represent over 40% of natural disasters globally. Different regions exhibit varying potentials for flood risk based on geomorphological, hydroclimatic, and land cover factors. The northern regions of Iran, especially the eastern areas bordering the Caspian Sea, are particularly susceptible to flooding due to high rainfall and specific geomorphological conditions. This vulnerability has led to significant flood events in recent years, including the notable flood of April 2018. Given the importance of this issue, this research employed remote sensing methods to identify vulnerable areas in the western regions of Golestan Province and analyze the factors influencing flood occurrence. The study focused on parts of western Golestan Province and the eastern Caspian Sea selected for their flood potential. Geomorphologically, this area lies between the Alborz and Caspian Plain units. Its topography is predominantly low-lying with elevations below 50 m above sea level. Despite receiving an average annual rainfall of approximately 800 mm—less than that of the western Caspian coasts—the region's topography has facilitated the occurrence of numerous floods. Materials & Methods This research utilized several key datasets, including Sentinel 1 and 2 radar images, MODIS, CHIRPS, Landsat 9 satellite imagery, and a 30-m digital elevation model. The main tools employed in this study were Google Earth Engine (for generating maps of flooded areas and land cover), IDRISI (for running the Weighted Linear Combination (WLC) model), and ArcGIS (for map preparation). The research was conducted in several stages: Identification of Flood-Affected Areas: In the first stage, flood-affected regions were identified using the Google Earth Engine. Analysis of Environmental Factors: The second stage involved analyzing the influence of various environmental factors on flooding, including rainfall, distance from rivers, elevation, slope, vegetation density, and soil type. The selection of these parameters was informed by the specific conditions of the region and previous research. To create a map of regional rainfall, both Google Earth Engine and CHIRPS satellite images were utilized. Additionally, vegetation density maps were developed using Google Earth Engine, MODIS satellite imagery, and the NDVI index. For mapping elevation, slope, and distance from rivers, the SRTM 30-m digital elevation model was employed. Vulnerability Assessment: In the third stage, the results and parameters from the previous stages—including precipitation, distance from rivers, elevation, slope, vegetation density, and soil type—were analyzed to identify areas vulnerable to flooding. Information layers for each parameter were prepared and standardized. These layers were then weighted based on the correlation coefficients obtained in the earlier analysis, which indicated the relationship between each parameter and areas prone to flooding. After applying the calculated weights to each layer, the information layers were imported into IDRISI software, where they were combined using the WLC model to generate the final map of flood-prone areas. Research Findings The research findings indicated significant insights into the flood events of April 2018 in Golestan Province, particularly regarding the environmental factors influencing flood risk. Flood-Affected Areas The analysis confirmed that substantial flooding occurred in urban and suburban areas, notably Aqh Qhala, Siminshahr, and Gomishtappeh. The data derived from Sentinel 1 and 2 radar images, along with other remote sensing tools, highlighted the extensive reach of the floodwaters. Environmental Factors The study evaluated the correlation between the flooded areas and several environmental parameters, yielding the following correlation coefficients: altitude (0.652), slope (0.619), vegetation density (0.543), rainfall (-0.517) (indicating an inverse relationship), and distance from river (0.437). These coefficients illustrated that lower altitudes and gentle slopes significantly contributed to the flooding. Additionally, areas with reduced vegetation density were particularly susceptible. Soil Type Impact The findings also identified that Aridisol soils were the most affected during the flood, suggesting a specific vulnerability linked to soil characteristics. Flood Risk Mapping Using the Weighted Linear Combination (WLC) model, flood-prone areas were delineated. The northern and western sectors of the study area characterized by low altitude, low slope, minimal vegetation, and proximity to rivers, were classified as having high flood potential. Implications for Urban Planning Given the high flood risk in western Golestan Province, the research underscored the necessity of integrating flood risk considerations into environmental planning and urban development strategies. The results advocate for heightened awareness and preparedness among local authorities and communities to mitigate future flood impacts. Overall, the study provides a comprehensive assessment of the factors contributing to flooding and serves as a critical resource for future flood risk management in the region. Discussion of Results & Conclusion The findings indicated that during the floods of April 2018, a significant portion of the region, particularly the urban and suburban areas of Aqh Qhala, Siminshahr, and Gomishtappeh, experienced extensive flooding. After identifying the flooded areas, this research analyzed the relationships between these areas and various environmental factors, including average annual precipitation, distance from rivers, elevation, slope, vegetation density, and soil type. The correlation coefficient between flooded areas and average annual rainfall was found to be -0.517, suggesting an inverse relationship. In contrast, the correlation coefficients for vegetation density and distance from rivers were 0.543 and 0.437, respectively. The analysis revealed that low-slope and low-altitude areas were particularly susceptible to flooding with coefficients of 0.652 and 0.619 for elevation and slope, respectively, indicating a strong correlation. Additionally, flooding was most prevalent in areas with Aridisol soil types. The western regions of Golestan Province demonstrated a high potential for flooding due to various environmental factors. This vulnerability had posed significant threats to residential areas and agricultural lands in recent years. The April 2018 flood exemplified this risk, affecting numerous cities in the region. The analysis of environmental factors revealed that altitude, slope, and vegetation density were the most influential with coefficients of 0.652, 0.619, and 0.543, respectively. Areas with low elevation, gentle slopes, and sparse vegetation experienced the highest levels of flooding. This research also produced a map indicating flood-prone areas based on the relationship between radar imagery and environmental parameters. The results highlighted that the northern and western regions of the study area, including urban and suburban areas of Gorgan, Aqh Qhala, Siminshahr, Gomishtappeh, Kordkoy, and Bandar Turkaman, were classified as having high flood potential due to their low altitude, gentle slopes, low vegetation density, and proximity to rivers. Given these findings, it is crucial to consider flood risks in environmental planning and urban development strategies. Special attention should be given to the flood-prone areas in the western regions of Golestan Province to mitigate future flooding impacts and enhance community resilience. سیلاب از‌جمله مخاطره‌های طبیعی است که در‌طی سال‌های اخیر بسیاری از شهرهای ایران از‌جمله شهرهای غربی استان گلستان با آن رو‌به‌رو بوده است. از‌جمله سیلاب‌هایی که در این منطقه رخ داده سیلاب فروردین 1398 بوده است که بسیاری از شهرهای غربی استان گلستان با خسارت‌های جانی و مالی مواجه شد. با توجه به اهمیت موضوع در پژوهش حاضر به پایش مناطق سیل‌زدۀ غرب استان گلستان و تحلیل نفش عوامل محیطی در وقوع آن پرداخته شده است. در این مطالعه از تصاویر راداری سنتینل 1 و 2، تصاویر ماهوارۀ MODIS، CHIRPS و لندست 9 و مدل رقومی ارتفاعی 30 متر به‌عنوان مهم‌ترین داده‌های تحقیق استفاده شده است. مهم‌ترین ابزارهای این مطالعه سامانۀ گوگل ارث انجین، IDRISI و ArcGIS بوده است. در پژوهش حاضر ابتدا با استفاده از سامانۀ گوگل ارث انجین مناطق سیل‌زده شناسایی و در‌ادامه، ارتباط آن با عوامل محیطی تحلیل و در‌نهایت، مناطق مستعد وقوع سیلاب با استفاده از مدل WLC شناسایی شده است. نتایج حاصل‌شده از تصاویر راداری نشان داده است که در فروردین 1398 بخش زیادی از شهرهای منطقه از‌جمله مناطق شهری و حاشیۀ شهری آق‌قلا، سیمین‌شهر و گمیش‌تپه با سیلاب مواجه شده است. نتایج تحلیل نقش عوامل محیطی در سیلاب رخ‌داده نشان داده است که وضعیت ارتفاعی، شیب و تراکم پوشش گیاهی به‌ترتیب با ضرایب 652/0، 619/0 و 543/0 بیشترین تأثیر‌گذاری را داشته‌ است. همچنین، نتایج مدل WLC نشان داده است که مناطق شمالی و غربی منطقۀ مطالعه‌شده و از‌جمله مناطق شهری و حاشیۀ شهری شهرهای گرگان، آق‌قلا، سیمین‌شهر، گمیش‌تپه، کردکوی و بندرترکمن به‌دلیل ارتفاع و شیب کم، تراکم کم پوشش گیاهی و نزدیکی به رودخانه در طبقۀ پتانسیل سیل‌خیزی خیلی زیادی قرار دارند.

https://gep.ui.ac.ir/article_29010_8042f82d3688ecdde244a4a0348f0b4e.pdf

دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 Spatiotemporal Assessment of Land Surface Temperature (LST) Based on Natural and Human Parameters Using Frequency Ratio (FR) Technique (Case Study: East Qazvin Province) استخراج و تحلیل زمانی و مکانی دمای سطح زمین نسبت به متغیرهای طبیعی و انسانی توسط روش آماری نسبت فراوانی (FR) (مطالعۀ موردی: محدودۀ شرقی استان قزوین) 19 46 29107 10.22108/gep.2024.142334.1660 FA صالح عبدالهی استادیار گروه مهندسی عمران، واحد اصفهان (خوراسگان)، دانشگاه آزاد اسلامی، اصفهان، ایران Journal Article 2024 08 03 Abstract In recent decades, one of the environmental factors that has garnered significant attention from researchers is the increase in Land Surface Temperature (LST) and the emergence of urban heat islands in cities. This study aimed to analyze the spatial and temporal variations in LST in the eastern part of Qazvin Province across two phases. In the first phase, LST was calculated using Landsat 8 satellite images from 2016, 2019, and 2021, allowing for an examination of temperature trends during summer and winter. The second phase focused on evaluating the influence of various topographical and anthropogenic factors on temperature changes within the region. The relationships were extracted and analyzed using the statistical-spatial Frequency Ratio (FR) technique. The results indicated that during summer, areas experiencing temperatures above 35°C had increased from approximately 6,600 km² in 2016 to over 9,300 km² in 2021. Conversely, in winter, the extent of sub-zero temperatures had decreased from about 1,330 km² in 2016 to around 890 km² in 2021. Overall, this suggested a general increase in regional temperatures over the study period. Among the selected natural factors, the FR values for the aspect layer indicated it was more influential than other factors. Additionally, barren land cover exhibited an FR value of 0.75 in areas with temperatures exceeding 35°C in summer, while snow cover showed a frequency ratio of 0.89 in regions with temperatures below -13°C in winter. These findings underscored the impacts of land use and cover on LST. By extracting such spatial-temporal relationships from the studied area, we could take effective measures in urban planning, environmental management, and crisis response, thereby mitigating numerous negative environmental consequences. Keywords: Land Surface Temperature (LST), Spatiotemporal Analysis, Frequency Ratio (FR), Geographic Information System (GIS), Qazvin Province. Introduction In recent decades, the increase in Land Surface Temperature (LST) has become a significant environmental concern for researchers in urban and environmental studies. Numerous studies have demonstrated that urban areas typically experience higher temperatures than their surrounding environments due to human activities. These activities include heating and cooling processes, transportation and road construction, industrial operations, and excessive absorption of solar radiation by urban structures. Additionally, the scarcity of green spaces and poor air circulation exacerbate this issue. Today, LST calculations are primarily conducted using satellite imagery, particularly through the processing of thermal infrared bands. One of the most commonly utilized datasets in this context is the thermal bands from Landsat 8 satellite images (TIRS). Researchers have developed various algorithms for calculating LST, including the Split Window Algorithm (SWA), SEBAL method, and Single-Channel Algorithm (SCA). After computing surface temperatures, spatial relationships between various independent natural and human factors and the calculated temperatures can be analyzed using Geographic Information System (GIS) capabilities and statistical methods. Over the last two decades, application of statistical and probabilistic models within the GIS framework to assess the impacts of various factors on specific phenomena has gained considerable attention. The primary aim of this study was to extract trends in LST changes from 6 Landsat 8 images taken from the eastern part of Qazvin Province during both summer and winter in 2016, 2019, and 2021,. The secondary objective was to investigate the relationship between changes in LST treated as a dependent variable and several topographical and human factors of the study area considered as independent variables across the two seasons. Ultimately, the goal was to analyze the impacts of different classes of independent variables on the trends in surface temperature during the selected time period using the Frequency Ratio (FR) regression model. Materials & Methods The eastern part of Qazvin Province was chosen as the study area for this research. This region is significant for environmental assessments due to the presence of several industrial zones, Tehran-Karaj-Qazvin freeway, which connects a large portion of eastern and central Iran to the western provinces, and Shahid Rajaee Power Plant located along this route. To assess the trend in temperature changes, Landsat 8 satellite images from the winters and summers of 2016, 2019, and 2021 were utilized. After downloading the images from the website of United States Geological Survey, initial preprocessing steps were conducted using ENVI software. These steps included radiance and atmospheric calibration to correct for sensor and atmospheric errors, as well as cropping the images to focus on the study area. Following the calculation of surface temperature from the satellite images, the results were validated using the SEBAL method. The discrepancies were found to be less than 1°C. Additionally, the calculated temperatures were compared with hourly air temperature data from Qazvin meteorological stations recorded during the overpass of the Landsat satellite, with differences remaining under 1.5°C. The final LST maps produced in ENVI were then imported into ArcGIS software for further classification, comparison, and spatial-temporal evaluation. To achieve the second objective, various data layers related to natural and human factors—including a digital elevation model, slope, aspect, land cover map, and road network—were compiled. The relationship between LST (the dependent variable) and these variables (independent variables) was analyzed using the FR regression statistical model. The FR method is a widely used data mining approach for modeling and forecasting various natural hazards and urban trends. Research Findings The analysis of LST trends indicated that the northern part of the study area had generally experienced lower average temperatures due to its higher latitude and elevation. In contrast, the southern part had a higher average temperature attributed to its lower latitude, reduced altitude, and flatter terrain. Notably, while winter 2019 recorded lower temperatures than the other two years, the summer of 2019 exhibited higher minimum and maximum temperatures compared to the preceding years. A significant portion of the region had recorded temperatures ranging from 30 to 45°C. Overall, the temperature calculations for the summer across the three years suggested a warming trend from 2016 to 2021. When evaluating the impacts of various factors on surface temperature, the slope direction layer demonstrated a greater influence than either the elevation or slope layers. Generally, northern slopes (north, northwest, and northeast) exhibited higher frequency ratios during summer due to reduced sunlight exposure in winter. Conversely, southern and southeastern slopes showed higher FRs in summer compared to winter, reflecting increased solar radiation. By extracting relationships through the FR method, valuable insights into the influence of independent variables on regional temperature could be gleaned. In summer, barren lands were associated with a very high FR for the hottest temperature class, while this ratio was significantly lower in winter. Agricultural and grassland areas also exhibited considerable heat during summer compared to winter. Factors like soil type, terrain flatness, direct solar radiation exposure, and aspect were crucial contributors to the elevated temperatures in these regions. Additionally, the impact of industrial activities on surface temperature increases was significant across all three years, affecting both winter and summer seasons. In each season during the selected timeframe, industrial FR values were consistently higher at elevated temperatures. Areas designated as industrial zones and towns had recorded higher temperatures than surrounding urban areas. Furthermore, the influence of vehicle traffic on roadways was particularly pronounced in winter, with barren lands adjacent to roads contributing notably to ambient air warming. The FR calculation process assessed the occurrence of a phenomenon as a dependent variable in relation to independent variable classes. This evaluation could assist in predicting natural events, such as floods, earthquakes, landslides, and other phenomena. Discussion of Results & Conclusion This study aimed to analyze the Land Surface Temperature (LST) of the eastern part of Qazvin Province in two stages. In the first stage, surface temperature was estimated using two widely used calculation methods and the rate of temperature fluctuations during the selected time period was assessed. The results indicated a strong correlation between the two methods of estimating surface temperature, as well as a close alignment with the hourly air temperatures recorded by meteorological stations in Qazvin. This consistency could be attributed to the incorporation of key parameters, such as the emissivity coefficient, spectral radiation, and brightness temperature from thermal bands. A detailed statistical evaluation of LST classes revealed a clear increase in temperature from 2016 to 2021. In addition to identifying temperature trends, it was also crucial to examine the factors influencing these changes. In the second stage, the effects of various layers—such as elevation, slope, aspect, land cover, and proximity to road networks—were evaluated using the Frequency Ratio (FR) method. The results demonstrated significant impacts from both human and natural variables on surface temperature fluctuations. By extracting these relationships, valuable insights were gained regarding the influence of independent variables on the dependent variable—surface temperature. These findings can be applied in various fields, including urban planning, environmental management, and crisis response. Finally, for more accurate calculations and analyses of surface temperature, particularly in urban areas, the use of high-resolution data and images is recommended to effectively assess different urban land uses. افزایش دمای سطحی زمین و یا ایجاد جزایر حرارتی روی سطح شهرها یکی از عوامل زیست‌محیطی است که محققان در دهه‌های اخیر به آن توجه کرده‌اند. هدف از پژوهش حاضر تحلیل مکانی و زمانی دمای سطحی بخش شرقی استان قزوین در دو مرحله است. در مرحلۀ اول پس از محاسبۀ دمای سطحی با استفاده از تصاویر ماهواره‌ای لندست ۸ در سال‌های 2016، 2019 و 2021 روند تغییرات دما در تابستان و زمستان مطالعه شد. هدف از مرحلۀ دوم ارزیابی و تحلیل تأثیر برخی عوامل مختلف توپوگرافی و انسانی بر تغییرات دمای منطقه است. این ارتباط با روش آماری – مکانی نسبت فراوانی استخراج و تحلیل شد. نتایج نشان داد که در فصل تابستان وسعت نواحی با دمای بیش از 35 درجه از حدود 6600 کیلومتر مربع در سال 2016 به بیش از 9300 کیلومتر مربع در سال 2021 رسیده است. در‌مقابل، در فصل زمستان مساحت دمای زیر صفر درجه در سال 2016 از حدود 1330 کیلومتر مربع به حدود 890 کیلومتر مربع در سال 2021 رسیده است؛ بنابراین به‌طور کلی، دمای منطقه در‌طول دورۀ مد‌نظر افزایش داشته است. همچنین، از‌میان عوامل طبیعی انتخاب‌شده، ارقام محاسبه‌شدۀ نسبت فراوانی لایۀ جهت ‌شیب نشان‌دهندۀ تأثیرگذاری بیشتر این عامل نسبت به دیگر عوامل طبیعی بوده است. از طرف دیگر، پوشش اراضی بایر با رقم نسبت فراوانی 75/0 در کلاس دمایی بیش از 35 درجه سانتیگراد در فصل تابستان و پوشش برفی با رقم نسبت فراوانی 89/0 در کلاس دمایی زیر منفی 13 درجه در فصل زمستان نشان‌دهندۀ تأثیر نوع پوشش و کاربری زمین در تغییرات دمای محیط است. با استخراج چنین روابط مکانی – زمانی از منطقۀ مطالعه‌شده می‌توان اقدام‌های تأثیرگذاری را در‌زمینۀ مدیریت شهری، زیست‌محیطی و بحران انجام داد و سپس از پیامدهای منفی زیست‌محیطی بسیاری جلوگیری کرد.

https://gep.ui.ac.ir/article_29107_7274296ccc77c0ddec375013f075753b.pdf

دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 Landfill Site Selection for Construction and Demolition Waste in Plain and Desert Cities Using Spatial Information System and TOPSIS (Case Study: Yazd City) تعیین مکانهای بهینۀ دفن نخاله‌های ساختمانی در شهرهای هموار و بیابانی با استفاه از سیستم اطلاعات مکانی و تکنیک شباهت به گزینۀ ایدئال (مورد مطالعه: شهر یزد) 47 82 29072 10.22108/gep.2024.142139.1656 FA محمد شریفی دانشیار ژئومورفولوژی، گروه جغرافیا، دانشگاه یزد، یزد، ایران محسن ابراهیمی استادیار سنجش از دور، گروه جغرافیا، دانشگاه یزد، یزد، ایران فاطمه سیف اللهی کارشناسی ارشد ژئومورفولوژی، گروه جغرافیا، دانشگاه یزد، یزد، ایران Journal Article 2024 07 28 Abstract Construction and Demolition (C&D) waste is a significant urban solid waste issue arising from the construction, renovation, and demolition of buildings and infrastructure. This study focused on the optimal location for C&D waste disposal in Yazd, a rapidly developing city facing challenges related to urban waste management. The flat desert landscape of the area had become a common site for both legal and illegal dumping, leading to unsightly scenes and environmental consequences, including the influx of fine-grained materials into the city from floods and winds. Through a combination of library and field studies, we identified 20 critical criteria for locating C&D waste disposal sites, including distance from residential areas, water sources, land use, and soil conditions. These criteria were processed using Geographic Information Systems (GIS) to create layered information for analysis. The TOPSIS multi-criteria decision-making technique was then applied to prioritize potential landfill sites. Findings indicated that proximity to residential centers and water sources were crucial in the short term, while long-term considerations included the impact of wind and drainage patterns. The northeastern areas of Yazd were identified as the most suitable places for C&D waste disposal, with 4 legal sites—Shehneh, Kholdebarin, Gawd-e-Mahmoudi, and Kouhistan park area—scoring the highest based on the evaluation. The results underscored the need for effective waste management strategies to minimize environmental pollution and enhance urban aesthetics. Future research could explore the application of this model in other regions facing similar challenges. Keywords: Siting, Construction & Demolition (C&D) Wastes, Landfill, Yazd, Environment. Introduction Construction and Demolition (C&D) waste is classified as urban solid waste generated from the construction, renovation, and demolition of both residential and non-residential buildings, as well as infrastructure, such as roads and bridges. This issue is recognized as a significant global concern that requires careful consideration from economic, environmental, and technological perspectives. The purpose of this study was to identify optimal locations for C&D waste disposal in Yazd, a rapidly developing city that was simultaneously undertaking the renovation of its older structures. The flat terrain surrounding Yazd had made it a common site for landfilling urban solid waste, particularly C&D waste. The presence of local industries, including tile, ceramic, and brick manufacturing, along with numerous sand and stone quarries, had contributed to a significant increase in urban construction waste in recent years. Unfortunately, this waste was often dumped both legally and illegally in the areas surrounding Yazd, resulting in unsightly landscapes and various environmental repercussions. One notable consequence was the infiltration of fine-grained materials into the city due to floods and strong winds, which posed additional challenges for urban management and environmental sustainability. Materials & Methods This research involved a comprehensive approach that included both library and field studies, as well as direct observations of various construction waste landfill sites. In the initial phase, we identified key criteria for locating urban solid waste, specifically focusing on plain and desert cities, drawing on the insights of experts and existing research. We established 20 critical indicators, which included elevation, slope, vegetation, pasture, surface water sources, underground water sources (such as springs, wells, and Qanats), soil type, urban and rural land use, cultural centers, airports, railways, and the locations of water, gas, and electricity transmission lines, as well as communication roads, industrial areas, and mines. These data were then transformed into informational layers using Geographic Information Systems (GIS) for processing. Each indicator was analyzed and standardized based on expert opinions. The layers were subsequently overlaid to create a map indicating optimal locations for the landfill of construction and demolition waste. To combine the maps, we assigned weights to each layer, determined their boundaries in the GIS, and then overlaid the boundary layers. Following this, we evaluated the areas surrounding Yazd that had been designated by the municipality as legal disposal sites for construction waste using the TOPSIS multi-criteria decision-making technique. This process allowed us to prioritize the most suitable locations for waste disposal. Research Findings The findings indicated that all 20 parameters evaluated in this study played a crucial role in determining the optimal locations for urban C&D waste disposal. However, some parameters were particularly significant and warranted greater attention. For instance, the distance to residential centers—both urban and rural—as well as the proximity to surface and underground water sources, land use, geology, soil type, and suburban communication roads were more favorable in the short term. In contrast, factors, such as the direction of the dry river network and the orientation of both primary and secondary winds were more critical for long-term site selection. C&D waste could weather relatively quickly and was susceptible to wind and heavy rainfall, especially during monsoon seasons, leading to flooding in dry riverbeds. As a result, substantial amounts of this material could be transported back into the city. Based on the analysis of various human and natural factors influencing the siting of C&D waste disposal and the results obtained using the TOPSIS method, 4 legally designated areas identified by the municipality—Shehneh, Kholdebarin, Gawd-e-Mahmoudi, and Kouhistan Park—were evaluated, yielding scores of 0.402, 0.612, 0.403, and 0.443, respectively. Consequently, the northeastern areas of Yazd City emerged as the most suitable locations for the disposal of urban construction waste, considering the environmental, economic, and aesthetic indicators. Discussion of Results & Conclusion The significant production of Construction and Demolition (C&D) waste in Yazd City could be attributed to rapid urban development, extensive reconstruction and renovation of old buildings and infrastructure, and the presence of nearby tile, ceramic, and brick factories. Additionally, most of the surrounding land was flat and desert-like, encouraging residents and factory owners to dispose their construction waste in the nearest available locations. This tendency was further exacerbated by the desire to reduce transportation costs. Consequently, both legal and illegal dumping of construction waste had marred the aesthetics of the city's surroundings, particularly along the main access roads. Dumping C&D waste in natural environments leads to significant alterations in the landscape and geomorphological features. These changes disrupt natural processes, including drainage systems, soil permeability, and vegetation levels, while also increasing erosion rates and affecting wind patterns and land subsidence. Such alterations pose risks to environmental stability, including changes to ecosystems, increased vulnerability of aquifers, heightened flood risks, and exacerbated dust production. Therefore, selecting suitable locations for landfill sites is crucial to mitigate environmental degradation and pollution (Paz et al., 2020). Failure to address the environmental impacts of construction waste can lead to severe air, water, and land pollution (Chen et al., 2018; Moustakas et al., 2023). To identify optimal locations for burying C&D waste, all areas surrounding Yazd City were evaluated, including those legally designated by the municipality for waste disposal, as well as those used illegally by residents. While the land around Yazd was predominantly desert and deemed unsuitable for many uses, numerous areas were found to be unfavorable for landfilling C&D waste. According to the results obtained through the TOPSIS technique, which guided the creation of a final location map, the central urban areas were unsuitable for waste disposal due to the presence of residential buildings, surface and underground water sources, and power transmission lines. Greater emphasis was placed on factors, such as proximity to urban and rural areas, water resources, and power transmission lines when determining suitable sites for C&D waste. Additionally, other influencing factors, including the presence of mines, industrial sites, roads, topography, vegetation, rural settlements, soil type, and sediment characteristics, ruled out the western, eastern, and southern regions of Yazd as viable landfill sites. For instance, the west and southwest areas were characterized by numerous suburban roads, an airport, and both urban and rural residential zones, alongside mining and industrial facilities. These regions were also subject to prevailing winds from the west and northwest, as well as the drainage patterns of several dry rivers oriented south-north and southeast-northwest. Consequently, the northeastern parts of Yazd emerged as the preferred locations for C&D waste landfilling. The findings of this research highlight the effectiveness of multi-indicator evaluation techniques in identifying suitable sites for urban solid waste disposal, a strategy supported by similar studies conducted in various regions worldwide (Banias et al., 2010; Aragones et al., 2010; Araiza et al., 2019; Yuan et al., 2022). نخاله‌های ساختمانی یا زباله‌های ساخت‌و‌ساز و تخریب به‌عنوان زباله‌های جامد تعریف می‌شود که بر اثر ساختمان‌سازی، بازسازی، تخریب ساختمان‌های مسکونی و غیرمسکونی و زیرساخت‌هایی از‌قبیل جاده‌ها، پل‌ها و غیره به وجود آمده و به‌عنوان یکی از موضوعات مهم و حیاتی در‌سطح جهانی شناخته شده است. این نخاله‌ها از وجوه مختلف اقتصادی، محیط زیستی و تکنولوژیکی نیازمند ملاحظات بیشتری است. هدف از پژوهش حاضر مکان‌یابی دفن ضایعات ساختمانی در شهر هموار و بیابانی یزد (شهری که با سرعت در‌حال توسعۀ کالبدی و بازسازی و نوسازی بافت‌های قدیمی آن است) با استفاده از سیستم اطلاعات مکانی و تکنیک شباهت به گزینۀ ایدئال است. به‌علاوه، وجود صنایع کاشی و سرامیک، کوره‌های آجرپزی و سفال‌سازی، معادن متعدّد شن و ماسه، سنگ‌های ساختمانی و موارد متعدّد دیگر سبب تولید حجم انبوهی از ضایعات ساختمانی شهری در سال‌های اخیر شده است که این ضایعات به شکل قانونی و غیرقانونی در مناطق پیرامونی شهر یزد تخلیه و رها شده و علاوه‌بر ایجاد مناظر بسیار زشت و ناپسند در حومۀ شهر آثار و پیامدهای زیست‌محیطی زیادی را بر‌جای گذاشته است. از‌جمله پیامد‌های زیست‌محیطی می‌توان به وارد‌شدن حجم زیادی از این رسوبات به معابر شهری پس از بارندگی‌های رگباری دو-سه سال اخیر و یا ایجاد گردو‌غبار و آلودگی هوا اشاره کرد. برای انجام‌دادن این پژوهش در ابتدا شاخص‌های بیست‌گانه‌ای بر‌پایۀ نظر‌های کارشناسان (ارتفاع، شیب، پوشش گیاهی، مرتع، منابع آب‌های سطحی، آب‌های زیرزمینی چشمه، چاه، قنات، خاک، مناطق شهری و روستایی، مراکز فرهنگی، فرودگاه، راه‌آهن، خطوط انتقال آب، گاز و برق، راه‌های ارتباطی و مناطق صنعتی و معادن) انتخاب و سپس این داده‌ها در سامانۀ اطلاعات جغرافیایی به شکل لایه‌های اطلاعاتی در‌آمد و در‌نهایت، هر‌یک از این شاخص‌ها بر‌پایۀ نظر متخصصان حریم و چارچوب لحاظ شد. در پایان، همۀ لایه‌ها روی‌هم‌گذاری شد تا نقشۀ مکان‌های بهینه برای دفن ضایعات ساختمانی مشخص شود. در‌ادامه، مناطق پیرامونی شهر یزد که شهرداری آنها را به‌عنوان مکان‌های مجاز دفن نخاله‌ها معین کرده بود، بر‌اساس تکنیک چند‌شاخصۀ تاپسیس ارزیابی و به‌دنبال آن مکان‌های بهینۀ دفن ضایعات ساختمانی اولویت‌بندی شد. بر‌اساس نتایج حاصل از روش شباهت به گزینۀ ایدئال چهار منطقۀ مجاز تعیین‌شدۀ شهرداری شامل شحنه، خلدبرین، گود محمودی و محدوده پارک کوهستان به‌ترتیب 402/0، 612/0، 403/0 و 443/0 امتیاز گرفتند. بدین ترتیب، بر‌مبنای همۀ شاخص‌های زیست‌محیطی، اقتصادی و زیبایی‌شناسی منظر مناطق شمال شرق شهر یزد برای دفن نخاله‌های ساختمانی شهری بر سایر گزینه‌ها ارجحیت دارد.

https://gep.ui.ac.ir/article_29072_796af8542b97488f4651f281a2ca546c.pdf

دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 An Analysis on the Compactness of the Spatial Structure of Rural Settlements in Meshkinshahr County and the Influence of Geographical Factors تحلیلی بر فشردگی ساختار فضایی سکونتگاه‌های روستایی شهرستان مشکین‌شهر و تأثیر بنیانهای جغرافیایی بر آن 83 112 29192 10.22108/gep.2025.140620.1634 FA بهنام باقری استادیار گروه جغرافیا، دانشگاه پیام نور، تهران، ایران Journal Article 2024 02 04 Abstract The spatial distribution of villages is influenced by various characteristics and factors, including elevation, land slope, slope direction, distance from roads and cities, and proximity to rivers. This study employed an applied, descriptive, analytical, and correlational approach to examine the compactness of the spatial distribution structure of rural settlements in Meshkinshahr County, as well as the impact of geographical features on this distribution. In the first part of the research, ArcGIS analytical tools were utilized to assess the spatial distribution patterns of villages. Techniques such as Average Nearest Neighbor, high/low clustering, and spatial autocorrelation were employed. Additionally, cluster and outlier analysis, along with hotspot analysis, were conducted to create maps of village clusters. Finally, kernel density analysis was performed to explore the spatial density characteristics of village distributions. The second part of the study investigated the relationship between the spatial distribution of villages and geographical features. The minimum elevation in the region was 595 m, while the maximum reached 4800 m. Most villages were located at elevations between 1200 and 1400 m with the highest concentration found on slopes ranging from 6 to 15 percent. Notably, 71% of the villages were situated on slopes of less than 15% and 68% were located in shaded areas. Furthermore, the majority of the county's rural population resided near Meshkinshahr County with proximity to rivers playing a crucial role in the establishment of these villages. Keywords: Spatial Structure, Village, Meshkinshahr County, Geographical Foundations, GIS. Introduction In the past, our ancestors relied heavily on experience for the placement of settlements, often without aligning their choices with contemporary scientific understanding. Today, however, the simplicity of earlier times has given way to the complexities of urban life. Modern cities characterized by advanced infrastructure, such as sewage systems, subways, and skyscrapers, stand in stark contrast to traditional villages, which serve as reservoirs for preserving human achievements and cultural heritage. Recently, we have witnessed rapid urbanization, leading to a significant decline in the rural population. Fragmentation of agricultural lands coupled with severe environmental pollution and emergence of numerous systemic risks has diminished rural areas, causing many traditional landscapes to vanish. Despite their critical roles in agricultural production, ecological preservation, and cultural heritage, rural areas face significant challenges due to prolonged industrialization and urbanization, making rural decline a pressing global issue. Materials & Methods This research was categorized as applied research, employing descriptive, analytical, and correlational methods. The study focused on the spatial distribution of 414 villages in Meshkinshahr County. In the first part of the research, we utilized analytical tools within ArcGIS software to conduct 3 analyses: the average nearest neighbor index, high/low clustering, and spatial autocorrelation, which together helped analyze the spatial distribution patterns of the villages. Additionally, we performed cluster and outlier analysis, along with hot spot analysis, to create a map of village clusters. Finally, kernel density function analysis was employed to investigate the spatial density distribution of villages in Meshkinshahr County. In the second part of the research, we examined the relationship between the spatial distribution of villages and various natural and human geographical factors. The data obtained from spatial analysis in the Geographic Information System (GIS) environment was then transferred to Excel software to calculate correlations between the spatial distribution of villages and the influencing factors. Research Findings Meshkinshahr County consists of 12 divisions and 414 villages. To analyze the spatial distribution pattern of these villages, the average nearest neighbor, cluster and outlier analysis, and spatial autocorrelation tools were employed. The results indicated that the villages in Meshkinshahr County exhibited a random distribution, lacking significant clustering. To examine the homogeneity and heterogeneity of the spatial distribution of villages, the local Moran's I index was applied at the division level. This analysis revealed that Arshq North Dehistan was an outlier with low values surrounded by villages with high values, while Noqdi Dehistan formed a cluster of low values surrounded by similar low-value villages. Additionally, to analyze the distribution of hot spots, the 12 divisions were assessed based on the number of villages within them. The findings indicated the presence of cold spots with a confidence level of 90% in the Lahrud and Noqdi divisions, while the other divisions of the county did not exhibit significant hot or cold spots. Most villages in Meshkinshahr County are situated at altitudes between 1200 and 1400 m. Approximately 50% of the villages are located below 1400 m and about 80% are below 1600 m—an altitude deemed suitable for rural settlement according to existing studies. The analysis also indicated an inverse relationship between altitude and rural population distribution, showing a decline in population as altitude increased. Furthermore, as the distance from rivers increased, the number of villages decreased, reinforcing the inverse relationship between river proximity and the spatial distribution of the rural population. Discussion of Results & Conclusion The analysis of clusters, outliers, and spatial autocorrelation regarding the spatial distribution of villages and their divisions revealed two key findings. First, the spatial distribution of villages within the county was random. Second, the distribution of the 12 divisions in Meshkinshahr County based on the number of villages they contained also exhibited a random pattern and lacked significant clustering. The slope was another critical factor influencing the spatial distribution of villages. Notably, 71% of the villages were situated on slopes of less than 15%, which was conducive to cultivation. Given that the study area was located in the northern hemisphere, the optimal slope directions for establishing villages were the eastern, southeastern, southern, and southwestern slopes (sunny slopes). However, this factor had not significantly influenced village establishment as only 38% of the villages were found on sunny slopes. This inconsistency might stem from the predominant location of Meshkinshahr County on the northern and northwestern slopes of Mount Sabalan. Additionally, the region's hydrographic network greatly affected the settlement and distribution of villages, primarily due to the rural population's reliance on water sources in this mountainous area. As the distance from rivers increased, both the number of villages and rural population diminished. Furthermore, proximity to main and intercity roads was another vital factor affecting village settlement and distribution. Most villages and the majority of the rural population were concentrated near these roads, highlighting the significant impact of transportation infrastructure on densely populated areas. استقرار و توزیع فضایی روستاها از ویژگی‌های متعدّد طبیعی و انسانی تأثیر می‌پذیرد و عواملی مانند ارتفاع، شیب زمین، جهت شیب، فاصله از جاده و شهرها و فاصله از رودخانه در آن مؤثر است. پژوهش حاضر از‌نوع پژوهش‌های کاربردی، توصیفی-تحلیلی و همبستگی بوده است. هدف از این مطالعه تحلیل فشردگی ساختار توزیع فضایی سکونتگاه‌‌های روستایی در شهرستان مشکین‌شهر و تأثیر بنیان‌های جغرافیایی بر آن است. پژوهش در دو فاز جداگانه انجام شده است. در این مطالعه با بهره‌گیری از ابزارهای تحلیلی ArcGIS در بخش اول پژوهش از تحلیل‌های متوسط نزدیک‌ترین همسایه، خوشه‌بندی زیاد/کم و خود‌‌همبستگی فضایی برای تحلیل الگوی توزیع فضایی روستاها، از تحلیل‌های خوشه و ناخوشه و لکه‌های داغ برای تهیۀ نقشۀ خوشه‌های روستاها و از تحلیل تراکم کرنل برای بررسی ویژگی‌های تراکم فضایی توزیع روستاها استفاده شده است. در بخش دوم ارتباط بین توزیع فضایی روستاها با بنیان‌های جغرافیایی بررسی شده است. نتایج نشان می‌دهد هر‌چند تحلیل در‌مقیاس دهستانی نشان‌دهندۀ خوشه‌بندی روستاها و جمعیت روستایی است، توزیع فضایی روستاها در شهرستان فاقد خوشه‌بندی است. علاوه بر این، استقرار روستاهای شهرستان بیشتر تحت‌تأثیر بنیان‌های جغرافیایی است؛ به‌‌طوری که کمینه ارتفاع در شهرستان برابر 595 متر و بیشینۀ آن مساوی 4800 متر بوده و بیشتر روستاها در ارتفاع 1200 تا 1400 متری قرار داشته و در شیب‌های 6 تا 15 درصد است. 71 درصد از روستاها در شیب کمتر از 15 درصد استقرار یافته‌ و 68 درصد آنها نیز در دامنه‌های سایه واقع شده است. بیشتر جمعیت روستایی شهرستان به‌علت دارا‌بودن زمین‌های کشاورزی و باغ‌های میوه در مجاورت شهر مشکین‌‌‌شهر بوده است. در این میان، نزدیکی به رودخانه‌ها نیز در استقرار و پراکنش روستاها به‌طور کامل، نقش اساسی داشته است.

https://gep.ui.ac.ir/article_29192_8e7ff5cbda513f98bccf9670128d8cab.pdf

دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 Utilizing GIS with SWARA, EDAS, and TOPSIS Multi-Criteria Decision-Making (MCDM) Methods for Solar Power Plant Site Selection in Fars Province استفاده از سیستم اطلاعات مکانی و روش‌های تصمیم‌گیری چندمعیارۀ سوارا، ایداس و تاپسیس به‌منظور مکان‌یابی نیروگاه خورشیدی در استان فارس 113 136 29260 10.22108/gep.2025.143337.1681 FA وحیدرضا عتباتی کارشناسی ارشد سیستم های اطلاعات مکانی، گروه مهندسی نقشه برداری، دانشکدۀ مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران" 0009-0001-5186-6969 مهدی عربی دانشیار گروه مهندسی نقشه‌برداری، دانشکدۀ مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران Journal Article 2024 11 09 Abstract Utilization of solar energy through construction of solar power plants has become a pivotal sector in the global energy landscape. Iran, with its advantageous geographical and climatic conditions, possesses substantial potential for the development of solar power facilities and electricity generation. This study employed a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to identify optimal sites for solar power plants in Fars Province. 9 critical criteria identified through prior research were mapped using GIS. The SWARA method was utilized to assign weights to these criteria, establishing a hierarchy of importance with photovoltaic potential ranked highest and altitude ranked lowest. The TOPSIS and EDAS methods were then applied to produce a land suitability map, indicating that 21 and 9% of Fars Province were highly suitable for the construction of solar power plants, respectively. This research prioritized the counties of Fars Province for the establishment of solar energy facilities. Keywords: Solar Power Plant Site Selection, Multi-Criteria Decision-Making (MCDM), Geospatial Information System (GIS). Introduction The demand for renewable energy as a substitute for fossil fuels has been rising in recent years. Solar energy, one of the most prevalent forms of renewable energy, has garnered significant attention in developing countries due to its potential for sustainable, cost-effective, and environmentally friendly energy production. Iran, a major oil and gas producer, primarily generates electricity from fossil fuel power plants, with solar energy representing only a minor portion of its electricity generation. Despite having high solar radiation levels and numerous sunny days throughout the year, Iran has yet to fully capitalize on its solar power potential. Determining the optimal location for a solar power plant is a complex task influenced by environmental, climatic, and topographical factors, requiring scientific approaches. Site selection involves evaluating potential areas and prioritizing locations that ensure high efficiency in electricity generation. By integrating Multi-Criteria Decision-Making (MCDM) methods with Geographic Information Systems (GIS), it becomes possible to effectively manage spatial information, analyze various locations, and assess their suitability for solar power plant establishment. This study employed an integrated MCDM approach based on GIS for site selection of a solar power plant in Fars Province. GIS was utilized to manage and prepare spatial data, while MCDM methods facilitated criteria weighting and development of a land suitability map. The SWARA method was applied to assign weights to the criteria and two MCDM techniques, TOPSIS and EDAS, were used to integrate the spatial layers. Materials & Methods In this study, Geographic Information System (GIS) technology was employed for the collection, management, and visualization of spatial data, while Multi-Criteria Decision-Making (MCDM) techniques were utilized for weighting and integrating the decision-making criteria. Initially, a comprehensive review of existing literature on solar power plant site selection identified 9 key criteria: photovoltaic potential, distance from protected areas, temperature, precipitation, elevation, slope, distance from urban centers, distance from roads, and distance from fault lines. Subsequently, expert opinions were gathered and the SWARA method was applied to establish the weights of the decision-making criteria. Relevant spatial data were collected and spatial layers for each criterion were generated through appropriate analyses within the GIS environment. These spatial layers were then integrated based on the calculated weights using two MCDM methods: TOPSIS and EDAS. The findings were analyzed, conclusions drawn, and results compared with previous studies to identify priority counties for solar power plant establishment. The SWARA method enhanced decision-making accuracy and efficiency compared to traditional methods like AHP by minimizing the number of pairwise comparisons. In this approach, experts ranked the criteria according to their importance and a reduction coefficient was applied to derive the weights. For generating the land suitability map, the TOPSIS method was employed, evaluating each location based on its proximity to an ideal solution (best conditions) and its distance from a negative ideal solution (worst conditions). The site closest to the ideal solution and farthest from the negative solution was deemed the most suitable. Additionally, the EDAS method was utilized to prioritize sites based on their distance from the average value, incorporating two key measures: Positive Distance from Average (PDA) and Negative Distance from Average (NDA). The site with the highest PDA and the lowest NDA was ranked as the optimal choice. Research Findings The results obtained from the SWARA method indicated that photovoltaic potential was the most critical criterion, while elevation was deemed the least significant. For land suitability mapping, both the TOPSIS and EDAS methods were employed. TOPSIS, a widely recognized method for solar power plant site selection, demonstrated high efficiency, while the EDAS method, being relatively newer and less explored, also yielded effective results. The findings from both methods were consistent, showing that the northern and northeastern regions of Fars Province were more suitable for solar power plant development. According to the TOPSIS method, classification of land suitability for Fars Province was as follows: very high suitability (21%), high suitability (45%), moderate suitability (24%), low suitability (8%), and very low suitability (2%). In contrast, the EDAS method classified the province as follows: very high suitability (9%), high suitability (27%), moderate suitability (34%), low suitability (22%), and very low suitability (8%). Additionally, the study prioritized counties in Fars Province for solar power plant development. Both TOPSIS and EDAS methods identified Abadeh, Khorrambid, and Bavanat as the top three priority counties, while Rostam, Mohr, Kuhchenar, Kazerun, and Farashband were ranked as the least suitable locations. Discussion of Results & Conclusion This study aimed to identify suitable locations for the establishment of solar power plants in Fars Province. The results demonstrated the effectiveness of integrating Multi-Criteria Decision-Making (MCDM) methods with Geographic Information Systems (GIS) for spatial decision-making. Both the TOPSIS and EDAS methods were employed to analyze and prioritize potential sites. The findings revealed a strong correlation between the results of these two methods, indicating that the northern and northeastern regions of Fars Province were more suitable for solar power plants, while the southern and southwestern areas were less favorable. Additionally, this study highlighted the EDAS method as a more robust and stable alternative to TOPSIS, suggesting that EDAS provides greater decision-making stability and reinforcing its potential for future applications. Given the favorable solar conditions in Fars Province, application of these methodologies could significantly contribute to the development of sustainable energy and enhance energy efficiency in Iran. The comprehensive framework utilized in this study—including problem identification, criteria selection, weighting, and decision-making—can be effectively adapted for solar power plant site selection in other provinces. بهره‌برداری از انرژی خورشیدی و احداث نیروگاه خورشیدی برای تولید برق به یکی از حوزه‌های مهم انرژی جهان تبدیل شده است. کشور ایران به‌دلیل شرایط جغرافیایی و آب‌‌هوایی ویژه پتانسیل بالایی در‌زمینۀ احداث نیروگاه‌های خورشیدی و تولید برق دارد. محققان در پژوهش حاضر با توجه به اهمیت مکان احداث نیروگاه خورشیدی در عملکرد و میزان برق تولیدی آن و با هدف مکان‌یابی بهینۀ نیروگاه خورشیدی در استان فارس از یک رویکرد ترکیبی تصمیم‌گیری چند‌معیاره مبتنی بر سیستم اطلاعات مکانی استفاده کرده‌اند. در این مطالعه براساس پژوهش‌های پیشین تعداد 9 معیار مؤثر بر مکان‌یابی نیروگاه خورشیدی انتخاب و لایۀ فضایی هر معیار با استفاده از سیستم اطلاعات مکانی تهیه و سپس وزن معیارها با استفاده از روش وزن‌دهی سوارا محاسبه شده است. براساس نتایج روش وزن‌دهی ترتیب اهمیت معیارها به‌صورت پتانسیل فتوولتائیک (172/0)، دما (137/0)، بارش (119/0)، فاصله از مراکز شهری (116/0)، شیب (111/0)، فاصله از راه‌ها (092/0)، فاصله از گسل‌ها (088/0)، فاصله از مناطق حفاظت‌شده (086/0) و ارتفاع (077/0) بوده است. در این مطالعه برای تهیۀ نقشۀ تناسب اراضی از دو روش تاپسیس و ایداس استفاده شده است. براساس نتایج روش تاپسیس و ایداس به‌ترتیب 21 درصد و 9 درصد از استان فارس تناسب بسیار بالایی برای احداث نیروگاه خورشیدی دارند. محققان در پژوهش حاضر شهرستان‌های استان فارس را ازنظر تناسب برای احداث نیروگاه خورشیدی اولویت‌بندی کرده‌اند. براساس نتایج دو روش تاپسیس و ایداس شهرستان‌های آباده، خرمبید و بوانات در اولویت اول تا سوم بوده است.

https://gep.ui.ac.ir/article_29260_f76ab6f51cd9f9cffafc46d0102aaf0f.pdf

دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 Investigating Land Use Changes and Their Effects on Soil Erosion in Meshkinshahr County بررسی تغییرات کاربری اراضی و اثرهای آن بر فرسایش خاک در شهرستان مشکین‌شهر 137 164 29339 10.22108/gep.2025.143700.1695 FA الناز پیروزی پژوهشگر پسادکتری ژئومورفولوژی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران صیاد اصغری سرسکانرود استاد ژئومورفولوژی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران بتول زینالی استاد آب‌و‌هوا‌شناسی، گروه جغرافیای طبیعی، دانشکده علوم اجتماعی، دانشگاه محقق اردبیلی، اردبیل، ایران Journal Article 2024 12 16 Abstract Soil erosion represents a critical environmental challenge, with land use changes being the primary factors that exacerbate its potential. Meshkinshahr County has long been susceptible to erosion due to its environmental characteristics, while population growth and unprincipled land use changes have further heightened this risk at the county level in recent years. This study aims to evaluate the impact of land use changes on soil erosion in Meshkinshahr County. To achieve the research objectives, land use maps for the years 2002 and 2024 were generated using an object-oriented approach. Subsequently, additional layers of factors influencing erosion were prepared using Geographic Information Systems (GIS). Erosion zoning was then conducted by standardizing these layers with a fuzzy function, weighting the criteria using the CRITIC method, and modeling with the VIKOR multi-criteria decision-making algorithm. The analysis of land use changes revealed that, in both time periods, poor pastures and dryland agriculture occupied the largest areas within the county. According to the erosion zoning map, in 2002, the areas classified as very high-risk and high-risk constituted 10.88 and 26.55%, respectively. By 2024, these figures increased to 14.14 and 27.33%. Overall, the study indicated that the reduction of pastures, gardens, and forest cover combined with the increase in agricultural (both irrigated and dryland) and residential land uses were the primary drivers behind the heightened potential for soil erosion in the county. Keywords: Land Use, Erosion, Object-Based Method, Multi-Criteria Analysis. Introduction Soil is one of a country's most vital natural resources and erosion is a significant factor contributing to the degradation and loss of soil fertility (Motamedirad et al., 2023, p. 147). In recent years, ongoing changes in land use driven by the need to meet the diverse demands of the growing global population have become a crucial aspect of environmental change (Taloor et al., 2020, p. 38; Qingge et al., 2020, p. 147; Hussain et al., 2020, p. 2). Inadequate management practices related to land use changes can exacerbate adverse effects on soil properties, increasing its vulnerability to erosion (Samie et al., 2022, p. 60; Costea et al., 2022, p. 2). Meshkinshahr County has long been susceptible to erosion due to its environmental characteristics. Recently, however, population growth and unprincipled land use changes have heightened the risk of this hazard at the county level. Therefore, this study aimed to evaluate the impact of land use changes on soil erosion in Meshkinshahr County. Materials & Methods In this study, we investigated land use changes by utilizing Landsat satellite images from the US Geological Survey, specifically from the OLI-TM sensors for the years 2002 and 2024. To prepare these images, we applied geometric and atmospheric corrections using ENVI 5.3 software. Next, we extracted land use maps for the two study periods by employing an object-based classification method and the nearest neighbor algorithm in Ecognition software. Following this, we identified the factors influencing erosion in the region, which included land use, slope, lithology, soil type, distance from communication roads, distance from rivers, and precipitation. Information layers were prepared for each criterion in the Geographic Information System (GIS). The evaluation and standardization of these layers were conducted using the fuzzy membership function and the criteria were weighted using the CRITIC method. Finally, the analysis and modeling were carried out using the VIKOR multi-criteria analysis method. Research Findings The analysis of land use changes revealed that, in both study periods, poor pastures and rainfed agriculture accounted for the largest areas within the county. Conversely, irrigated lands and snow-covered areas represented the smallest portions of the total county area in both 2002 and 2024. Throughout the study period, there was an increase in irrigated agriculture, rainfed agriculture, and residential areas, while the extent of gardens, forested lands, good pastures, poor pastures, and snow-covered lands had decreased. Notably, the most significant land use change in the county was the conversion of poor pastures to rainfed agriculture. By 2024, approximately 452.02 km2 of poor pastureland had been converted to rainfed agriculture compared to 2002. In terms of the weight coefficients of the criteria, in 2002, the most influential factors were slope, land use, lithology, and soil; while in 2024, the order had shifted slightly to land use, slope, lithology, and soil. According to the erosion zoning map, in 2002, the areas classified as very high-risk and high-risk had encompassed 422.14 and 1,030.03 km2, respectively. By 2024, these figures had increased to 548.58 and 1,060.36 km2. Furthermore, the erosion maps for both study periods indicated that the areas classified as very high-risk and high-risk were predominantly located within agricultural zones (both rainfed and irrigated), as well as in poor pastures and residential areas. Discussion of Results & Conclusion The results indicated that Meshkinshahr County exhibited a very high potential for erosion due to various environmental factors, including loose soils, sensitive and erodible formations, steep slopes, significant rainfall, and the presence of numerous waterways. Moreover, the decline in both good and poor pasture areas, alongside reductions in orchards and forest cover, had exacerbated the potential for soil erosion as agricultural land use (both irrigated and dryland) and residential developments had increased. In light of these findings, several recommendations are proposed to manage land in accordance with sustainable development principles. These include converting low-yield rainfed lands into forage and medicinal plant areas, strengthening and restoring rangeland vegetation, preventing overgrazing, and alleviating the pressure on rangelands by creating alternative job opportunities. Additionally, raising public awareness about the consequences of unsustainable land use practices, delegating the management of rainfed lands to local communities, and educating farmers on conservation measures are crucial emergency actions to protect and sustainably utilize water and soil resources. It is anticipated that the land use change map and erosion risk zoning map generated in this study will serve as valuable resources for managers and planners. These tools will provide essential insights into the status of land use changes and soil erosion potential in the county, facilitating the implementation of effective control and management measures to mitigate erosion in high-risk areas. فرسایش خاک از مهم‌ترین چالش‌های زیست‌محیطی است که تغییرات کاربری اراضی یکی از عوامل اصلی افرایش پتانسیل آن است. شهرستان مشکین‌شهر به‌دلیل ویژگی‌های محیطی از دیرباز تحت‌تأثیر وقوع فرسایش بوده و در طی سال‌های اخیر با توجه به رشد جمعیت و تغییرات غیراصولی، کاربری اراضی پتانسیل رخداد این مخاطره در‌سطح شهرستان افزایش یافته است. بر این اساس، محققان در پژوهش حاضر به‌دنبال ارزیابی اثر تغییرات کاربری اراضی بر فرسایش خاک در شهرستان مشکین‌شهر هستند. در راستای دستیابی به هدف‌های پژوهش نقشۀ کاربری اراضی با استفاده از روش شیءمبنا برای دو سال 2002 و 2024 استخراج و در مرحلۀ بعد لایه‌های اطلاعاتی سایر عوامل مؤثر بر فرسایش در محیط سیستم اطلاعات جغرافیایی تهیه شد. در‌نهایت، برای پهنه‌بندی خطر فرسایش، استانداردسازی لایه‌ها با استفاده از تابع فازی، وزن‌دهی معیارها با بهره‌گیری از روش کریتیک و مدل‌سازی با استفاده از الگوریتم چندمعیارۀ ویکور صورت پذیرفت. براساس نتایج حاصل از تحلیل تغییرات کاربری اراضی در هر دو دورۀ زمانی مطالعه‌شده کاربری‌های مراتع ضعیف و زراعت دیم بیشترین مساحت شهرستان را پوشش می‌دهند. با نظر به نقشۀ پهنه‌‌بندی فرسایش نیز در سال 2002 مساحت طبقۀ بسیار پرخطر و پرخطر 88/10 و 55/26درصد بوده است که مقدار این طبقات خطر در سال 2024 به‌ترتیب به 14/14 و 33/27 درصد افزایش یافته است. به‌طور کلی، با توجه به نتایج پژوهش می‌توان کاهش سطح مراتع، باغ‌ها و پوشش جنگلی و در‌مقابل افزایش کاربری‌های زراعی (آبی و دیم) و مسکونی را از دلایل اصلی افزایش پتانسیل فرسایش خاک شهرستان مشکین‌شهر دانست.

https://gep.ui.ac.ir/article_29339_54443a90bfb121330eff310ba2e3625d.pdf

دانشگاه اصفهان جغرافیا و برنامه ریزی محیطی 2008-5362 35 4 2025 01 20 Monitoring the Water Quality of the Karun Bozorg River Based on the IRWQIsc Index in the Spring of 2022 پایش کیفیت آب رودخانۀ کارون بزرگ بر‌اساس شاخص IRWQIsc در بهار 1401 165 192 29322 10.22108/gep.2025.140054.1622 FA مهرداد الرعنایی دانشجوی دکترای علوم و مهندسی محیط زیست،گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران سعید پورمنافی دانشیار گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران محمد نعمتی ورنوسفادرانی استادیار گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران علی لطفی استادیار گروه محیط زیست، دانشکده منابع طبیعی، دانشگاه صنعتی اصفهان، اصفهان، ایران Journal Article 2024 04 02 Abstract This study assessed the water quality of the Karun Bozorg (greater) River, from the junction of the Dez River to the junction with the Arvand River, using the IRWQIsc index. Twelve sampling stations were established at relatively equal distances (between 25 and 35 km) based on the main access points to the river. To analyze the relationships between water quality parameters and their distribution, we employed Spearman's correlation coefficient, hierarchical cluster analysis, and land use regression using the support vector machine method. The calculated IRWQIsc values ranged from 30.65 to 48.98 within the study area. The results indicated that, with the exception of two stations (1: Dehkhoda Sugarcane Cultivation and Industry and 4: Ahvaz City Entrance) that exhibited average conditions (48.98 and 46.32, respectively), the remaining stations were in relatively poor conditions. Furthermore, BOD and electrical conductivity levels at all stations exceeded Iran's drinking water quality standards. Phosphate concentrations ranged from 0.006 to 0.021 mg/L, while ammonium levels varied from 0.4 to 2.08 mg/L, with significant increases observed at downstream stations. Spearman’s correlation analysis revealed a strong relationship between electrical conductivity and total hardness (0.81). The hierarchical clustering of the stations indicated that Stations 9 to 12 experienced the highest pollution levels, primarily due to the influx of agricultural, industrial, and urban pollutants. The land use regression analysis demonstrated an R² value of 0.78, suggesting that spatial patterns of water quality could be effectively predicted. These findings underscored the necessity for improved pollutant management and continuous monitoring to enhance the river's water quality. Keywords: Water Resource, Water Quality Classification, Cluster Analysis, Physicochemical Parameters, Land Use Regression. Introduction Water bodies, particularly rivers, are vital for the sustainability and development of human societies. Therefore, maintaining water quality within permissible limits is essential, depending on the characteristics of surface water and its intended use. Previous studies on the water quality of the Karun River have typically been limited in scope, focusing on a small number of sampling stations primarily located around major cities like Ahvaz and Khorramshahr. As a result, they fail to capture the changes occurring along the entire length of the Karun Bozorg (greater) River. This river, which extends from the junction of the Dez and Karun Rivers to the junction with the Arvand River, is crucial for supplying water to cities, villages, large industries, fisheries, and agricultural activities. Given the fluctuations in water volume over the past few years, this study aimed to achieve the following objectives: (1) to map and display the current status and spatial variations of water quality parameters along the Karun Bozorg River, (2) to investigate the relationships between these water quality parameters, and (3) to cluster sampling stations based on their water quality. Materials & Methods In this study, the water quality index of the Karun Bozorg River was calculated by establishing 12 sampling stations at relatively equal distances from one another based on the main access points to the river—from the Dez River inlet to the outlet at the Arvand River. Sampling was conducted on May 13, 2022, during which 11 parameters were measured: BOD5, COD, Electrical Conductivity (EC), Dissolved Oxygen Percentage (DO%), turbidity, Total Water Hardness (TWH), phosphate, nitrate, ammonium, temperature, and pH. To analyze the relationships among the measured parameters, Spearman's correlation coefficient was employed to group them based on their degree of similarity. Hierarchical cluster analysis was then utilized to categorize the sampling stations. Additionally, to visualize the spatial distribution of water quality in the Karun River, land use regression was conducted using the support vector machine method in R software. For this analysis, Bands 1 to 7 of Sentinel 2 satellite images, which were captured from the study area at 2-day intervals, were utilized. Given the limited number of sampling stations, the data collected from all stations were used for modeling and validation. The accuracy of the constructed model was assessed using R² and RMSE statistics. Research Findings The assessment of water quality in the Karun Bozorg River revealed significant insights based on the IRWQIsc index, with calculated values ranging from 30.65 to 48.98 across 12 sampling stations. Notably, only two stations (1: Dehkhoda Sugarcane Cultivation and Industry and 4: Ahvaz City Entrance) demonstrated an average status (48.98 and 46.32, respectively), while the remaining stations were classified as being in relatively poor conditions. Key findings included: BOD and EC: All sampling stations exhibited BOD and EC levels that exceeded Iran's drinking water quality standards, indicating serious concerns regarding water safety. Nutrient Levels: Phosphate concentrations ranged from 0.006 to 0.021 mg/L and ammonium levels ranged from 0.4 to 2.08 mg/L with significant increases observed at downstream stations. Correlation Analysis: Spearman’s correlation coefficient revealed a strong positive relationship between EC and TWH (0.81), as well as between ammonium and EC (0.87). This suggested that as conductivity increased, so did hardness and ammonium levels. Cluster Analysis: Hierarchical clustering categorized the sampling stations, revealing that Stations 9 to 12 had the highest pollution levels primarily attributed to urban, agricultural, and industrial runoff. Land Use Regression: The land use regression analysis, with an R² value of 0.78, showed that the spatial patterns of water quality could be effectively predicted, highlighting the influence of land use on water quality dynamics. The results underscored the urgent need for improved pollutant management and continuous monitoring to enhance the water quality of the Karun Bozorg River. The integration of spatial distribution maps aids decision-makers in visualizing and addressing the deteriorating conditions of the river's water quality, emphasizing the necessity for critical conservation measures and better agricultural practices. Discussion of Results & Conclusion The calculated Water Quality Index (WQI) results indicated a decreasing trend from Station 1 to Station 12, with WQI values ranging from 30.65 at Station 12 to 48.98 at Station 1. The findings revealed that all stations were in relatively poor conditions with the exception of two stations (1 and 4), which exhibited mediocre water quality. Notably, fecal coliform and phosphate parameters had the highest average effective weights compared to other factors. This situation was primarily attributed to the inflow of urban, rural, and hospital sewage, along with the discharge from extensive industrial activities along the river. Correlation matrix analysis showed a significant positive correlation between ammonium and EC (0.87), as well as between TWH and EC (0.81). Cluster analysis further revealed that Stations 1 (Dehkhoda Sugarcane Cultivation and Industry) and 12 (Khoramshahr) differed significantly from the other sampled stations. Additionally, mapping the river's water quality status using the support vector machine method demonstrated an accuracy of 78%, highlighting the efficacy of this approach even with a limited number of training points. The correlation results indicated that EC, ammonium, TWH, and phosphate levels increased from Station 1 (upstream of the Dez River junction) to Station 12 (Khoramshahr). While phosphate concentrations remained below the standard at all stations, other parameters exceeded acceptable limits at several locations. The tidal phenomenon might also exacerbate sudden spikes in EC, particularly at Station 12. Due to the adverse effects of human pollutants, the water quality of the Karun Bozorg River was deteriorating. Consequently, it is imperative for responsible managers and decision-makers to implement critical conservation measures, such as improved agricultural practices, in conjunction with planned river usage. Spatial distribution maps can enhance the effectiveness of this information, enabling decision-makers to visualize the river's water quality conditions more clearly in the study area. در پژوهش حاضر کیفیت آب رودخانۀ کارون بزرگ از ابتدای ورودی رودخانۀ دز تا محل اتصال به رودخانۀ اروند با استفاده از شاخص IRWQIscارزیابی شد. در این مطالعه برای محاسبۀ‌ این شاخص 12 ایستگاه نمونه‌برداری با فاصله‌های به‌نسبت مساوی از یکدیگر (بین 25 تا 35 کیلومتر) و نیز بر‌مبنای ورودی‌های اصلی به رودخانه تعیین شد. در‌ادامه، از ضریب همبستگی اسپیرمن و تحلیل خوشه‌ای سلسله‌مراتبی و رگرسیون کاربری زمین با روش ماشین‌بردار پشتیبان برای درک بیشتر روابط میان پارامترهای کیفیت آب و توزیع آنها در رودخانه استفاده شد. مقدار‌های IRWQIsc محاسبه‌شده بین 65/30 و 98/48 در منطقۀ مطالعه‌شده متغیر بود. نتایج نشان داد که به‌جزء دو ایستگاه 1 (کشت و صنعت نیشکر دهخدا) و 4 (ورودی شهر اهواز) با وضعیت متوسط (به‌ترتیب 98/48 و32/46) سایر ایستگاه‌ها در وضعیت به‌نسبت بد قرار دارد. همچنین، نتایج نشان داد که مقدار‌های BOD و هدایت الکتریکی در تمامی ایستگاه‌ها از استانداردهای کیفیت آب شرب ایران فراتر رفته ‌است. مقدار‌های فسفات (006/0 تا 021/0 mg/L) و آمونیوم (4/0 تا 08/2 mg/L) نیز در ایستگاه‌های پایین‌دست افزایش چشمگیری داشته است. تحلیل همبستگی اسپیرمن نشان‌دهندۀ رابطۀ قوی میان هدایت الکتریکی و سختی کل (81/0) بود. خوشه‌بندی سلسله‌مراتبی ایستگاه‌ها نشان داد که ایستگاه‌های 10،9 و 12 بیشترین آلودگی را دارد که ناشی از ورود آلاینده‌های کشاورزی، صنعتی و شهری است. تحلیل رگرسیون کاربری زمین (78/0 R2= ) نشان داد که الگوهای فضایی کیفیت آب را می‌توان پیش‌بینی کرد. این نتایج بر لزوم مدیریت آلاینده‌ها و پایش مستمر برای بهبود کیفیت آب رودخانه تأکید دارد.

https://gep.ui.ac.ir/article_29322_fabc441af149dd8f73e036c4a9b6775b.pdf