دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Long Term Enclosure Assessment on Stability of Soil Physical and Chemical Properties in Rangeland Ecosystems (Case Study: Khash County Rangelands) ارزیابی اثرات قرق بلندمدت بر پایداری خصوصیات فیزیکی و شیمیایی خاک در اکوسیستم‌های مرتعی (مطالعه موردی: مراتع شهرستان خاش) 1 20 4026 10.22126/ges.2025.12480.2898 FA مرتضی صابری گروه مرتع و آبخیزداری، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران. 0000-0003-1596-9714 مهدیه خدادوست کارشناس‎ارشد مدیریت مرتع، دانشکده آب و خاک، دانشگاه زابل، زابل، ایران.‏ Journal Article 2025 07 23 This study aimed to investigate the effects of long-term (15-year) enclosure on the physical and chemical properties of soil in the arid and semi-arid rangelands of the Koteh region in Khash County. For this purpose, two sites were selected for comparison: an enclosed rangeland and a grazed rangeland with similar topography. Vegetation and soil samples were collected at two depths (0–15 cm and 15–30 cm), and parameters such as soil texture, pH, electrical conductivity, organic carbon, total nitrogen, phosphorus, potassium, and bulk density were measured. The results of data analysis showed that enclosure significantly increased canopy cover (32.3% vs. 10.9%), litter, and plant species diversity. At the 0–15 cm depth, organic carbon (0.48% vs. 0.24%) and total nitrogen (0.052% vs. 0.022%) were significantly higher in the enclosed area, while bulk density, available phosphorus, and potassium were higher in the grazed rangeland. At the 15–30 cm depth, organic carbon (0.33% vs. 0.23%) and total nitrogen were also higher in the enclosed area, with no observed differences in soil texture between the two sites. Additionally, pH and electrical conductivity values at both depths in the enclosed area were lower than or similar to those in the grazed rangeland, indicating a relative improvement in soil quality under long-term enclosure. These findings suggest that long-term enclosure can enhance the sustainability and ecological function of rangelands in arid and semi-arid environments by improving key soil indicators. Introduction Rangeland enclosure not only helps restore vegetation cover but also enhances the long-term sustainability and productivity of rangeland ecosystems by improving soil physical and chemical factors. Although ample evidence confirms the positive effects of enclosure, the results reported in some studies have been diverse and contradictory due to differences in climate, soil type, vegetation composition, and duration of enclosure. Considering the arid and semi-arid climatic conditions of the Koteh region in Khash County and the high vulnerability of its soils to overgrazing, a detailed study of the effects of long-term enclosure on the physical and chemical properties of the soil in this area is of great importance. Therefore, this study aims to scientifically assess the impact of enclosure on key soil indices in rangeland ecosystems in this region. Materials and Methods This study aimed to investigate the effect of long-term enclosure on the physical and chemical properties of soil in arid and semi-arid rangelands of Khash County. The Koteh rangeland was chosen as the enclosure site with a 15-year history of enclosure, and an adjacent rangeland with similar topographic conditions was chosen as the grazed site. Vegetation sampling was conducted in June by establishing six 100-meter transects at each site (three transects in the direction of the slope and three transects perpendicular to it) and sampling 10 one-meter plots in each transect using a random-systematic method. Data on canopy cover, litter, bare soil, and plant species were recorded. Soil sampling was performed from two depths of 0–15 and 15–30 cm at the beginning, middle, and end of the transects, and a total of 24 composite samples were prepared. Soil properties were measured, including texture, pH, electrical conductivity, total nitrogen, phosphorus, potassium, calcium, magnesium, sodium, organic carbon, and bulk density using standard methods. Data were analyzed using independent and paired t-tests in SPSS software. Correlation analysis was performed in R software. Results and Discussion The results showed that the vegetation of the area consisted of perennial and annual species with diverse growth forms. In the enclosure area, the canopy cover was significantly higher (32.3%) than in the grazed area (10.9%). More stable and diverse species were found in the enclosure area, and the amount of litter was also higher in this area (3.72%), indicating higher biological activity and reduced soil erosion. Regarding soil characteristics, the percentage of silt was higher in the grazed area, and the bulk density of the soil was also higher in the grazed area, indicating greater soil compaction due to livestock grazing. The amounts of organic carbon and total nitrogen at both depths (0–15 and 15–30 cm) in the enclosure area were significantly higher than in the grazed area, which could be due to the accumulation of plant residues, reduced erosion, increased biological activity, and improved microclimatic conditions. However, available phosphorus and potassium were higher in the grazed area. Also, soil acidity and electrical conductivity were lower in the enclosure area, indicating improved soil conditions. Soil texture composition was not significantly different at the depth of 15 to 30 cm, but bulk density and nutrients were still significantly different. These findings emphasize that livestock grazing management through enclosure improves vegetation cover and soil physical and chemical quality in semi-arid ecosystems. Conclusion According to the results of this study, long-term enclosure, as an effective management strategy, plays a significant role in restoring and improving the ecological conditions of arid and semi-arid rangelands. This method increases the percentage of vegetation cover and litter, preserves native and valuable rangeland species, and improves ecosystem dynamics. The decrease in soil bulk density indicates an improvement in the physical structure of the soil and a decrease in compaction caused by livestock grazing. From the perspective of chemical properties, a significant increase in organic carbon and total nitrogen, along with a decrease in acidity and electrical conductivity, indicates an improvement in soil quality and a strengthening of the nutrient cycle. Although the accumulation of phosphorus, potassium, and calcium in grazed areas was observed due to livestock waste, these elements are not stably stabilized and cannot replace the positive effects of enclosure. Therefore, it is recommended that enclosures be developed as a sustainable method in rangeland management and, along with continuous monitoring of soil and vegetation, help maintain and improve the health of the rangeland ecosystem. این پژوهش با هدف بررسی اثر قرق بلندمدت (۱۵ ساله) بر ویژگی‌های فیزیکی و شیمیایی خاک در مراتع خشک و نیمه‌خشک منطقه کوته شهرستان خاش انجام شد. برای این منظور، دو سایت شامل مرتع قرق‌شده و مرتع چراشده با شرایط توپوگرافی مشابه انتخاب شدند. نمونه‌برداری پوشش گیاهی و خاک از دو عمق ۰ تا ۱۵ و ۱۵ تا ۳۰ سانتی‌متر انجام شد و پارامترهایی نظیر بافت خاک، اسیدیته، هدایت الکتریکی، کربن آلی، نیتروژن کل، فسفر، پتاسیم و جرم مخصوص ظاهری خاک مورد تجزیه و تحلیل قرار گرفتند. نتایج حاصل از تجزیه ‌و تحلیل داده‌ها نشان داد که قرق موجب افزایش معنی‎دار پوشش تاجی (۳/۳۲ در مقابل ۹/۱۰%)، لاشبرگ و تنوع گونه‌های گیاهی شده است. همچنین در عمق ۰ تا ۱۵ سانتی‌متر، میزان کربن آلی (۴۸/۰ در برابر ۲۴/۰%) و نیتروژن کل (۰۵۲/۰ در برابر ۰۲۲/۰%) در منطقه قرق به‌طور معنی‎داری بیشتر بود، در حالی که وزن مخصوص ظاهری، فسفر و پتاسیم قابل دسترس در مرتع چراشده بالاتر گزارش شد. در عمق ۱۵ تا ۳۰ سانتی‌متر نیز کربن آلی (۳۳/۰ در برابر ۲۳/۰%) و نیتروژن کل در قرق بیشتر بود، اما تفاوتی در ترکیب بافت خاک میان دو منطقه مشاهده نشد. همچنین مقادیر pH و هدایت الکتریکی در هر دو عمق در منطقه قرق کمتر یا مشابه مراتع چراشده بود که بیانگر بهبود نسبی کیفیت خاک تحت تأثیر قرق بلندمدت است. این نتایج نشان می‌دهد که قرق بلندمدت با بهبود شاخص‌های کلیدی خاک می‌تواند زمینه‌ساز افزایش پایداری و کارکرد اکولوژیکی مراتع در شرایط خشک و نیمه‌خشک باشد.

https://ges.razi.ac.ir/article_4026_81abfe16005a02662e723567becb2b7b.pdf

دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Exploring the Impact of Future Land Uses on Flood Risks in Python (Case study: Tangrah Basin, Golestan Province, Iran) تحلیل اثر تغییرات کاربری اراضی بر پهنه‌های سیلابی (مطالعه موردی: حوضه تنگراه، استان گلستان، ایران) 21 47 4336 10.22126/ges.2026.12729.2910 FA فریبا پاکنژاد دانش‌آموخته دکتری ژئومورفولوژی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران. علی احمدآبادی دانشیار ژئومورفولوژی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران. 0000-0002-3623-6192 Journal Article 2025 10 24 Detection and prediction of land use and land cover (LULC) changes provide useful information to regional decision-makers for natural resource management and environmental sustainability. In this study, land use maps of the Tangrah Basin in northeastern Iran were extracted and analyzed for the past three decades (1986, 2006, and 2020) using satellite imagery and remote sensing techniques. Future land use changes for the year 2040 were then simulated using the CA-Markov integrated model under the current management process continuation scenario.The results of the CA-Markov model show that from 1986 to 2020, the most significant change was related to forest land, which decreased from 479.13 km² in 1986 to 360.16 km² in 2020. The land use map for 2040 was also predicted using the CA-Markov model, indicating a substantial increase in urban and built-up areas by 0.99% compared to 1986. To determine the impact of land use changes on flood occurrence, flood hazard zoning was carried out for two time periods (1986 and 2040). By examining the parameters affecting flood modeling in Anaconda 4.20.0 software for both scenarios, it was determined that precipitation, distance from waterways, and land use changes played effective roles in increasing peak discharge, flood volume, and high-risk points.The results demonstrate that land use change analysis can significantly contribute to water resource management and planning in flood-prone areas. Introduction Land use and land cover (LULC) changes are among the most critical factors influencing environmental sustainability and natural resource management. These changes, driven by population growth, urbanization, agricultural expansion, and deforestation, have profound impacts on hydrological regimes, soil erosion, biodiversity loss, and climate change. In recent decades, remote sensing technology and geographic information systems (GIS) have become indispensable tools for detecting, monitoring, and predicting LULC changes over time. The integration of satellite imagery with spatial modeling techniques enables researchers and decision-makers to understand past trends and simulate future scenarios. Among various modeling approaches, the CA-Markov (Cellular Automata-Markov Chain) model has gained widespread acceptance due to its ability to combine temporal dynamics with spatial distribution patterns. This model is particularly useful for predicting land use transitions and assessing their environmental consequences. The Tangrah Basin, located in Golestan Province, northeastern Iran, has experienced significant LULC changes over the past three decades, accompanied by an increasing frequency of devastating floods. Understanding the relationship between land use dynamics and flood susceptibility is essential for sustainable land management and disaster risk reduction in this region. This study aims to (1) detect and analyze LULC changes in the Tangrah Basin from 1986 to 2020 using satellite imagery, (2) predict future LULC changes for 2040 using the CA-Markov model, and (3) assess the impact of these changes on flood risk zoning. Materials and Methods The Tangrah Basin is part of the Gorganrud watershed in Golestan Province, covering an area of approximately 625 km². The region has a semi-arid to Mediterranean climate with average annual precipitation ranging from 202 mm to 903 mm across different elevations. For LULC classification, Landsat TM (1986), ETM+ (2006), and OLI-TIRS (2020) satellite images were obtained from the USGS Earth Explorer website. Pre-processing steps included radiometric and atmospheric corrections using the FLAASH module in ENVI 5.6 software. Pan-sharpening was performed using the Gram-Schmidt method to enhance spatial resolution from 30 m to 15 m for the 2020 image. Six LULC classes were defined: forest, urban/built-up areas, agricultural land, water bodies, good rangeland, and poor rangeland. Classification was conducted using the Random Forest algorithm within the EnMap-Box 2.2 plugin in ENVI 5.6. Accuracy assessment was performed using ground control points and the Kappa coefficient. For prediction of future LULC, the CA-Markov model was implemented in TERRSET2020 software. Transition probability matrices were derived from LULC maps of 1986 and 2006, and the model was validated by comparing simulated and actual LULC for 2020. For flood susceptibility mapping, ten conditioning factors were selected based on literature review and field surveys: elevation, slope, aspect, distance from streams, lithology, annual rainfall, LULC, stream power index (SPI), topographic wetness index (TWI), and drainage density. A total of 605 flood inventory points were identified using satellite imagery, historical records, and field observations. The Random Forest algorithm was implemented in Python (Spyder 3.13) within Anaconda 4.20.0 to determine the weight of each factor. The final flood hazard maps were produced in ArcGIS 10.7 and classified into five susceptibility classes: very low, low, medium, high, and very high. Results and Discussion The LULC classification results revealed significant changes over the 34-year study period. Forest land decreased substantially from 479.13 km² (76.6%) in 1986 to 376.63 km² (60.26%) in 2006 and 360.16 km² (57.6%) in 2020, representing a total loss of 118.97 km² (24.8%) between 1986 and 2020. Urban areas expanded from 1.85 km² (0.28%) in 1986 to 5.97 km² (0.95%) in 2020, reflecting rapid urbanization and infrastructure development. Agricultural land showed a slight decrease between 1986 and 2006 but increased to 71.19 km² (11.39%) by 2020. Good rangeland increased substantially from 81.20 km² (12.99%) in 1986 to 190.49 km² (30.47%) in 2020, largely due to forest degradation and conversion to rangeland. The Kappa coefficients for classification accuracy were 0.82, 0.85, and 0.91 for 1986, 2006, and 2020, respectively, indicating high reliability of the classification results. The CA-Markov model successfully simulated LULC for 2020 with an overall accuracy of 82.6%, and the predicted map for 2040 indicates continued forest loss (256.54 km²; 42.96%) and urban expansion (7.97 km²; 1.27%). Flood susceptibility mapping showed that very high and high flood risk zones increased by 26% from 1986 to 2040, while low and very low risk zones decreased by 19%. The Random Forest model performed excellently, with AUC values of 0.84 and 0.86 for 1986 and 2040, respectively. The most influential factors in flood modeling were distance from streams, stream power index, LULC changes, and precipitation. Conclusion This study demonstrates that LULC changes in the Tangrah Basin have significantly increased flood susceptibility over the past three decades, and this trend is projected to continue through 2040. The integration of remote sensing, CA-Markov modeling, and Random Forest algorithms provides a robust framework for detecting, predicting, and assessing the environmental impacts of land use dynamics. The findings highlight the urgent need for sustainable land management practices, including reforestation programs, strict land-use regulations, and integrated watershed management to mitigate future flood hazards. The methodology developed in this research can be applied to other flood-prone regions, supporting evidence-based decision-making for environmental planning and disaster risk reduction. تشخیص و پیش‌بینی تغییرات (LULC) در مدیریت منابع طبیعی و پایداری محیطی، اطلاعات مفیدی را در اختیار تصمیم‌گیرندگان منطقه‌ای قرار می‌دهد. در این پژوهش، نقشه‌های کاربری اراضی حوضه تنگراه در شمال شرق ایران برای سه دهه گذشته (2020، 2006، 1986) با استفاده از تصاویر ماهواره‌ای زمین و با تکنیک سنجش از دور استخراج و تحلیل شده است. سپس تغییرات کاربری اراضی آتی برای سال 2040 با استفاده از مدل یکپارچه CA-Markov با توجه به سناریوی تداوم فرایند مدیریت فعلی شبیه‎سازی شده است. نتایج مدل CA–Markov نشان می‌دهد که در بازه زمانی 1986 تا 2020 بیشترین تغییر شکل مربوط به اراضی جنگلی است که از مقدار 13/479 کیلومتر مربع در سال 1986 به 16/360 کیلومتر مربع در سال 2020 کاهش یافته است. همچنین نقشه کاربری زمین برای سال 2040 توسط مدل CA–Markov پیش‌بینی شده است. نتایج پیش‌بینی مدل حاکی از افزایش معنی‌دار وسعت نواحی تکمیل‌شده و شهری به میزان 99/0% نسبت به سال 1986 است. به‌منظور میزان تأثیر تغییرات کاربری در سیلاب‎های رخ‌داده در منطقه، پهنه‌بندی در دو بازه زمانی 1986، 2040 در حوضه مورد نظر انجام گرفت. با بررسی پارامترها تأثیرگذار در مدل‌سازی سیلاب در نرم‌افزار، Anaconda4.20.0 برای دو سناریو سال‌های 1986 و 2040 مشخص شد بارش، فاصله از آبراهه و تغییرات کاربری نقش مؤثری در افزایش دبی اوج و حجم سیلاب و نقاط پرخطر داشته است. نتایج نشان داد که تغییرات کاربری اراضی بر پاسخ هیدرولوژیکی تأثیر زیادی دارد. این می‌تواند به مدیریت و برنامه‌ریزی منابع آب در مناطقی که مستعد سیل هستند کمک کند.

https://ges.razi.ac.ir/article_4336_a255b5f198de495b3b599ea802cc5138.pdf

دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Assessment of Land Subsidence Induced by Groundwater Level Decline and Land. Use Changes in the Mahidasht Plain, Kermanshah ارزیابی فرونشست زمین متأثر از افت تراز آبخوان‌ها و تغییرات کاربری اراضی در دشت ماهیدشت کرمانشاه 49 67 4339 10.22126/ges.2026.13047.2924 FA کیمیا دلپاک گروه ژئومورفولوژی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران امجد ملکی گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه رازی، کرمانشاه، ایران 0000-0003-3232-2736 امیر صفاری گروه ژئومورفولوژی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران. سید مروت افتخاری گروه ژئومورفولوژی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران. Journal Article 2025 10 24 Land subsidence has become a critical geomorphological hazard in many arid and semi-arid regions of Iran, where intensive groundwater extraction and rapid land-use transitions have accelerated ground deformation. This study examines the spatial–temporal dynamics of subsidence in the Mahidasht Plain of Kermanshah Province by integrating SBAS-InSAR time-series analysis, long-term piezometric records, and multi-temporal Landsat-based land-use classifications. Thirty Sentinel-1 SLC images (2022–2024) were processed using SNAP and SARscape to derive vertical displacement rates, while groundwater-level variations from 21 piezometric wells (2001–2023) were interpolated using the Inverse Distance Weighting (IDW) method. Land-use maps for 2001 and 2023 were generated through artificial neural network classification to evaluate the impact of agricultural transformation on subsidence patterns. Results indicate substantial deformation, ranging from +22 mm of uplift to −40 mm of annual subsidence, with the most severe rates concentrated in the eastern and northern sectors of the plain. These hotspots coincide with significant groundwater decline (up to −26.98 m) and extensive conversion of rain-fed agriculture to irrigated farming. The spatial overlap among subsidence zones, declining water tables, and intensified land-use changes confirms the dominant influence of anthropogenic pressures on ground deformation. In contrast, central and western areas exhibit lower deformation rates due to more stable groundwater conditions and limited land-use alteration. Overall, the findings reveal an escalating subsidence crisis driven primarily by unsustainable groundwater exploitation and agricultural expansion. If current trends continue, they may threaten soil stability, water quality, infrastructure integrity, and regional food security. This study underscores the urgent need for sustainable groundwater management, reduction of high-water-demand crops, and stricter land-use regulations to mitigate future risks effectively. Introduction Land subsidence has become an increasingly critical geomorphological hazard in many arid and semi‑arid regions of Iran, where rapid agricultural intensification, groundwater over‑extraction, and climate‑driven water scarcity have accelerated ground deformation. The Mahidasht Plain in Kermanshah Province is one of the regions most severely affected by this phenomenon. Subsidence in such environments typically results from the compaction of unconsolidated sediments, triggered primarily by declining groundwater levels and exacerbated by land‑use changes, particularly the conversion of rain‑fed agriculture to irrigated farming. Given the region’s semi‑arid climate and its heavy reliance on groundwater for irrigation, understanding the interplay between hydrological changes and land‑surface response is critical for sustainable planning. Given the growing pressure on groundwater resources and the rapid transformation of agricultural practices in Mahidasht, a comprehensive assessment of subsidence dynamics is essential. This study integrates radar interferometry, hydrogeological observations, and multi‑temporal land‑use analysis to evaluate the spatial‑temporal patterns of subsidence and identify the dominant drivers influencing ground deformation in the region. Materials and Methods A multi‑source analytical framework was employed, combining SBAS‑InSAR time‑series analysis, piezometric data interpretation, and long‑term land‑use change assessment. Thirty Sentinel‑1 SLC images acquired between January 2022 and July 2024 were processed using SNAP and SARscape to derive vertical displacement rates. Interferograms were generated using small baseline pairs, followed by Goldstein filtering, phase unwrapping, and geocoding. Ground control points were selected in stable zones to enhance phase correction accuracy. The IDW interpolation was performed with a power parameter of 2, and cross‑validation was used to assess the accuracy of the groundwater‑level maps. Additionally, the land‑use classification achieved an overall accuracy exceeding 85% based on field‑collected ground truth points. Hydrogeological data from 21 piezometric wells (2001–2023) were analyzed using the IDW interpolation method to map groundwater‑level variations. Land‑use maps for 2001 and 2023 were produced using an artificial neural network classifier applied to Landsat imagery, supported by field‑based ground control points. The integration of these datasets enabled a detailed evaluation of the relationship between subsidence, groundwater decline, and land‑use transitions. Results and Discussion The SBAS‑InSAR analysis revealed significant vertical deformation across the Mahidasht Plain, with displacement ranging from +22 mm uplift to −40 mm annual subsidence. The most severe subsidence occurred in the eastern and northern sectors, where groundwater extraction is most intensive. Piezometric data indicated a maximum groundwater decline of −26.98 m over the study period, with steep hydraulic gradients surrounding high‑extraction well clusters. These zones correspond closely with subsidence hotspots, demonstrating a strong spatial correlation between aquifer depletion and ground settlement. Land‑use analysis showed substantial expansion of irrigated agriculture between 2001 and 2023, replacing large areas of rain‑fed croplands. This transition significantly increased groundwater demand, accelerating aquifer compaction and subsidence. The growth of barren lands further reduced soil moisture retention and increased susceptibility to erosion, contributing to weakened soil structure. The spatial overlap between subsidence zones and areas with high irrigation density was quantified, revealing that regions with more than 50% irrigated crop cover experienced subsidence rates nearly double those with lower irrigation intensity. Moreover, the temporal analysis indicates an acceleration of subsidence after 2020, coinciding with a period of reduced rainfall. In contrast, central and western areas exhibited lower subsidence rates due to more stable groundwater conditions and limited land‑use alteration. Comparisons with previous studies in other Iranian plains such as Kermanshah, Harris, and Shahr‑e Kord confirm that subsidence in Mahidasht is both more intense and spatially distinct. While many regions exhibit central subsidence patterns, Mahidasht shows maximum deformation along its eastern and northern margins, indicating a shift in subsidence dynamics driven by localized human pressures. The combined evidence highlights that anthropogenic factors particularly groundwater over‑extraction and agricultural intensification are the primary drivers of subsidence in the region. The findings also reveal that continued land‑use transitions, especially the conversion of rain‑fed to irrigated farming, have amplified environmental stress, reduced soil stability, and increased vulnerability to long‑term degradation. These changes have broader implications for water quality, agricultural productivity, and the resilience of rural communities. Conclusion This study demonstrates that the Mahidasht Plain is experiencing a rapidly intensifying subsidence crisis driven primarily by unsustainable groundwater exploitation and extensive land‑use transformation. Maximum annual subsidence reached 40 mm, with the most affected areas corresponding to zones of severe groundwater decline and widespread conversion of rain‑fed to irrigated agriculture. Continued trends pose serious risks to soil stability, water quality, agricultural productivity, and infrastructure integrity. Effective mitigation requires strict groundwater management, reduction of high‑water‑demand crops, and regulatory control over land‑use practices. Integrating these findings into local water‑resource management plans for instance, through managed aquifer recharge and promoting drought‑resistant crops could substantially reduce future impacts. The study also emphasises the need for continuous satellite‑based monitoring to track deformation trends and inform adaptive management strategies. The findings provide a scientific foundation for regional planning and highlight the urgent need for sustainable resource management to prevent irreversible environmental degradation. فرونشست زمین به‌عنوان یکی از مهم‌ترین مخاطرات ژئومورفولوژیکی در مناطق خشک و نیمه‌خشک ایران، طی سال‌های اخیر شدت یافته و پیامدهای گسترده‌ای بر منابع آب، خاک و زیرساخت‌های انسانی بر جای گذاشته است. این پژوهش با هدف تحلیل الگوی مکانی–زمانی فرونشست در دشت ماهیدشت استان کرمانشاه، از تلفیق تکنیک SBAS-InSAR، داده‌های بلندمدت چاه‌های پیزومتری و تحلیل تغییرات کاربری اراضی بهره گرفت. برای این منظور، ۳۰ تصویر سنتینل-۱ در بازه ۲۰۲۲ تا ۲۰۲۴ پردازش شد و داده‌های ۲۱ حلقه چاه پیزومتری طی سال‌های ۱۳۸۰ تا ۱۴۰۲ با روش IDW تحلیل گردید. همچنین، نقشه‌های کاربری اراضی سال‌های ۱۳۸۰ و ۱۴۰۲ با استفاده از الگوریتم شبکه عصبی مصنوعی تهیه شد. نتایج نشان داد بیشینه فرونشست سالانه در دشت ماهیدشت حدود ۴۰ میلی‌متر و بیشینه بالاآمدگی ۲۲ میلی‌متر بوده است. بیشترین نرخ فرونشست در نواحی شرقی و شمالی دشت مشاهده شد؛ مناطقی که با افت شدید سطح ایستابی (تا ۲۶٫۹۸- متر) و تبدیل گسترده کشت دیم به آبی هم‌پوشانی دارند. این هم‌زمانی مکانی بیانگر نقش غالب عوامل انسانی در تشدید فرونشست است. در مقابل، نواحی مرکزی و غربی دشت به دلیل پایداری نسبی سطح آب زیرزمینی و تغییرات محدود کاربری، نرخ نشست کمتری را تجربه کرده‌اند. به‌طور کلی، یافته‌ها نشان می‌دهد که بهره‌برداری بی‌رویه از منابع آب زیرزمینی و تغییرات شدید کاربری اراضی، مهم‌ترین محرک‌های فرونشست در ماهیدشت هستند. تداوم روند فعلی می‌تواند تهدیدی جدی برای پایداری خاک، کیفیت آب، زیرساخت‌ها و امنیت غذایی منطقه ایجاد کند. این نتایج ضرورت مدیریت پایدار منابع آب و بازنگری در الگوی کشت را برجسته می‌سازد.

https://ges.razi.ac.ir/article_4339_a121be8e5e584b7bb315933730964099.pdf

دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Explaining the Factors Influencing Sustainable Water Resource Governance in Rural Areas at Risk of Water Scarcity (Case Study: Rural Areas Surrounding Parishan Lake) تبیین عوامل مؤثر بر حکمروایی پایدار منابع آب در روستاهای معرض مخاطره کم‌آبی (مطالعه موردی: مناطق روستایی پیرامون دریاچه پریشان) 69 87 4342 10.22126/ges.2026.13484.2960 FA نازنین آذرنیو گروه جغرافیا و برنامه‌ریزی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران. 0009-0002-1616-1207 مهدی پورطاهری مدرس گروه جغرافیا و برنامه‌ریزی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران. 0000-0002-6388-8823 ذبیح الله ترابی گروه جغرافیا و برنامه‌ریزی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران. 0000-0002-9347-2722 محمد رضا شهبازبگیان گروه مهندسی آب، دانشکده فنی و مهندسی، دانشگاه تربیت مدرس، تهران، ایران. 0000-0002-7572-9264 Journal Article 2026 01 04 The water scarcity crisis in Iran's rural settlements, especially in inland water basins, is not only caused by natural limitations and climate change but is also closely linked to weak decision-making and water resource governance. In recent years, villages surrounding Parishan Lake have faced a sharp decline in surface and groundwater resources, reduced agricultural production, and livelihood instability, intensifying local conflicts and weakening social participation. This highlights the need to examine the factors affecting sustainable water resource governance. This study aimed to explain the causal structure and network weights of six dimensions—cultural, infrastructural, environmental, social, economic, and institutional–organizational—and 22 related variables. Data were collected through documentary and field methods using a DEMATEL questionnaire completed by 30 local experts and were analyzed through the integrated DANP model. The results showed that the institutional–organizational dimension was the most influential factor (Ri = 8.3934; Ri − Cj = +8.1204), while the social dimension was the most affected (Cj = 8.3688; Ri − Cj = −8.1252). Causal analysis placed the institutional–organizational and cultural dimensions in the cause group, the social, economic, and infrastructural dimensions in the effect group, and the environmental dimension in a balanced position. Network weighting highlighted the cultural factor as the most important (0.2046) while ranking the institutional–organizational dimension fifth (0.1501), indicating that sustainable water governance requires strengthening both institutional mechanisms and cultural components. Introduction Water is the foundation of rural settlement sustainability, food security, agricultural activity, and environmental balance. In recent decades, water resource shortages, increased withdrawals from surface water and groundwater resources, climate change, growing demand, and institutional incoherence have caused the water crisis to move beyond a purely natural issue and become a multidimensional governance challenge. This issue is particularly acute in arid and semi-arid rural areas, where household livelihoods are highly dependent on water resources. The villages surrounding Parishan Lake in Kazerun County provide a clear example of this situation. The drying up of the lake, the decline in groundwater levels, the increase in licensed and unlicensed wells, the reduction in cultivated land, the rising cost of water supply, and the emergence of local conflicts indicate that the existing crisis is not merely the result of drought but is also closely linked to weaknesses in water resource governance. Focusing on six dimensions—cultural, infrastructural, environmental, social, economic, and institutional–organizational—this study explains the causal structure and relative importance of the factors affecting sustainable water resource governance in the villages surrounding Parishan Lake. Materials and Methods This study is applied in purpose and descriptive–analytical in nature, and it was conducted using a deductive approach. The required data were collected through documentary and field studies. First, by reviewing the theoretical and empirical literature, the factors affecting sustainable water resource governance were identified and classified into six main categories: cultural, infrastructural, environmental, social, economic, and institutional–organizational. The study area included 27 villages located around Parishan Lake in the two rural districts of Balyan and Famur. The statistical population consisted of local managers, experts from executive agencies related to water and natural resources, and local individuals familiar with the issue of water scarcity. A purposive sampling method was used, and ultimately 30 completed questionnaires formed the basis of the analysis. The data collection instrument was a specialized DEMATEL questionnaire, in which the degree of influence of each factor on the others was measured through pairwise comparisons using a five-point scale. The content validity of the questionnaire was confirmed by experts, and its reliability was evaluated using Cronbach's alpha coefficient, which was 0.90, indicating an acceptable level of reliability. The data were analyzed using the combined DANP model, which integrates DEMATEL and network-based weighting to identify causal relationships, distinguish influential and influenced factors, and determine the final weights. Results and Discussion The results obtained from the total relation matrix indicated that the examined factors exhibit reciprocal, network-based, and nonlinear relationships, and that their degrees of influence and dependence are unequal. Based on the row-sum value, the institutional–organizational factor, with a value of 8.3934, had the greatest influence and was identified as the most important driver of sustainable water resource governance. This finding highlights the role of institutional coordination, transparency, accountability, managerial capacity, and law enforcement in organizing the region's water resources. In contrast, the social factor, with a dependence value of 8.3688, showed the highest level of dependence on other factors. Therefore, public participation, cooperation among water users, and local conflicts are affected by institutional performance, economic conditions, infrastructure, and cultural patterns. The difference between influence and dependence indicated that the institutional–organizational and cultural factors belong to the causal group, whereas the social, economic, and infrastructural factors fall into the effect group. The environmental factor occupied a position close to equilibrium. In the network-weighting stage, the DANP results showed that the cultural factor, with a weight of 0.2046, had the highest structural importance. It was followed by the infrastructural factor with a weight of 0.1792, the economic factor with 0.1666, the social factor with 0.1614, the institutional–organizational factor with 0.1501, and the environmental factor with 0.1381. At the variable level, "culture of participation in water resources management" with a weight of 0.0552, "coordination among different institutions" with 0.0545, "status of licensed and unlicensed wells" with 0.0538, and "regional rainfall and drought conditions" with 0.0518 were identified as the most important variables in the network. Conclusion The findings showed that the water crisis in the villages surrounding Parishan Lake is not a one-dimensional phenomenon and cannot be explained solely by reduced rainfall or drought. The structure of water resource governance in the region follows a pattern of "institutional causal concentration and social reflection"; in other words, the weakness or strength of responsible institutions has the greatest effect on the entire system, and its consequences become evident in public participation, cooperation, and local conflicts. The weighting results also indicated that the cultural factor holds the most important position in the structural sustainability of the system. Therefore, achieving sustainable water resource governance requires simultaneous attention to two main axes: first, reforming institutional structures, increasing inter-agency coordination, strengthening supervision over water withdrawals, and improving transparency; and second, promoting a culture of participation, reforming consumption patterns, and raising public awareness. Policies focused solely on technical or environmental measures, without considering institutional and cultural drivers, will not lead to the long-term sustainability of water resources. The simultaneous activation of the institutional lever and the cultural lever is therefore the most effective pathway for improving sustainable water resource governance in the villages surrounding Parishan Lake. بحران کم‌آبی در سکونت‎گاه‌های روستایی ایران، به‌ویژه در پهنه‌های آبی داخلی، تنها ناشی از محدودیت‌های طبیعی و تغییرات اقلیمی نیست، بلکه به‌طور معناداری با ضعف سازوکارهای تصمیم‌گیری و حکمروایی منابع آب مرتبط است. روستاهای پیرامون دریاچه پریشان طی سال‌های اخیر با کاهش شدید منابع سطحی و زیرزمینی، افت تولیدات کشاورزی و ناپایداری معیشت مواجه شده‌اند؛ پیامدهایی که تشدید تعارضات محلی و کاهش مشارکت اجتماعی را به دنبال داشته و ضرورت تحلیل عوامل مؤثر بر حکمروایی پایدار منابع آب را برجسته می‌کند. هدف پژوهش حاضر، تبیین ساختار علّی و وزن شبکه‌ای شش بعد فرهنگی، زیرساختی، محیط‌زیستی، اجتماعی، اقتصادی و نهادی–سازمانی و ۲۲ متغیر مرتبط در نظام حکمروایی منابع آب بود. داده‌ها به‌صورت اسنادی و میدانی گردآوری و با پرسش‎نامه دیمتل از ۳۰ خبرگان محلی جمع‌آوری شد و تحلیل با مدل ترکیبی دنپ انجام شد. نتایج کمی نشان داد بعد نهادی–سازمانی با 3934/8= Ri و 1204/8+= Ri−Cj تأثیرگذارترین عامل و بعد اجتماعی با 3688/8=Cj و 1252/8-= Ri−Cj بیشترین اثرپذیری را دارد. تحلیل علّی–معلولی ابعاد نهادی–سازمانی و فرهنگی را در گروه علّی، ابعاد اجتماعی، اقتصادی و زیرساختی را در گروه معلولی و بعد محیط‌زیستی را در وضعیت تعادلی نشان داد. وزن‌دهی شبکه‌ای نیز اهمیت بالای عامل فرهنگی (2046/0) و رتبه پنجم بعد نهادی–سازمانی (1501/0) را آشکار کرد که تأکید می‌کند گذار به حکمروایی پایدار منابع آب نیازمند تقویت هم‌زمان سازوکارهای نهادی و مؤلفه‌های فرهنگی است.

https://ges.razi.ac.ir/article_4342_cb98e58048387f503a5ac6f8d748b547.pdf

دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Food Providers Deprived of Food: A Spatiotemporal Analysis and GeoAI Based Forecasting of Food Insecurity in Iran Rural Areas over the Recent Decade تأمین‌کنندگان غذای محروم از غذا: تحلیل مکانی-زمانی و پیش‌بینی ناامنی غذایی با استفاده از هوش مصنوعی مکانی (GeoAI) در نواحی روستایی ایران در دهه اخیر 89 109 4352 10.22126/ges.2026.13727.2970 FA علی گلی بخش جامعه‌شناسی و برنامه‌ریزی اجتماعی، دانشکده اقتصاد، مدیریت و علوم اجتماعی دانشگاه شیراز، شیراز، ایران 0000-0001-7512-3353 وحید ملکی بخش مهندسی عمران و محیط‌زیست، دانشکده عمران، دانشگاه شیراز، شیراز، ایران. 0000-0001-7512-3355 Journal Article 2026 04 13 Food insecurity in Iran has intensified in recent years due to high inflation, declining household purchasing power, and major reforms in food subsidy policies. Although food insecurity has long exhibited pronounced regional disparities, it has increasingly expanded into rural areas, yet conventional measurement approaches often fail to capture intra-national spatial heterogeneity. This study addresses this limitation by examining the spatiotemporal dynamics and spatial heterogeneity of food insecurity across Iran and forecasting its potential evolution using a GeoAI-based analytical framework. The analysis employs Foster–Greer–Thorbecke (FGT) indices derived from annual Household Income and Expenditure Survey (HIES) data for the period 2013–2024, while spatial clustering and local spatial dynamics are assessed using Moran's I and Local Indicators of Spatial Association (LISA). Results show that in 2013, approximately 39% of sampled households in 150 counties experienced food insecurity, largely concentrated in central, eastern, and southern regions, but between 2021 and 2023, the prevalence rose to over 60%, with its geographic extent expanding to 290 counties nationwide. The food insecurity gap—defined as the shortfall from the minimum daily requirement of 2,100 kilocalories—increased from 24% in 2013 to more than 27% in 2024, while the severity of food insecurity rose by approximately 13%. These trends coincided with subsidy policy revisions and changes in the preferential exchange rate for food imports, which triggered sharp food price increases. Projections to 2026 suggest that more than 60% of households may face food insecurity, highlighting the urgent need for economic stabilization measures and regionally targeted food security policies. Introduction Food insecurity, as a growing global challenge, reflects deep structural inequalities in access to healthy and affordable food. While much research has focused on urban areas, the rural dimension—critical for food production—remains underexplored. This multidimensional concept, encompassing availability, access, utilization, and stability, is difficult to measure. Despite the development of various indicators, no single global standard has yet been established. In Iran, although national food insecurity has somewhat decreased, over 3.5 million people still face severe food insecurity, with rural populations disproportionately affected due to lower incomes, limited market access, and greater dependence on agricultural livelihoods. Given recent socioeconomic shifts, such as the removal of food subsidies and rising inflation, there is a critical need for fine‑scale spatiotemporal analysis to capture local variations. Traditional survey‑based methods lack the capacity to capture local vulnerabilities, whereas spatiotemporal analytical frameworks and Geospatial Artificial Intelligence (GeoAI) offer powerful tools for modeling complex patterns and making accurate predictions. This study addresses this gap by calculating the FGT (Foster‑Greer‑Thorbecke) food insecurity index, identifying critical hotspots, analyzing spatiotemporal changes from 2013 to 2023, and predicting future patterns in rural districts of Iran using GeoAI. Materials and Methods This applied research employed a descriptive‑analytical approach utilizing Exploratory Spatial Data Analysis (ESDA). The primary data source was the raw Household Income and Expenditure Survey (HIES) data for rural households, collected annually by the Statistical Center of Iran from 2013 to 2023. Food consumption data were used to estimate daily per capita caloric intake, with the food insecurity threshold set at 2100 kilocalories per person per day. Food insecurity was measured using the FGT index (P₀, P₁, P₂), which captures prevalence, depth, and severity. Spatial distribution patterns and clusters were analyzed using Moran's I, Local Indicators of Spatial Association (LISA), and Hot Spot Analysis. Finally, a Long Short‑Term Memory (LSTM) neural network model was employed to predict food insecurity levels for the year 2026. The model's performance was validated using cross‑validation and the Root Mean Square Error (RMSE). Results and Discussion The findings reveal a clear and significant worsening of rural food insecurity in Iran from 2013 to 2023. The P₀ index (prevalence) increased from 39% to over 60% of rural households falling below the caloric threshold. Substantial spatial heterogeneity was observed, with prevalence in 2023 ranging from 21% in Semnan Province to nearly 88% in West Azerbaijan Province. Over the decade, Tehran Province had the highest average prevalence (~69%), while Kermanshah had the lowest (19%). The number of districts with severe food insecurity rose dramatically from 110 to 250. The P₁ index (depth) increased from 24% to 27%, indicating a decline in average caloric intake among food‑insecure households. The P₂ index (severity) grew by over 13% during the study period. Spatial analysis using Moran's I showed a decreasing concentration of food insecurity, indicating a shift from a clustered pattern to a more dispersed and widespread national issue. Early hotspots were concentrated in central and southeastern provinces but gradually expanded to northern and northwestern regions. In contrast, some western provinces consistently showed below‑average food insecurity levels. Predictions using the LSTM model for 2024–2026 suggest a continued high prevalence, with the P₀ index slightly exceeding 60%. However, scenario analysis indicates a wide range of possible outcomes: from a drastic increase to 82% in a pessimistic scenario to a reduction below 45% in an optimistic scenario. Projected spatial patterns for 2026 still show significant clusters of food insecurity in the northwest, north, center, south, and parts of the east, with the emergence of new hotspots. Conclusion The study confirms that caloric intake and access to sufficient food in many rural areas of Iran fall below standard levels, with significant spatial inequalities. While food insecurity showed a slight decline until 2017, policy shifts such as reduced government support in 2018 and the removal of food subsidies in 2022 led to a sharp increase in caloric deficits and insecurity, affecting over 250 districts. Income deprivation creates a detrimental cycle of reduced purchasing power, inadequate nutrition, and adverse social and health outcomes. Projections for 2026 indicate that persistent food insecurity may affect more than half of the country's districts. The findings underscore the urgent need for targeted, region‑specific interventions, price stabilization, and improved food subsidy policies. ناامنی غذایی در ایران طی سال‌های اخیر تحت تأثیر تورم بالا، کاهش قدرت خرید خانوارها و اصلاحات اساسی در سیاست‌های یارانه‌ای تشدید شده است. اگرچه ناامنی غذایی همواره با نابرابری‌های منطقه‌ای قابل‌توجهی همراه بوده، اما در سال‌های اخیر به‌طور فزاینده‌ای به مناطق روستایی گسترش یافته است. رویکردهای متعارف سنجش ناامنی غذایی غالباً از درک ناهمگنی فضایی درون‌سرزمینی ناتوان‌اند. این مطالعه با هدف رفع این کاستی، پویایی‌های فضایی–زمانی و ناهمگنی مکانی ناامنی غذایی در نواحی روستایی ایران را بررسی کرده و الگوهای احتمالی آن را با بهره‌گیری از چارچوبی مبتنی بر هوش مصنوعی مکانی (GeoAI) پیش‌بینی می‌کند. در این پژوهش از شاخص‌های فاستر–گریر–ثوربک (FGT) مبتنی بر داده‌های سالانه طرح آمارگیری هزینه و درآمد خانوار (HIES) طی دوره ۱۳۹۲ تا ۱۴۰۲ استفاده شده است. الگوهای خوشه‌بندی و پویایی‌های مکانی با بهره‌گیری از شاخص موران (Moran’s I) و شاخص‌های محلی وابستگی فضایی (LISA) ارزیابی شده‌اند. نتایج نشان داد که در سال ۱۳۹۲ حدود 39% از خانوارهای نمونه در 110 شهرستان با ناامنی غذایی مواجه بوده‌اند که عمدتاً در مناطق مرکزی، شرقی و جنوبی کشور متمرکز بوده است. در بازه ۱۴۰۰ تا ۱۴۰۲، شیوع ناامنی غذایی به بیش از 60% افزایش یافته و دامنه جغرافیایی آن به 250 شهرستان گسترش یافته است. شکاف ناامنی غذایی - به‌عنوان فاصله از حداقل نیاز روزانه ۲۱۰۰ کیلوکالری - از 24% در سال ۱۳۹۲ به بیش از 27% در سال ۱۴۰۲ افزایش یافته و شدت ناامنی غذایی نیز حدود 13% رشد کرده است. برآوردها تا سال ۱۴۰5 نشان می‌دهد که بیش از 60% خانوارها ممکن است با ناامنی غذایی مواجه شوند. یافته‌ها بر ضرورت فوری اتخاذ سیاست‌های تثبیت اقتصادی و مداخلات منطقه‌ای هدفمند در حوزه امنیت غذایی تأکید دارد.

https://ges.razi.ac.ir/article_4352_6a9dbe2ebb86ff5259c257c1290685d1.pdf

دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Spatiotemporal Assessment of Water Inundation Changes in the Mighan Wetland Based on Satellite Image Time Series in the Google Earth Engine Platform ارزیابی تغییرات زمانی–مکانی آبگیری تالاب میقان بر پایه سری زمانی تصاویر ماهواره‌ای در محیط GOOGLE EARTH ENGINE 111 130 4391 10.22126/ges.2026.14018.2992 FA سمانه رضوی زاده استادیار پژوهش، بخش تحقیقات بیابان، مؤسسه تحقیقات جنگل‏ها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران. 0009-0005-2104-092X Journal Article 2026 06 05 Mighan Wetland has experienced severe hydrological fluctuations and declining water stability in recent decades. In this study, the wetland was monitored over the period 1995–2024 using Landsat 5, 7, and 8 imagery within the Google Earth Engine (GEE) platform. The aim was to analyze the spatiotemporal changes of the water body and assess hydrological instability using the Normalized Difference Water Index (NDWI) and the Transition and Seasonality algorithms from the JRC Global Surface Water dataset. The results showed that the average water body area decreased from 10.7 km² (1995–2004) to 3.9 km² (2005–2014), representing a 63% decline, then increased to 19.8 km² (2015–2024). More than 80% of annual inundation occurs in winter and spring, corresponding to snowmelt and higher precipitation periods. The Seasonality algorithm revealed that 25% of the wetland is permanently dry, approximately 70% holds water for only 1 to 5 months per year, and only 4.5% has persistent water cover. The Transition results indicated the conversion of some seasonal areas to permanent zones and the emergence of new seasonal water bodies. Overall, the hydrological regime of the wetland has become highly unstable over three decades, transforming into a seasonal system dependent on short-term climatic events, posing a serious threat to its ecological functions, including habitat loss for migratory birds and increased dust emission from dry playa surfaces. The novelty of this research lies in integrating long-term monthly analyses with the NDWI index and the Transition and Seasonality algorithms within the GEE platform, enabling continuous monitoring, improved water resource management, and effective wetland restoration planning. Introduction Wetlands in arid and semi-arid regions are among the most vulnerable natural ecosystems due to their high dependence on limited precipitation and intermittent runoff. These ecosystems provide critical ecological services, including groundwater recharge, flood regulation, biodiversity conservation, and climate moderation. However, they are increasingly threatened by anthropogenic pressures such as excessive groundwater extraction, land-use changes, upstream water diversion, and climate change. Mighan Wetland, located in the central plateau of Iran near Arak City, is one of the most important salt wetlands in the region, which has experienced severe hydrological fluctuations and declining inundation stability in recent decades. The desiccation of this wetland can lead to widespread environmental consequences, including the activation of dust sources, increased wind erosion, degradation of surrounding agricultural lands, loss of habitat for migratory birds, and weakening of ecological functions. Despite its ecological and economic significance, long-term monitoring of its hydrological dynamics has been limited. The main purpose of this study is to monitor and analyze the spatiotemporal changes in the water body area of Mighan Wetland over a 30-year period (1995–2024) and assess its hydrological instability trend using remote sensing data within the Google Earth Engine platform. Materials and Methods In this study, Landsat 5, 7, and 8 satellite images (time series from 1995 to 2024) were used to extract and analyze water body changes. The Google Earth Engine platform was employed for all processing due to its powerful cloud-computing capabilities, access to extensive satellite archives, and efficient handling of large-scale time-series data. The Normalized Difference Water Index (NDWI), a widely used spectral index for water body extraction, was applied with a threshold of 0.2 to delineate water surfaces. The NDWI leverages the reflectance differences between visible green and near-infrared bands to effectively distinguish water bodies from other land cover types. To analyze the stability and structural changes of water bodies, the Transition and Seasonality algorithms from the JRC Global Surface Water dataset were used. These algorithms provide detailed classification of water presence over time, identifying patterns such as permanent, seasonal, ephemeral, and transitional water bodies. Water surface areas were calculated at monthly, seasonal, annual, and decadal scales, and their trends were analyzed to understand both short-term fluctuations and long-term patterns. Additionally, inundation classification maps were prepared based on 11 Transition classes and 12 Seasonality classes, enabling a comprehensive assessment of hydrological stability and change dynamics across the wetland. Results and Discussion The results showed that the average water body area of Mighan Wetland decreased from 10.7 km² in the first decade (1995–2004) to 3.9 km² in the second decade (2005–2014), representing a 63% decline. This significant reduction reflects the combined effects of prolonged drought, upstream water diversion, and increased groundwater extraction. However, the wetland area then increased to 19.8 km² in the third decade (2015–2024), largely due to wetter climatic conditions and increased rainfall events. The maximum inundation area was recorded in 2019 with 29.5 km², while the minimum was recorded in 1999 with only 0.1 km², indicating extreme interannual variability. Seasonal analysis indicated that the largest water area occurs in winter (15 km²) and spring (14.6 km²), corresponding to snowmelt and higher precipitation periods, while the smallest occurs in summer (1.5 km²) and autumn (4.4 km²), reflecting high evaporation rates and reduced runoff. Seasonality algorithm results revealed that 25% of the wetland area remains permanently dry, approximately 70% is inundated for only 1–5 months annually, and stable inundation areas (more than 6 months) comprise merely 4.5% of the total area. Transition algorithm results showed that the "No Change" class (21.9 km²), "Permanent" class (24.5 km²), "New Seasonal" class (2 km²), "New Permanent" class (2 km²), and "Seasonal to Permanent" class (6.4 km²) are among the most important change classes, highlighting the dynamic nature of wetland hydrology. Conclusion The findings of this study indicate that the hydrological regime of Mighan Wetland has become extremely unstable over the past three decades, and the wetland has transformed into a seasonal system dependent on short-term climatic events rather than a stable, permanent water body. Although the increase in water area in the third decade is significant, it is mainly due to the expansion of seasonal zones and the wetland's response to short-term wet periods, not the return of structural stability to the permanent cores of the wetland. The novelty of this research lies in the integration of long-term monthly analyses with NDWI and Transition and Seasonality algorithms within the Google Earth Engine environment, enabling continuous monitoring, improved water resource management, and restoration planning for Mighan Wetland. Based on the results, determining and providing sustainable environmental water rights, integrated watershed management, controlling excessive groundwater extraction, and improving water use efficiency in the agricultural sector are among the most essential actions needed to preserve the stability of this valuable ecosystem. Without timely intervention, the wetland faces the risk of permanent desiccation, with severe environmental and socio-economic consequences for the region. تالاب میقان طی دهه‌های اخیر با تغییرپذیری هیدرولوژیکی و کاهش تداوم آبگیری مواجه بوده است. در این پژوهش، تالاب میقان طی بازه ۱۹۹۵ تا ۲۰۲۴ با استفاده از تصاویر لندست ۵، ۷ و ۸ در محیط Google Earth Engine پایش شده است. هدف، تحلیل تغییرات مکانی‑زمانی پهنه آبی و ارزیابی ناپایداری هیدرولوژیکی با بهره‌گیری از شاخص NDWI و الگوریتم‌های Transition و Seasonality از مجموعه داده JRC-GSW بود. نتایج نشان داد متوسط مساحت پهنه آبی از 10.7 کیلومترمربع (۲۰۰۴‑۱۹۹۵) به 3.9 کیلومترمربع (۲۰۱۴‑۲۰۰۵) کاهش و سپس به 19.8 کیلومترمربع (۲۰۲۴‑۲۰۱۵) افزایش یافته است. بیش از ۸۰ درصد آبگیری سالانه در زمستان و بهار رخ می‌دهد. الگوریتم Seasonality مشخص کرد ۲۵ درصد تالاب همیشه خشک، حدود ۷۰ درصد تنها ۱ تا ۵ ماه در سال آبدار و فقط 4.5 درصد دارای پوشش آبی پایدار است. نتایج Transition نیز نشان‌دهنده تبدیل بخشی از پهنه‌های فصلی به دائمی و ظهور پهنه‌های فصلی جدید است. به‌طور کلی، رژیم هیدرولوژیکی تالاب طی سه دهه شدیداً ناپایدار شده و به سیستمی فصلی و وابسته به رویدادهای کوتاه‌مدت اقلیمی تبدیل گردیده که تهدیدی جدی برای کارکردهای اکولوژیکی آن به شمار می‌رود. نوآوری پژوهش در تلفیق تحلیل‌های ماهانه بلندمدت با NDWI و الگوریتم‌های Transition و Seasonality در بستر GEE است که پایش مستمر، مدیریت منابع آب و برنامه‌ریزی احیای تالاب را فراهم می‌سازد.

https://ges.razi.ac.ir/article_4391_02f6376ef3320ef11a59bf846bb761ae.pdf

دانشگاه رازی جغرافیا و پایداری محیط 2322-3197 16 3 2026 09 23 Evaluating the Spatiotemporal Dynamics and Recovery of Ecological Environmental Quality Post Flood in Lorestan Province Using Remote Sensing ارزیابی پویایی مکانی-زمانی کیفیت محیط اکولوژیکی استان لرستان پس از سیلاب با رویکرد سنجش‎ازدور 131 156 4469 10.22126/ges.2026.13792.2975 FA علی مهرابی گروه جغرافیا، دانشکده ادبیات و علوم انسانی، دانشگاه شهید باهنر کرمان، کرمان، ایران. 0000-0003-3287-7037 Journal Article 2026 04 19 Natural disasters typically cause environmental degradation. A scientific and quantitative assessment of ecological environmental quality and its post-disaster recovery trajectory can provide valuable insights for mitigating future hazards. In this study, the April 2019 flood event in Lorestan Province, Iran, was selected as a case study to investigate the spatiotemporal variations and driving factors of ecological environmental quality prior to and following the flood. Initially, MODIS data were employed to construct the Remote Sensing Ecological Index (RSEI). Subsequently, the spatiotemporal dynamics of ecological environmental quality across Lorestan Province were evaluated from 2015 to 2024, alongside an analysis of spatial autocorrelation relationships. Finally, the Geographical Detector (GeoDetector) model was implemented to identify the key factors influencing post-flood environmental recovery. The results demonstrated that the flood event caused a notable decline in the RSEI from 0.7658 to 0.6081, which subsequently recovered to 0.6514, reflecting the significant impact of the flood phenomenon on the province's environmental quality. Spatially, areas with higher ecological quality were predominantly concentrated in the central and eastern regions of the province, whereas the northwestern and southeastern areas exhibited lower quality. Moran's I index exceeded 0.75, indicating a strong positive spatial autocorrelation and clustering pattern of ecological environmental quality. Based on the GeoDetector analysis, elevation and population density played the most prominent roles in driving the post-flood ecological quality dynamics. Among the interactive factors, the interaction between elevation and slope exhibited the strongest explanatory power regarding the ecological recovery process Introduction In recent years, natural disasters such as floods, earthquakes, land subsidence, and landslides have frequently occurred in Iran. Floods, as one of the most severe natural hazards, not only cause extensive human casualties and financial losses but also severely degrade the ecological structure of the environment. Ecosystems serve as the material foundation for human life and development, playing a vital role in climate regulation, environmental purification, pest control, and biodiversity conservation. In April 2019, Lorestan Province experienced a catastrophic flood event that caused severe financial damage to the province's infrastructure, claimed 15 lives, destroyed numerous homes, and left profound psychological impacts on survivors. However, the environmental impact of this flood and its subsequent recovery trajectory required rigorous scientific investigation, which constitutes the primary objective of this study. Despite numerous studies on the Lorestan flood (focusing on landslides, psychological trauma, and social responses), post-disaster ecological recovery monitoring remained unaddressed in this region. Most previous studies focused solely on vegetation cover or land-use changes (Mehrabi, 2021; Moradi et al., 2024), whereas the Remote Sensing Ecological Index (RSEI) provides a more comprehensive assessment of environmental health by integrating multiple core indicators, including greenness, wetness, heat, and dryness. Therefore, this study combines the RSEI and the Geographical Detector (GeoDetector) model to evaluate ecological quality variations and recovery following the flood event. This research aims to support improved environmental management planning and post-disaster sustainable development, thereby contributing to the Sustainable Development Goals (SDGs), particularly in building resilient and sustainable cities and human settlements. Materials and Methods This study utilized MODIS satellite data spanning from 2015 to 2024. Two primary datasets were employed: MOD09A1 for surface reflectance across various spectral bands and MOD11A2 for Land Surface Temperature (LST). The key advantages of MODIS data include broad spatial coverage, high temporal resolution (8-day composites), and suitability for time-series analyses, making it ideal for long-term ecological monitoring. The primary software used included the MODIS Reprojection Tool (MRT), ENVI, and ArcGIS. First, the MODIS data for the study area were batch-processed using MRT, which involved image mosaicking, spatial reprojection to a common coordinate system, and format conversion. To address cloud contamination, cloud-affected pixels were identified and masked, and composite clean images were generated to cover the entire study area. Subsequently, using ENVI 5.3, four primary indicators—Normalized Difference Vegetation Index (NDVI) representing greenness, Wetness index from tasseled cap transformation, Land Surface Temperature (LST) representing heat, and Normalized Difference Built-up and Soil Index (NDBSI) representing dryness—were extracted and integrated via Principal Component Analysis (PCA) to construct the composite RSEI. Results and Discussion In the first Principal Component (PC1), with the exception of the year 2020, the loadings for NDVI and WET—both positively correlated with favorable ecological conditions—consistently exhibited positive signs. The percentage contribution of eigenvalues for PC1 across the six temporal stages were 71.35%, 63.86%, 67.09%, 62.49%, 52.88%, and 63.29%, respectively, all of which were significantly higher than those of the other components. This indicates that PC1 captured the vast majority of critical information from the four selected indicators. Consequently, the RSEI was constructed based on PC1 to analyze the pre- and post-flood EEQ dynamics. Higher RSEI values denote ecological improvement, whereas lower values indicate EEQ degradation. As presented in Table 2, the mean RSEI value prior to the flood (2015–2019) remained relatively stable above 0.65, reflecting favorable environmental quality across the province. The highest and lowest RSEI values were recorded in 2017 and 2020 at 0.7658 and 0.6081, respectively. This decline of 0.1577 units is directly attributable to the impacts of the flood event, which caused widespread vegetation loss, soil erosion, and surface degradation. Following 2020, RSEI values exhibited a steady increase, indicating substantial environmental recovery and a return toward pre-disaster conditions. Overall, the spatiotemporal variations in EEQ from 2015 to 2024 can be categorized into three distinct phases: the pre-flood period (2015–2019) with favorable ecological conditions; the flood occurrence period (2019–2020) characterized by a marked reduction in mean RSEI due to ecosystem damage; and the post-flood recovery period (2020–2024) marked by a rising mean RSEI and progressive environmental restoration. Conclusion The main findings of this study are summarized as follows: - The temporal RSEI values were recorded at 0.6741, 0.7658, 0.6658, 0.6081, 0.6236, and 0.6514, respectively. The overall trajectory from 2015 to 2024 exhibited a clear "Stability – Decline – Recovery" pattern, which closely corresponds to the EEQ dynamics. These results indicate that while the flood degraded the province's ecological quality, gradual environmental recovery ensued, ultimately restoring overall ecological quality to near pre-crisis levels. - Area-based statistics comparing pre- and post-flood RSEI revealed that areas experiencing an RSEI increase accounted for only 33% of the total area, whereas areas exhibiting a decrease covered 44% of the study region, indicating that recovery has been uneven across the province. - Spatial autocorrelation analysis revealed that Moran's I values from 2015 to 2024 were consistently positive, indicating that the spatial distribution of EEQ is significantly clustered rather than randomly distributed. Furthermore, the GeoDetector analysis identified elevation and population density as the dominant drivers of RSEI spatial variation. In terms of factor interactions, the interactive effect of elevation and slope exerted the strongest explanatory power on ecological recovery, consistently explaining over 40% of the variance throughout the study period. بلایای طبیعی معمولاً باعث تخریب محیط‌زیست می‌شوند. ارزیابی علمی و کمی از کیفیت محیط‌زیست اکولوژیکی و روند بهبودی آن پس از وقوع بلایا، می‌تواند اطلاعات ارزشمندی برای کاهش خطرات آینده فراهم کند. در این پژوهش، سیلاب فروردین‌ماه سال 1398 استان لرستان به‌عنوان نمونه‌ای برای بررسی تغییرات زمانی و مکانی و همچنین عوامل مؤثر بر کیفیت محیط اکولوژیکی قبل و بعد از سیلاب انتخاب شد. ابتدا از داده‌های مودیس برای ساخت شاخص اکولوژیکی مبتنی بر سنجش‎ازدور استفاده شد. سپس تغییرات زمانی و مکانی کیفیت محیط اکولوژیکی استان لرستان از سال 1394 تا 1403 بررسی شد و روابط خودهمبستگی فضایی نیز تحلیل گردید. درنهایت، مدل GeoDetector برای شناسایی عوامل مؤثر در بازیابی محیط‌زیست پس از سیلاب به ‌کار گرفته شد. نتایج نشان داد که درنتیجه سیلاب شاخص RSEI از عدد 7658/0 به 6081/0 کاهش یافته و دوباره به عدد 6514/0 رسیده است. این تغییرات نشان از تأثیر پدیده سیلاب در کیفیت محیط‌زیست استان دارد. از نظر مکانی، مناطق با کیفیت بالاتر بیشتر در بخش‌های مرکزی و شرقی استان قرار دارند، در حالی که بخش‌های شمال ‌غربی و جنوب ‌شرقی کیفیت پایین‌تری دارند. شاخص موران بالاتر از 75/0 بود که نشان‌دهنده همبستگی و خوشه‌بندی مکانی قوی کیفیت محیط‌زیست است. بر اساس مدل GeoDetector، دو عامل ارتفاع و تراکم جمعیت، مهم‌ترین نقش را در تغییرات کیفیت محیط اکولوژیکی پس از رخداد سیلاب داشتند. در میان عوامل ترکیبی، تعامل بین ارتفاع و شیب، قوی‌ترین اثر را در روند بهبودی اکولوژیکی نشان داد.

https://ges.razi.ac.ir/article_4469_b585b5819421e1f9296f13700964dda8.pdf