https://hsmsp.modares.ac.ir/ The Journal of Spatial Planning and Geomatics en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://hsmsp.modares.ac.ir/article_28901.html Land Surface Temperature (LST) is a key parameter for monitoring the Earth’s surface energy balance and water cycle, playing an important role in assessing environmental changes from local to global scales. Despite advances in remote sensing and the availability of time-series data, limitations in sensor design and the trade-off between spatial and temporal resolution remain major challenges for generating accurate LST time series. To address these issues, various spatiotemporal fusion (STF) methods have been developed. In this study, four ensemble learning algorithms, namely Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF), and Deep Forest, were used as the core of a spatiotemporal fusion framework to simulate daily LST from Landsat 8 and Landsat 9 imagery. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was also employed as a benchmark for comparison. The results showed that XGBoost achieved the best performance, with Root Mean Square Error (RMSE) values of 2.76 K, 1.60 K, 1.68 K, and 1.54 K for 2016, 2021, 2022, and 2023, respectively. Deep Forest showed the highest errors among the ensemble models, while GBM and Random Forest performed at an intermediate level. In addition, ESTARFM produced higher RMSE values ranging from 1.69 K to 2.91 K, indicating lower accuracy compared to the machine learning approaches. Overall, the results demonstrate that XGBoost provides the most accurate and robust performance for LST spatiotemporal fusion, outperforming both other ensemble methods and the ESTARFM model. https://hsmsp.modares.ac.ir/article_28924.html This research was conducted with the aim of identifying the key drivers affecting the sustainability of Sistan’s ecological resources, including soil, water, and vegetation. 67 drivers with Delphi method and systems thinking were identified and then analyzed using MICMAC to determine their impact and influence within the framework of direct, indirect, probable direct, and probable indirect relationships. The results showed that the drivers of self-interest, discouragement or indifference of managers and decision-makers, corruption and managerial inefficiency, and the lack of commitment of parliamentarians and local councils to the Sistan conservation and development vision programs have the greatest influence and the lowest dependency, and were therefore identified as the key drivers. Moreover, drivers such as reduced biodiversity and ecosystem services, aesthetic decline in natural and artificial landscapes, environmental and human health, failure to implement vegetation restoration plans, degradation of afforestation projects and off-road traffic inside the Hamoon Wetland, tourism and the local economy, lack of participation of people and non-governmental organizations in implementing desertification-control programs, public distrust of officials, migration, and increased poverty were identified as response drivers with the highest dependency and lowest influence. The examination of driver displacement showed that the system has structural stability based on the influence and dependence relationships between the variables. The findings indicate that key drivers are concentrated in managerial, political, and governance domains, while response drivers lie within economic, social, cultural, and ecological dimensions. Accordingly, it can be inferred that improving response drivers depends on enhancing conditions of managerial, political, and governance spheres. https://hsmsp.modares.ac.ir/article_29162.html This study aims to provide a comprehensive assessment and strategic planning framework for the social–economic resilience of counties in Tehran Province, using the DPSIR analytical framework. The main objective is to identify the current conditions, analyze regional inequalities, and propose practical strategies to enhance resilience across the province.The research adopts a mixed quantitative–qualitative methodology and evaluates resilience across fourteen key dimensions, including health and social welfare, human capital, sustainable economy, infrastructure, environment, and food security. Data were collected from official sources, normalized using the Z-value method, and indicator weights were determined through the Analytic Hierarchy Process based on expert judgment.The findings reveal significant spatial inequalities among the counties. Tehran County, with a resilience score of 10.45, along with Firuzkuh (7.39) and Rey (5.91), ranks at the top, while Pishva (0.44) and Varamin (0.71) are in the most disadvantaged positions. This distribution confirms a clear center–periphery pattern and a north–south divide in access to infrastructure, services, and human capital.DPSIR analysis indicates that macro-level drivers such as population growth and economic policies have intensified pressures on employment, housing, and public services. These pressures have weakened resilience and led to adverse impacts, including declining quality of life and increasing social inequality.Finally, the study emphasizes the need for region-specific strategic responses, including immediate investment in health and educational infrastructure in deprived counties, the development of sustainable and diversified employment in densely populated areas, and a focus on knowledge-based economy and sustainable tourism in more developed regions. https://hsmsp.modares.ac.ir/article_29344.html Subsidence is the gradual downward movement of the Earth’s surface, affecting both urban and rural areas. More than 300 locations across Iran are experiencing surface deformation and land subsidence due to natural and anthropogenic factors. This study focuses on quantifying and comparing land deformation in Iran’s major densely populated cities by classifying deformation intensity into three levels (low, moderate, and high). The aim of this research is to rank the provincial capitals based on the severity of hazardous land deformation and, consequently, identify the province with the highest subsidence risk. Using the SBAS technique, the subsidence rate in the provincial capitals of Tehran, Alborz, Razavi Khorasan, Isfahan, Khuzestan, Fars, East Azerbaijan, Kerman, and Qom was analyzed through a one‑year time‑series (from November 2024 to November 2025). The results indicate that Tehran, with a subsidence rate of 22 cm per year, ranks first in terms of subsidence risk. In the remaining cities—Isfahan, Karaj, Kerman, Mashhad, Tabriz, Shiraz, Ahvaz, and Qom—the affected areas are also extensive, highlighting the urgent need for preventive measures. https://hsmsp.modares.ac.ir/article_28925.html Shortwave radiation (SWR) as one of the fundamental components of the Earth’s climate system plays a key role in regulating the surface energy balance, controlling ecological processes, and shaping patterns of human activities. The present study aims to investigate the spatiotemporal variability of shortwave radiation in western Iran over the 2000–2022 period using satellite data and products, focusing on spatial and seasonal patterns across the provinces of Kurdistan, Kermanshah, Ilam, Hamedan, and Lorestan. In this research, MODIS sensor data were used to estimate net surface incoming radiation, and regional-scale spatiotemporal analyses were performed. The results indicate that western Iran has a high potential for solar energy reception, with an estimated annual mean radiation of approximately 1800 to 2200 kWh per square meter and more than 280 sunny days per year. Spatially, the highest radiation values were observed in the elevated and low-cloud regions of northwestern areas, while lower values were recorded in southern and southwestern regions due to higher humidity and cloud cover. Temporally, the seasonal pattern shows maximum radiation in summer and minimum in winter. This pattern is primarily driven by variations in solar zenith angle, day length, cloud conditions, humidity, and topographic characteristics. Overall, the findings suggest that western Iran, particularly the Zagros highlands, has considerable potential for solar energy development, and the results can support strategic planning for renewable energy expansion and sustainable resource management. https://hsmsp.modares.ac.ir/article_29345.html This study aimed to examine the implications of the existing water resources governance pattern for the sustainability of rural settlements surrounding Lake Parishan. The research was applied in purpose, quantitative in approach, and based on field data. The statistical population comprised local residents, local managers, and experts involved in water resources management, and a total of 415 questionnaires were analyzed. Data were analyzed using the one-sample t-test, one-way analysis of variance (ANOVA), and the Preference Selection Index (PSI). The results showed that the environmental, economic, and social dimensions were all below the theoretical midpoint, with the environmental dimension exhibiting the most unfavorable condition. Significant differences among stakeholder groups were observed only in the environmental dimension, primarily between local residents and experts. The PSI results identified land economic value, economic water productivity, farmers’ literacy level, unemployment, and annual migration as the most influential indicators differentiating rural settlements. Spatial ranking also revealed considerable heterogeneity among the studied villages. Overall, the findings highlight the need for an integrated, spatially differentiated approach to water management that simultaneously enhances water productivity, human capacities, and institutional coordination.