Journal of Spatial Analysis

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he spread of Covid-19 in the rural areas of the country has caused more dangers due to the common rural cultures, and the ignorance and lack of efficient management of this crisis in the villages has caused irreparable consequences for these areas. In such cases, the existence of social capital can be very vital in creating national consensus and successful policies to pass this critical stage. Leading research is applied in terms of purpose and based on descriptive-analytical nature. To determine the sample size of villagers, using Cochran's formula, from the total of 6903 households in sample villages, 362 households were calculated as sample households to complete the questionnaire. In order to investigate the effects of social capital on economic and social indicators that are effective in reducing the vulnerability of the outbreak of Covid-19 in the border villages of Sistan, a wide range of indicators was determined, and from the one-sample T-test and the analysis of variance of the regression model to measure the The evaluation of the effects of social capital on socio-economic indicators effective in reducing the vulnerability of the outbreak of Covid-19 in the border villages of Sistan was used. The results of the research showed that the higher the level of people's participation and their trust towards each other, the higher the level of responsibility and knowledge of people, it has a positive role and effect on social and economic indicators in order to reduce the vulnerability of the spread of the covid disease. has had 19
 

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Subsidence as an environmental hazard is caused by various natural and human factors. The drastic changes in land use, the increase in the number of deep wells, and the effects of the subsidence phenomenon in Dehgolan plain show the need to investigate these influencing factors. In such a situation, adequate understanding of the degree of vulnerability and investigation of the influencing factors in that process provides the opportunity for planning and environmental preparation of the space in order to reduce vulnerability. In this research, first, the NDVI index of the plain was investigated with the help of 15 Sentinel-2 and Landsat 8 satellite images, and the best date was selected for the Sentinel-1 images. In this way, 8 Sentinel-1 satellite images were analyzed over a period of 8 years (2014-2021) and all the images were analyzed and processed in eight stages with the help of SNAP software. 3 Landsat 7 and 8 satellite images were used to investigate land use changes (2000-2021).By applying atmospheric and radiometric corrections and finally performing the supervised classification method using Arc GIS software, land use was extracted and its changes were checked. The interferometric results showed that the Dehgolan plain suffered a total of 480 mm of subsidence. So that 60 mm of subsidence has occurred in this plain every year. In the end, with the preparation of the map of land use changes, the classes of irrigated agricultural and residential lands increased by 6.98, 1.47 percent, and the uses of pasture, forest and rainfed lands were faced with a sharp decrease, so that irrigated lands increased by 8477 and residential by 672 hectares. Is. The results obtained from the analysis of the relationship between water use and subsidence showed that rapid subsidence occurs mainly in water and urban land use classes. This is a consequence of increasing water extraction for agriculture and drinking. Usually, the pattern of land use conversion with more human influences has increased the rate of subsidence.
 

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Human health depends on living in a healthy environment. Various factors determine environmental health, which should be explored to be able to improve them. The purpose of this research is to analyze the factors affecting the health level of urban environment in district 20 of Tehran municipality and to find out their spatial inequality. This is a descriptive-analytical research based on documentary data of 6 indicators and survey data of 26 indicators. For the analysis, various tools of the GIS, especially the spatial analysis tool of Interpolation, are used. Quantitative analyzes are carried out by calculating statistical parameters in attribute tables. Findings show that the score of 2.29 out of 5 in the evaluation range of 1 to 5, indicates a poor situation of environmental health. Higher weakness belongs to the 6 indicators of the economic dimension including saving, sufficient income for buying cultural goods or going to recreational journeys, and residential home per capita. Access to urban parks and green spaces with scores of 4.55 and 3.43 respectively, show a superiority compared to other indicators. In the outskirts, the environmental health condition is much weaker than in the interior areas. Despite the vastness of worn-out fabric and informal constructions and abandoned spaces, what is more effective are the impacts of external factors, 3 sides of the district are limited to fringe lands, where the establishment of polluting industries, the passage of sewage canals and the replacement of foreign and the population involved in informal and illegal jobs are affected the health level of the urban environment.
 

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In this research, by preparing a distribution map of landslide areas and assessing the potential of landslide vulnerability in the Sivan basin, it has been tried to identify the resources at risk in different zones of vulnerability, then the environmental stability of the basin against the potential Vulnerability due to landslides should be investigated.. For this purpose, the information layers of landslide areas, agricultural lands, forests, residential areas and roads were prepared in the GIS environment. Then, the prepared layers based on the model (Index Overlay) were weighted and classified using the Class Maps Multi method and using expert opinion.According to the results obtained from the research, 44 landslides were determined in the Sivan basin, 17 of which could be measured and investigated in the field, 10 of which were in mountainous areas and were inaccessible and 12 of which could not be reached at all. The arena was not identified and 5 cases have been leveled due to the change of use to gardens and facilities. In the following, the potential vulnerability map of Sivan basin landslides in five vulnerability classes: very low (19846 hectares), low (1793 hectares), medium (741 hectares), high (2089 hectares) and very high (871 hectares) was prepared. In terms of environmental stability, the most damages caused by landslides in the Sivan basin are respectively related to communication roads and forest areas, which are in the range of high and very high vulnerability, agricultural lands are in the range of damage. Medium and lowest acceptability is related to residential areas, which is in a very low range. In the investigation of the environmental stability of the basin, it has been determined that there is a potential for vulnerability in all the mentioned cases, but it will be more intense in the direction of communication roads.
 

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Drought is one of the most important challenges faced by pastoralists around the world. This phenomenon can have significant negative effects on livestock health, production, and livelihoods. However, pastoralists can adapt to drought and reduce its negative effects by adopting various strategies. Semi-nomadic people in Darbandi, Kalat-Naderi County, have been facing drought since 2007 due to their livestock farming. Since livestock farming has profound impacts on the lifestyle and livelihoods of these communities, the present study examines their experience in facing drought and identifies their management strategies in these conditions. The research method is qualitative and the research tool is in-depth interviews with 20 semi-nomadic people in Darbandi, Kalat-Naderi. Sampling was purposeful and carried out until theoretical saturation was reached to ensure that a wide range of perspectives and experiences were collected. The data from the interviews were analyzed using a qualitative grounded theory approach to extract key patterns and concepts. According to the findings, the semi-nomadic Darbandi people of Kalat County have adopted various strategies in the face of drought, which are classified into four categories: rangeland and grazing management strategies, livestock nutrition management, water consumption management, and livelihood diversification. These results can be used as a basis for formulating better policies in the field of crisis management and rural development. Also, these results can be used for more effective planning to reduce the vulnerability of nomads to drought.

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The occurrence of persistent rainfall, resulting from the integration of multi-scale cyclonic systems, is prone to producing heavy to severe precipitation. Therefore, it is highly significant due to its potential to cause disasters such as floods and landslides, as well as its importance in water resource management for agricultural purposes. In this study, persistent rainfall refers to rainfall events lasting at least three consecutive days with a total precipitation of more than 1 millimeter. The aim of this research is to identify and classify these types of rainfall for the western region of Iran over a 30-year statistical period (1993 to 2022) for the first time using the Lamb-Jenkinson method. In this method, the central coordinates of the study area are used as the reference point in the calculations. Sixteen additional points are also determined around the reference point. With the availability of instantaneous standard sea-level pressure data for these points, it becomes possible to calculate the values of geostrophic wind and vorticity. By comparing these two quantities, the types of weather patterns in the L-J method—which include four types: directional, cyclonic, hybrid, and undefined—are identified and categorized. Disregarding the undefined category, it was ultimately determined that the provinces of Kermanshah, Kurdistan, Hamedan, Lorestan, and Ilam each had 50, 50, 50, 40, and 39 occurrences of the directional state, respectively. Moreover, the frequencies of the cyclonic state for these provinces were 69, 94, 65, 66, and 38, respectively, with cyclonic rotation percentages of 100%, 98%, 97%, 95%, and 97%, respectively. As for the hybrid state, the frequencies obtained for each province were 49, 53, 43, 41, and 38, respectively.
 

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The vulnerability of rural settlements to environmental hazards necessitates attention to local resilience. The aim of this study was to prioritize the factors affecting the physical-environmental and institutional resilience of Divshal rural district. The approach of the present study is descriptive-analytical and survey. Data collection was done in two forms: documentary and field (expert questionnaire). The statistical population of the study is 16 Rural managers of Divshal rural district. Using DEMATEL and ANP techniques, the obtained data were analyzed. According to the results of the ANP method, the sub-criteria of respecting the privacy of roads and preserving indigenous and cultural identity in new constructions have the greatest impact on the resilience of the study area. Also in the quality index of passages, sub-criteria of observing the privacy of passages; In the index of quality of access to services and infrastructure, quality of access to welfare and administrative services; In the shelter index, the existence of a strong public shelter in critical situations; In the index of quality of housing and constructions, preservation of indigenous and cultural identity in new constructions; In the environmental quality index, waste collection and disposal; In land use quality index, proper location of land uses; In the context of institutions, the existence of administrative organizations and institutions to help people; And in the Index of Institutional Relations, the cooperation of institutions in facilitating laws and giving credit to the people has the greatest effect on physical-environmental and institutional resilience.

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Introduction: Assessing the vulnerability and pollution of the aquifer is necessary for the management, development and allocation of land use, quality monitoring, prevention and protection of groundwater pollution. The purpose of this research is to identify and analyze the qualitative vulnerability of the Mashhad plain aquifer in order to monitor and manage underground water resources and prevent its future pollution.
Methodology: Mashhad plain is located in the northeast of Iran between Binaloud and Hezarmasjed mountains and in the watershed of the Kasfroud river, and its area is 2527 square kilometers. In this research, the vulnerability of the Mashhad Plain aquifer was evaluated with DRASTIC and SI models, and ArcGIS was used to analyze the parameters and prepare the vulnerability map. DRASTIC model is one of the overlap and index methods. In this method, the seven measurable parameters for the hydrogeological system include the depth of the groundwater level(D), net recharge(R), aquifer environment(A), soil environment(S), topography(T), Impact of the unsaturated Zone(I) and hydraulic conductivity(C) is used. The ratings for the sub-layers of each criterion vary from one to ten depending on their impact on the vulnerability potential. In SI method, five parameters of groundwater depth(D), net recharge (R), aquifer lithology(A), topography(T) and landuse(LU) are used for aquifer vulnerability. After preparing the SI model layers and weighting each of the layer classes using the functions available in the ArcGIS, the sensitivity index is obtained from the weighted sum of the mentioned parameters.
Conclusion: Study area is divided into four zones with very low vulnerability(21.85%), low(32.09%), medium to low(31.05%) and medium to high vulnerability(14.59%). Also, based on the results of the SI model, the study area is divided into five areas with very low vulnerability(0.4%), low(24.63%), medium to low(23.98%), medium to high(18.71%) and high vulnerability(32.25%). In general, the vulnerability of the aquifer increases from the southeast to the northwest.For verification, statistical method and calculation of correlation coefficient between vulnerability maps and TDS layer was used in TerrSet software and the results showed that both DRASTIC and SI models have high accuracy in zoning the vulnerability of Mashhad plain aquifer, so that the correlation coefficient of vulnerability maps with index The quality of TDS in Drastic model is (0.996) and in SI model (0.995); Therefore, the results of the following research can be used in environmental assessments and analysis of various pollutions and can be used as a basis for management decisions.

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Reducing the emissions caused by deforestation and forest degradation REDD is a strategy to moderate climate change, which is used to reduce the intensity of deforestation and greenhouse gas emissions in developing countries. In the last few decades, drastic changes in land use have caused a significant decrease in Hyrkan forests located in Mazandaran province. For this purpose, the aim of this study is to investigate the changes in land use and its prediction for the year 2050 using the Markov chain and the REDD project to reduce carbon dioxide emissions for the cities of Nowshahr and Chalus. Using the images of TM and ETM⁺ sensors of Landsat satellite, a land use map has been prepared in three time periods related to the years 1989, 2000 and 2021. Maximum likelihood method was used to classify images from supervised classification. From the error matrix, the Kappa coefficient in this evaluation was equal to 0.83 for 1989, 0.81 for 2000, and 0.92 for 2021. The results show that the forest cover decreases in 2050. In contrast, the area of ​​range land, city, barren land, agriculture and wetland will increase. Based on the goals of the REDD project, the amount of carbon dioxide emissions was calculated until 2050. If the REDD project is not implemented, a large area of ​​forest cover will be destroyed and a lot of carbon dioxide is released. The amount of carbon dioxide in the project area in 2021 is 49,681 tons and will reach 806,732 tons by 2051, and with the implementation of the REDD project in the region, this amount of gas can be increased to the equivalent of 402,321 tons. 404411 tons of carbon dioxide was prevented from entering the upper atmosphere of the earth. Examining changes using satellite images can help managers and planners to make more informed decisions.
 

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Objective: Investigating the occurrence of land subsidence in the country and the extent to which rural settlements are exposed to the phenomenon of land subsidence.
Methods: The present study was based on library surveys and studies, field observations and impressions. Using information received from the National Mapping Organization, a map of the country's subsidence zones and the degree of risk of each zone, including very low, low, medium, high and very high risk, was drawn, and the aforementioned maps were compared with the location of the villages.
Results: According to the analysis of the available data, 302 villages are at very high risk, 768 villages are at high risk, 834 villages are in the medium risk zone, and 573 villages are in the low risk zone. In terms of percentage weight, about 4 percent of the country's villages are at medium to very high risk of subsidence, of which 1,904 villages are at medium to very high risk, and 573 villages are at low risk.
Conclusions: The highest provincial distribution of villages at risk of subsidence in the country with a very high degree is in the provinces of Alborz, Tehran, Khorasan Razavi, Qazvin, Kerman, Golestan and Hamedan, and the highest provincial distribution with a high degree is in the provinces of West Azerbaijan, Isfahan, Alborz, Tehran, Khorasan Razavi, Semnan, Qazvin, Kerman, Golestan, East Azerbaijan, Hamedan and Yazd. Also, the highest provincial distribution of villages at risk of medium-level subsidence is in the provinces of East and West Azerbaijan, Isfahan, Alborz, Tehran, Semnan, Qazvin, Kerman, Golestan, Mazandaran, Markazi, Hormozgan, Hamedan, and Yazd.

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Abstract
Objective:The Kabul Basin is one of the most vulnerable regions in Afghanistan due to the frequency of heavy rainfalls and devastating floods. This study aims to identify heavy rainfall events (above 20 mm) and analyze their synoptic mechanisms, focusing on their causes and patterns.
Methods:The study uses a descriptive-analytical approach based on daily rainfall data from 18 hydrometeorological stations in the Kabul Basin over the statistical period of 2008 to 2022. Heavy and flooding rainfall events were identified using the environmental-circulation method. Cluster analysis was conducted using Ward’s hierarchical clustering technique, and GrADS software was employed to extract and interpret synoptic maps.
Results:The analysis revealed three main synoptic circulation patterns responsible for heavy rainfalls in the basin. Three representative days were selected for detailed analysis: March 23, 2009 (31 mm rainfall at Qala-e-Malak), March 17, 2014 (59 mm at Bagh-e-Umumi), and February 5, 2017 (60 mm at Qala-e-Malak). These events were associated with Mediterranean troughs, cold Siberian air intrusions, and Indian anticyclone influence, which collectively intensified rainfall. The findings suggest that these systems can be monitored in advance for early warning.
Conclusions:Heavy and flooding rainfalls in the Kabul Basin are strongly influenced by specific synoptic systems and atmospheric interactions. Recognizing these patterns enables early detection of risk and can improve the efficiency of disaster preparedness, water resource management, and regional warning systems. This study provides valuable insight for reducing vulnerabilities and mitigating the impacts of extreme weather events in the region.

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Climate change stands out as one of the most pressing environmental challenges of the modern era, exerting profound impacts on aquatic ecosystems—particularly wetlands. This study investigates the influence of climate change on three wetlands in Chaldoran County, West Azerbaijan Province—Pir-Ahmadkandi, Naver, and Zavieh-ye Sofla—spanning the period from 1984 to 2023. To achieve this, climate data were obtained from the TerraClimate database and CMIP6 model outputs under four emission scenarios. Landsat and Sentinel-2 satellite imagery, along with JRC/GSW data, were processed to evaluate changes in wetland surface areas. Annual wetland extents were extracted and compared against climatic parameters (temperature, precipitation, actual evapotranspiration, and snow water equivalent) using time-series analysis, Pearson correlation, and multivariate regression. Additionally, the Delta Method was employed for downscaled climate data to project possible trends over the next 20 years.
The results indicate that rising temperatures and evapotranspiration constitute the primary drivers of wetland shrinkage. Pir-Ahmadkandi and Naver have lost over 27% and around 20% of their surface area, respectively, whereas Zavieh-ye Sofla exhibits an irregular, seasonal reduction due to human interventions and agricultural runoff. Projections suggest that wetland surfaces—especially in Pir-Ahmadkandi and Naver—will continue to decline, potentially exacerbating drought conditions, diminishing biodiversity, and reducing water quality. These findings underscore the necessity of implementing sustainable water resource policies, controlling evaporation, and incorporating human impact assessments into conservation measures. Moreover, harnessing advanced hydrological modeling techniques and integrating remote sensing data with machine learning approaches may offer more effective strategies for safeguarding these vital wetland ecosystems.
 

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Objective: During a landscape, it is not facile to discriminate land parts that have dissimilar amounts and types of vegetation. Plant Ecological Units (PEUs) are known as management units and are a reflection of the management actions and natural disturbances in the region. This research aims to fuse different resolutions of satellite images to increase the PEUs classification accuracy.
Methods: For this purpose, the Marjan-Borujen watershed in Chaharmahal va Bakhtiari province was selected. After field monitoring and surveys, four dominant PEUs groups were identified in the study area. In this study, bands from the Landsat_8 satellite images with 30 m  spatial resolution (bands 7_2) and a 15 m panchromatic band (band 8) were used, as well as the Sentinel_2 satellite images including panchromatic bands (8, 4, 2, 3) with 10 m spatial resolution. First step, using the Landsat panchromatic band, the 30-m bands were upgraded to 15 m through the pen-sharpening process; so the 15 m  data set was prepared from the Landsat_8 satellite. Then, to increase the spatial resolution of the 15-meter data set to 10 m, the Sentinel_2 panchromatic bands were used. In this way, the Sentinel_2 panchromatic bands were geometrically matched with the Landsat_8 15 m data set, and the Co-Registration process was performed with the minimum RMSE(0.05). Finally,  two data sets (2 to 8 bands) of the Landsat_8 satellite images with 15 m and 10 m spatial resolution, the PEUs classification maps were prepared using the RF classification algorithm, and the maps' accuracy was displayed as an error matrix.
Results: The results show that increasing the spatial resolution significantly enhances the accuracy of PEUs classification maps. The 15 m set shows an overall classification map accuracy of 66%, while increasing the spatial resolution to 10 m enhances the overall accuracy to 82%. As well as, the error matrix results show that the classification map procured from the 10 m set, all four PEUs groups have improved the producer accuracy, user accuracy, and kappa agreement index. So, in this map, PEU 2 and PEU 3 have the highest kappa agreement coefficient (83 percent).
Conclusions: This study shows that using the Gram-Schmidt fusion algorithm and consequently increasing the spatial resolution of Landsat 8 images from 30 m to 10 m reduces mixed pixels and increases pure pixels, which in turn improves the quality of PEU classification maps.
 

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Objective: This study aims to analyze greenhouse gas variations across Iran and to identify the gases that exert the greatest influence on their overall dynamics. The findings enhance understanding of atmospheric pollution patterns and support the development of effective mitigation strategies. These results provide a scientific basis for climate-change mitigation planning in Iran. The study relies on satellite-based remote sensing datasets.
Methods: This study analyzes the temporal and spatial variations of major greenhouse gases including carbon monoxide, nitrogen dioxide, ozone, water vapor, and methane across Iran from 2019 to 2024. Sentinel-5P satellite data were extracted via the Google Earth Engine platform, and after filtering and removing low-quality observations, the data were standardized using the Z-Score method to enhance comparability and correlation analysis. Principal Component Analysis (PCA) was applied to reduce data dimensionality and identify dominant variation patterns. Temporal and spatial trends were then quantified using complementary statistical techniques.
Results:
Methane exhibited a consistent increasing trend from late 2021 through 2024 and accounted for the largest share of total variance (R² = 0.87), likely reflecting intensified anthropogenic activities and regional climatic shifts. CO, NO₂, and O₃ were mainly affected by seasonal fluctuations and nonlinear factors, and no clear long-term increasing or decreasing trends were observed. Water vapor showed a direct relationship with temperature variations, water sources, and atmospheric patterns, with its lowest concentrations recorded during the cold months and increases observed in the warm months. PCA analysis indicated that the first two principal components explained more than 70% of the total data variance, with CH₄, O₃, and NO₂ contributing the most to the overall variations.
Conclusions: The study results indicated that greenhouse gas variations in Iran are simultaneously influenced by natural factors and human activities. The combination of satellite data, statistical analysis, and PCA enabled a precise assessment of the temporal and spatial trends of greenhouse gases, providing valuable information for planning pollutant reduction and developing strategies to combat climate change.



 

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This study investigates the spatio-temporal dynamics of evapotranspiration (ET) and its modulation by biophysical variables and land use/land cover (LULC) changes in the Karun River Basin, southwestern Iran, from 2000 to 2023. The basin, spanning 67,257 km² and characterized by diverse topography, experiences significant annual water loss (72% of 413 billion m³ national precipitation) due to ET, leading to salt and sediment accumulation. Data from MODIS products (MCD12Q1, MOD13A1, MCD43A3, MOD11A2, MOD16A3, CHIRPS) provided land cover, NDVI, albedo, LST, precipitation, and ET at 500-meter resolution, supplemented by Landsat imagery (30-meter resolution) for validation. Multiple regression and Geographically Weighted Regression (GWR) analyses revealed a 39.5% ET increase (31.48 to 43.92 mm/year), a 32.78% NDVI rise (0.18 to 0.239), and a 16.35% LST decrease (33.52°C to 28.05°C), correlated with a 6.90% agricultural decline (6,939,225 to 6,460,335 ha), a 6.94% rangeland increase (3,840,375 to 4,106,780 ha), and a 42.76% forest expansion (156,000 to 222,700 ha). GWR (AdjR² > 0.97, peak 0.9887 in 2010) identified spatial non-stationarity, with overprediction in mountainous northeast regions and underprediction in agricultural southwest plains, reflecting LULC influences. Landsat-derived false color composites and classifications (overall accuracy 85–90%, Kappa 0.85–0.90) validated a 2,477 km² forest loss to high-ET rangelands/agriculture, driving warm-season ET elevation. Results emphasize the need for integrated hydrological models incorporating irrigation data and high-resolution analyses to enhance sustainable water management in this water-stressed region.

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This study employs the Analytic Hierarchy Process (AHP) to assess the vulnerability and resilience of the urban water distribution network in the Feyzabad and Baharan districts of Sanandaj against the parameter of Peak Ground Velocity (PGV). The main objective is to identify the key factors influencing network vulnerability and to propose strategies for enhancing the resilience of this critical infrastructure. PGV values were derived based on data from 40 faults longer than 10 km within a 70 km radius of the city, using empirical attenuation relationships. Geological, geomorphological, soil type, and pipe diameter and material data were collected from reliable local sources.In the AHP model, the main criteria including PGV, geology, soil, pipe material, and pipe diameter were integrated with weights of 0.460, 0.112, 0.243, and 0.182, respectively, and vulnerability maps of the network were generated. Results showed that PGV values across the city range between 35 and 39 cm/s. In Feyzabad, lower PGV values combined with thick steel pipes and Quaternary alluvial soils resulted in 81% of the network falling into the low-vulnerability class and only 2.1% into the high-vulnerability class. Conversely, in Baharan, higher PGV values (39 cm/s), combined with small-diameter asbestos pipes and shale bedrock, placed 34% of the network in the very high-vulnerability class.

                               

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Objective: Over the past two decades, land subsidence has emerged as a significant geomorphological hazard and one of the most critical environmental crises in Iran, causing irreversible damage to many plains each year. Among its primary current causes is the excessive and unregulated extraction of groundwater. The Eshtehard Plain, recognized as one of the industrial and agricultural hubs of Alborz province, is no exception. Due to severe groundwater depletion, it has been officially declared a critical zone by the Ministry of Energy. The objective of this study is to model the risk of land subsidence in this plain using the Random Forest algorithm and to analyze the contributing factors influencing its occurrence
Methods: In this study, twelve independent spatial layers were utilized, including: digital elevation model (DEM), distance to rivers, distance to qanats, distance to wells, distance to faults, groundwater depth, drainage density, soil type, lithology, land use, topographic wetness index (TWI), and solar radiation. The dependent layer consisted of subsidence zones. The Random Forest model was implemented in the R software environment. Two key importance measures—Mean Decrease Accuracy and Mean Decrease Gini—were employed to rank, assess the significance of, and assign weights to the contributing factors of land subsidence. Finally, model performance was evaluated using three complementary metrics: Accuracy, Kappa, and AUCResults: The results demonstrated that the Random Forest model achieved high accuracy in classifying land subsidence risk. Model evaluation showed strong performance with an overall accuracy of 0.963, a Kappa coefficient of 0.611, and an AUC value of 0.955, indicating that the model is highly effective for spatial risk zoning of land subsidence. The most influential variables in subsidence occurrence were identified as groundwater depth, distance to wells, geology, and land use. Furthermore, more than 65% of the study area was categorized as high-risk and very high-risk, reflecting the critical condition of the Eshtehard Plain. Notably, the share of urban land use has shown a steady increase from 2011 to 2023, with a significant spike in 2023, where increased population concentration has placed additional pressure on groundwater resources, leading to an intensification of subsidence in affected areas
Conclusions: The Random Forest algorithm successfully modeled the spatial distribution of land subsidence risk with high accuracy. This method can serve as an effective tool for informed decision-making in groundwater resource management, sustainable development planning, and hazard mitigation in similar regions.

 

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Dust storms rank among the most significant natural hazards in the world’s arid and semi-arid regions, inflicting irreparable damage across multiple sectors each year. Given the rising frequency of dust storms in Kerman Province and other desert and arid areas of Iran, it is imperative to undertake a study aimed at identifying the synoptic patterns that precipitate dust events and at determining their source regions as well as their transport and dispersion pathways. In this research, the conditions and origins of dust storm formation over the 2000–2023 period were examined using synoptic and remote-sensing methods. The HYSPLIT model was applied to track airflow trajectories, and factor analysis together with cluster analysis were used to identify the synoptic patterns responsible for dust generation. Finally, the principal source regions of dust were delineated.
The results revealed that 63% of the province’s dust storms originate from domestic sources, whereas 37% originate from other areas. Three main atmospheric patterns were identified as drivers of dust activity in Kerman Province:
1. The co-advection of simultaneous low-pressure and high-pressure systems;
2. A lower-tropospheric cutoff low pressure in conjunction with the Siberian high;
3. A pressure-gradient regime featuring a core of elevated wind speeds.
Modeling of transport and dispersion pathways indicated that 60% of externally sourced dust is advected from the Arabian Peninsula, while 55% of dust emitted disperses southward, impacting the Makran coast and the Sea of Oman. Analysis of source regions further showed that the desert areas of Saudi Arabia, Iraq, Syria, and Jordan, as well as those of North Africa, together with internal sources such as the dried Jazmourian wetland, the Lut Desert, the Hamun region, and the Tabas Desert, contribute most substantially to the dust events observed in Kerman Province.
 

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Land use is one of the most important aspects of studying natural resources management and reviewing environmental changes, and studying it is also very important in understanding the microclimate of urban areas. Therefore, according to the importance of the topic in this research, the spatial pattern of land use changes and surface temperature in Bukan city in the statistical period of 1990-2020 using Landsat satellite images and sensors (OLI-TIRS, ETM+, TM) and the separate window algorithm. was evaluated. The results showed that the land use of the area has changed a lot during the period under review, so that the residential use has increased and the agricultural use has decreased. The results of the survey of the earth's surface temperature also showed that in 1990, the highest temperature was related to pasture areas and barren lands with a temperature between 32 and 40 degrees Celsius and the lowest temperature was related to areas with dense vegetation with a temperature between 15 and 20 degrees. It is Celsius. The temperature in residential and urban areas varies between 28 and 31 degrees Celsius. In 2020, the average temperature of pasture use was 35 degrees Celsius, residential use was 30 degrees Celsius, and garden and agricultural land was 14 and 24 degrees Celsius, which, apart from pasture use, which did not change significantly, other studied uses increased. They show a temperature of 2 to 4 degrees Celsius compared to 1990. The examination of the temperature in relation to the land use changes showed that there is a high correlation between the land cover and the surface temperature of the land, so that in some of the sampled places, it showed that the change in the use of gardens Residential use or pasture has caused an increase of 15 to 20 degrees Celsius in the temperature of the earth's surface in these areas. Based on the results of land use and overall vegetation, it has an indirect and strong relationship with the surface temperature of the earth, and with the increase in the area of residential and barren lands and the decrease of vegetation and agricultural lands, the surface temperature of the earth will increase.