Behnaz Attaeian; Ali Badrestani; Saeid Khosrobeigi Bozchelui; Mohammad Mehdi Artimani
Abstract
Soil organic carbon as a key factor in soil stability and fertility is considered as one of the important environmental challenges in the context of climate change. The aim of this study was to determine soil organic carbon zonation in Gonbad paired-watershed, Hamedan province. In this research, the ...
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Soil organic carbon as a key factor in soil stability and fertility is considered as one of the important environmental challenges in the context of climate change. The aim of this study was to determine soil organic carbon zonation in Gonbad paired-watershed, Hamedan province. In this research, the information of meteorology, soil science and erosion and sedimentation study of Gombad watershed was used, including the information of 49 profiles in the 0-15 cm soil layer. After collecting data, tests of normality (Shapiro-Wilkα test <0.05), homogeneity of variance, and then the relationship between independent variables and organic carbon were performed using Pearson's linear correlation in SAS software. Also, determining the most effective independent variable using multivariate analysis, PCA factor analysis was used in XlStat 2.1 software. In order to determine the distribution and amount of soil organic carbon in the Gonbad representative watershed, modeling using SVM support vector machine learning algorithms and RF random forest was used in R software.The results showed that 78.18% of soil organic carbon changes depend on four components. Clay and nitrogen percentage were selected as the most effective variables on soil organic carbon content, so that the first component of clay content explained 34% and the second component nitrogen explained 18% of variations. According to the results of the implementation of the SVM and RF Models, the SVM model with a CE factor of 0.86 and RMSE of 0.05 in the test stage is a more accurate model in this study.
Mehdi Ghorbani; Seyed Amirhossein Garakani; Mina Hamidi; Sajad Amiri; Majid Rahimi
Abstract
Social capital is defined as a set of relationships, networks, trust, needs, and participatory systems within a society that enables it to confront challenges and improve the living conditions of its members. This study was conducted to analyze the intra-group social capital in three villages: Eskelabad, ...
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Social capital is defined as a set of relationships, networks, trust, needs, and participatory systems within a society that enables it to confront challenges and improve the living conditions of its members. This study was conducted to analyze the intra-group social capital in three villages: Eskelabad, Eslamabad Kalleh-ye Espid, and Chah-e Ahmad in Tafatan city. Given the importance of participatory management and strengthening social capital, analyzing the link between trust and participation among individuals in these villages is essential. The network analysis method was used to examine the links of trust and participation among individuals and villages of interest. The results before the project implementation showed low levels of trust, participation, cohesion, and social capital. However, after the project implementation, these indicators improved, and trust, participation, and the speed of interaction among individuals increased. The project implementation has led to increased unity and cohesion among village residents, increasing the intra-group social capital. In other words, improved communications have led to increased social welfare and the expansion of trust and participation among the residents.
Leila Biabani; Hassan Khosravi; Gholamreza Zehtabian; Esmaeil Heydari Alamdarloo; Behzad Raygani
Abstract
Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. Wind erosion is a significant cause of ...
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Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. Wind erosion is a significant cause of land degradation and desertification, negatively impacting the economy, society, and environment, particularly in arid and semi-arid regions. To control and reduce the effects of wind erosion, the first step is to identify sensitive areas. The aim of this research is to identify areas susceptible to wind erosion using the ILSWE model in the watershed regions of the Dar Anjir-Saghand Desert, Namak Lake, and Sefidrud. This model has been calculated by combining five erosion indices, including climate erosivity, soil erodibility, surface crust, vegetation cover, and surface roughness. The model's results indicate that climatic factors, such as precipitation, evaporation, and wind, vary across these regions. These factors play a significant role in determining areas that are susceptible to wind erosion. It is noted that other factors, including differences in vegetation cover, soil characteristics, topographic conditions, and the extent of bare lands, salt marshes, sandy dunes, low-density pastures, and rainfed agricultural lands, have also influenced the results of this. In general, this study presents a new method for identifying wind erosion-sensitive areas in various climates. This method can prioritize regions that require further research and corrective measures.
Mohammad Gholami; Majid Kazemzadeh
Abstract
The inappropriate utilization of water resources has led to water shortages and numerous challenges for humanity. Assessing water scarcity can significantly contribute to sustainable water resource management. The Water Poverty Index (WPI), as a composite tool, assesses the factors affecting the water ...
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The inappropriate utilization of water resources has led to water shortages and numerous challenges for humanity. Assessing water scarcity can significantly contribute to sustainable water resource management. The Water Poverty Index (WPI), as a composite tool, assesses the factors affecting the water condition in a specific area using five critical criteria: Resources, Access, Capacity, Use, and Environment. in the present study, Razavi Khorasan province was divided into 22 study areas, then the water poverty index was used to assess the water scarcity in each of them. WPI results revealed that the Darghz study area scored 50.63, indicating the best water condition, while the Kashmir study area, Red Mountain, and Khalilabad had the worst water conditions, scoring 20/20 compared to other regions. The average of WPI for the entire province was 34.41, indicating an unfavorable state of water scarcity in this region. The average values for the five criteria—resources, access, capacity, consumption, and environment—across the entire province were 19.41, 36.35, 31.23, 38.06, and 47.05, respectively, that according to Use and Resource criteria, water utilization in this province exceeds available water resources by a factor of 1.96. According to this, overexploitation of water resources, particularly in the agricultural sector, and neglecting sustainable development and resilience thresholds of natural ecosystems were identified as significant management strategic mistakes contributing to water scarcity in this province. Therefore, make a balance between water utilization and available resources, allocating a portion of water for the natural ecosystems requirements, and reducing economic reliance on agriculture, can serves as a roadmap for water resources management to effective water resource management and prevent further deterioration of the current situation.
soroush namjoofar; Ahmad Nohegar; Zeinab Sazvar
Abstract
Horul Azim Wetland is located in the southwestern Iran in Khuzestan Province, has 1180 km2. About 70% of the wetland is located in Iraq, which has faced the challenges of drought and oil drilling, and many of its southern lands have dried up and become dust center. The present study was conducted with ...
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Horul Azim Wetland is located in the southwestern Iran in Khuzestan Province, has 1180 km2. About 70% of the wetland is located in Iraq, which has faced the challenges of drought and oil drilling, and many of its southern lands have dried up and become dust center. The present study was conducted with the aim of assessing the levels of heavy metal pollution in the dried part of this wetland. At first, 15 stations were identified on the LANDSAT image and the dried soil of the wetland was sampled by quadrat and the Cd, Cu, Pb, Fe, Mn and Ni were measured by ICP-MS device. To assess the pollution levels, Igeo, ER, PLI, RI, CSI and mCd were used. The results showed that the concentration of Cd, Cu, Ni and Pb is higher than the shale standard. According to the Igeo, Cd pollution is high (moderate to severe). According to the PI, Cd and Pb pollution are high and moderate, respectively. The ER shows that the ecological pollution of Cd is significant, but other metals don't have ecological risk. The average of the cumulative indices ER and PLI were 193 and 9.7, respectively, indicating a possible ecological risk for all points. The CSI pollution safety for Cd, Cu, Ni and Pb was 0.52, 0.25, 4.02 and 0.34, and the mCd index was 0.27, 10.4, 13.9 and 8.9, respectively, indicating a very high severity of pollution safety for Ni and Cd.
Zainab Afzali; Somayeh Amirtaimoori; Mohammad Reza Zare Mehrjerdi
Abstract
Rangelands play an important role in the sustainability of the ecosystem and providing financial support to many people in the world. Rangelands destruction has caused many concerns at the global level. The results of the studies show that the level of rangelands, rangeland productions and the satisfaction ...
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Rangelands play an important role in the sustainability of the ecosystem and providing financial support to many people in the world. Rangelands destruction has caused many concerns at the global level. The results of the studies show that the level of rangelands, rangeland productions and the satisfaction of rangeland users have decreased in Iran. Therefore, the purpose of this research is the pathology and complications of range management policies in Iran towards sustainable exploitation of rangelands. This research is applicable in terms of nature and purpose and mixed (qualitative-quantitative) in terms of research strategy. In the qualitative part, using the meta-synthesis method, the complications of pasture policy towards their sustainable use was determined, and then based on that, a research questionnaire was made and then based on the Delphi method, relying on the opinions of academic experts, for The significance of revealed complications was used. Also, in the qualitative part, articles related to the research topic in the last 15 years (2008-2023) were used, and the criteria for selecting sources was based on the theoretical saturation of the data. The statistical population of quantitative part was academic experts familiar with the subject and interested in cooperation, who formed the Delphi panel. In order to achieve the goal of the research, the opinions of 9 experts were used, and their selection was based on non-probability snowball sampling. The findings of the research indicate that 12 complications can be introduced as the main complications of range management policies in Iran.
Abdulmajed Bostani; Sharareh Pourebrahim; Afshin Danehkar
Abstract
Recognizing and mapping the sensitivity of forests to fires is crucial for the preservation of ecosystems and biodiversity. This study, utilizing the time-series capability of Landsat 8 satellite imagery and developing an efficient model within the Google Earth Engine (GEE) platform, managed to map the ...
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Recognizing and mapping the sensitivity of forests to fires is crucial for the preservation of ecosystems and biodiversity. This study, utilizing the time-series capability of Landsat 8 satellite imagery and developing an efficient model within the Google Earth Engine (GEE) platform, managed to map the sensitivity of Kurdistan province forests to fires over the the past decade, from 2013 to 2023, in two study areas located in the Marivan, Sarvabad, and Baneh counties. It provided valuable information for land use management and effective resource allocation to prevent and mitigate the impacts of forest fires in the Kurdistan region. The Normalized Burn Ratio (NBR) index was applied to pre- and post-fire season images to detect forest fires. To enhance classification results, areas such as vegetation, residential zones, and water bodies were highlighted as non-fire regions. The Random Forest (RF) model within the GEE platform was employed to achieve the highest classification accuracy. Appropriate training samples were derived from the highlighted results, and image classification using the RF model with 50 decision trees was performed on the GEE platform.To ensure the reliability of the selected training samples, the fire mapping results were compared with point-based fire data from the Kurdistan Province Natural Resources Department. The classification results for the two forest study areas- Marivan and Sarvabad regions in 2016, 2018, and 2020, and the Baneh region in 2018-demonstrated an overall accuracy of 99% and a Kappa coefficient of 0.97. The findings of this study underscore the capability of Landsat 8 imagery in mapping forest fire susceptibility and confirm the acceptable accuracy of the RF model in this context.
Farzaneh Parsaie; Ahmad Farrokhian Firouzi; Masoud Davari; Ruhollah Taghizadeh-Mehrjardi
Abstract
Mechanical properties of soil, such as shear strength and penetration resistance, play a crucial role in optimizing crop productivity and proper soil management. The objective of the research was to produce digital map of soil shear strength and penetration resistance in Kielaneh watershed, located in ...
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Mechanical properties of soil, such as shear strength and penetration resistance, play a crucial role in optimizing crop productivity and proper soil management. The objective of the research was to produce digital map of soil shear strength and penetration resistance in Kielaneh watershed, located in Kurdistan Province, covering an area of 12,000 hectares using Gradient Boosted Decision Trees (XGBoost), Random Forest (RF), and k-Nearest Neighbors (KNN). Soil penetration resistance and shear strength were measured using handheld penetrometers and vane shear devices at 150 observation points from the surface soil layer (0 to 10 centimeters). Spectral data and auxiliary variables derived from the Digital Elevation Model and Sentinel-2 satellite images were used to predict soil shear strength and penetration resistance. These variables include CHND, VD, RSP, CHNBL, Brightness, WE, NDVI, Band12, Greenness, PLC, as well as soil parameters such as organic matter, calcium carbonate, bulk density, geometric mean particle size, soil texture (percentages of clay, sand, silt), and visible near-infrared spectral data as latent variable (LT), representing soil formation factors. The results showed that the XGBoost had higher accuracy compared to other models for predicting shear strength in surface soil layer with an (R2) of 0.61 and an nRMSE of 0.16, as well as for predicting penetration resistance in the surface soil layer with an (R2) of 0.60 and an nRMSE of 0.11. In conclusion, the XGBoost model, using spectral data along with topographic variables and soil parameters, was able to estimate the spatial variability of soil mechanical properties with acceptable accuracy in the study area. The generated maps can be used to make necessary management decisions regarding of the region.
