land use

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.

Characterized by 25% global biodiversity, the soil is known as the unique substrate sustaining Earth's inhabitants. On the other hand, soil erosion caused by water is a primary factor in land degradation. Approximately 75 billion tons of soil and living organisms are removed from soil ecosystems annually due to soil degradation and erosion. Moreover, soil erosion may endanger the quality and health of soil and water. The primary determinants of water erosion are static topographical features such as steepness and slope direction, and dynamic climatic factors, including precipitation and wind, which may cause changes in soil’s hydrological processes, indirectly or indirectly affecting other environmental factors. The main point concerning the initiation of water erosion is the impact of raindrops on the soil surface. However, processes affected by the raindrops from the time of descent to the impact on the soil surface have been underresearched, neglecting the influential role of raindrops in creating and changing the behavior of water erosion. Therefore, this study was conducted on the soil collected from the Kojour Watershed in Mazandaran Province, Iran, mainly focusing on the interaction of rain and wind with interrill erosion under laboratory conditions.

The soil samples, especially those collected from the summer rangelands of the Northern Alborz Range of Kojur Watershed, Mazandaran Province, Iran, were transported to the Rainfall and Erosion Simulation Laboratory of the Faculty of Natural Resources, Tarbiat Modares University. For this o this end, three laboratory plots with dimensions of 0.5 × 1 × 6 m were used at a slope of about 30%. Moreover, the intensity of rainfall was approximately 50 mm h-1 with a duration of 30 min under no-wind control conditions and two wind velocities of 3 and 6 m s-1 at a slope of 30%. The wind direction of the study area was also considered, opposite the slope (from bottom to top of the plot), as it mainly occurred during the storms in the study region. Furthermore, the wind velocity generated during the experiments was closely monitored using an anemometer to ensure accuracy and consistency throughout the study. Finally, the effectiveness of the interrill erosion process caused by wind-affected rain was demonstrated through statistical comparisons against the control plot.

According to the results of the study, the mean runoff start time varied significantly based on wind velocity. In this regard, the runoff started after approximately 4:19 minutes under calm conditions. Moreover, when wind velocities increased to 3 m s-1, the mean runoff start time was delayed to 7:28 minutes, and the delay extended further to 10:16 minutes at a wind velocity of 6 m s-1. The aforementioned results indicated that the mean runoff volume was influenced by wind velocity. On the other hand, the mean runoff volume was found to be 114.95 l under wind-less conditions. Accordingly, as wind velocity increased to 3 m s-1, the mean runoff volume rose slightly to 123.89 l. However, at a wind velocity of 6 m s-1, the runoff volume slightly decreased to 120.59 l. The runoff coefficients were also found to be 108.33, 114.20 and, 111.27%, respectively.Furthermore, the study found that the mean soil loss increased under higher wind velocities. On the other hand, the mean soil loss was reported as 1933.88 g under calm conditions. In this regard, when the wind velocity increased to 3 m s-1, the soil lossrose to 2107.25 g, and it further escalated to 2858.58 in cases where the velocity reached 6 m s-1. The sediment concentrations were calculated as 142.20 g l-1 under calm conditions, reaching 212.69 g l-1 and 410.13 g l-1 at wind velocities of 3 m s-1 and 6 m s-1, respectively. These results suggest a progressive increase in sediment concentration with rising wind velocities, underscoring the significant influence of wind-induced turbulence on sediment mobilization and transport in runoff. Moreover, the analysis of the results revealed that wind velocity exerted a statistically significant influence on runoff and interrill erosion components at the 5% significance level.

The results of this study confirm that wind-driven rain significantly influences the components of runoff and soil loss. These findings can be a game-changer, providing valuable insights for accurately modeling the water erosion process under natural circumstances. Furthermore, the findings can significantly help executive department managers estimate soil erosion caused by wind storms, thereby fostering sound management and prioritization of erosion-prone areas and soil and water conservation measures.

Analyzing the morphological patterns of rivers is crucial for better comprehension of the rivers’ current status and their potential for prospective alterations, thereby enabling effective river management. Meandering is one of the most important river issues that influences human activities along riverbanks. Therefore, this study sought to prepare the map of meander formation-prone areas using Kernel Logistic Regression (KLR), Bagging Kernel Logistic Regression (BKLR), and Support Vector Machine (SVM) models in the northern part of Khorramabad River, Lorestan Province.

The data used in this study included the land use data extracted from Sentinel-2 satellite images, digital elevation model, slope and aspect, geology, and lithology, distance from the fault, distance from the road, topographic wetness index, and some geometric attributes such as topographic position index, profile curvature index, plain curvature index, and terrain ruggedness index, together with the data obtained from field surveys. To this end, a Sentinel-2 satellite image of the study area taken on 15/05/2021 with less than 10% cloud cover and no geometric errors was extracted from the European Space Agency website. Moreover, pre-processing (geometric and radiometric) of the image was performed in ENVI5.3 software and SNAP software, which are specifically designed for processing Sentinel satellite images. Then, atmospheric correction was applied using the Sen2Cor plugin in SNAP software. Subsequently, land use was estimated using a supervised maximum likelihood classification method based on pixel-level training samples. To study meandering in the northern part of the Khorramabad River, a 200-meter buffer zone was considered based on river topography, agricultural lands, access network, and other relevant factors. All input layers in Arc GIS and SAGA-GIS were clipped based on the 200-meter buffer map and entered into the model. On the other hand, 70% of the total data collected for meandering analysis was used for training the model and the remaining 30% was used for testing the model. It should be noted that the training samples used in this study consisted of samples of meandered cases along the studied waterway. Finally, to compare the performance of different models and select the best one for generating meandering potential maps, RMSE, MAPE, NS, and CORR error metrics were used.

This study investigated the potential of the Khorramabad River for meandering and meander formation. To analyze different parts of the river, three main segments were designated, including the northern part consisting of kilometers 0 to 4, the middle part consisting the kilometers 4 to 8, and the southern part consisting the kilometers 8 to 14. The meandering potential was generally assessed in all three parts mentioned above. Accordingly, the highest level of meandering was observed in the middle and southern parts of the river. On the other hand, most of the meanders found in the northern part were well-developed and stable, as they are located in mountainous areas, thus being relatively tough and erosion-resistant. In the coastal zone, due to the soft and erodible nature of the sediments, meandering may expand, and meanders can develop into horseshoe shapes, an example of which can be observed in the middle part of the river, i.e., at kilometer 8.5. As the topographic position index along the river displays a concave pattern, this factor tends to promote meandering both in the longitudinal and lateral directions of the river. However, in areas with a convex pattern, the factor hinders meandering.The high numerical value of the topographic wetness index along the banks of the Khorramabad River which indicates high moisture and wetness conditions makes the banks susceptible to landslides and erosion, which in turn contributes to meandering.Moreover, it can be argued that the overall condition of convex curvature in Khorramabad River and its floodplain has contributed to a decrease in meandering, as the flow velocity is higher in convex bends compared to the concave ones, thereby acting as an inhibiting factor for meandering. While the curvature of the profile varies at nearly every pixel level, its values tend towards negative in parts where the river is located near ridges, indicating increased erosion. Conversely, positive values suggest increased sedimentation due to a decrease in flow velocity.Characterized by simpler conditions, the Kernel Logistic Regression Method examines areas with the potential for meandering, thus selecting a smaller and narrower extent for prediction. In contrast, the Support Vector Machine Method acts more strictlyand selects a broader range. However, the method is considered to be more accurate, as RMSE and MAPE evaluation statistics bear the minimum values of 0.15 and 0.20, respectively, while the NS and CORR statistics enjoy the maximum values of 0.83 and 0.85 compared to other methods, respectively.

Meandering in the river located north of Khorramabad is a process influenced by geomorphological and hydrological features, leading to agricultural land degradation, reduced production, and increased suspended sediment load. From a geomorphological perspective, this study can play a significant role in river engineering. Therefore, activities such as channel modification, shoreline protection, dredging, and sand mining cannot be carried out without knowledge of river engineering.

Remote sensing has become a valuable tool for acquiring integrated spatial data regarding land cover and land use across various temporal and spatial scales. One of the primary challenges in multi-temporal land cover and land use mapping is the availability and integrity of training data for supervised classification algorithms. Collecting training samples for each class of land cover and land use over different time periods can be time-consuming and, particularly in rapidly changing environments, fieldwork can be challenging. This issue is further exacerbated by the potential spectral and phenological changes in land cover features over time, which can diminish the transferability of training samples. The concept of "migration" or "transfer" of training samples from a reference year to target years has been explored as a means to overcome the limitations of training data. In this context, the use of Google Earth Engine (GEE) has facilitated multi-temporal land cover and land use mapping. GEE's ability to integrate diverse data sources, including Sentinel-2 imagery and a wide range of spectral indices, enables researchers to develop robust and scalable applications for land cover and land use classification. Monitoring changes in land use and cover over time is crucial for understanding and managing the environment. However, when there are limitations in training data for different time periods, this can pose significant challenges. This study presents an innovative approach to classify Sentinel-2 satellite images from different years using a set of reference training samples.

In this study, an innovative application was explored using migrated training samples from a reference year (Sentinel-2 imagery from 2019), along with bands from Sentinel-2 images and spectral indices, to classify land cover and land use in the dynamic and ecologically significant mangrove region (Khoran Protected Area). The use of machine learning algorithms within the Google Earth Engine (GEE) framework was examined to achieve high classification accuracy and monitor land cover changes over time. Satellite images from Sentinel-2 covering the study area for the target years 2022 and 2024, as well as the reference year 2019, were retrieved in GEE. Ground truth data, including the location and classification of various land cover types for the reference year, were collected using the European Space Agency's land use maps. Subsequently, high-quality ground truth data and their corresponding image samples from the reference year (2019) were transferred to the target year images using the Spectral Angle Distance (SAD) algorithm. Classification algorithms such as Random Forest (RF), Gradient Boosting Trees (GBT), and Classification and Regression Trees (CART) were employed to classify the target year images using the variable training samples. The classified images were evaluated using various accuracy metrics, including overall accuracy, Kappa coefficient, producer accuracy, user accuracy, as well as commission and omission errors. Finally, the importance of different spectral bands from Sentinel-2 and spectral indices in the classification process was analyzed to identify the most suitable features for distinguishing various phenomena in the study area.

The results of this study indicated that among the classification algorithms, the highest accuracy for overall accuracy and Kappa coefficient in the classified images for 2024 and 2022 was achieved using the Random Forest classification, with accuracies of 0.9104 and 0.8742, and Kappa values of 0.8955 and 0.8570, respectively. Additionally, the findings revealed that the area of mangrove forests decreased from 7530.74 hectares to 6546.51 hectares during the study period, representing a reduction of approximately 984.23 hectares, or about 164 hectares per year.The area of residential and construction zones increased from 72.52 hectares in 2019 to 96.48 hectares in 2024, indicating rapid growth in these land uses over the last two years. The synthesis and summary of various classification methods across different years highlighted the relative importance of bands and indices. Specifically, the EMVI and mNDWI indices demonstrated greater dominance due to their effectiveness in capturing the phenomena of mangrove forests and aquatic areas within the study region. Consequently, for the band combinations aimed at distinguishing various phenomena, the use of green, red, and near-infrared bands, along with the mangrove index and optimized water body index, were identified as the most suitable for the study area. These findings are recommended for similar areas in southern Iran and other mangrove regions. This research employed a simple and effective application by transferring ground truth points from the reference year to their corresponding images as training samples for the target year images, utilizing the Google Earth Engine platform. This approach holds the potential for extension to other regions.

In summary, this study demonstrates the potential of using migrated training samples and advanced machine learning algorithms such as Random Forest, Gradient Boosted Trees, and Classification and Regression Trees, along with spectral indices as auxiliary data, for the accurate classification of multi-temporal satellite imagery. The development of spatial tools, including the online Google Earth Engine (GEE) platform, is essential for the up-to-date management of land uses, particularly in wetland and mangrove areas. In this research, high-quality training samples were successfully migrated from the reference year to the target year, resulting in high classification accuracy using the Random Forest classification algorithm compared to other methods such as boosted regression or regression and classification trees. This method offers a viable solution in multi-temporal land use studies, especially in cases of insufficient or inadequate training samples within the GEE system. For future studies, it is recommended to employ a combination of Euclidean Distance (ED), Spectral Angle Distance (SAD), and K-means clustering for generating variable training samples, and to compare and analyze the classification results obtained through these methods. This approach presents a promising solution for producing up-to-date land use/land cover maps, even in challenging environments with limited training data. The findings of this study can guide future research aimed at monitoring land cover, managing, and effectively protecting valuable natural resources, such as mangrove forests, in other regions.

Desertification is a serious threat to both natural and human environments. UNCCD (1994) defines desertification as land degradation in arid, semi-arid and dry sub-humid areas that is caused by various factors, including climatic variations and human activities. Despite being an international definition, it does not address the decrease in biological diversity. Additionally, this process cannot be reversed and has expanded to other biological climates, including the European continent, resulting in deficiencies that require attention. The world's environment has become one of the most important concerns due to the changing land use since the last century (22, 29). The sustainability of the environment is threatened by many agricultural activities, even though agriculture is a vital factor in sustainable development and poverty reduction (19). The relationship between food security and basic resources such as water, soil, as well as natural resources and the environment is of particular importance (3, 9, 31). The expansion of desertification will occur when the productive capacity of land resources decreases. The objective of this research was to evaluate these two effects, namely Examining some criteria for agricultural development, as well as the key criteria for land degradation and desertification, and emphasizing the issue of food security in Balajam Plain, Torbat Jam County, in Khorasan Razavi province.

This study evaluated and analyzed positive criteria for agricultural development, including performance, production, employment, and investment. The Iranian Model of Desertification Potential Assessment (IMDPA) was used to assess desertification intensity based on key indicators such as climate, underground water, soil, agriculture and landuse changes criteria (2, 5, 33, 41). This research was carried out between 2011 and 2021. Finally, the outcomes obtained from the food security indicators supplied by FAO were compared and evaluated (16, 37, 40). No research has been conducted to assess the positive and negative aspects of agricultural development, with a focus on food security. This research was conducted in Balajam Plain in Torbat Jam county of Khorasan Razavi province.

The foundation of economic, social, cultural, and political security is food security. Food security is based on ecological security, which is the security of fundamental natural resources and genetic diversity. The agricultural development sector has seen an increase in investment in institutions and human resources from 2011 to 2021. Over the past 10 years, from 2011 to 2021, agriculture has developed in Balajam Plain of Torbat Jam County, and the amount of investment in this sector has also increased (Tables 4-7). The results showed that the area under cultivation of crops in the Balajam plain has increased from 2011 to 2021, reaching from 50.25km2 in 2011 to 77.76km2 in 2021 (Table 8). The statistics on agricultural and horticultural crops indicate that some crops have seen a decrease in production and yield, while others have seen an increase. The increase in saffron cultivation has resulted in an upward trend in the added value of the products, which indicates increased income and increased food security. The results showed that land use change, a decrease in soil quality indicators and an increase in drought have reduced groundwater in terms of has reduced qualitatively and quantitatively (Figures 2 and 5). According to the IMDPA model's findings, desertification intensity in the study area has increased from moderate to moderate and severe in 2016 and 2021 (Figure 6). The desertification in Balajam Plain increased in 2016 and 2021, and many areas of the study areas are now dealing with an increased desertification phenomenon. The intensity of desertification in the Balajam Plain has increased despite the development of the agricultural sector in the past 10 years.

FAO has emphasized the need to implement sustainable land management practices, food production, and prevent the expansion of desertification in order to achieve food security and agricultural development (12). Unsustainable agricultural development, climate change, soil degradation, and the reduction in the quantity and quality of groundwater resources are among the human factors affecting desertification (24). The results of some studies showed that agricultural development has improved food access and promoted food security (28), but the results of this study showed that despite agricultural development and increased investment in the Balajam Plain, desertification occurred between 2011 and 2021. The results of this study showed that land use changes to agricultural lands, soil degradation, quantitative and qualitative decline in groundwater, and climate change in the study area have caused desertification, and if current conditions continue, desertification will intensify. Although agricultural development has expanded between 2011 and 2021, the adverse consequences of desertification, land degradation, and the reduction of the ecological capacity of water and soil resources have not been addressed. Therefore, implementing sustainable agricultural development is one of the important strategies for sustainable land management according to FAO (12). In the years 2011 to 2021, investment in agriculture and mechanization has increased and the cultivation pattern has changed. Currently, instead of plants with high water requirements, plants with low water requirements but high economic potential are used. Therefore, it is expected that by changing the cultivation pattern and achieving sustainable development in the future, the expansion of desertification will be reduced in the study area. Investment in institutional infrastructure, employment, and agricultural product yield all benefit from development in Balajam Plain, and is a key example of creating food security in society. The negative impacts of agricultural development, such as the quantitative and qualitative degradation of underground water, soil degradation, and the switch to agricultural land use, as well as the climatic expansion of desertification, are visible. Agriculture and food security are particularly important in the 7th five-year development plan of the country politicians, decision-makers should also consider knowledge-based agricultural development management plans and methods instead of source-oriented. According to these results, it can be seen that the Balajam Plain has been experiencing an increase in desertification between 2011 and 2021. Therefore, agricultural development is very effective in enhancing food security, and with development in this sector, food security will increase. But the region's production of agricultural products has decreased due to the intensity of desertification. The authors of this study suggest solutions such as - cultivating crops with low water requirements and modifying planting patterns, - investing in implementing projects to combat desertification and developing knowledge-based rather than resource-based agriculture.

Maharlou Lake in Fars Province is one of the saline lakes of Iran, located 10 kilometers southeast of Shiraz. It holds significant economic, social, and environmental importance for the region. Therefore, assessing the long-term changes in the lake's surface area is crucial for making informed decisions by managers. In this context, the present study investigates the temporal changes in the water surface area of Maharlou Lake.

The study area was the Maharlou Watershed. The lake's surface area was determined for each satellite image using Landsat data over the period from 1986 to 2020. Then, the trend in the lake’s surface area changes during this period was analyzed using the Mann-Kendall test. Additionally, factors influencing the lake's surface area changes, such as precipitation changes, inflow to the lake, groundwater level, population, and vegetation cover area within the watershed, were evaluated during this period.

The results of this study showed that the trend in the surface area of Maharlou Lake during the study period was significant and decreasing (3km²/year). The change point in the time series occurred in September 2007, after which a sharp decline in the lake’s surface area was observed. Furthermore, the results of the precipitation analysis indicated that the trend in precipitation was negative but not significant. However, the trends in the inflow to the lake and evaporation were both negative and significant. The trends in population growth and vegetation cover were found to be increasing and significant. Additionally, the study's findings showed that the groundwater level trend in this area was negative and significant.

The results of this study indicated that the surface area of Maharlou Lake has significantly decreased. The analysis of the factors affecting the lake’s surface area showed no significant trend in the precipitation within the watershed, which is one of the primary contributors to the inflow to the lake. Although the trend in precipitation was not significant and the evaporation trend was also decreasing, the increasing trends in population and vegetation cover within the watershed inevitably demand more water in this region. This situation has led to excessive extraction from groundwater resources and surface flows. Consequently, the trends in groundwater levels and inflow to the lake were both decreasing and significant. Moreover, the interplay of these factors has resulted in a negative trend in the lake's surface area. Therefore, given the importance of Maharlou Lake within the watershed and to prevent further reductions in the lake's surface area, managers must pay close attention to implementing sustainable land management plans.

Characterized by fragile ecosystems, dry regions are prone to frequent damage and wind erosion, making it crucial to develop effective strategies for accurately assessing wind erosion. Therefore, this study sought to model and prepare a wind erosion risk map using remote sensing and machine learning approaches in Sabzevar City. Throughout the past decades, this region has continuously suffered from land degradation, water and wind erosion, land use conversion, and groundwater depletion. Thus, this study identified the most important parameters contributing to wind erosion in the study area after reviewing the sources of wind erosion and its evaluation models. Moreover, a distance measurement index was considered for each individual parameter. Also, machine learning methods were used to model and prepare the wind erosion map of Sabzevar city using the remote sensing indicators prepared and auxiliary points obtained by reviewing previous studies. Finally, to reduce the uncertainty of the results, the combined modeling method was used to prepare the wind erosion map. 

fifty ground data points were collected through performing field visits and reviewing the previous studies conducted in the study area. Accordingly, eight factors, including wind speed, land use, soil salinity, soil moisture, soil texture, vegetation density, precipitation, and digital elevation model (DEM) were selected based on the review of the related literature review. Then, to model wind erosion in the study area, the values found for each of the aforementioned factors were correlated with the field data. Moreover, four machine learning techniques, including SVM, GBM, GLM, and RF were used to predict wind erosion risk in the study area. In addition, the kappa coefficient, ROC curve, and True Skill Statistics (TSS) were used to assess model performance. Finally, a combined model was employed in the SDM statistical package to minimize the uncertainty of regional modeling. 

The SVM model demonstrated the best performance with AUC = 0.95, TSS = 0.97, and kappa = 0.87. The results indicated that the eastern regions of the study area were affected the most by severe wind erosion. Moreover, the combined model revealed that 44% of the area fell within the low wind erosion class (5340 square kilometers), 16% in the medium class (2007 square kilometers), 15% in the severe class (1916 square kilometers), and 25% in the very severe class (3122 square kilometers). The results of the significance of the variables showed that wind speed, precipitation, vegetation density, and surface moisture (NDMI) were the most important factors that contributed to the region’s wind erosion, respectively. Furthermore, land use change, soil salinity, and soil surface texture were found to have the least contribution to wind erosion in the study area, respectively. These findings can help the relevant decision-makers to set managerial policies and practical plans to avoid the risks of wind erosion. 

effective planning, optimal management, and corrective measures can be made and implemented to prevent land degradation and wind erosion in the affected areas by using the combined model with the least uncertainty. Generally, modeling wind erosion and preparing its map play a fundamental role in environmental studies. In this study, four models were applied in the form of an SDM statistical package, where the accuracy of the evaluation results clearly identified the best model. The difference in the results obtained from the performance of the models confirmed the uncertainty between them. Therefore, it appears that using a combined approach can be a suitable solution to reduce the uncertainty of modeling. The results of the study also suggested that the western areas of Sabzevar City were the most important centers of wind erosion production, where the surface of the hills was loamy and sandy. Thus, to avoid wind erosion in the study area, some corrective and biological measures are suggested, including the cultivation and development of salinity-resistant plant community cover, the cultivation of sand-loving plants, and the construction of windbreaks for sand dunes. While the measures performed so far in combating dust storms and wind erosion in Sabzevar city have proved to be ineffective, the intensified occurrence of dust in the region throughout the recent years and the irreparable consequences it has brought about make it necessary to devise and implement a comprehensive plan to deal with those phenomena. In this regard, the wind erosion risk intensity maps prepared in this study can be an efficient and appropriate tool for managing and reducing the effects of wind erosion and land destruction.

Carbon storage in terrestrial ecosystems is an important part of global carbon storage and plays a vital role in mitigating climate change. The purpose of this research is to investigate the effects of different types of land uses/land covers (dense rangelands, semi-dense rangelands, low density rangelands, barren lands and agricultural lands) on the amount of soil carbon and plant biomass sequestration as an important ecosystem service in Sefiddasht watershed in in Chaharmahal va Bakhtiari province. Sampling of soil and vegetation was done randomly and systematically. For this purpose, 60 plots of 4 square meters were used for collecting soil, plant material and litter. Plant biomass was sampled by direct measurement method. Soil samples were also collected from the depth of 0-30 cm and 20 samples in each area. The results showed that there is a significant difference between the investigated regions in terms of total carbon storage. The total carbon storage from the highest to the lowest in order includes dense rangelands (46.42 tons/ha), semi-dense rangelands (38.49 tons/ha), agricultural lands (31.62 tons/ha), low-density rangelands (26.12 tons) per hectare) and barren lands (17.21 tons per hectare). The economic value of the total carbon storage per hectare of the examined uses, including dense rangelands, semi-dense rangelands, agricultural lands, low-dense rangelands, and barren lands, was determined to be $5446, $4516, $3710, $3065, and $2019, respectively. Therefore, it can be concluded that the implementation of appropriate policies to prevent or minimize the conversion of land uses with a higher carbon storage capacity to land uses with a lower carbon storage capacity is of particular importance.

Ecological spaces play a crucial role in addressing climate change and global warming with focusing on storing and capturing CO2 from atmosphere. Consequently, quantifying and monitoring carbon storage content is the main step to toward achieving sustainable development goals. The main objective of this study is to develop a precise model for calculating carbon storage content at four pools i.e., aboveground carbon (AGC), belowground carbon (BGC), soil organic carbon (SOC) and dead organic carbon (DOC) under different land uses. For this, a site with an area extent about 243204 ha was selected from Miandoab region (West Azerbaijan Province) by the fact that it represents various land uses. Since application of integrated remotely-sensed data and novel technologies facilitates predictions as well as increases the modeling accuracy, the Google Earth Engine (GEE) platform and the InVEST model were implemented in preparing land use/land cover (LULC) maps and then carbon storage content. The prediction for 2023 showed that the total carbon storage in the aforementioned region would be approximately 11.52 × 106 ton. In addition, the detailed evaluations illustrated that the highest carbon storage content related to SOC and AGC as expected. A possible reason is that living mass is located on the surface. In terms of different land-uses without paying attention to their areas in themselves, the highest carbon storage content was for pasture, followed by tree cover, agriculture, urban area, water bodies and barren. Finally, it is concluded that applying the respective remote sensing data integrating with a suitable technique uncovers the impact of LULC changes on carbon storage with success.

Urbanization which has been considered the main contributor to climatic problems in cities, not only changed the physical environment in cities, but also has affect the characteristics of local climate zone. This study aims to investigate the relationship between surface properties of Local Climate Zones (LCZ) extracted from satellite resolution images and Land Surface Temperatures (LST) in a semi-arid region. By employing the LCZ approach, the study seeks to understand how LCZs influence LSTs in this specific environmental context. This study consisted of four main steps: Image preprocessing, LST retrieval, LCZ map preparation and spatial analysis. In this way, the LCZ scheme was used to classify the study area based on two sets of built-up types and land-cover types. Google Earth was used to specify training areas under study and to define different LCZ types. The Split Window Algorithm (SWA) was used to retrieve LST from Landsat-8 TIRS. The results showed that the built type and the land-cover type behave like two independent sets of patterns in the study area. The build type set fluctuated in a higher LST range than the cover land set (forest, farmlands and bushes). By shedding light on the relationship between the surface properties of LCZs and LSTs, the study provides valuable insights into the factors influencing thermal dynamics in semi-arid environments. Armed with this knowledge, urban planners can develop more informed strategies and interventions aimed at mitigating heat-related challenges and improving overall urban livability.

Vegetation cover is one of the basic and key ecological factors in arid regions, which will be severely affected by the drought process. As a result of these impacts, there will be increased intensity of soil erosion and soil degradation. Therefore, the present research was conducted to evaluate the process of drought and its impact on changes in vegetation in Sarakhs county in Khorasan Razavi province. In this research, the climate data of precipitation and monthly temperature of synoptic and evapotranspiration stations in a period of 21 years (2000 to 2021) were used to estimate the standardized precipitation-evaporation-transpiration index (SPEI). To investigate the vegetation changes, the images of ETM+ and OLI satellite sensors and the difference vegetation index (NDVI) were used. The index SPEI was calculated in the software environment R. Prediction of future vegetation cover changes is done using the Markov model and in the IDRISI-TerrSet software environment. Finally, the correlation between two indicators of drought and vegetation was compared with a linear regression test. The results of this research showed that the highest level of drought in the years 2000, 2010, and 2020 occurred in Sarkhas city at moderate and severe levels. The investigation of changes in vegetation cover in 2030 showed that the soil without cover increased compared to the year 2020, and the area of land with other classes, including vegetation with medium, high, and thin density, has decreased. Correlation analysis between vegetation cover and drought indicators showed that the highest correlation value of 0.59 at the significance level of 0.01 has been observed, and in the years 2000 and 2020, the correlation coefficient was higher at 0.52 and 0.51, respectively. The results of examining the relationship between the NDVI vegetation cover index and SPEI climatic index indicated the existence of a positive relationship between these indices.

The largest part of the biodiversity of terrestrial ecosystems is in pastures and agricultural lands. In terms of biological importance, the Zagros mountain range has the richest and most diverse biodiversity, which faces the dangers of land use change and invasive species. Earthworms, soil ecosystem engineers, have been of great interest in soil physicochemical processes and evolutionary studies. Recent studies indicate the adverse effect of human activities and land use change on the diversity of earthworms. Since the identification of earthworms, using morphological methods is ambiguous; therefore, we need to use genetic methods and molecular markers to separate worms. In this study, we identified taxonomic functional units (OTUs) in earthworms using morphological information and molecular marker COI. Then, we have investigated the effect of agricultural uses on the genetic diversity of earthworms in Central Zagros, Borujerd County using multifactorial statistical analysis. We separated into 13 OTUs belonging to seven genera based on the results of the genealogical tree. The results of the comparison of wheat, alfalfa, sugar beet and clover pastures and farmlands showed that the OTUs of the farmlands all belong to the Lumbricidae family and the OTUs of the pastures, in addition to this family, were in the Megascolecida and Hormogastridae families. In total, we observed four genera of Helodrilus, Aporrectodea, Bimastos and Allolobophora in agricultural lands and six genera of Metaphire, Aporrectodea, Bimastos, Esenia, Allolobophora and Hormogaster in pastures. The results of the ranking analysis of the multifactorial analysis method in land use groups, soil physicochemical characteristics and indicators of abundance and biodiversity of earthworm OTUs with nine factors in 36 variables showed that OTUs C, H and I, all of Aporrectodea rosea species, are the most effective based on multi-factorial analysis in abundance and biodiversity. The biodiversity efficiency index of earthworms of grassland ecosystems was higher than agricultural ecosystems in all cases. Compared to other agricultural ecosystems (wheat, sugar beet and clover), alfalfa cultivation had the highest efficiency index in earthworm biodiversity and the lowest efficiency index was in wheat cultivation. Sugar beet and alfalfa croplands had more OTU diversity compared to clover and wheat croplands. The selection of alfalfa cultivation in crop rotation will increase the biodiversity of earthworms and the stability of agricultural ecosystems. Considering the importance of earthworms in providing ecosystem services and soil quality, it is necessary to conduct more research on the effect of common agricultural ecosystems on the biodiversity of earthworms.

Sustainable development and food security rely heavily on the preservation and restoration of natural areas. Soil health is fundamental for rangeland ecosystem function, and land management practices significantly impact its quality and productivity. Rangeland restoration practices and exclosures have been widely implemented to improve vegetation cover and soil health. This study investigates the effects of different rangeland management practices on key soil physicochemical and biological properties in the Khamesan representative watershed, Kurdistan province, Iran.

The Khamesan watershed (4193 ha), located 35 km from Kamyaran city, was selected for the study. Three management regimes were evaluated: restored rangelands (mechanical and biological operations since   2006), exclosures (established in 2007), and livestock grazing areas. Within each management area, three homogenous sites with similar physiographic characteristics were chosen. Five soil profiles were sampled at each site (one central and four surrounding) from a depth of 0-30 cm. Analyzed soil properties included bulk density, texture (silt, clay), porosity, average particle diameter, electrical conductivity, pH, phosphorus, potassium, calcium, nitrogen, organic carbon, carbon-to-nitrogen ratio, basal respiration, stimulated respiration, microbial biomass carbon, microbial biomass nitrogen, microbial biomass carbon-to-nitrogen ratio, and microbial metabolic coefficient. Data were analyzed using SPSS 19 software.

Management practices significantly influenced soil physicochemical and biological properties. Notably, restoration and exclosure treatments impacted soil texture (silt and clay percentage), bulk density, porosity, average particle diameter, organic carbon, phosphorus, soil carbon-to-nitrogen ratio, basal respiration, stimulated respiration, microbial biomass carbon, microbial biomass nitrogen, microbial biomass carbon-to-nitrogen ratio, and microbial metabolic coefficient. Biological indicators were more sensitive to management interventions compared to other soil properties. The highest basal respiration (0.93 mgCO₂g⁻¹dm²h⁻¹) was observed in the grazed area, while the lowest (0.57 mgCO₂g⁻¹dm²h⁻¹) occurred in the restored area. Similarly, bulk density was highest (1.69 g/cm³) in the grazed area and lowest (1.57 g/cm³) in the restored area. The carbon-to-nitrogen ratio was also higher in restored pastures (12.18) compared to grazed areas (6.91).

Healthy soil is essential for ecosystem life support, including vegetation and biodiversity. Rangelands are often subjected to prolonged overgrazing, necessitating management interventions to improve protection levels. Our findings indicate that biological indicators are more sensitive to management practices and environmental changes than other soil properties, making them valuable tools for assessing soil health in rangeland ecosystems. Restoration practices and proper management enhance vegetation cover, promote soil aggregation and structure, increase carbon input, and stimulate soil microbial populations, ultimately improving soil health and quality. This study demonstrates the positive impacts of restoration operations and exclosures on soil improvement in the Khamesan watershed.

 The soil erosion is one of the effective factors in reducing agricultural production and threatening food security, in which the soil fertile layer is destroyed. The risk of soil erosion is different depending on the watershed topography, soil characteristics, conditions of local weather, land use and methods of land management, which the land use change can be exacerbated. Land use changes are one of the important factors in changing hydrological flow and soil erosion. As a result of land use change and vegetation destruction, a large part of the rainfall has turned into runoff, which in addition to soil loss and sediment production, causes a lot of financial and human losses caused by floods. Therefore, the knowledge from erosion process in different time periods can help in determination of the distribution pattern of erosion and its effects over time. So that, the decision-makers, while monitoring and evaluating the conducted measures about the soil and water conservation take the appropriate decisions for the current and future conditions of watersheds. Therefore, in the present study, the effects of land use change on soil erosion have been evaluated using the RUSLE model in Chardavel watershed of Ilam province for the years 2005 and 2020.

 In the present study, the effective factors in the Revised Universal Soil Loss Equation were prepared in the form of raster layer. These factors include the rainfall erosivity, length and degree of slope, vegetation management, soil erodibility and conservation operations. The factor of rainfall erosivity was calculated for each of the stations inside and outside the watershed and the erosivity layer was prepared in the GIS environment, by using the Inverse Distance Weighting method. For the length and degree of slope were used from the digital elevation model map and cumulative flow map. The layer of soil erodibility was obtained using the soil texture map. The NDVI index was calculated using Landsat 8 of OLI sensor (2020) and satellite images of five ETM Landsat satellite images (2005) and using bands of 4 and 5 (for Landsat 8) and bands of 3 and 4 (for Landsat 5) in the remote sensing software, and then, the management factor was obtained in years of 2005 and 2020. In order to obtain the factor of conservation operation, the land use map was used for years of 2005 and 2020, and according to each land use was determined the its conservation value. In the next step, the amount of annual soil loss was estimated by multiplying these layers in the GIS environment. Finally, to determine the amount of erosion in each of land uses, the layers of erosion and land use placed on top of each other to the years of 2005 and 2020.

 The results showed that the rainfall erosivity was from 220.5 to 309.8 MJ mm ha-1 h-1, the length and degree of slope were 0 – 403.7 at the pixel level, the vegetation management was 0.16 - 0.57 (in year of 2005) and 0.18 - 0.61 (in year of 2020), the soil erodibility was 0.03 - 0.06 t. ha h ha-1 MJ-1 mm-1 and protection operation was 0-1 for both years of 2005 and 2020. The average erosion was 13.23 t ha-1. year-1 for year of 2005 and this value was 20.13 t ha-1 year-1 for year of 2020. The barren land uses due to the lack of vegetation produces the highest amount of erosion, so that its amount has increased years of 2003 to 2005 from 0.65 to 3.8 t, and residential areas due to the presence insulation, asphalt and proper drainage have the more role of transferring runoff. In agricultural areas, it has been exposed to erosion during this period due to not following the principles of proper cultivation and usage of proper tools.

 Considering the extensive effects of soil erosion on various aspects of human life, its continuous monitoring and evaluation can help to make the right decisions to confronting with this crisis. In the present study, the RUSLE model was used to investigate the effects of land use change on soil erosion for the years of 2005 and 2020 in Chardavol watershed. In general, the results showed that the erosion amount has increased in the studied watershed from 2005 to 2020, which it indicates that there is the dangerous situation for the watershed in the future. Therefore, it is suggested to use soil and water conservation operations, especially in the steep areas and also in the upstream areas of the watershed. Also, according to the change of land use in Chardavol watershed, the measurements should be taken for soil and water conservation in areas with high erosion potential. It also seems that the adoption of appropriate laws to prevent land use change can help reduce soil erosion.  But, because the most important part of the watershed are its residents, these laws should be in such a way that while attracting people's participation and satisfaction, it guarantees their stable income. Therefore, the development of management scenarios in the region will help natural resource managers in prioritizing and usage of appropriate management methods in controlling soil erosion in high-risk areas of Chardavol watershed.

With the rapid development of society, economy and population growth, the impact of human activities on ecosystems is increasing, which has caused the relationship between human well-being and ecosystem to be in the spotlight. To that end, ecosystem services as an essential element for human development and survival emphasizes the benefits that natural ecosystems provide to humans. These services are divided into four categories based on the Millennium Ecosystem Assessment (MEA): provision, regulation, support and cultural. Cultural ecosystem services are important component of ecosystem services that refer to intangible benefits of the human beings from ecosystems and play an essential role in enhancing human well-being. Cultural ecosystem services can include the use of natural resources directly or indirectly.

The present study was conducted with the aim of modeling recreation services in Semnan province. For this purpose, the integration of the analytic hierarchy process (AHP) of one as the multicriteria decision-making methods and the recreation opportunity spectrum (ROS) model was used. In the ROS model, recreational service is estimated based on recreation potential index and accessibility- remoteness index. In the recreation potential index, three elements: the degree of naturalness, the protected regions, and the attractiveness of water bodies, and in the accessibility- remoteness index, the distance from roads and human-made areas were mapped. In this research, Sentinel-2 satellite images were used to prepare the land use map of 2021. Then, using the maximum likelihood algorithm, land use maps were prepared in 18 classes.

According to the land use map, pastures, barren lands and rainfed agriculture have the highest levels of land use in the studied area with 23.57, 22.27 and 17.31% respectively. After that, waterbodies, salt marshes and irrigated agriculture are the dominant land uses with 15.65, 10.98 and 7.84%, respectively. It is worth mentioning that the smallest area of the region is dedicated to wetlands and urban uses. Based on the results, Forest and pasture areas have the highest potential in providing recreation services in the study area. It was also seen that, the third category (low provision- difficult access) and the seventh category (high provision- easy access) respectively have the highest and lowest land surface coverage in terms of the provision of recreational ecosystem services in the province. Overall, the results showed that recreation ecosystem service is affected by various factors caused by human development, especially land cover/use changes. Considering the concentration of both variables of ecosystem service provision and urban and industrial development in the northern half of the province, it is vital to pay attention to the conflicts between the development and management of ecosystem services. If this is ignored, in addition to intensifying the instability of development, the destruction of natural assets of the province, especially forests and pastures, which are the main source of provision of various ecosystem services such as recreation, will intensify.

In general, the results indicate that most of the places in the province have a medium to low ability to provide recreation services. Considering that recreational potential is important for the well-being of urban populations, increasing urban parks and green infrastructure areas is a useful way to improve the quality of the recreational experience. Therefore, urban development strategies should increasingly aim to strengthen the natural and artificial network of green space, diversity of plant compositions and biomass structure, and proper management of green space. This is necessary for the design of green space with regard to maximizing ecosystem services in Semnan province.

The degradation of soil structure and reduced water infiltration into the soil are indicators of soil degradation, which lead to decreased stability and production quality, as well as environmental problems. Soil conservation methods are widely used to curb soil degradation processes and improve soil structure and permeability. The effectiveness of these methods in enhancing these indicators and controlling soil degradation requires further study and evaluation. This research aims to investigate the stability of soil structure and soil infiltration rate affected by various soil management and conservation operations, and to compare them in the Razin Watershed area of Kermanshah Province.

For this purpose, eight common reclamation and conservation operations in the study area were selected and evaluated. These operations include pit-seeding and seeding, rangeland audit plan - conversion of dryfarming to rangeland, rangeland audit plan - conversion of dryfarming to forage cultivation, almond tree planting alongside digging holes, land leveling, seedling planting, conversion of dryfarming to rangeland, forage cultivation and orchard, and forest area. For evaluation, random systematic sampling points were selected within the area of each operation and their corresponding control area for study and sampling. After digging profiles and sampling the soil, the laboratory measured and calculated soil aggregate stability indices, including Mean Weight Diameter (MWD), Geometric Mean Diameter (GMD), and the proportion of stable aggregates larger than 0.25 mm (WSA>0.25). The final infiltration rate was also measured in the operation areas and their controls using a disc infiltrometer. Finally, a statistical comparison of the mean values of MWD, GMD, WSA>0.25, and final infiltration rate in soil conservation operations and their control areas was performed using the T-test for independent samples and the Duncan test for comparing the means of these indices in various operations using SPSS software.

The evaluation results showed that the lowest MWD index was related to land leveling and sapling planting at 0.15 and 0.35 mm, respectively, while the highest values at 1.9, 1.8, and 1.6 mm were related to 20-year-old almond planting, 10-year-old almond planting, and forest areas, respectively. Additionally, the highest WSA>0.25 index values were found in fodder cultivation, orchard establishment, and 10-year-old almond planting operations, indicating the formation of large and stable aggregates due to conservation operations. Among the eight operations studied, the 20-year-old almond planting operation showed the greatest improvements in MWD and WSA>0.25 indices. The results for the final infiltration rate indicated that soil conservation operations, particularly converting low-yield rain-fed lands to sapling planting and changing rangeland use to 10-year-old almond planting, led to the highest increases in final infiltration rate, at 21.8% and 16%, respectively, compared to the control.

Overall, soil conservation operations increased the relative share of larger soil aggregates. However, the soil infiltration rate index showed conflicting results, as factors other than the evaluated conservation operations also influenced water infiltration into the soil, necessitating further studies. Consequently, examining the role of management, especially land use management, is essential for sustainable soil resource utilization.

Among natural disasters, flood is undoubtedly the most catastrophic hazard in the world. One of the basic strategies for reducing the damage caused by floods is to prepare a flood sensitivity map. Spatial prediction of the flooding probability using models created from spatial and historical data, which ultimately leads to the preparation of flood sensitivity maps is an appropriate solution for land management planners in different areas to prevent the occurrence of this phenomenon. In this research, in order to determine flood-prone areas, the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of imperial competition (ANFIS-ICA) and the hybrid model of adaptive neural and fuzzy inference and the metaexploratory optimization algorithm of particle swarm (ANFIS-PSO) are used.

The Zarine River watershed has an area of 4485 km2 and is located in the northwest of Kurdistan province between the longitude of 45°48ʹ30ʺand 46°48ʹ20ʺ east and the latitude of 35°42ʹ20ʺ and 36°23ʹ15ʺ north. The climate of the region is humid and the average annual rainfall is 480 mm. Locations of flood events were randomly divided into two groups: training (70%) and validation (30%). Various environmental factors (height, direction, slope, surface curvature, land use, lithology, rainfall, flow power index, distance from river and topographic wetness index) were selected as independent variables in the modeling and their digital layers were prepared. The ANFIS-ICA and ANFIS-PSO models were used in this research and their prediction results were evaluated based on the criterion (AUC) and the true skill statistic (TSS).

On the basis of these findings, in the validation stage, the model (ANFIS-PSO) with an AUC of 0.98 and a true skill statistic (TSS) of 0.89 had the highest accuracy. The results also showed that the factor of distance from the stream was identified as the most important environmental factor. In addition, ground slope and TWI were ranked second and third in importance, respectively.

Based on the results, the hybridization approach, which combines machine learning models and meta-exploratory optimization algorithms, improves the learning power as well as the predictive power of the model. The results of this research showed that the distance from the stream and the slope of the land are the most important factors affecting flooding. Based on the results and analysis, it can be concluded that machine learning models have a high capability for predicting flood potential. The flood potential maps prepared in this research can be very useful for managers and experts and can be used in planning flood prevention measures. Directing flood control facilities and measures in situations with a high flood potential will improve flood management from an economic and technical point of view.

Desertification is one of the most significant process of land degradation in all the world, especially in arid and semi-arid regions. In the United Nations Convention to Combat Desertification report (1994), Desertification is land degradation in dry-lands, resulting from various factors, including both climatic variations and human activities. This process is always associated with soil degradation, pollution and reduction of water resources, vegetation and other biological resources in natural and ecological conditions. Therefore, determining the main factors of this process can be very effective in proper land management. This research was conducted with the aim of zoning sensitivity areas to the desertification intensity based on ESAs model and providing management strategies based on scenario planning in Razavi Khorasan province.

In this research, five criteria such as climatic quality, soil, vegetation, erosion and management (human activities), and 20 indicators such as soil texture, soil depth, rock fragment, parent Material, vegetation density, fire risk, rainfall. drought, water and wind erosion, land use change, mining, livestock density in pasture, etc. were used. were used. Scoring and weighting of the indicators was done using a questionnaire and based on the expert opinions of specialists, executive managers and field experiments. The designed questionnaire included 21 questions, that classified in the form of 5 criteria of soil, climate, vegetation, erosion and management, which were distributed among the statistical population in two rounds. Management strategies and policies were also obtained in the Delphi method in the form of expert panels (brainstorming).

The results showed that the vegetation criteria with indicators such as the reduction of vegetation, the increase in the risk of fire in pastures and forests due to the continuous droughts of the past two decades and the increase in the intensity of wind and water erosion, with a value of 1.419, has had the greatest value and importance in expansion the intensity of desertification. After that, factors such as human activities (management), climate, erosion and soil respectively have had the greatest impact on the sensitivity areas to desertification in Khorasan Razavi province. In terms of the critical situation, the high percentage of the areas in the northwestern regions such as Quchan and Chenaran township due to the impacts of drought, severe changes in land use and water has become desertification. In the central, eastern and southern parts due to drought, land use changes, lack of optimal land management and wind erosion, main reason has been the expansion of desertification. Besides that, all the townships of Khorasan Razavi province have fragile conditions. This issue can be considered as an important and serious warning for the future management of Khorasan Razavi province. Therefore, in order to provide effective ways to deal with the intensity of desertification, based on the opinion of experts and specialists in the current research, by considering only the priority indicators of each of the criteria, the number of 10 cross matrix of the important indicators of each of the criteria, 20 scenarios, 6 strategies and more than 40 strategies were obtained. So that, by using the suggested strategies could be planned and managed according to regional and local conditions, to reduce the intensity of desertification.

The ESAs model uses four criteria of management, vegetation, soil quality and climate mainly to evaluate areas sensitive to desertification. So that, in this research, due to the importance of the erosion (water and wind erosion), for zoning map of sensitivity areas to desertification were used. In order to provide management plans (strategies and policy), to reduce and combat desertification in Khorasan Razavi province, management strategies such as; -sustainable management of vegetation, - water resources management and watershed management, - soil management, - livestock management and reduction of environmental hazards caused by human activities, -education, promotion and culture and sustainable management. Therefore, in this research, only the important strategies and policy that can provide a comprehensive vision of land management in Khorasan Razavi province, were suggested.

Investigating changes in vegetation density and land use is one of the most important aspects of natural resource management and reviewing environmental changes. In this research, using remote sensing technique, the monitoring of changes in vegetation cover and its relationship with surface temperature and land use in Khoda Afarin and Kalibar cities were investigated using remote sensing technology during a period of 22 years (2000-2022). To perform statistical and visual analysis on satellite images, Envi 5.6 and Arc GIS 10.8 software were used. NDVI index was used to investigate changes in vegetation area and quality. Also, in order to investigate qualitative changes in vegetation, the numerical values of this index were classified into three classes: dense, semi-dense, weak or no vegetation, and the temperature changes of the earth's surface during the study period using It was calculated from Landsat satellite images and then the land use maps were extracted based on the supervised classification method and through the maximum similarity algorithm. Based on the analysis, it was determined that in the studied period, 58,196 hectares, about 15.6% of dense vegetation density, and 38,415 hectares, about 10.2% of semi-dense vegetation density, have been lost. In fact, 96,611 hectares, about 25.8% of the density of dense and semi-dense vegetation, have been converted to other uses in 22 years. The study of land use in the region showed that the changes in vegetation density are mostly related to the use of agricultural and garden lands and pastures, and during the study period, the most destruction is related to dense vegetation density. Finally, investigating the relationship between vegetation cover and land surface temperature (LST) showed that the vegetation cover index (NDVI) has a negative and inverse correlation with the land surface temperature and in most areas that have denser vegetation cover; shows a lower temperature. The results of the research show that the most important factor in the changes in vegetation density in the region is human activities such as agriculture, construction and road construction, which have caused many changes in the land surface, the analysis of the area of these land uses shows He said that the level of agricultural lands and pastures has increased significantly, which is mainly the result of the conversion of dense vegetation, especially forests, to agriculture. The results of this research can be used by the organizations of agricultural jihad, natural resources and the Ministry of Interior.