احسان قلعه

The depletion of surface water resources has necessitated uncontrolled groundwater abstraction in various regions worldwide, resulting in substantial reductions in groundwater table levels. As populations continue to expand, the extraction of these essential resources has intensified, posing a significant threat to natural reserves. This study aims to monitor groundwater levels through the analysis of satellite imagery and to investigate the correlation between these levels and land use patterns. To accomplish this objective, relevant satellite images were acquired and subjected to appropriate pre-processing. An object-oriented methodology was employed to generate land use classification maps for two distinct years, alongside a land use change map covering a fifteen-year period from 2000 to 2015. Moreover, groundwater level maps for the study area were produced for both years utilizing the Gaussian method, recognized as the most accurate approach. The findings indicate a robust and significant relationship between land use and groundwater levels, revealing that areas with higher vegetation exhibit lower groundwater levels compared to other regions. This phenomenon can be attributed to the hydrological dynamics that facilitate the movement of water from higher potential zones to these areas. Additionally, irrigated agricultural practices demonstrated the most pronounced average decline in water levels relative to other land uses, underscoring the excessive reliance on groundwater for irrigation in the study area. The results further illustrate that the conventional kriging method with Gaussian variance surpasses other techniques in estimating groundwater table depths across both statistical periods. Analysis through conventional kriging reveals a general decline in groundwater levels throughout the majority of the plain during the study period, with a maximum decrease of 40 meters and an average reduction of 15 meters.

Water erosion is the result of interactions between various environmental factors such as topography, soil characteristics, climate characteristics, runoff and land use and management. Sediment production is highly dependent on runoff, so doubling the speed of runoff increases its leaching capacity and transportability up to five and six times. Knowing the effective factors in sediment production plays an important role in determining the amount of sedimentation of a watershed and understanding the phenomenon of erosion and its consequences, and it can be used in prioritizing the sub-basins of a watershed. The purpose of this research is to investigate the relationship between morphological characteristics and the amount of sediment production in the catchment area of the Gharasu River in Ardabil using principal component analysis and multivariate regression, in this regard, using 16 independent variables and 1 dependent variable in 13 sub-basins of the Gharasu River.

As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude. In this study, 1:100,000 scale geological maps, 1:25,000 scale topographic maps, and discharge and suspended sediment statistics and information of 13 hydrometric stations of Gharasu River sub-basin were used in the 50-year period from 1350 to 1399. In order to check the correlation between independent and dependent variables and to test the normality and normal distribution of data, Shapiro-Wilk and Kolmogorov-Smirnov tests were used in SPSS software. Also, in this research, the OLI Landsat 8 satellite images were used to extract the vegetation cover index (NDVI). For this purpose, step-by-step linear regression is used to determine the most effective variables and to determine the most appropriate statistical relationship between suspended sedimentation and the variables used, and principal component analysis is used to determine the most effective factors of suspended sediment production in the watershed.

In this study, the average annual suspended sediment of the basin was considered as dependent variable and other parameters as independent variables, and Pearson correlation method was used to check the correlation between independent variables and dependent variable. The variables of time of concentration, length of the main waterway, area, Slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basin than other variables. Based on the models obtained from sediment correlation analysis, the amount of sediment produced with the concentration time variable had a positive correlation and was significant at the 5% level. The results of the analysis of the principal components show that the four factors of area, length of the main waterway, concentration time and slenderness ratio of the basin have an eigenvalue greater than 1, and the number of extracted factors is 4 factors. The first factor (area) has been able to explain 37.230 percent of the variance of all research variables. This value is 24.735, 16.950 and 9.849 percent for the second factor (length of the main waterway), the third factor (concentration time) and the fourth factor (slenderness ratio) respectively.

The present research was conducted with the aim of investigating the relationship between geomorphic parameters, vegetation and erodible formations of selected sub-basins of Gharasu basin with the average annual sediment. For this purpose, the statistics and information of 17 variables (16 independent variables and 1 dependent variable) were obtained for 13 sub-basins of Gharasu basin from the regional water of Ardabil province. The relationship between geomorphic parameters and annual precipitation was determined using stepwise multivariate regression method. The research results indicate that geomorphic parameters have a high correlation with the amount of annual sediment production and can be used in sediment prediction. Meanwhile, the variables of concentration time, length of the main waterway, area, slenderness ratio, perimeter and slope have a higher correlation with the amount of sediment production in the basins than other variables. Among these variables, the basin concentration time variable was used in the final step-by-step regression model and was selected as the sediment predictor variable. This variable alone can predict 98% of annual precipitation. In order to ensure the appropriateness of the data, the KMO coefficient was used to analyze the main components. The value of KMO = 0.78, as a result, the data will be suitable for factor analysis. The results showed that the four factors of area, main waterway length, concentration time and elongation factor could explain 88.754% of the variance of all research variables.

Sediment that consists of solid particles with organic matter transported by water is called suspended sediment. In other words, the sediment load is the flow of the total sediment output from the catchment or drainage basin that can be measured in the desired cross-section and in a certain period of time. In most natural rivers, most of the sediments are transported as suspended load. The sediments collected by the rivers cause many problems, including sedimentation in the reservoirs of dams and reducing their useful volume, changing the course of the river due to sedimentation in their bed, reducing the capacity of canals and water transfer facilities, and changing the quality of water in terms of drinking and agriculture. . However, no direct or indirect experimental model developed to evaluate this process has been universally accepted. Therefore, sediment transport has been considered by engineers from different aspects and different methods have been used to estimate it. One of the methods of estimating suspended sediment is artificial neural network. Artificial neural network is a computing mechanism that is able to provide a series of new information by taking information and calculating it. Considering that the structure of the human brain has a very high ability to process complex, non-linear and parallel information.

As one of the sub-basins of the Aras catchment basin, Gharasu catchment is located in the geographical coordinates of 47°31' to 48°47' east longitude and 37°47' to 38°52' north latitude.In this study, the statistics and information of 17 variables in 13 sub-basins of the Gharasu River, which were extracted by the regional water organization of Ardabil province, were obtained from this organization. In order to model the artificial neural network from the data of 3834 daily sediments recorded in 13 sediment measuring stations in the studied sub-basins during a 50-year statistical period corresponding to the statistical period of 1350 to 1399 and also from the digital topographic maps of the 1:25000 scale of the Geographical Organization of the Armed Forces to Validation of basin demarcation was used. In choosing this common time base, criteria of completeness, sufficient length of data and use of the latest available data were taken into consideration. Then the normality and correlation between the obtained data were evaluated and two methods of Radius Axis Function (RBF) and Multilayer Perceptron (MLP) were used in SPSS software to model the artificial neural network.

recorded suspended sediment (3834 cases) in the relevant statistical period was considered as a dependent variable and flow rate as an independent variable separately for each sub-basin, and Pearson's correlation method was used to check the correlation between the independent variable and the dependent variable.According to the correlation matrix of the variables, it can be seen that Barouk sub-basin has the highest correlation and Arrab Kendi and Pol Almas sub-basins have the lowest correlation. After modeling the data by two artificial neural network models (RBF and MLP), the amount of sediment for each year was predicted by these models and R2 and RMSE values were also calculated for them. In order to determine the number of neurons in the hidden layer, the values of the neurons in this layer were evaluated by trial and error, and according to the results, choosing the number of 4 neurons for the RBF model and 3 neurons for the MLP model has the highest prediction accuracy in the evaluation data and It also shows in the test data. The accuracy of prediction in RBF model with correlation coefficient R2=0.941 and RMSE=65.002 is compared to MLP model with R2=0.917 and RMSE=88.244.Based on the scatter diagram between the real data and the estimated data, it was determined that the average of the real values is 4.636, which is 4.367 for the RBF model and 3.534 for the LMP model, which indicates better accuracy in Modeling and the closeness of the RBF model value to the real value. Regarding the median index and the mode index, which represent the most repeated data in the statistical collection, for the real values, the numbers are 4.117 and 3.246, respectively, and for the RBF model, the numbers are 4.425 and 4.213, respectively, which are the closest values. It is considered as a real amount.

So far, various forecasting models have been used to estimate river sedimentation. Some of these models have estimated the amount of sediment by combining different physical parameters of the basin, climate and even the output of satellite images. Artificial neural network models are widely used in forecasting geographic models today.In this research, two artificial neural network models, radial axis function (RBF) and multi-layer perceptron (MLP) in SPSS software have been used to estimate the sediment of Gharasu River in Ardabil province. In this study, recorded suspended sediment (3834 cases) in a 50-year statistical period was considered as a dependent variable and flow rate as an independent variable separately for each sub-basin, and Pearson's correlation method was used to check the correlation between the independent variable and the dependent variable. It was found that Barouk sub-basin had the highest correlation and Arbab Kendi and Pol Almas sub-basins had the lowest correlation. After modeling the data by artificial neural network model, the amount of sediment for each year was predicted by these models and R2 and RMSE values were also calculated for them. The prediction accuracy of RBF model with correlation coefficient R2=0.941 and RMSE=65.002 is higher than MLP model with R2=0.917 and RMSE=88.244, and it has a better performance in estimating suspended sediment in the study basin.Also, the average value of the real values is equal to 4.636, which is equal to 4.367 for the RBF model. This research showed that in all studied stations, the RBF method provides more accurate estimates of suspended sediment than the MLP model. Of course, due to the existence of complex relationships between flow rate and suspended sediment, the appropriate model should be determined in each hydrometric station to estimate this variable more accurately,

The earthquake is one of the most important environmental hazards in many Iranian cities. In this regard, spatial assessment and micro-zoning of seismic vulnerability of cities are essential requirements in earthquake mitigation planning. In the present study, seismic vulnerability of Kermanshah metropolis is investigated. Crossing three important fault lines in the vicinity of Kermanshah city has caused earthquake hazard in this urban area. One of the main measures to reduce the vulnerability of Kermanshah city to earthquake risk is seismic assessment and zoning of the city in order to implement earthquake mitigation plans. In this context, 11 variables affecting the seismic vulnerability of the city were used. These variables are: fault distance, slope, lithology, population density, land use, residential density, and major communication networks, access to hospitals, operating radius of fire stations, and distance from hazardous facilities, and access to urban open spaces. Fuzzy TOPSIS technique was used to combine these variables in the context of GIS. The results show that about 32% of the area of Kermanshah is in a very high vulnerability class. Neighborhoods located in the central part of the city (the core of the city) are among the most vulnerable to earthquake hazards in Kermanshah. In this context, factors such as proximity to faults in the southern margin of the city, high population density, high residential density, marginalization, the presence of worn urban texture, and the lack or severe shortage of open urban spaces are among the most important causes of high vulnerability of these neighborhoods.

Forests have an extremely important place in the ecosystem in terms of providing social and environmental balances. Fire is the greatest danger to forests. Therefore, estimating fire formation and its behavioral characteristics is very important to deal with this issue. Every year, large areas of forests in the Zagros region in western Iran are burned and destroyed. The extent and distribution of fires, the mountainous terrain, and the difficulty of crossing the predominantly forested areas of the Zagros Mountain Range have hampered managers’ abilities to obtain quantitatively reliable information about the burned areas, levels of damage, as well as their statistics and factual information. Utilizing appropriate and available satellite data and images can provide useful information about pre- and post-forest fire conditions. The aim of this study was to estimate fire affected areas and be aware of the efficiency and capability of Landsat satellite data and NBR and dNBR indices in identifying, evaluating, and mapping the burned forests of Zagros region. For this purpose, after preparing the required images, the fires that occurred in June 2016 in the forests of Zagros were investigated by using remote sensing techniques and the necessary processing. The results showed that NBR and dNBR indices provided good information about the impact of fire and its changes. 13685 hectares of Zagros forests were burned in this fire in 2016.

Identification of landforms and the way of their distribution is one of the basic needs in applied geomorphology and other environmental sciences and landform maps show the shapes of the earth's surface. This project aims to identify the landforms of the Qaranqu catchment area using object-oriented classification methods including nearest neighbor algorithm and thresholding. Landsat satellite imagery for 1990 (TM) and 2020 (OLI) was used for this purpose. First, to apply the classification, atmospheric and radiometric corrections were applied to the images, then to better identify and extract the phenomena, principal component analysis (PCA), and MNF algorithms were used to classify satellite images using classification methods. Object-oriented, which included the nearest neighbor method and thresholding was used. For the accuracy of the maps produced using the two methods of Kappa index and the overall accuracy of the use, the results revealed that the nearest neighbor method is more accurate than the thresholding method. The classification results showed that the highest rate of decreasing changes during 1990-2020 is related to dense rangeland because it has decreased by 12.49 percent and the highest rate of incremental changes is related to irrigate agriculture which is 10.83 percent. The most important reason for this increase is the construction of Sahand Dam over time. In the absence of well-organized planning and the adoption of appropriate policies, the destruction of rangeland will continue and turning it into arable land, which leads to irreparable environmental and economic losses in the region.

Land surface temperature is an important indicator in the study of ground energy balance models on a regional and global scale. Due to the limitations of meteorological stations, remote sensing can be a good alternative to estimating surface temperature. In the present study, the relationship between different land uses and ground surface temperature in Kosar city and spatial autocorrelation has been investigated using Moran index. Landsat 8 (OLI) and Landsat 5 (TM) satellite images were used for this purpose. The present study was carried out in four main stages: a) preparing images for 1987 and 2015, b) making the necessary predictions, c) preparing a classification map using the object-oriented method, and d) extracting the earth's surface temperature with Separate window algorithm. The results showed that dryland and rangeland agricultural uses in 1987 and 2015 have higher temperatures than other uses, due to the dryness of selected agricultural products in the selected period (June), which caused the temperature to rise. Is. In contrast, the use of water areas due to their high heat capacity in these years have the minimum average temperature. In addition, hot and cold clusters of Kosar thermal islands were extracted using the hot spot analysis index (Hotspot). Finally, the analysis of spatial autocorrelation with global Moran indices showed that the surface temperature of Kosar city has a spatial structure and is distributed in the form of clusters.

 
Landslides are one of the major geological problems around the world that cause compaction of subsurface layers. The cause of this phenomenon can be attributed to human activities such as uncontrolled abstraction of groundwater and natural activities such as earthquakes. Landslides are also one of the main geological hazards in the region, causing serious damage to buildings, roads, infrastructure and bridges. According to the US Geological Survey, subsidence involves the collapse or subsidence of the earth's surface, which can have a small displacement vector. Therefore, identifying and monitoring the subsidence phenomenon is one of the most important and vital issues to maintain stability in the regions. For this purpose, researchers use various methods to study and observe this phenomenon, which are divided into three categories based on the position of the tools used: Subsurface methods, Ground Based methods and Remote Sensing methods. One of the most important of these methods is remote sensing, which collects data from the air or space using satellites, airplanes, or unmanned vehicles and balloons. Radar interference or InSAR, as one of the methods of processing radar images in active remote sensing, is one of the most accurate and economical methods that allows the detection and detection of differences in altitudes created in the shortest time and for large areas. 

The study area is located in western Iran and one of the parts of Kermanshah city located in Kermanshah province is located in the area between latitudes 42 degrees and 9 minutes north and longitude 36 degrees and 2 minutes east. Radar interferometry studies in this study were taken by sentinel 1 satellite imagery of the C band. In this study, data from Sentinel 1 satellite in C-band were used. Using image processing with SARSCAPE 5.2 plugin in ENVI 5.3 software platform and using interferometry method, areas exposed to subsidence and the amount of subsidence in each area were determined. In the present study, the relationship between groundwater level drop and subsidence of Mahidasht plain has been investigated. For this purpose, groundwater depth data of 31 piezometer wells in Mahidasht plain were used. In terms of time, considering the accurate and available statistics, the statistics of 1394 and 1398 were cited. The steps of the research were as follows: after preparing the statistics of piezometric wells, the data reconstruction method was used to eliminate the deficiencies in the study data. The reconstruction method used, which was used only to correct the defects in the data, is the interpolation method, which was performed by Neural Power software (based on artificial neural network). To normalize the data, logarithmic transformations were used in SPSS 16 software and GS+ software was used for geostatistical analysis.

 Examination of subsidence status during the study years shows that from 2015 to 2020, the rate of subsidence has increased. In Figure 1, the areas marked in red have the highest subsidence, the areas marked in green and yellow have the least subsidence, and the areas marked in black have no subsidence. According to the subsidence map of the region, the maximum average subsidence rate in the study area reaches 16 cm. According to the extracted subsidence map of the study area, it is observed that the highest amount of subsidence occurred in the eastern part of Mahidasht plain, which decreased to the west of the plain, so that in the western and south western part of the plain, the lowest Subsidence is observed.Relationship between groundwater extraction and land subsidence phenomenon Considering that groundwater abstraction is one of the important causes of subsidence in Mahidasht plain, in order to explain the trend of groundwater level changes, the general trend of annual water level of all wells were examined. According to the groundwater level interpolation map, the groundwater depth in Mahidasht plain varies from a maximum of 21.62 meters in 1994 to a maximum of 24.71 meters in 1998.

Land subsidence is a pervasive phenomenon in the world, which has had a significant quantitative and qualitative manifestation in recent decades, mainly due to the improper exploitation of groundwater resources and the intensification of its decline. In the present study, the relationship between groundwater level drop and subsidence of Mahidasht plain has been investigated. Examination of statistics related to the depth of study wells as well as groundwater level zoning maps confirm the decrease of groundwater level. In fact, over-harvesting and lack of balance between feeding the aquifers and draining them has caused the water table in the region to follow a continuous downward trend, with the emptying of water in the cavities of the aquifer and the displacement of water in these cavities with Air reduces the equilibrium pressure between the layers. Due to the disturbance of the balance between the pressure and the weight of the upper layers, due to the force of the weight of the upper layers, the aqueous layers are compressed and the water table decreases and because this decrease has a direct relationship with subsidence. Therefore, in order to deal with this environmental problem, it is recommended to prevent the development of subsidence in the region or to minimize the occurrence of this phenomenon as much as possible by managing land use in the area of subsidence and also adequate supervision over the extraction of underground resources.

The main purpose of this research is to monitor the groundwater level using remote sensing science and satellite images and its relationship with land use. For this purpose, the relevant images were taken first and the necessary pre-processing was applied on each of them. Then the images were modelled and classified. In order to study land use changes, the land use classification map was extracted for 2002 and 2018 years using the object-oriented classification method and then to study land use changes, the land use change map was extracted for a period of 16 years. The highest rate of change is related to the use of rangeland to rainfed agriculture, rangeland to irrigated agriculture, forest to irrigated agriculture and rainfed agriculture to irrigated agriculture. Also, among the modified land uses, forest land use has the lowest increase. After extracting the land use change map in order to select the best interpolation model from different models, all models were evaluated and the Kriging method was more accurate than other methods, which among the different modes of the kriging method Also, K-Bessel model for 2002 and Circular model for 2018 have the highest accuracy. The results of groundwater survey showed that the highest and lowest average water level in 2002 belongs to rainfed agricultural use and water use and in 2018 rainfed agricultural use has the highest average water level and forest use has the lowest average water level.

Studies have shown that the role of thermal temperature measurement in studying and estimating surface temperature is very important. Earth surface temperature is an important indicator in the study of equilibrium energy models on the ground at the regional and global scale. Due to the limitation of meteorological stations, remote sensing can be a good alternative to earth surface temperature estimation. The main objective of this study is to monitor the surface temperature of the Earth using satellite imagery and a relationship that can have a surface temperature with land use. For this purpose, the relevant images were first obtained and the necessary pre-processes were applied to each one. Then it was compared to modeling and classification of images. Firstly, in order to study land use change, land use classification map was extracted for each two years using a controlled classification method. Then, to study the land use change, the land use change map was extracted for a period of 28 years (1987-2015). Became finally, in order to monitor the surface temperature, the surface temperature map of Meshginshahr was extracted. The results showed that there is a strong relationship between land use and surface temperature. High-vegetation areas and low-temperature blue areas. Also, rainfed farming has the highest average temperature relative to adjacent areas, which indicates the dryness of agricultural products in the Meshginshahr city.

Due to climate change and reduction of precipitation as one of its main components, especially in the mountainous areas of the northwest of Iran, it is important to study the important phenomenon of water and snow due to the importance of snow reservoirs in feeding water reserves. Due to the importance of spectral indices in measuring distance and spectral similarity of snow and water phenomena, in the present study, normalized indices of normalized water and snow, as well as normalized indices without snow/ice and water background and Otsu threshold method on the image Landsat 8, has been used as a study area by selecting a part of Sabalan Mountain and the Caspian Sea. The results of the present study showed that, according to the histogram and variance of the bands, the normalized differential indices of water without snow and snow without water, with 100% accuracy and 97% accuracy, respectively, showed high accuracy in extracting and separating water and snow cover phenomena. The results of the implementation of the Otsu binary algorithm also confirmed this and showed the high ability of the desired indicators to extract snow and water phenomena.

The land surface temperature is the highest layer of the earth's surface and depends on the level of surface emission, vegetation and the types of ground cover. Earth's surface temperature provides important information about the physical characteristics of the Earth's surface from local and global scales, and plays an important role in many applications. From Earth's surface temperature to study water resources management, agriculture, resource management, drought, environmental geochemical processing, meteorological research, global changes in land surface temperature, weather forecasting, hydrology, ecology, plant status survey, urban climate, studies Environmental and geophysical variables such as evaporation-transpiration and soil moisture are used. In general, temperature measurements at ground level are performed by meteorological stations, including synoptic and climatological. It should be noted that meteorological stations are only able to measure the temperature at specific points that have already been installed there. What is considered to be a major flaw in ground temperature monitoring is the lack of sufficient meteorological stations to know the temperature values in stations without stations, which have been partially remedied today by remote sensing technology. Earth's surface temperature is one of the most important components in global studies, which is used as one of the important factors in controlling the biological, chemical and physical processes of the earth.

Moghan plain is located in the northwest of Iran and is subject to Ardabil province and is located in the northern part of the province. ParsAbad Moghan city is a relatively large plain with an area of 143494 hectares, which occupies about 14% of Ardabil area and is the northernmost city of the province. The data used in this study included the Landsat 8 satellite image, which used its OLI sensor to extract land use maps and its TIRS gauge used to extract the Earth's surface temperature for 2018, as well as the ETM + Landsat imager to prepare land use maps. Using visible and infrared bands and surface temperature using thermal bands for 2002. The city's meteorological data were also used to check the temperature recorded by the stations. The ENVI 5.3 software was used for atmospheric and radiometric corrections, and the ARC GIS 10.5 software was used to extract the relevant maps. In order to classify land use, object-oriented classification method was used in eCognition Developer64 software. In object-oriented classification, spectral information is integrated with spatial information, and pixels are segmented based on the shape, texture, and gray tone of the image at a specific scale, and image classification is based on these components.

The largest area in 2002 belongs to the rangeland class with an area of 58,138 hectares. The second area belongs to the dryland agricultural class, which has the largest area with 52369 hectares. The smallest area belongs to the use of water with 543 hectares. For 2018, rainfed agriculture had the highest area with 41906 and then pasture with 30943 had the highest area. Looking at the uses of 2018, the results show a significant difference that the use of soil has increased from 11143 in 2002 to 30943 in 2018, as well as the use of irrigated agriculture and residential areas and forests. However, the use of irrigated areas has decreased significantly from 543 hectares to 262 hectares, and the use of rainfed and rangeland agriculture has also decreased compared to 2002. The water temperature during 2018 was almost constant and did not differ significantly, and the lowest temperature in both years is 31 degrees. Due to the fact that water has a high heat capacity, water has a lower surface temperature to the soil has the highest temperature in both years with 40.80 for 2002 and 42.92 for 2018. Aquaculture in 2003 was 33.12. C, which in 2018 increased to 34.41. C. The rangeland use has had a high temperature in both years of study and has increased from 38.22 in 2002 to 39.26 in 2018.

In this research, in the first step, in order to classify and then examine the changes that occurred in a certain period of time in the city of ParsAbad, action was taken. For this purpose, in the first stage of this research, in order to classify and record changes in a 16-year period, object-oriented images were classified in eCognition software and in ArcGIS10.5 software, extraction maps were extracted. Was. The classification accuracy in 2000 has a total accuracy of 0.90 and a coefficient of 0.87. While the classification in 2018 with an overall accuracy of 92% and a Cape coefficient of 0.90 has provided a relatively higher accuracy. After classification, an attempt was made to examine the changes that occurred in the region over a 16-year period, and a map of land use changes was drawn up for the area under study. Land surface temperature is one of the main parameters in the study of cities. Because it is almost the same as the air temperature in the lower layers of the city, which is the energy balance of the surface and determines the climate between the buildings and affects the life and comfort of the urban residents. Soil use has the highest temperature in both years with 40.80 for 2002 and 42.92 for 2018. Also noteworthy about residential areas is that residential use has increased from 34/51 in 2002 to 40/09 in 2018, which indicates that in 2018, with the expansion of the city compared to 16 years ago, the heat concentration will also increase. Increased. This use is due to the presence of man-made and heat-absorbing factors such as asphalt, concrete, the existence of various machinery and factories, as well as the creation of tall buildings that prevent heat from escaping around and prevent wind from moving into the city.

Groundwater is one of the main sources of drinking water, industry and agriculture in Iranchr('39')s arid and semi-arid regions. Improper harvesting and entry of pollutants into these waters have caused changes in the quality of these waters in different regions. The purpose of this study is to compare definitive introspection and statistical methods for zoning qualitative data of Borkhar plain in Isfahan province. In this study, the qualitative information of 29 groundwater wells was used to evaluate the qualitative parameters including: EC, TDS, Cl, SAR, SO4, Na, Mg and HCO3 in 2006 and 2016. GS + software and GIS environment were used to determine and map zoning maps using different introspection methods. Then, the qualitative parameters were evaluated using definitive internalization methods and zoning statistics and using error evaluation criteria, the best method for each qualitative parameter in each period was determined and their zoning map in the GIS was drawn. The results showed that in definitive internalization methods in all qualitative parameters, the RBF method provided less error. However, in the geo-statistical method, the results of fitting the models to the mutual variogram showed that different qualitative elements in 2006 and 2016 follow different models. Also, a map of qualitative characteristics was drawn during the study period and the results showed that the average qualitative characteristics of EC, TDS, Cl, SAR, SO4, Na, Mg and HCO3 in 2006 were equal to 942, 281.7, 27.28, 6.428, 10.409, 22.602, 8.936 and 4.178, which in 2016, this amount is equal to 1002, 352.4, 21.604, 6.059, 10.645, 18.869, 5.185 and 7.126, which indicates a decrease in water quality.

One of the most important wealth of a nation is the water that is flowing in the rivers of that country, and because of its impact on the settlement, site selection and development of villages, cities, communication networks and agriculture, it is of great importance since the past. Because of this, human attention has been given to river systems as one of the most vital constituents of the earth's surface in a variety of respects. River morphology is a multifaceted branch of earth science that may be considered as a specific geomorphological subject. One of the most important purposes of investigating river morphology is the geometric description of river bed parameters and interpretation of its main causes with the help of the theoretical knowledge base of water flow, solids and sediment transport. Changes in river morphology can cause many problems, including river diversion, flooding of surrounding areas, damage to hydraulic structures, as well as some environmental impacts. Short-term river variability may be gradual and continuous, but the long-term variability or under certain conditions, it is inconsistent and abrupt. One of the factors that can severely impair the stability of rivers is the construction of dams. Flow changes by the dam can affect the amount, timing, and duration of upstream and downstream currents. The purpose of this study was to investigate the morphological changes (patterns and dynamics) of the Gharasu River (from Samian Bridge to Sabalan Dam) over a period of 19-years (2000–2019).

The study area of the Gharasu River is approximately 51 km 2 in geographical coordinates of 48 ° 2 min to 48 ° 18 min east longitude and 38 ° 22 min to 38 ° 30 min north latitude in the political-administrative boundary of the city of Ardabil. The highlands of the region are mainly composed of Eocene volcanic rocks. There are also two Quaternary units of Qsc and Qst related to Sabalan volcanic activity. The study area is located in the geological division of Iran in the Alborz-Azerbaijan zone. In most of Iran's construction divisions, the Azerbaijan region has been considered the continuation of central Iran. Topographic maps, geological maps, Landsat, Sentinel and Google Earth satellite imagery, digital elevation model (DEM) images, and climatic and hydrometric data were used in the study. In order to quantify the meandering development of alluvial rivers and determine the behavioral pattern and its changes over time, the geometrical characteristics of rivers such as central angle, meander radius, wavelength, valley length and bending coefficient ( Sinusitis), were measured and analyzed to determine changes in the course of the river channel from the past up to the  present.

The mean radius of meanders for the entire Gharasu River channel during the study periods of 2000, 2010, and 2019 were 11.02, 100.90, and 99.40 meters; respectively, indicating a decreasing trend. The average length of arches during these years was 254.29, 250.24 and 251.74 meters; respectively. For this reason, over the years under the study, the mean central angle has an increasing trend from 138/50 in 2000 to 153/15 in 2010 and 157/41 in 2019. The mean curvature coefficients for the entire study period from the Gharasu River for the periods 2000, 2010 and 2019 were 1.58, 1.61 and 1.63;respectively. The values of this index during the years 2000, 2010 and 2019 were about 2.40, 2.52 and 2.58; respectively, which is of a severe meander type. The average rate of migration (Rm) of the study period from the Gharasu River during the years 2000 to 2010 was about 0.5 m / year. The value of this indicator for the period 2010 to 2019 has decreased to about 0.3 meters per year. According to calculations, the index has grown to about 0.4 m / year over the past 19 years (from 2000 to 2019). Another method called transect method was used to evaluate lateral variations of the study through the Gharasu River channel. According to calculations using the transect method over the past 19 years, a total of approximately 22.45 hectares of the Gharasu River margin lands have been lost due to erosion processes during the study period. This amount was about 13.75 ha during the period 2000 to 2010 and about 10.22 ha during the period 2010 to 2019.

In this study, the morphology and lateral variations of the ditch of Gharasu River in Ardabil province were evaluated. Based on geomorphological conditions, slope and width of the flood plain as well as other factors (such as river discharge variations), the river was divided into three sub-intervals and 30 transects in the study area. In addition to the visual interpretation, in order to evaluate and analyse the morphology of the Gharasu River, quantitative indicators were required. The present study used four indices of curvature, central angle of the cornice, channel migration rate and transect method to detect and identify the river pattern as well as lateral channel changes. These  indices were calculated over the time periods of 2000- 2010, and 2010-2019 for the study river channel. The results showed that the Gharasu River has had a developed meander pattern in most of the studies. The results of the two channel migration rate indices and the transect method also confirmed that, overall, the transverse changes of the Gharasu River channel have been low. Also, these two indices, similar to the Cornis curve coefficient and the central angle, showed that transverse dynamics have declined sharply in the last decade.

Due to increasing land-use changes, mainly for human activities, it is necessary to monitor vegetation changes, evaluate their trends and their environmental impacts for future planning and resource management. With the increase in population and the development of technologies, human beings are, currently, considered the most important and powerful tool of environmental change in the biosphere. Land use is the type of land use in the current situation, which includes all land uses in various sectors of agriculture, natural resources, and industry. Due to the provision of a wide and integrated view of an area, reproducibility, easy access, high accuracy of information obtained, and high speed of analysis, using satellite data is a good way to prepare a land-use map, especially in large geographical areas. One of the most widely used methods of extracting information from satellite images is classification, which allows users to generate different information. According to the type of classification method of the study area, the characteristics of the educational points get different results to separate the thematic phenomena and extract information more accurately.

Mordagh River, which is known as Mordi Chai in the region, originates from the southern slope of Sahand Mountain located in East Azerbaijan and flows south. By connecting the sub-branches, it continues its way to the city of Maragheh, passes through the city of Malekan, and enters Lake Urmia. In the present study, Landsat satellite images, TM, and OLI sensors from 2000 and 2020 were used to identify the area and prepare a land-use map. To prepare for classification and processing on them, the necessary pre-processing was first done on the images. Images were pre-processed in ENVI5.3 software using the FLAASH method. Finally, ENVI5.3 software was used to classify the base pixel and eCognition Developer 64 software was used for object-oriented classification. To evaluate the classification results, the Kappa coefficient and overall accuracy were used to evaluate the classification accuracy of the maps.

According to the obtained results, it is observed that the most area in the study area in 2000 with the method of minimum distance belongs to the use of medium and dense rangeland. The lowest area for the year 2000 is the use of residential areas. In 2020, the highest area of land use is 173.875 square kilometers. The lowest area is related to the use of snow with a rate of 0.199 square kilometers and the use of residential areas, which compared to 2000, has an increase of up to 5.54 square kilometers. In the maximum likelihood method in 2000 and 2020, the highest areas are related to medium rangeland and soil uses, respectively. The lowest area for 2000 is related to vegetation and for 2020 is snow use. In addition, in the support vector machine method, the highest and lowest areas for 2000 are related to medium rangeland and vegetation uses, respectively, and for 2020, medium rangeland and snow uses have the highest and lowest areas, respectively. According to the maps obtained from the object-oriented method, the highest area in 2000 is related to medium rangeland with 156.406 square kilometers and then dense rangeland with 96.514 square kilometers. The lowest area is related to the use of residential areas with 11.141 square kilometers. In 2020, the highest area is related to the use of dense rangeland (126.907 square kilometers). In addition, the lowest area is snow use with an amount of 5.199 square kilometers.

According to the results of this study and other studies, it can be suggested that the object-oriented classification method for land-use change studies is a more appropriate and accurate method than the pixel-based method. One of the most important reasons for achieving high accuracy in the object-oriented classification method is that in this method, in addition to spectral information, information related to texture, shape, position, and content is also used in the classification process. The study of pixel-based classification showed that in selecting educational examples, the more uniform the user is and free of mixed pixels, the more accurate the classification process is. So that the land use classification and vegetation in the pixel-based method had the highest accuracy, which due to the uniform surface of both land use and homogeneous texture, the selection of training samples in these uses with the highest accuracy and have played an important role in improving overall accuracy and kappa coefficient. Based on the results of the extent of different classes related to the land use of the basin studied in 2000 and 2020, we see a decreasing trend of dense rangeland, medium rangeland, and vegetation and increasing land use of residential areas and soil. What is very clear in these maps is the excessive reduction of pastures and their conversion to other uses.

Given the growing population and the need for food and economic issues, this transformation is obvious and it cannot be said that this change can be prevented.

Snow and ice cover is a vital resource and is very sensitive to climate change. Accurate measurement of the spatial distribution of snow and ice at regular intervals is essential for water resources management. In this research, the snow cover of Sabalan Mountain is extracted using Landsat 5 satellite images for 2000 and Landsat 8 for 2020 using object-oriented classification and also using NDSI and NDSINW spectral indices. The results of the classification maps extracted from the object-oriented method showed that the area covered by snow for the years 2000 and 2020 is 2500 and 1954 square kilometers, respectively, with a kappa coefficient and an overall accuracy of 99%. And 0/99 for both years. Then the snow covered area was extracted using NDSI spectral indices and NDSINW index. Both of these indicators are able to extract the snow cover surface with appropriate relative accuracy in cold and winter regions. However, because the NDSI index does not distinguish between snow and water, it may produce different results in areas with waterlogging associated with snow extraction. The NDSINW index, on the other hand, is more accurate in eliminating waterborne backgrounds. In relation to the present study, the NDSI index has extracted 2610 and 2577 square kilometers of snow cover and the NDSINW index has extracted 2577 and 1937 square kilometers for 2000 and 2020, respectively. The area obtained from the NDSINW index has been extracted by the object-oriented classification is closer.

Today, soil erosion and sediment production are considered a danger to humans and their lives, so in areas where soil erosion is not controlled, soils gradually erode and lose their fertility. The aim of this study is to model the relationship between the amount of suspended sediment load and the geomorphic features of the Nirchai River Basin.

In this study, GIS was used to extract geomorphic features and its relationship with sedimentation. Stepwise multivariate regression analysis was used to determine the relationship between geomorphic characteristics and sedimentation of each sub-basin. The results of investigating the relationship between geomorphic characteristics and sediment in sub-basins showed that the amount of sediment produced had a positive correlation with the volume of flow and the form factor of the basin and was significant at the level of 5 percent. Also, in order to identify the factors affecting the amount of sediment in the basin among the existing variables, the principal component analysis method was used.

The results show that among 25 variables affecting sediment production, four factors of area, basin coefficient, environment and discharge were 37/78, 25/47, 18/5 and 11/22 percent of variance of all variables of research.

Therefore, it is concluded that in total, all the effective factors of the four final extracted factors were able to explain 92.9 percent of the variance of all research variables.

The use of satellite imagery and remote sensing models has been a convenient and inexpensive tool for estimating solar radiation in recent years.images related to the two years 2019 and 2020 of Landsat 8 satellites, OLI sensors, TIRS sensors and Sabal algorithm were used. The results show that the average of the highest input shortwave radiation was 914 watts per square meter in June and the lowest in November was 548 watts per square meter. The highest amount of net radiation in June was 505 watts per square meter and the lowest in February was 467 watts per square meter. Finally, the highest amount of net radiation reached the earth's surface in June at an average of 505 watts per square meter, the lowest average amount was in November at 296 watts per square meter. The difference in the amount of net radiation reaching the earth in the study area is due to the difference in the angle of the sun and the number of hours of sunshine in different months of the year. Finally, it can be concluded that solar radiation in the region, in the two years under study has the necessary potential to implement solar photovoltaic projects.

Changes in land cover and land use affect many natural processes such as soil erosion and sediment production and the physical and chemical properties of the soil. The purpose of this study is to investigate the trend of land use change in Givi Chay catchment area and also its effect on soil erosion using WLC algorithm. For the present study, Landsat satellite images for 1985 (TM) and 2020 (OLI) were used and the land use map was prepared using the object-oriented method. The results of detection of changes showed that the highest rate of increase is for irrigated cultivation, which has increased by 17.89% compared to 1985. The highest rate of change is for low density pastures, which has decreased by 13.02%. According to the erosion zoning map of 1985, 27.76 and 13.06%, and according to the erosion zoning of 2020, 24.98 and 16.39% of the Givi Chay catchment area in two high-risk floors and very risky, so that these classes are also in both time periods in residential use, rainfed cultivation and medium rangeland.

Background and Objective :

Groundwater is the most important source of fresh water in the world. Drinking water for two billion people is supplied directly from groundwater and is used to irrigate the world's largest food supply. Improper harvesting of groundwater reservoirs has led to the fact that the amount of feeder feed is not responsive to harvesting and the groundwater level has dropped. The drop in groundwater levels has led to problems such as drying up water wells, declining river and lake discharge, lowering water quality, increasing pumping costs and water extraction and land subsidence. Awareness of water level changes is necessary to understand the status of groundwater aquifers and their optimal management. By assessing groundwater level fluctuations, it can be used to manage water resources. One of the major applications of remote sensing is the detection and determination of land use changes. Using remote sensing, it is possible to study and identify various phenomena. The aim of this study was to investigate the effect of different land use on groundwater using interpolation geostatistical methods as well as object-oriented classification methods for land use mapping.

Materials and Methods:

 Ardabil plain is a mountainous plain located in northwestern Iran and east of the Azerbaijani plateau. The plain covers an area of 990 km2 among the highlands around it and in terms of political divisions includes parts of the cities of Ardabil and Namin. The data used in this study included a Landsat 8 satellite image of the OLI surveyor for the 2015 land use map, as well as a Landsat TM 5 surveyor for the 1987 land use plan. Also, in this study, the groundwater depth data of 43 piezometer wells in Ardabil plain were used. In this research, after preparing the statistics of piezometric wells, the data reconstruction method was used to eliminate the deficiencies in the study data. Reconstruction, which was used only to correct deficiencies in the data, is an interpolation method performed by the Neural Power software (based on the artificial neural network). To normalize the data, logarithmic transformations were used in SPSS and GS+ software was used for geostatistical analysis. ENVI software was used for atmospheric and radiometric corrections and ArcGIS software was used to extract the layer map.

Results and Discussion:

 The largest area in 1987 belongs to the irrigated agricultural class with an area of 51840 hectares. The second area belongs to the rainfed agricultural class, which has the largest area with 48,790 hectares. The smallest area also belongs to the use of water with 88.65 hectares. Looking at the uses of 1394, the results showed significant differences in such a way that the use of irrigated agriculture with 10.17 hectares has increased significantly compared to 1987. After extracting the land use change map to select the best intrusion model from among the various models, all models were evaluated and only the models that were more accurate than the other models were selected. The highest average water level was recorded in 1987 for agricultural agriculture and the lowest average water level was recorded for the forest area. Considering the land use map and the groundwater level map of 1394, the above analysis is confirmed and as it is known, the highest average water level this year belongs to the use of irrigated agriculture with 20.17 meters and the lowest average recorded water level is related to the use of the forest is 11.45 meters. Compared to 1987, water use has had a decrease in water level, which has reduced the water level of dams and also reduced the volume of water in rivers and even dried up several of these rivers. After water use, one of the most interesting uses that need to be analyzed and the reason for its search is the use of irrigated agriculture. This land use has the highest water level drop in 1987 and in 2015 it has faced the highest water level drop. The reason can be attributed to the over-harvesting of groundwater for irrigated crops that need more irrigation. Due to the fact that the Rain-fed crops in the study area are mostly wheat and do not need water or needless, but the amount of groundwater level in 2015 compared to 1987 has been accompanied by a significant decline. The use of pastures in 2015 compared to 1987 has dropped significantly, which indicates the critical situation of groundwater and excessive use of these resources.

Conclusion:

 In this study, in the first step, in order to classify and then examine the changes that occurred in a certain period of time in the study area. In order to classify the relevant images, An object-oriented classification method was used in eCognition software and the relevant outputs were extracted in ArcGIS software. Evaluation of classification accuracy for 2015 has a very high accuracy so that the overall accuracy and coefficient of the extracted Kapa at the highest possible level, the overall accuracy of 100% and the coefficient of Kapa 0.99 and for the year 1987 was extracted with less accuracy and general accuracy for In 1987, 98% and the Kappa coefficient was 0.95. After extracting the land use change map to select the best intrusion model from among the various models, all models were evaluated. Due to ME and RMSE values, the curing method has higher accuracy than other methods. Among the various modes of the curing method, the Gaussian model has the highest accuracy. According to the results, the most changed use in this area has been the use of pastures in irrigated agriculture and Rain-fed agriculture. This change shows that the increase in the use of irrigated agriculture and Rain-fed agriculture in this area has been accompanied by a decrease in the use of rangelands, which indicates the destruction of pastures. According to the groundwater level map, the highest average water level was recorded in 1987 for irrigated agricultural use and the lowest average water level was recorded for the forest area. Also, the highest average water level in 2015 belongs to the use of irrigated agriculture with 20.17 meters and the lowest average recorded water level is related to forest use with 11.45 meters. One of the interesting uses that need to be analyzed and the reason for its search is the use of irrigated agriculture. This land use has the highest water level drop in 1987 and in 2015 it has faced the highest water level drop. The reason can be attributed to the over-harvesting of groundwater for irrigated crops that need more irrigation. In general, all uses in 2015 compared to 1366 have faced a decrease in water balance. As a result of these changes, farmers have made more use of groundwater resources, which has led to a drop in groundwater levels over a 28 years period in the study area. This overuse is enough to reduce the average level of the plain by 4.9 meters during the mentioned period.

The purpose of this research is to evaluate the land use of Ghouchan city by using object-oriented and pixel-based classification as well as predicting these changes using the CA Markov model until 2030. In this research, Landsat satellite images of ETM and OLI sensors for the years 2000 and 2018 (August) were used. After the images were taken, radiometric corrections were applied to the images, and then using the ground-object and object-oriented pixel methods, a land use map was extracted. In order to evaluate the classification accuracy, general accuracy and Kappa coefficients were used. The results obtained in the object-oriented classification in both of the general accuracy and kappa coefficients were 94% and 97%, respectively, which is more accurate than the pixel-based method. Most of the area in the region, using Object Oriented Classification in 2000, is related to land use and mountainous land use, and to base land use and land use, respectively. According to the Classification for 2018 using Object Oriented Classification, most of the area had the weakest land use and dry land use. Using the CA Markov modeling and considering the two land use maps, the probability matrix was calculated, and the CA mapping prediction map for the next 12 years, 2030, was obtained, and the area and percentage of each Uses were calculated separately. The results showed that the greatest increase in the variation among the users would be for poor pasture users in 2030, which is also increased by 2019-27449 hectarrs. The largest reduction in area will be for dense pasture users with a total area of 236666 hectares. Man-made human consumption will grow at 62.530 hectares during this 12-year period. By predicting user variations, the extent to which resources can be expanded or degraded can be guided, and this can be done by directing these changes to appropriate paths.

Floods are one of the most destructive and most frequent natural disasters. In this regard, flood risk zoning is one of the most effective methods for managing and mitigating the effects of floods. In this study, the spatial and risk assessment of flood events at the surface of Ghorichai watershed in Ardabil province was investigated. In this regard, 10 measures affecting the flood event were applied. These criteria include elevation, slope, slope orientation, convexity, geological formations, drainage density, curve number (CN), waterway distance, land use and vegetation. In the meantime, the ground slope variable with a weight of 0.26 (excluding network analysis process model) plays a major role in identifying high risk flood zones. Two fuzzy logic models and a network analysis process (GIS) were used to integrate and overlap the aforementioned subject layers in order to prepare a flood risk zoning map. Flood hazard zoning of the Ghorichai watershed showed that about 18% of the studied watershed area was located in high and very high risk classes. Flood risk in the basins of the main valleys and downstream lands of the study basin due to Geomorphometric reasons such as formation and development of flood plains, low relative elevation, concave terrain, and rapid upstream runoff of the event potential. It is high. In addition, relocation of residential areas downstream of the basin has increased the risk of flooding in these areas. Flooding is called the dangerous increase in the flow of a river or a flood. This phenomenon has a long history in human history and is one of the most damaging and destructive natural events. Old towns are usually formed alongside rivers due to easier access to water; they are therefore affected by floods, causing casualties. Floods occur when water flows out of rivers, streams and canals, in other words leaving its natural channel. We see an event when the canal or river is completely filled with water and enters the floodplains and areas where people live. In the present study, the risk of flooding in the Ghorichai watershed is investigated. Due to the large extent of the semi-arid climate and the presence of numerous settlements in the region, it is important to assess the importance of flood risk assessment and zoning in the Ghorichai catchment area. This is especially important in the presence of large human settlements, extensive agricultural lands and conservation of water and soil resources. Therefore, flood risk zoning at the basin level of the study is one of the essential steps in the management of flood risk mitigation and management measures.

The present study investigates the risk of flooding in the Ghorichai watershed. This basin is located in Ardebil province within the administrative districts of Nair, Ardebil and Kosar. Much of this basin is located in the Nair area. The study area is located at 48 degrees 2 minutes to 48 degrees 31 minutes east longitude and 37 degrees 46 minutes 38 degrees 11 minutes north latitude in mathematical position. The basin has an area of about 824 km2 and an area of about 240 km. In this study, the following data and tools were used to analyzed flood risk in the Ghorichai catchment: 1: 50000 area topographic maps, 1 scale geological maps : 100000 including Ardebil and Givi Sheets, 1: 250000 Scale Soil Map, Digital Elevation Model (DEM) for ALOS - PALSAR Satellite Area with 12.5 m Resolution, Sentinel2 Satellite Images with 10 m spatial resolution and meteorological and climatic data including Ardabil synoptic station data and rain gauge station data located within the watershed. Two models of fuzzy logic and network analysis process (ANP) in the framework of Geographic Information System (GIS) were used to model flood risk in the Ghorichai catchment area.

The final layer resulting from the composition and overlap of the subject layer indicates the potential for flood risk at the Ghorichai watershed. The resulting map was classified into five classes of very low, low, medium, high and very high flood risk. According to the flood risk zoning map of the Ghorichai catchment, it can be stated that approximately 49 km2 of the catchment area is in a very high flood risk class, covering about 5.9% of the catchment area. In addition, more than 101 km2 or about 12% of the study area is in high risk class. Most of these high-risk areas are adjacent to either side of the basin's main waterways or floodplains adjacent to them. This can be attributed to a number of reasons, such as the smoothness or slowness of these areas (and thus the possibility of easier spreading and spreading floods), the existence of a valley extended by the flood plain below it. , Counts the crossing of the Ghorichai River through these zones and the low altitude of these zones.

In this study, in order to map the flood hazard in the Ghorichai catchment area, 10 factors influencing flood event were applied. These criteria can be divided into three main categories: geomorphological criteria including altitude, slope, slope direction, ground convexity and geological formations; hydrological criteria including drainage density variables, curve number (CN) and distance from the waterway. And land cover criteria including land use and vegetation (NDVI). To integrate and overlap the research thematic layers with the aim of mapping flood risk zoning at the Ghorichai watershed, we use two models of fuzzy logic and ANP in the form of GIS. There was action. In this regard, all subject layers affecting flood occurrence were applied with different fuzzy functions, in the range between 0 and 1 standard and having the same units. Then all relevant subject layers were combined with the weights obtained from the ANP model. Flood hazard zoning of the Quriichai watershed showed that about 18% of the studied watershed area was located in high and very high risk classes.

 Landslide is a term that encompasses a variety of amplitude motions and causes the movement of a mass of material in the slopes. And creep is classified. Natural slope instability is one of the geomorphological and geological phenomena that plays an effective role in deforming the earth's surface. Identifying areas with potential for landslides and their zoning is one of the key steps in managing environmental hazards and reducing the damage caused by this phenomenon, because this phenomenon causes financial and human costs, soil and land degradation and increased sediment production at the basin outlet. It becomes. Iran with its predominantly mountainous topography, high tectonic activity and seismicity, diverse geological and climatic conditions, has the most natural conditions to create a wide range of landslides. The purpose of this study is to zoning the risk of landslides on the Sarab-Nir road. In this research, two models of network analysis and fuzzy logic are examined and evaluated. It is hoped that eventually, by preparing a landslide risk zoning map, it will be of great help to planners and managers in order to reduce potential damages and find safer locations for development, construction and road construction.

Sarab-Nir road is located between East Azarbaijan province and Ardabil province and is a communication route between these two provinces, whose geographical coordinates are 37 degrees and 94 minutes to 38 degrees and 03 minutes north latitude and 47 degrees and 53 minutes to 48 degrees and 01 minutes. It is east longitude. In this study, network analysis model was used to determine areas prone to fall and zoning. In order to better understand the causes of landslides and also to organize the research in the field, the study area was visited and 15 geographical points from different areas of the study area were recorded. The geographical location of the points prone to fall was also recorded with GPS. Then, according to the network analysis model, information layers were prepared in ArcGIS software. The information layers for landslide risk zoning are: fault, slope, slope direction, distance from road, and distance from waterway, land use, geology (lithology), precipitation, altitude and vegetation. The elevation file or digital model of the elevation of the area was prepared with an accuracy of 30 meters from the USGS site and the desired DEM is a digital file obtained from the AST‌ER sensor and according to this DEM, the information layer such as streams, slope and direction ‌The slope was obtained.

Four maps have been developed to investigate landslide hazards, which are rainfall, slope, elevation and land use layers. After creating information layers in order to prepare the final landslide hazard map, fuzzy information layer maps were created. In this study, in order to determine the effect of different classes of criteria on landslide sensitivity zoning, the layers are based on the type of performance of each in the landslide event using fuzzy membership functions in the range of zero to 1 fuzzy. Were made. The results obtained from the information layers and finally the landslide hazard map show that altitudes of more than 2000 meters have the highest share of landslides, and altitudes of 1400 meters have been significant landslides due to the instability of the slopes against Climatic and environmental factors. Also, most of the landslides occurred at a distance of 3 to 6 km from the faults, which shows the importance of faults against landslides. About 40% of landslides occur in very high-risk classes. This indicates that the model has a high capability in predicting landslides. It is necessary to explain that most of the landslides occur in the area of Saein pass, which have very favourable conditions for the occurrence of range movements that start from 25 km of mirage and continue for a distance of 15 km of Nir.

Factors such as slope, precipitation and geology play a more important role in landslides than other factors. Slopes of 60 to 80% have the greatest impact on landslides, which are more pronounced at altitudes above 2000 meters. Therefore, altitudes above 2000 meters have the most landslides. Also, due to the direct relationship between altitude and climatic fluctuations in these altitudes, the amount of precipitation is higher and, of course, has a great impact on the occurrence of landslides. In these areas, vegetation is at a minimum and due to the cold region, the vegetation in these areas is very small, which prepares the conditions for landslides and due to the presence of sedimentary formations such as sandstone, Siltstone mudstone with tuff interbreeds in the area, the conditions for landslides have become more prone and because these formations lose their stability sooner and are strongly influenced by physicochemical factors, they are more prone to landslides than other formations. To be. According to the results, the low risk floor with the highest value, 405.44 square kilometers, occupies approximately 30.87 percent of the area, but the very high risk floor with 288.2 square kilometers and the high risk class with 23.23 square kilometers. , Occupy a total of 37.25% of the area of risk classes.

Landslides as one of the natural hazards can cause a lot of damage every year. Kahman catchment with potential mountainous features and different natural conditions has potential landslide potential. The purpose of this study was to investigate landslide hazard using fuzzy logic model in Kahman basin of Lorestan province. In this study, first, effective parameters on landslide occurrence were extracted and then the relevant layers were prepared. Then the landslide distribution map of the basin occurred. Then, by integrating the landslide map with the landslide distribution map, the effect of each of the slope factors, slope direction, lithology, precipitation, land use, distance from fault and waterway were calculated in ArcGIS software environment. In this study, fuzzy logic model with fuzzy community operator, fuzzy sharing operator, fuzzy algebraic multiplier operator, fuzzy algebraic sum operator, and fuzzy gamma operator were applied. Based on zoning using gamma-fuzzy operator, 64.37, 7.45, 8.93, 12.49 and 6.76 percent of the area were classified as very low, low, medium, high and very high risk classes has taken.

Rapid development of cities due to extensive changes in land use and land cover has had negative effects on global environmental quality. Land cover and  land use changes, and the development of urban and agricultural regions and deforestation are changing the regional and local temperature regime. Knowing the land surface temperature degrees contribute significantly to a wide range of issues relating to the Earth science such as urban climate, global environmental changes, and the study of the interaction of human and the environment. The lack of sufficient meteorological stations to be aware of temperature values in regions lacking a station is considered as a major flaw in monitoring the land surface temperature. Due to the information limitations, collecting data especially to a large extent,  is associated with many problems and obstacles, and the real-time access is difficult or impossible. Therefore, the need to use remote sensing technology with time conditions along with the feature of continuity and data collection in wide ranges can be very effective. The purpose of this study is to investigate the land surface temperature of Namin county in a period of 28 years and to compare the obtained results with land use and vegetation changes.

The data used in this study included  Landsat 8 satellite image of the OLI sensor in order to extract land use map and  TIRS sensor image to extract land surface temperature for the year 2015. Moreover, Landsat 5 satellite image of the TM sensor were used to extract land use map by using visible and infrared bands, and also to extract land surface temperature by using thermal bands for the year 1987. Images were taken in late spring and early summer due to the lack of high cloudy and snowy covers , as well as the high intensity of sunlight. The eCognition8.9 software was used for object-based classification. Classification in five classes (dry and irrigated farming, rangeland, forest and residential) and six classes (dry and irrigated farming, rangeland, forest, residential and water bodies) were selected  for the years 1987 and 2015 respectively. To assess the accuracy and comparison of the obtained maps, the error matrix, overall accuracy, and kappa statistics were used. Split-Window method was used to extract the land surface temperature of the study area. Finally, in order to analyze the relationship between land surface temperature with vegetation index, the correlation coefficients between land surface temperature and vegetation index were calculated based on land use types in the years 1987 and 2015.

The highest land use area in the years 1987 and 2015 belongs to the rangeland use with 43781 and 34114 hectares  respectively and the second land use area belongs to dry farming use with 23854 and 33277 hectares respectively. Moreover due to the lack of water use , the lowest land use area in 1987 belongs to residential use with 1301 hectares, while in 2015 with the construction of water structures, water use with an area of 86 hectares has the lowest land use area. The highest land use area increase was in the dry farming with 9423 hectares, which is a significant increase compared to 1987. The highest recorded temperature for Namin county in 1987 and 2015 was related to dry farming use (34°C and 27°C, respectively), indicating the concentration of heat in these regions. This type of land use has the highest temperature due to the factors such as the dryness of the products at this time and the harvest of the products. In 1987, dry farming use had the highest temperature (34°C), but in 2015 it experienced a decrease in temperature (27°C), despite the fact that it had the highest land surface temperature compared to other  types of land uses in 2015. The reason can be attributed to the factor of harvesting crops. Due to the fact that the rainfed crops in the study area are mostly wheat, and at this time of the season, most of the wheat is ripe or harvested, so the transpiration of these products is insignificant. The lowest recorded temperatures in Namin county are related to the uses of water bodies (21°C), forest (21°C) and irrigated farming (22°C), respectively.  Since water has a high heat capacity, it has the greatest effect on reducing the temperature. In forest and irrigated farming land uses, due to the higher vegetation density, the land surface temperature has the lowest value (23°C and 24°C in 1987 and 21°C and 22°C in 2015 respectively) compared to the other land use types. Agricultural land use in this area has the lowest land surface temperature (24°C in 1987 and 21°C in 2015) after forest areas. Due to the fact that the crops cultivated in this area are plants such as potatoes and these plants have more water needs, therefore these plants have a high greenness value at June to early July, which has led to more transpiration in the area where they are cultivated than other areas, thus it has been very effective in keeping the land surface temperature cool. The rangeland use has had high land surface temperatures (27°C and 25°C, respectively) in the two study  years, and there is little difference between the two years. According to the study season which was late June to early July, the high temperature of this land use type is due to the increase in the areas lacking canopy cover or areas having low or scattered vegetation. Due to the fact that in August, most of the leaves and brunches of the existing plants are dry and the transpiration is low, high temperatures are also recorded. The relationship between land surface temperature and vegetation index in rangeland use in the two study years had the highest correlation (0.91 in 1987 and 0.83 in 2015), while the correlation coefficient of the forest use was the lowest (0.46 in 1987 and 0.23 in 2015).

Land use type and land use and vegetation changes have a significant effect on land surface temperature changes. However, areas without vegetation have a higher land surface temperature than the areas with vegetation. The results showed that there was no significant correlation between vegetation cover and land surface temperature, which is mainly due to sufficient vegetation. In general, the results showed that in most areas with lower temperatures, there is high density vegetation indicating an inverse relationship between vegetation index and land surface temperature.