فصلنامه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی
سال دوازدهم شماره 3 (پاییز 1400)

Recently, a lot of studies have been done in Anzali wetland as one of the most important wetlands of Ramsar Convention, which has a high cost due to the nature and geographical location of the wetland. Advances in technology have made it possible to evaluate natural environments more accurately, fast, and low cost with remote sensing data due to their easy accessibility, high accuracy, extensive and reproducible coverage in terms of time and space, and information extraction in a relatively short time. Because one of the most important problems in studying vegetation changes is the lack of accurate spatial information over time. Satellite imagery and remote sensing technology make it possible to achieve a better program for environmental management by relying on the information produced by it.  In this study, the vegetation classification of Anzali wetland was done by using the technique of Object base classifications of Landsat image incorporation with fieldwork based on the wetland index of plants as well as the vegetation index (NDVI) of the study area were analyzed. Wetland vegetation classification maps can be used to identify the amount and type of cover and planning to maintain and rehabilitate the wetland.

 In this study, a vegetation map based on the wetland index is considered as one of the required criteria for ecological demarcation of wetlands. First, the general vegetation areas of the wetland on the coast and around it were identified.  Then, vegetation data of wetland aquatic species were collected from different wetland areas in 0.25 m2 plots. In the land margin area, the wetland species of the wetland margin were collected with a 1 m2 plot. A total of 42 plots were collected during the spring and summer of 2019. After preparing the required images, their preprocessing including geometric, atmospheric, radiometric corrections and image enhancement were performed using ENVI. Landsat 8 Image on July 29, 1998, with a spatial resolution of 30 meters was used to classify vegetation and prepare a map of vegetation index (NDVI) and image of Sentinel-2 satellite (July 98) due to 10 m of the ground resolution was used to combine with Landsat 8 data as auxiliary data in image classification. The combining of these two images improves the spatial resolution also preserves the spectral values ​​of the multispectral image. The object-based classification was performed on the integrated Landsat 8 image using training data from field work. The classification accuracy was evaluated for each class using experimental samples as ground control points and the classification error matrix was extracted.

 First, the dominant plants and representatives of their wetland index were identified by field work. Then, the relative percentage of dominant plant cover at the sampling site was calculated according to the standard list of identified plant species, and Plants were divided into two groups of wetland and non-wetland based on the wetland index. From the classification of plot species in 42 plots, 180 plant species were identified in 124 genera and 48 families. Also, four groups of wetland plants were: obligate wetland plants (OBL), facultative and obligate wetland plants (OBL & FACW), facultative upland, and facultative wetland plants (FACU & FACW), and facultative wetland plants (FACW). A vegetation map was prepared from a combination of terrestrial samples and object base classification of the 2019 Landsat satellite OLI image sensor.  The accuracy of the classified maps was evaluated based on the kappa coefficient and overall accuracy. The overall accuracy is 88.62% and the kappa coefficient is 84%. The Plant distribution was determined based on satellite image classification: OBL plants were observed in the water zone (west and Sorkhankol wetland margin), FACW plants were observed mostly in the dry margin and mainly in the southwest of the wetland (Siahkeshim wetland) and Choukam Wildlife Sanctuary in the eastern part of the wetland, OBL & FACW group with less uniform distribution was observed in the whole area and FACU & FACW group was observed in a small part in Choukam, north, and northwest of the wetland. The percentage of vegetation density map retrieved from the NDVI index shows the distribution of dense vegetation cover in different parts of the wetland and the limitation of the water level of the wetland bed.

The results of the satellite imagery study and their classification according to terrestrial samples showed that the spread and dispersal of obligate wetland species (OBL) were limited to water parts of the wetlands so that the highest distribution of these plants were in the west of the Anzali wetland and Sorkhankol. The spread of facultative wetland species (FACW) was in the arid areas of the wetland, which indicates the upland areas of the wetland in Siahkeshim (southwest) and Choukam (east). The result of image classification showed the percentage of plant group in each class: the agricultural class (with a present level of 23.9%) and the group of facultative species (FACW) (with a present level of 23.6% and mostly Phragmites, Alnus, and Salix species) have the top percentage of image classification classes of ​​Anzali Wetland. This indicates more presence of facultative species compared to obligate species of wetland (OBL) (with a present level of 10.1%) and the level of agricultural land occupation, showed the wetland drying. The percentage of vegetation at the wetland level was assessed with the vegetation index (NDVI), most of which belongs to dense vegetation. Due to the fact that the satellite image is related to the summer season, this map shows the distribution of vegetation in different parts and the water level of the wetland bed, which has reduced the amount of water levels in the wetland. Periodic review of vegetation and its ecological changes provides useful information on changes in the water and ecological resources of the wetland to plan for its maintenance as an important ecosystem in the region.

A method to reduce the absorption of solar radiation and prevent the creation of urban heat islands is to increase shade by vegetation. A shadow creating on buildings, causes houses to cool down, reduces energy consumption and costs, increases the value of houses, and creates a proper visual effect and a sense of well-being and vitality. Although economically, the amount of savings due to shade and cooling of the air for a tree during its lifetime in different climatic regions is different and depends on the type of tree, the amount of shade during the day and in different seasons of the year, but its effect on energy savings and costs are definite.  The subject of the present study is strategic planning to increase the shade coverage of trees in urban residential areas. A simple way to create plenty of shade is to plant numerous trees around buildings. However, this method is impractical in many areas that face water shortages due to its high costs. In addition, the presence of additional shadows on the rooftop of the buildings will reduce the ability to be exposed to sunlight and the potential of using solar panels to generate electricity. So the main challenge is using a method that can provide maximum shade coverage on the facade surface and minimum shadow coverage on the rooftop with a few trees in optimal locations. The issue of locating trees with the aim of optimizing shade coverage, i.e. maximizing shade coverage on facades and opening components, and minimizing shadow coverage on the rooftop, is a Non-deterministic Polynomial hard (NP-hard) problem and has no exact solution. Therefore, the 3D Geographic Information System and the Ant Colony Optimization algorithm have been used for this purpose. Previous studies have often examined the effects of tree canopy shade on a single building. But in most cities in Iran, buildings are connected together and form a building block. So, instead of a single building, a building block is examined. In addition, in most previous studies, the effect of shade coverage of a maximum of two trees on the building has been investigated; while in this study, we examine the effect of shade coverage of 15 trees on the building block. None of the studies on optimizing the shade of trees on the facade of the building has used the meta-heuristic optimization methods and its combination with GIS. In this study, a hybrid model of GIS in a three-dimensional environment and ACO is used for maximizing the shade of trees on the facade and opening components of buildings, and minimizing the shade of trees on the rooftop.

 Two types of data are required to perform the analysis; The building block properties, for example, dimensions, position, and size of the facade, rooftop, and opening components, and the tree properties (height and position). 3D GIS and ACO algorithms have been used to model tree shade coverage optimization. 3D GIS provides abilities for storing, analyzing, and creating 3D topologies, and ACO is used to summarize real-world conditions in a mathematical problem. GIS and trigonometric rules have been used to store geographical information and spatial topology. After storing the position, composition, and description information of 2D and 3D objects by topological data, Duffie and Beckman relations (2013) is used to extract the position of the shadow. Then, according to Church and Revelle, the Maximal Covering Location Problem (MCLP) is defined. For the following 3 reasons, ACO has been used for three-dimensional optimization; 1) The complex trigonometric rules in calculating the shadow coverage on buildings, 2) There is no deterministic solution for optimization problems because of nonlinear constraints including trigonometric functions, 3) The existence of continuous space around the building block that It is possible to place a tree in any position. The details of the steps are; 1) Define the set of possible locations for the tree based on the height, diameter of the canopy, and around space of the building block, 2) Use a method to place the first tree in all possible places around the building block during hot hours on certain days of the summer and calculate the maximum shade coverage on the building block based on the weight of the building components, 3) Remove the places that may be done in the tree canopy to prevent overlapping of tree canopies, 4) Repeat steps 2 and 3 to place the next trees in the possible places around the building block until the number of trees reaches the desired number of trees to create shade. Considering the infinite possible positions, a simplification step is required to limit the number of available positions. Therefore, the constant space is reduced to possible positions for locating Ni trees with two-meter spacing in the N-S and E-W directions. Further, the possible tree positions in front of the opening components are eliminated to make daylight available, have an outlook from the building, and comment through the doors. The minimum spacing of two meters between the trees and the building is set to prevent unnecessary shading on the rooftop.

MATLAB environment is used to optimize the shade coverage of trees using the ACO algorithm. For this purpose, properties of the buildings block such as length, width, height, are modeled in a struct in MATLAB. This struct has separate matrices for the north, east, south, and west views of the building block. Another matrix is also used to model the rooftop. Each element of the mentioned matrices is equal to 10× 10 cm from the surface of the building block and has a value of zero. To model the dimensions and location of doors and windows in each facade, another struct includes separate matrices for each facade is used. In these matrices, the amount of elements in the location of doors and windows is one. The characteristics of the sun in the study area are used, including azimuth and altitude of the sun on the studied days in 15-minute intervals from 9 to 15 hours.  The shadow is created on building components, by placing the tree in any of the possible locations, and movement of the sun. The elements of the matrices equivalent to the shaded building components change from zero to one. The sum of the values of the matrix elements determines the amount of shadow created by the tree on each component of the building. The sum of the point multiplication of the door/window matrix elements in the facade matrix elements determines the amount of shadow created on the doors/windows. The objective function is defined and the ACO algorithm is used to maximize the shadow coverage of trees on the facade, doors/windows, and minimize the shadow coverage on the rooftop. The results of the ACO show that the optimal shade coverage on the buildings block, which creates the most shade on the facade and doors and windows and the least shade on the roof, depends on the number of trees and the position of the doors and windows in buildings block. In general, as the number of trees increases, the amount of shadow created on the building block components increases.

The results of the ACO showed that for buildings, in the northern hemisphere, the trees in the north of the buildings have no effect on casting shadows on the components of the building. Due to the fact that in arid and tropical regions there are restrictions on planting trees, finding a suitable position for trees plays an important role in optimizing the shade coverage. Due to the high heat transfer through the doors and windows compared to the facade and rooftop, the higher weight is considered for these components in the objective function. Finding the optimal position of the trees depends a lot on the position of the doors and windows in the building to create the most shadow on these components. For a buildings block with the number and dimensions of buildings assumed in the research and according to the dimensions and position of doors and windows, planting a tree in one of the positions K10, K16, K22, or K28 creates the most optimal shade. These positions are 2 meters from south of the buildings and in the middle of two windows. On average, this tree provides 7.48, 9.22, and 0.85% shade respectively on the facade, doors /windows, and rooftop from 9 to 15 o'clock in four days studied. In the case of planting two trees, two positions from positions K10, K16, K22, or K28 still provide the optimal shade. On average, these two trees provide 13.88%, 18.64%, and 1.69% of shade respectively on the whole facade, doors /windows, and rooftop at 9:00 AM to 3:00 PM. In the case of three trees, positions K8, K18, and K22, in the case of four trees, positions K14, K20, K26, and K32, in the case of five trees, positions K8, K14, K20, K26, and K32 create the optimal shadow. Shading coverage in the case of three trees, is 21.07, 28.54, and 2.54%, respectively on the facade, doors/windows, and rooftop, in the case of four trees, is 24.96, 35.36 and 3.39% respectively on the façade, doors/windows, and rooftop and in the case of five trees is 33.26, 44.70 and 3.95% respectively on the facade, doors/windows, and rooftop. By planting five trees, more than 88% of the south façade and more than 90% of the south façade doors/windows of the building will be covered with shade. However, due to the goal of optimizing the shadow on the building and the greater weight of the doors and windows, the ACO has optimized the position of the trees in such a way that more surfaces of the doors and windows are exposed to the shadows. Due to the fact that in the case of five trees, 90% of the southern facade is in the shade of trees, in the case of six trees, in addition to the southern facade, the eastern and western facades are also considered for planting trees. So that the positions K8, K14, K20, and K30 are chosen in the distance of 2 meters from the south and the position of H2 is chosen in the distance f 2 meters from the west, and the position of H36 is chosen in the distance of 2 meters from the east. On average, these trees provide 33.95%, 42.29%, and 3.64% shade respectively on the facade, doors/windows, and rooftop.

In recent year’s groundwater pomping in the Mashhad plain and decreasing of rainfall in the Mashhad, plain is cause subsidence and creat damage to province infrastructure. This problem is causing more application for deep well water in agriculture, industries, and drinking water. Follow by this demand the number of illegal wells dicking by customers is increasing, therefore the water level of groundwater in Mashhad plain decreasing and the subsidence rate is growing. Mashhad plain is one of the significant plains in the Khorasan Razavi province which is the main water source to support the cropland and industries. High pressure in Groundwater pumping and rainfall is decreasing it causes aquifer recharge reduction.  Groundwater depletion induced a variety of inadequate in the Mashhad plain such as reducing well discharge, Qanat destructive, Water quality decreasing and land subsidence, etc. In this research, the rate of land subsidence by satellite radar data of ALOS-1 and Sentinel-1 and its relationship with groundwater depletion are investigated. For this purpose the time-series InSAR with multiple SAR data in L and C- bands are used for land subsidence analysis for ten years from 2007 to 2018.

The main goal of this research is to find the land subsidence rate in relationship with groundwater depletion of the Mashhad plain for a period of 2007-2018 using the InSAR technique. For achieving the research goal the three pairs of SAR images of ALOS data and three pairs of Sentinel-1 data are used. For analyzing the water delation with land subsidence the ten years piezometric well data for a period of 2006-2017 are modeled to create the groundwater table contour line. This map is used for finding the relationship with land subsidence. The final result of the subsidence map was assessed with field observation and previous work.

 InSAR result of ALOS-1 data in this research is shown the subsidence maximum rate of 5.2 cm in the period of 2007.10.16 to 2008.10.16 for 92 days, subsidence maximum rate of 3.8 cm in the period of 2008.01.16 to 2008.03.02 for 46 days, and subsidence maximum rate of 4.7 cm in the period of 2008.03.02 to 2008.06.02 for 92 days. In addition, the Sentinel-1 data processing for InSAR analysis has shown the subsidence maximum rate of 16.1 cm between 2015.05.28 to 2016.05.22 for a year, subsidence maximum rate of 17.4 cm from 2016.05.22 to 2017.05.29 for 372 days, and subsidence maximum rate of 20.3 cm from 2017.05.29 to 2018.05.24 in a year. The spatial distribution of the subsidence area is mostly in the central and southeast of Mashhad plain. The subsidence area is extended in the area with a 39 km length and 8 km wide. The Mashhad plain does not have a permanent river therefore most of the water demand in agriculture, industries, and drink water is supplying by groundwater pumping. The correlation between the subsidence map and groundwater level contour map obviously has shown that groundwater depletion affects land subsidence. Field observation was also confirmed the subsidence by wall and building crack, wellhead uplifting in the test site.

The result showed that the area with the maximum rate of subsidence is the counterpart to cropland and garden which have more influence on groundwater pumping. In addition, the piezometric well date is shown the groundwater table continuously decreasing. According to the result of this research, the main reason for subsidence is a force to groundwater pumping. The field observation approved that the subsidence is happening in the Mashhad plain by some cracks in the wall, bridge, road, well destructive.

Among many natural hazards, landslides are one of the most widespread and destructive. Due to the high mountainous topography, tectonic activity, high seismicity, diverse geological and climatic conditions, basically, Iran has a natural condition for creating a wide range of landslides and these landslides annually cause both life loss and financial damage to the country. Since it is difficult to predict the timing of landslides, identifying susceptible areas to landslides, and zoning these areas based on potential risk are highly important. Therefore landslide-prone areas need to be identified in order to reduce such damage. In this respect, landslide susceptibility assessment can provide valuable information essential for hazard mitigation. The main goal of landslide susceptibility analysis is to identify dangerous and high-risk areas and thus reduce landslide damage through suitable mitigation measures. Since the exact prediction of landslides occurrence isn’t possible by human sciences, thus, we can prevent the damages of this phenomenon by identification of landslide susceptible areas and prioritizing them. Binalood Mountain in Khorasan Razavi Province, Due to its geological location, geomorphology, topography, climate, vegetation, has kinds of mass movement. The results of these studies can be used as fundamental information by environmental managers and planners. Landslide hazard zonation was challenged by several researchers in recent years. In order to provide landslide hazard, zonation maps various methods such as Fuzzy logic, statistic methods and Analytic Hierarchy Process (AHP) can be used. Since the early 1970s, many scientists have attempted to assess landslide hazards and produced hazard zonation maps portraying their spatial distribution by applying many different GIS-based methods. Different models and methods have been proposed to produce Landslide hazard zonation. The aim of this study is to develop and compare detailed landslide susceptibility maps (LSM) for Binalood Mountain, using Fuzzy and AHP methods in the framework of the GIS.

 The study area is the northern and southern slopes of the Binalood Mountains that are located in the Khorasan Razavi Province. The present study area fallows under 36 ° 1' to 36 ° 15' north latitudes and 58° 38' to 59 ° 35' east longitudes. According to Geological, Geomorphologic, Hydrological, Climatic, Human and Environmental characteristics of the study area and using comparative studies and results of other researchers, 20 criteria and sub-criteria were identified to achieve the goals. The needed Layers of landslide hazard zonation were prepared using ArcGIS software. These layers are slope, aspect, altitude classes, geology, distance from the river, river density, distance from the road, road density, distance from the fault, fault density, morphological units, topographic indexes (stream power index (SPI), topographic wetness index (TWI) and slope length index (LS)), geomorphological indexes (topographic position index (TPI), topographic roughness index (TRI) and surface curvature index, land use, isothermal lines, and Rainfall lines. Thun, The landslide inventory map has been created in the study area. Subsequently, landslide susceptibility maps were produced using Fuzzy Logic and Analytical Hierarchy Process (AHP) models. After preparing the layers, the next step was to assign weight values to the raster layers, and to the classes of each layer, respectively. This step was realized with the use of the AHP method. So, the landslide hazard zonation map of the study area was presented using weight exertion of factors in their layers and integration of them by Arc GIS software. In the Fuzzy method, after fuzzyizing the layers in the ArcGIS environment, the landslide risk zoning was performed using fuzzy gamma 0.8. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated. Finally, the ratio of the percentage of landslides was in each zone to the percentage of the total area of the zone was calculated.

 The results of weighting the parameters affecting the landslide using the Analytic Hierarchy Process (AHP) showed that geological, slope, and fault factors have the greatest impact on the occurrence of landslide risk in the study area, respectively. The class of very high and high susceptibility covers 47.8% of the total area in the landslide susceptibility map generated with the AHP model. Low and moderate susceptible classes make up 13.4 and 38.8% of the total area, respectively. According to the landslide susceptibility map based on the Fuzzy Method, 27.7% of the total area was determined to be very high and high susceptibility to landslide. Low and moderate susceptible classes constitute 56.8%, and 15.5% of the area, respectively. The AUC values were 0.817 and 0.752 for AHP and Fuzzy models and the training accuracy was 81.7 and 75.2%, respectively. It can be concluded that both models utilized in this study showed reasonably good accuracy in predicting the landslide susceptibility of the study area. Finally, the ratio of the percentage of landslides was ineach zone to the percentage of the total area of zone showed the NRi values in each susceptible class for the AHP model more than the Fuzzy method. The larger ratio in the AHP method indicates its better consistency than the Fuzzy method, implying more coverage of landslides in a smaller area by the AHP method. This result represents the better accuracy of the AHP method than the Fuzzy method in the landslide susceptibility map.

In this study, the most widely accepted models, AHP and Fuzzy were used for producing Landslide Susceptibility Map (LSM) and their performances were compared. The LSMs were divided into five landslide susceptibility classes. The performance of the resulting LSMs was verified by the ROC curves and Numerical Ratio (NRi). The results show that the AHP and Fuzzy models are successful estimators. The map produced by the AHP model exhibited a slightly better result for landslide susceptibility mapping in the study area. These two techniques may be characterized by incorporating a wide range of conditioning factors. Also, they can discriminate the causative factors for understanding the importance of each factor. The interpretation of the susceptibility map indicates that geological, slope, and fault play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps like the one produced in this study should provide a valuable tool for the use of planners and engineers for reorganizing or planning new programs.

Rangelands are one of the natural ecosystems that have an important part of soil carbon reservoirs and also, as very diverse genetic reservoirs guarantee the dynamics of the ecosystem. Fire is a natural factor in rangelands burning most of the existing natural cover. Rangeland fires directly alter soil microbial activity by burning soil microorganisms and indirectly by reducing organic matter, altering soil organic matter quality and other soil properties. Investigating the positive and negative effects of fire on ecosystems, especially on soil properties, has led researchers to look for alternative methods, instead of direct methods, which are generally very costly and time-consuming. One of the new methods and technologies that are very useful in the field of natural resources is satellite remote sensing. The purpose of this study was to investigate the short-term effect of fire on organic carbon, acidity, and electrical conductivity of rangeland soils in the Gonbad region of Hamadan, and to investigate the capability of remotely sensed data in the indirect estimation of soil surface carbon in semi-arid rangelands after the fire.  

 In this study, 20 soil samples were taken from each site from a depth of 0-10 cm (40 samples in total) and the coordinates of each sampling point were recorded with a GPS device. Sampling was performed 15 to 20 days after the fire in early October. After transferring to the laboratory, the samples were used to measure the amount of soil organic carbon. Then, the statistical relationship between non-burned areas and burned areas was examined and analyzed by an independent t-test. Indirect estimation of soil surface organic carbon at non-burned and burned sites was also investigated and their changes were evaluated using remote sensing satellite imagery. For this purpose, after performing the pre and post-processing on satellite data, the corresponding values ​​of spectral reflectance of each pixel with sampling points at different wavelengths and spectral indices were extracted, and the correlation and regression equation of indices with the Carbon reservoirs were analyzed.

 The results of the Pearson correlation test showed that among all spectral indices, only the HI index was correlated with soil organic carbon in the short time and in the non-burned site. Besides, among all indices, BI, NDBI, NDVI, SAVI, VCI, and VHI indices were correlated with the EC value in the non-burned site. At the non-burned site, there was a significant correlation between most spectral indices and soil EC, which was eliminated after the fire at the burned site. Regarding the correlation between pH and spectral indices, it was observed that there is a correlation between some spectral indices and pH. As a matter of fact, it can be concluded that the fire has caused a large change in the rate of reflection and propagation of waves from the soil surface so that in the non-burned site, the indices were correlated with EC, but in the burned site, the correlation between indices and EC was completely eliminated, and instead, a correlation has been established between the indices and the pH. Furthermore, none of the spectral indices in April 2017 at the non-burned site had a significant positive or negative correlation with soil organic carbon, and the results showed that after six months of the fire, the soil carbon changes were not such that the spectral indices could be examined its process. Comparing the results of October 2016 with the results of April 2017 on the non-burned site, it was found that after six months, the NBR index has found a significant correlation with the EC rate, but the BI and VHI indices have lost their correlation. According to the NBR index and the SWIR2 band, it seems that after six months from the occurrence of the fire, changes have occurred in the control site, which has led to a correlation between this index and soil EC. Since the amount of reflected energy from the earth's surface depends on several factors such as soil moisture, changes in soil organic matter content, and surface cover, so the effect of these factors on the soil reflectance should be considered in the growing season. Failure to change these results after six months can prove that the positive and negative effects of the fire have not disappeared in a short period of six months and a longer time is needed for the situation to return to normal.

 According to the results, it was found that soil organic carbon reservoirs in burned rangelands in comparison with non-burned rangelands is not significantly different. Deformation and stabilization of soil organic matter due to fire have been studied by many researchers, but the transformation of soil organic matter by fire has often led to heterogeneous and different results. At a depth of 10-20 cm, the fire was found to have no effect on soil organic carbon content, but other researchers found that 6 months after the fire, the amount of carbon in the burned soils increased compared to the non-burned soils. It was also found that the percentage of soil organic carbon decreased significantly three months after the fire. Moreover, in another study on the effect of fire on soil organic carbon, it was found that in the area affected by the fire compared to the control area in one year and two years after the fire, the amount of soil organic carbon has decreased significantly. Since the effect of fire on the physical and chemical properties of soil is strongly influenced by fire intensity, soil moisture, climate, and vegetation, so all these factors have led to different results in investigating the effect of fire on soil organic carbon. Due to environmental conditions, climate, the slope of the area, soil texture and structure, and factors related to fire such as its intensity and duration, the amount of soil carbon has changed. For example, in the event of a medium-sized fire, the conditions for vegetation regrowth are faster, but in the event of a severe fire, the entire organic layer of the soil surface is generally removed and carbon is reduced over time. Also, in examining the correlation between spectral indices and soil organic carbon, it was found that only the HI index with soil organic carbon was significant at the non-burned site, but no correlation was observed at the burned site. This can be examined by examining the spectrum of visible blue and green wavelengths in the mathematical relationship of this index because only in this index the green and blue wavelength spectrum have been used. According to the results of other researchers, it seems that estimating soil organic carbon using remote sensing has certain complexities. Since soil organic carbon has the greatest impact on soil color, it is difficult to estimate it using remotely sensed data if its amount is low. The occurrence of fire in the region has a major impact on the spectral reflectance of surface soil so that after the fire in a short time the correlation of HI index with soil organic carbon is lost. According to the results of the present research, it seems that the main point about the impact of fire on soil organic carbon is the time and the opportunity for soil to change.

Land use reflects the interactive features between humans and the environment and describes how humans are exploited for one or more purposes on earth. Land use is usually defined based on human use of land, with an emphasis on the functional role of land in economic activities. Land use map is one of the main factors in the study of natural resources and environmental management. Knowing the changes in land use and examining their causes and factors in a period of time can be of interest to planners and managers. The use of satellite data is a good tool for land use mapping, especially in large geographical areas, due to the provision of a wide and integrated view of an area, reproducibility, easy access, high accuracy of information obtained, and high-speed analysis. One of the most widely used methods of extracting information from satellite images is image classification, which allows users to generate different information. Google Earth Engine (GEE) is a web, cloud-based system developed by Google to store and analyze large amounts of data at the petabyte scale (including various satellite imagery, digital models, climatic and vector data). Speed in processing and access to diverse data is one of the issues and problems of land use change studies. The purpose of this paper is to classify satellite images using the support vector machine learning method in the two periods of 2000 and 2020 and to produce a land use map of these two periods in the Google Earth engine system.

In this paper, Urmia city and its surrounding areas (Urmia plain) have been evaluated. In order to prepare land use maps and study its changes, Landsat 7 ETM+ sensor for 2000 and Landsat OLI 8 for 2020 have been used. Images from June were used, when vegetation reached its maximum vegetative growth. Various methods have been developed to monitor and measure land cover and land use changes. In this paper, the efficiency of the Google Earth Engine system for collecting, managing, and processing remote sensing data has been evaluated in order to prove and introduce the speed and accuracy of this system. In order to produce the land use map, the Support Vector Machine classification method has been used. The main difference between this paper and other research is that the management and processing of images have been done in the Google Earth Engine system, which means that the researcher does not need expensive and licensed software such as ENVI and only by access to the Internet can do the processing. By developing the code for image classification using the support vector machine method, the images of 2000 and 2020 were classified. Six land use classes were identified, including barren lands, man-made lands, orchards, irrigated agriculture, rainfed agriculture, and irrigated areas. After classifying images, the results were stored in Google Drive and prepared for further analysis. The classification results were entered into ArcGIS software and the classification accuracy was evaluated using control points obtained from Google Earth images as well as data related to the land use management plan of West Azerbaijan province. In this paper, in addition to preparing a land use map in the Google Earth Engine system, it was used to forecast and model land uses for 2040 using the CA-Markov transfer estimator.

 After calling and classification of images in the Google Earth engine environment using the SVM method, land use map for 2000 and 2020 was produced. The prepared maps include man-made lands, orchards, irrigated agriculture, rainfed agriculture, and barren lands. A comparison of different land use in 2000 and 2020 shows that extensive changes have taken place in them. Some of these changes are positive and some are negative. The area of barren lands in 2020 compared to 2000 has increased by about 10 square kilometers, man-made lands, 42.62 square kilometers, orchards 67 square kilometers, and water bodies 0.39 square kilometers. In contrast, rainfed agriculture has lost 39.45 and irrigated agriculture has lost 80 square kilometers. The reason for the increase in orchards can be seen in the change of irrigated agricultural uses to orchards, as well as urban development and the creation of various human infrastructures, which is very evident in recent years. Most of the changes are related to the use of orchards with a positive trend during which many irrigated agricultural lands have become garden lands. These changes have increased the production of horticultural products in Urmia and become one of the hubs of horticultural production, especially apples. the area of man-made land has almost doubled, which usually happens in other parts of the country and is normal. Usually, with the increase in the population of cities as well as villages and the need to build new buildings and infrastructure facilities such as factories, sports fields, roads, entertainment spaces, etc., man-made uses have increased. According to the forecast for 2040 using the CA-Markov method in Idrisi software, the highest growth is related to rainfed agricultural use. It is predicted that during this period, the area of rainfed lands will reach 73.40 square kilometers. The man-made land will increase to 90.9 square kilometers. While its value in 2020 was 76.38 square kilometers. On the other hand, the area of orchards will increase from 31.61 square kilometers in 2020 to 72.15 square kilometers. Irrigated agriculture will increase to 27.38 square kilometers with an increasing trend.

 Studies show that the growth of man-made lands in Urmia city and its surroundings is not commensurate with other land uses and this has led to the growth of land use area of ​​the man-made lands compared to other uses and this issue has caused the phenomenon of expansion has become in Urmia city. On the other hand, the results show that the study of land use using the time series of satellite images is a time saver and cost, and as mentioned in the paper. different land uses for the years 2000 and 2020, prepared using the Google Earth system, and their changes were identified. Another important result of this paper is the high efficiency of the GEE system in processing large volumes of satellite images. Using this system does not require any specialized remote sensing software and the user can easily process various data using a computer browser or even a smartphone. Another important point is that in this system, there is no need to download different images, but the user can only download the processing result. This is very useful in terms of time and processing speed. The GEE system is able to process large volumes of time series data (here satellite imagery), different regions of the world with very high speed and very low time, and present the results in the form of various maps and graphs.