جستجوی مقالات مرتبط با کلیدواژه « Remote sensing » در نشریات گروه « منابع طبیعی »

In this research, the water quality of Sefidroud River during the years 2013-2018 using Landsat 8 satellite images as well as 10 qualitative chemical and physical parameters including Ca2+, Na+,Mg2+, (Cl-,SO4-2, HCO3-, TDS, EC, TH and pH were studied in three hydrometric stations. Drinking water quality index (WQI) was calculated and its relationship with satellite bands and band ratios (28 parameters) was analyzed using univariate and multivariate regression models. The results of the univariate regression model showed that the WQI index with band 5 and the ratio of band B4/B3 had a linear and power correlation at a significance level of 1% with coefficient of determination (R2) of 0.55 and 0.51, respectively. The implementation of the stepwise linear multivariate regression model of WQI with all the studied bands and ratios showed that the three band 5 variables and band ratios B4/B3 and B6/B5 were correlated with WQI, with an R2 of about 0.80 at a 5% significance level. After preparing the spatial changes map of WQI using multivariate linear regression model, the results indicated that the water quality in the head branches of Sefidroud, that is, the Qezaluzen and Shahroud rivers, was lower compared to the lower areas and Manjil Dam Lake, although the water quality of the Qezeluzen and Shahroud rivers was lower. Shahrood was weak, but after entering Manjil Dam, it became a good water class. However, the WQI of the water coming out of the dam had gradually increased by passing through the agricultural lands, residential and industrial areas along the river until it reached the Caspian Sea, and it had weakened water class. In general, the results of the research showed that the use of Landsat 8 satellite images and multivariable regression model has a high power for water quality monitoring.

Today, due to climate change and the occurrence of torrential rains, flood hazard is one of the major problems in arid areas. Zarand city in Kerman province is one of these areas that has suffered a lot of damage in infrastructure and agriculture due to this phenomenon. The purpose of this study is to identify and determine potential flood hazard areas using remote sensing and GIS techniques. In this regard, eight criteria were used to prepare the flood hazard index, these parameters include the criteria of flow accumulation, draining capability, elevation, distance to drinage, land cover, runoff coefficient, slope and geology. The mentioned layers were weighed and combined in GIS environment after forming a pairwise comparison matrix based on Fuzzy-AHP method. Finally, according to the flood hazard index (FHI), a flood hazard map related to the study area was prepared. The results show that about 5% of the study area is very high hazard (18800 hectares), 23% high hazard (94100 hectares), 44% medium hazard (179700 hectares), 22% low hazard (88200 hectares) and 6% very low (23,100 hectares). High and very high hazard areas are mostly located in the plains and agricultural areas. In order to validate the created flood hazard map, the map of flooded areas obtained by applying the threshold method on the Sentinel 1 image was used. A comparison of the two shows that about 32 and 49% of the total area of flooded areas are in high-hazard and very high-hazard classes, respectively. The results showed that the use of GIS-based multi-criteria analysis method can be effective in flood hazard analysis.

Drought is one of the most important natural hazards in Iran that has many destructive environmental and economic effects. Drought is affected by various factors, and different indices have been developed to monitor it. Drought studies have been performed using temperature and vegetation data, but few studies have used satellite evapotranspiration data. In this research, vegetation condition index, temperature condition index, and evapotranspiration condition index have been used to study drought in Jarahi and Zohreh catchments. For this purpose, drought indices have been calculated on a monthly basis using MODIS satellite products from the 2000 to 2017 period. The six-month Standardized Precipitation Index was used to evaluate the remote sensing-based drought indices. The results showed that the evapotranspiration condition Index had the highest correlation with the six-month SPI index and was considered the most appropriate index to study the drought. The correlation of ETCI with SPI was equal to -0.57 and the RMSE was 0.47. A drought severity map was prepared using remote sensing indices to depict six classes of drought severity including severe drought, moderate drought, mild drought, near normal, and without drought for 2008, 2009, and 2016, when the study area was suffering from drought. The results of the spatial assessment showed that the central part of the basin which contains ecologically important protected areas was the most vulnerable part during dry years, and during the study period, it has experienced over 10 months of severe drought. In this period, the coastal part had the lowest drought intensities. However, during different years, the wetland area of the basin, which is one of the protected water ecosystems, has decreased. The results showed that compared to other indices, the satellite-based evapotranspiration data can provide a good tool for monitoring drought in hot areas with sparse vegetation such as Iran.

In this study, the goal is; Find suitable statistical and experimental models for estimating ground carbon storage by combining spectral and radar data from Sentinel 1, 2. There are 150 random circular samples with an area of 10 acres and a total of 150 samples. With global coverage, all height classes were selected. Species of species type, the total height of trees, and diameter equal to the chest of trees with more than 7.5 cm were recorded in each sample plot. After that, the amount of biomass at the surface of the sample parts was calculated based on the FAO global model and the amount of carbon storage on the ground by applying a coefficient. Radar and spectral images were subjected to various preprocessing operations and necessary processing. Then, the numerical values corresponding to the ground sample plots were extracted from the spectral bands and considered as independent variables. Modeling was performed by non-parametric methods of RF, SVM, kNN, and parametric methods of multiple linear regressions. The results showed that the average ground biomass was 469.07 tons per hectare and carbon storage was 234.53 tons per hectare. Also, the highest correlation was obtained between the main and artificial bands with the two characteristics related to the near-infrared band. The results of modeling validation showed the combination of optical and radar data of Sentinel 1, 2 satellites with biomass and surface carbon storage; Random forest method with the RMSE%, and percentage of bias. The studied characteristics (32.79, -2.24) and (30.79 and 0.01), respectively, have had a better performance in modeling. In general, the results obtained from the validation showed that in estimating the two characteristics the RF method showed better results if the Sentinel 1, 2 data were combined, and in contrast to the SVM.

The distinction between barren and build-up areas is one of the most important issues in land use/land cover mapping ‎in arid and semi-arid climates. In this regard, many researchers have tried to increase the accuracy of classification ‎using different methods that, some of which are complex and time-consuming. Therefore, the present study conducted ‎aimed to apply micro-climate change through the implementation of Local Climate Zoning (LCZ) algorithm in land ‎use identification with emphasis on the separation of build-up areas in one of the arid cities of Iran, and the efficiency ‎of the method by investigation the classification accuracy was compared with various supervised methods including ‎maximum likelihood, minimum distance, Fisher, KNN, fuzzy, artificial neural network and support vector machine. ‎The study area is Zahedan city, which has a very significant growth of build-up areas in recent decades. For this ‎purpose, four periods of Landsat satellite images year 2020 were used. Training samples were extracted from Google ‎Earth and the validation of the classification results was performed using 218 random points. The accuracy results ‎showed that the use of LCZ algorithm with overall accuracy and kappa coefficient of 96.33% and 0.95, respectively is ‎the highest and then the support vector machine and Fisher methods with overall accuracy of 86.61 and 83.03 and ‎kappa coefficient of 0.82 and 0.75, respectively. Therefore, for land use / land cover studies, the LCZ method that ‎considers the micro-climate, is proposed.‎

Knowing the extent and severity of drought in a region and planning to reduce its effects is one of the most important principles of management in regional planning to combat drought. Drought monitoring and management in an area using remote sensing data and satellite imagery as a suitable tool in temporal and spatial monitoring of agricultural drought has always been the focus of regional managers. The purpose of this study is to investigate the efficiency of remote sensing data and satellite images in the zoning of agricultural drought in the years 2000 to 2021 in Neyriz city. For this purpose, three vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) were extracted from MODIS satellite images for the desired time period. The results of these indices were compared with the values of the standard precipitation index (SPI) in time series of 1, 3, 6, 9, 12, 18, 24, and 48 months.

The study area in this study is Neyriz city located in the southeast of Fars province with an area of 10787 Km2 and is part of one of the watersheds of Bakhtegan Lake. The average altitude of the region is 1798 meters, the maximum altitude of the region is 3235 meters and the minimum altitude is 1476 meters above sea level. The average annual rainfall, temperature, and evapotranspiration of the basin are 204.8 mm, 19 °C, and 1058.3 mm, respectively. In this study, the rainfall data of Neyriz synoptic station during the statistical period of 22 years (2000-2021) were used to calculate the SPI index in time series of 1, 3, 6, 9, 12, 18, 24, and 48 months. Then, 3 indices based on satellite imagery including vegetation condition (VCI), temperature condition index (TCI), and plant health index (VHI) were extracted from Modis measured data for May month from 2008 to 2021 and with standard precipitation index (SPI) were compared in time series of 1, 3, 6, 9, 12, 18, 24 and 48 months based on the correlation coefficient. Finally, the most appropriate drought index based on satellite images was selected from the indices and the percentage of drought classes was determined based on the selected index in the study area.

 The results of calculating the values of the SPI index using DIP software in time series of 1, 3, 6, 9, 12, 18, 24, and 48 months in the statistical period of 2000-2021 showed that the trend of curves in some years is decreasing, in some years it has been increasing and in most years it has been almost normal. On average, the incidence of droughts and wetlands according to the SPI index in different time series during the statistical period is 68% in normal conditions, 18% in wet conditions, and 16% in drought conditions. The results of calculating the SPI index in different ground series were analyzed based on data from synoptic stations and remote sensing data. For this purpose, the values obtained from all indices based on satellite images including VCI, TCI, and VHI are extracted and compared and their correlation coefficient with the ground SPI index in time series 1, 3, 6, 9, 12, 18, 24, and 48 became. VCI index values in 2000 have the lowest value (32.1%) and in 2020 have the highest value (41.3%) during May. Therefore, based on the value of the VCI index during the statistical period in 2008, severe drought conditions prevailed in the region, and in 2020, more favorable vegetation and wetting conditions prevailed in the region. The results obtained from the SPI index in different time series also confirm the fact that the most severe drought and wet season during the statistical period studied in the two years 2000 and 2020, respectively, in the region. In addition, the VCI index is most correlated with the SPI index in different series and the SPI relationship is significant with the all-time series. TCI index has no significant correlation with any of the time series and has a weak correlation with the SPI index in different time series. In addition, the VHI index has a significant correlation with time series of one, three, six, and twelve months only at the level of 5% and its correlation with the SPI index in different time series is much less than the VCI index. Spatial distribution of drought intensity based on the values of the studied indices in May 2008 showed that the eastern parts of the region, which is also located at low altitudes, have been more affected by drought. The study of the area affected by drought classes based on the TCI index in 2008 showed that there is no very severe drought in the study area, 11% of the area suffers from moderate drought, 22% of the area suffers from mild drought and 67% has no drought. According to the VCI index, the level of severe drought on the date is 0.14%, severe at 0.33%, moderate at 17%, mild at 77%, and no drought at 6%. Also, according to the VHI index, there is no severe or severe drought in the study area only 9% of the area suffers from moderate drought and 91% does not have a drought. Spatial distribution of drought severity based on the values of the studied indices in May 2020 shows that in the study area according to the TCI index there is no very severe drought on the target date and 5% of the area has moderate drought, 22% drought Mild and 73% lack drought. According to the VCI index on the target date, the percentage of drought is very severe 0.5%, severe 0.8%, moderate 5%, mild 31%, and no drought 62%. Also, according to the VHI index in May 1999, 0.2% of the area has a moderate drought, 30% has a mild drought and 69% has no drought. According to this index, there is no very severe drought in the region.

 Drought is one of the most important natural disasters that affect millions of people and large parts of the world every year. This phenomenon, which starts slowly and has a creeping nature, can cause a lot of damage to agriculture, natural resources, and the environment. Knowing how to occur and preparing drought severity maps based on new methods has a very positive and serious impact on drought management in an area. One of the new and widely used methods in temporal and spatial monitoring of drought is the use of drought indices based on satellite images, which has recently been used in drought-related topics. The results of the SPI index analysis showed that in most time series, the most severe drought and wet season during the study period occurred in 2000 and 2020, respectively. The results also showed that the temperature condition index (TCI) has no significant correlation with any of the time series and has a weak correlation with the SPI index in different time series. The plant health index (VHI) with time series of one, three, six, and twelve months has a significant correlation at the level of 5% and its correlation with the SPI index in different time series is less than the vegetation condition index (VCI). The value of the VCI index in 2008 had the lowest value (32.1%) and in 2020 had the highest value (41.3%) during May, which is consistent with the results obtained from the SPI index in the region. A comparison of the results of this study with the results of other researchers shows the excellent accuracy of remote sensing indices in drought monitoring. Therefore, the use of remote sensing technology in drought monitoring in areas that do not have meteorological stations or have meteorological stations with low density or scattered is recommended.

Climate changes in Iran, which have mainly manifested in the form of intensifying periods of drought and increasing temperature, can have profound effects on the forest ecosystems of Zagros in the west of Iran. In this study, the drought trend (with PDSI index) and its effects on the spatial-temporal dynamics of EVI greenness index from MODIS sensor were investigated through correlational relationship in the statistical period from 2000 to 2020 in Zagros oak forests in Kohgiluyeh and Boyer-Ahmad province. To determine the greenness index threshold value, sampling at 20 points in the forests of the province and EVI index values in these areas were used. The results showed that the greenness threshold value of EVI in these forests was at the maximum level in June. The greenness index value and the area of the oak forests of the province have decreased during the study period. The peak greenness of the oak forests of the province had a significant sensitivity to changes in rainfall and drought at the end of the cold period of the year and in the spring season. The negative phases of the drought indices have been completely consistent with the periods of greenness loss. The EVI, as an index of forest greenness, has shown a high sensitivity to the PDSI drought index. The PDSI index has also been able to explain 0.74 of the spatial changes of EVI in the three months of April, May, and June.

The valuable Arasbaran forest is a complex and dynamic ecosystem that has always been subject to extensive fires. The purpose of the present research is to utilize the technology of remote sensing and geographic information system and the technical abilities of the Google Earth Engine system in order to prepare a fire occurrence map in the rangelands and forests of Arasbaran. In order to choose the appropriate method and type of satellite between Sentinel2 and Landsat8, the separability index was used. Accordingly, the RdNBR differential index was selected among the different indicators of fire detection to prepare the final map of the last 9 years and the cumulative fire map. Based on the accuracy assessment of the resulting map quantitatively, 84% of the actual fire points recorded by the General Directorate of Natural Resources and Watershed of East Azarbayjan province were placed at a distance of 200 meters from fire polygons extracted from satellite images, which showed the high accuracy of the fire map. The field visit also showed a good match among the fire areas resulting from the processing of satellite images with the existing situation of the region. The current research showed the high potential of these two satellites as well as the extraordinary ability and facilities of the Google Earth Engine system in providing a huge amount of remotely sensed data and advanced processing on them to prepare fire occurrence maps. The advantages of Landsat8 compared to Sentinel2 are having thermal bands and more time series. The spatial and radiometric resolution of both are almost similar, and the low-temporal resolution of Landsat8 will be compensated by combining it with Landsat 9 images. Therefore, in line with the results of similar studies, Landsat8 is generally preferable to Sentinel2. For the correct and scaled spatial data from Arasbaran region, it is suggested to create an integrated and large-scale Geo-database due to the lack of accuracy.

Background and Objective :

Karst is a composite image of all the roughness, shapes, pores, and phenomena caused by water corrosion, above and below the surface, in various soluble geological formations, which cover about 15% of the world's exposed rocks. Despite the importance of karst areas in the past, today, the study, identification, spatial analysis and management of these areas are of very interest to geologists, hydrologists, hydrogeologists, eco-tourists, geotourists and environmentalists at various scales. Karst phenomenon also has a special place in various dimensions from the point of view of geology and geomorphology, because the causes and methods of dissolution processes and forms and their extension in rocks and minerals are of great importance to East Mazandaran, especially in the study area, is geologically one of the areas with karst process potential and has been less studied due to environmental conditions, especially vegetation and access roads. This study is focused on identifying karst areas and their extent using RS and GIS method in the Zalem-Rud Sari basin in Mazandaran province in northern Iran. In order to identify karst areas and physicochemical characteristics of existing aquifers, it was first necessary to identify karst areas and then assess their physicochemical status. In the first step, using Landsat, ASTER and SRTM satellite data, geological maps, weather information and field visits, factors affecting karstification such as rock types, Fracture lineaments, vegetation, climate, condition of the drainages and the slope of the topography are extracted. It should be noted that in this step, to obtain a better result, fuzzy logic and Hierarchical Analysis Process have been used. In the second step, in order to investigate the physicochemical condition of the aquifer, the chemical parameters of some springs are analyzed and the dissolution parameters, saturation indices, the origin of water-soluble components and the general flow system in the existing aquifers are determined.

Materials and Methods:

 In this study, the composition and distribution of rock, based on field visits and georeferenced geological maps of one hundred thousandths of Behshahr and Sari, have been done. Three methods of manual, automatic and semi-automatic have been used to extract the lineaments due to fractures. In the manual method, the lineaments are highlighted and extracted by applying High Pass filters, PCA and the color combination of Landsat 8 satellite data. In the automatic method, the Segment Tracing Algorithm (STA) in PCI software is used. In the STA algorithm, linear pixels are identified based on the degree of gray difference and then converted to vectors based on RADI, GTHR, LTHR, FTHR, ATHR and DTHR parameters. The Normalized Difference Vegetation Index (NDVI) was used to detect and extract vegetation status on OLI sensor data from the Landsat 8 satellite. To generate DEM, photogrammetric techniques were performed on a pair of stereo images of ASTER sensor in Idrisi software based on parametric variables of external and external orientations and ground control points (GCPs). The topographic slope of the area has been calculated by DEM and based on the degree in GIS and its map has been prepared. Based on the weather data of the General Meteorological Department of Mazandaran Province, the weather condition is determined by the isothermal, isohyetal and isoevaporation curves and the type of weather is determined by the De Marton method.

Results and Discussion :

The result of the above activities has been the production of lithological maps, density and distance from fracture lineaments, density and distance from drainages, topographic slope, rainfall and vegetation. Due to the different criteria used in the generated maps and the need for a single, comparable and proportionate criteria for combining information layers, fuzzy logic has been used. In this case, all layers, except lithology, which has a definite boundary and is a function of Boolean logic, are fuzzy and then extracted as fuzzy layers in GIS. On the other hand, because the weight and effectiveness of the eight factors affecting the occurrence of the karst phenomenon are not the same, the Hierarchical Analysis (AHP) method has been used to determine the preference and prioritization of these factors. And the participation rate, in other words, the weight of each criterion with an incompatibility coefficient of less than one percent has been calculated in Expert Choice 12 software. Finally, by the weighted linear combination (WLC) method, fuzzy raster layers based on their effective weight are combined with Compromise Operator or Gamma and the karst distribution map is extracted with different probability coefficients in the study area. In the next step, in order to investigate the physicochemical status of the aquifers and evaluate the karst phenomenon, 18 springs with equal flow and more than three litres per second were selected and evaluated using SPPS, RockWorks2016 and PHREEQC2.6 software. In addition to extracting hydrogeochemical tables and graphs, the saturation index of various minerals has also been calculated.

Conclusion:

 The results of this study show that the karst phenomenon has the most spread in the central part and the lineaments resulting from fractures have played the most role in the formation of this phenomenon. The water type of these springs is calcium-magnesium bicarbonate and according to the Gibbs diagram, the role of aquifer rocks in determining the chemical composition of water is very clear. Also, the saturation indices of minerals and the type of flow in groundwater reservoirs are diffuse and diffuse-duct flows. To this research, the use of new remote sensing technology and GIS increases accuracy and speed and reduces costs in karst studies.

 Earthquake In addition to the destructive effects of man-made structures, earthquakes also have different effects on surface water resources. Earthquakes always increase or decrease the water flow depending on its intensity, time, direction, and profundity. It may even cause the springtime fountain to flow that has dried up over the years. The use of remote evaluation technology in various earth sciences is very common compared to geocentric methods due to the wide coverage of satellite images, the timeliness of images, and its little cost. Also, one of the important and unique capabilities of digital satellite data is its temporal, spatial, spectral, and radiometric resolution. These important features of satellite imagery allow for important studies such as the evaluation and monitoring of dynamic phenomena such as quantitative changes in water resources in temporal and spatial dimensions. Because in some cases the information obtained from a sensor alone does not meet the desired needs. Although optical multispectral data provide rich spectral information of various effects, it is significantly affected by environmental factors such as smoke, fog, clouds, and the amount of sunlight. Unlike optical sensors, radar data with virtual aperture (SAR) is independent of different weather and radiation conditions, as well as the sensitivity of its signal, scatter to target parameters such as structure (shape, orientation, size), roughness, and moisture content of the features can provide more information about the study area, but on the other hand, radar images cannot clearly identify the details and edges of objects. Therefore, combining different properties of optics images and radar data using image integration techniques can provide a more complete view of the target and provide higher accuracy and reliability for the results obtained from this method. In the present perusal, in order to achieve the above purpose, using satellite data and the image combination method, the data have been standardized in such a way that they can be used together in the form of a dataset for processing.

Using the method of combining satellite images of quantitative changes of surface water resources, affected by the SARPOL-E-ZAHAB earthquake in the course of 7 days, 11/11/2017 to 17/11/2017, using radar data (S_1A-IW-SLC), With 100 m spatial baseline and Landsat 8 (OLI) optical data, and obtained by applying remote evaluation techniques and indicators to detect changes in water resources, including Normalized difference vegetation index (NDVI), Modified normalized difference water index (MNDWI) in ENVI software environment and then analysis in ArcGIS software environment.

Examination of the results of quantitative changes in surface water resources shows that in the time period of 7 days after the earthquake in the study area, the highest amount of small changes (in a decreasing manner) occurred in dams (HAMMAM Strait and GILANGH-GHARB Dam). As the Strait of HAMMAM dam decreased by 0.13 square meters and the Gilan GHARB dam decreased by 0.07 square meters. Also, small changes occurred in the surface (SARPOL-E-ZAHAB SARAB-GARM) (drinking water source of the region), SIRVAN river and canals), and there were a total of 7523421 square meters of changes in the surface water resources of the study area after the earthquake.

The earthquake caused the outflow of groundwater and decreased the volume of dams in the region and fed the surface rivers of the region, including (the Sirvan river) and finally caused water loss and in some places caused the drying of springs and Damage to normal living conditions.

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.

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.

From the perspective of urban planning and management, the criteria for the distribution of parks, in terms of the scope of influence and the benefits of the surrounding areas, is of particular importance. The main purpose of this study; Determining and evaluating the influence area of each type of urban parks in Tabriz. The map of urban parks was produced based on updating the land use map extracted from the detailed plan of Tabriz using satellite imagery and fieldworks in a combined method. Then the map of Tabriz city parks according to their area were prepared in 5 categories of parks; Mini, Neighborhood, Regional, Community and Mountain parks and the influence radius of each park was applied in GIS as a buffer based on internal research. The results showed that none of the ten districts of Tabriz Municipality are desirable in terms of covering the influence of "neighborhood" parks and the need to develop this type of parks in the whole city is serious. District 1, 2 and 8 are in a good position to cover the area of ​​influence area of "Regional" parks. Most districts, except 9 and 5, are covered by "Community" parks. "Mountain" parks cover almost all the ten districts of Tabriz and district 1 is in the best condition in terms of being within the influence area of all types of parks. In terms of social and psychological functions of parks, although the existence of Metropolitan parks is necessary to attract large local populations and tourists, but the great impact of "Mini" and "Neighborhood" parks in addition to ecological functions, in family entertainment and ultimately increase social vitality should not be ignored.

Background and Objective:

Soil organic carbon in rangeland ecosystems has a variety of functions such as increasing soil fertility, controlling erosion, increasing soil water permeability and, reducing the effects of greenhouse gases. Therefore, it is a key indicator in determining soil health that affects all physical, chemical, and biological properties of soil. The large area of the country's rangelands causes a serious challenge to the use of traditional methods in estimating soil organic carbon. In such situations, the use of remote sensing capabilities can be considered as a suitable option for monitoring the organic carbon of the country's rangeland soils.  The aim of this study was to determine the most important spectral factors affecting topsoil organic carbon in two summer rangelands.

Materials and Methods:

 This research was carried out in two summer rangelands of Lazour and Asaran. The first rangeland (Lazour) with an area of 8150 hectares and an average height of 2875 meters is located in the range of eastern longitudes 52.514 to 52.694 degrees and northern latitudes 35.855 to 35.934 degrees in Tehran province. The second Rangeland (Asaran) with an area of 5642 hectares and an average height of 2465 meters is located in the range of eastern longitudes 53.265 to 53.392 degrees and northern latitudes 35.804 to 35.882 degrees in Semnan province. In this research, the data of the OLI sensor of the Landsat 8 satellite were used. After pre-processing satellite imagery of the studied areas, Top of Atmosphere (TOA) reflectance layers of bands 2 to 7 along with the variables of surface albedo, Clay index, Carbonate index, Grain Size index, NDVI, brightness, greenness, and wetness index of Tasseled cap transformation were calculated. In each of the target areas, using Digital Elevation Model (DEM) maps, the slope, aspect, and hypsometric maps were prepared and by combining the last three layers with each other, a map of homogeneous sampling units was obtained. Soil sampling was performed using the stratified-random sampling pattern. In this way, in each of the homogeneous units, according to its area, several soil samples were randomly taken from a depth of zero to 20 cm and the amount of organic carbon of the samples was measured using the Walkley-Black method.

Results and Discussion:

 The results of this study showed that the spectral variables of Top of Atmosphere (TOA) reflectance layers of bands 2 to 7 along with the variables of surface albedo, Clay index, NDVI, brightness, greenness, and wetness index of Tasseled cap transformation have a significant correlation with topsoil organic carbon (p < 0.01). Also, the results of factor analysis by principal component analysis (PCA) with eigenvalues greater than one showed that the total cumulative variance explained by the 12 variables is 91.74%, which was explained by two factors. The first factor (soil color) explained 76.6% of the variance and the second factor (vegetation and soil texture) explained 15.14% of the variance.

Conclusion:

 The results of this study confirm the existence of a significant relationship between topsoil organic carbon and spectral factors extracted from Landsat 8 OLI sensor data in semi-steppe rangelands. Because of the large area of rangelands in Iran, the use of traditional methods in estimating soil organic carbon is not possible due to the need to spend a lot of time and money. And in such situations, the use of Remote sensing (RS) capabilities can be considered as a suitable option for monitoring the topsoil organic carbon in the rangelands.

Background and Objective:

Over the past 100 years, the country has lost about 90 percent of its per capita renewable water. About 90% of the country's renewable water resources are allocated to the agricultural sector. With the increase in the area of pistachio orchards and the increase in demand for water on the one hand and the limited water resources in the region, on the other hand, the imbalance between supply and demand for water is sharply increasing. In this regard, the most important step to prevent water loss is the uniform distribution of water on the field, optimal at each stage of growth. About 99% of the water absorbed by the plant is used for evapotranspiration. Therefore, studying this phenomenon can play an important role in determining the water needs of plants. It is difficult to measure the actual evapotranspiration outside the laboratory. Many experimental methods have been developed to estimate actual and potential evapotranspiration using meteorological and climatic data. But most of these methods are only able to estimate potential evapotranspiration and do not estimate the actual amount of it.  In contrast, remote sensing methods have been developed that are a good solution for estimating the actual evapotranspiration. Satellite imagery with global coverage and repetitive Acquisition has made it possible to monitor evapotranspiration at the field level and during plant growth. Various studies have been conducted to estimate the actual evapotranspiration of agricultural areas using satellite images, which indicate the acceptable accuracy of these methods. However, most of this research is related to agricultural fields and no significant research has been done to estimate evapotranspiration at the orchards. Vegetation at the farms is uniform and homogeneous compared to orchards, so the estimation of vegetation index, which is one of the inputs of the SEBAL model in orchards is more difficult than agricultural fields, which can affect the final accuracy. Therefore, the main purpose of this study is to estimate the amount of evapotranspiration in the pistachio orchard using the SEBAL algorithm and evaluate the accuracy of estimation. Also, this research has been

Materials and Methods :

The present research has been carried out in pistachio orchards in Zarandieh city of Markazi province. The gardens had three different irrigation systems including flood irrigation systems, surface, and subsurface drip irrigation systems. Actual evapotranspiration is estimated using water balance and SEBAL algorithm. Meteorological data from Imam Airport Synoptic Station and Landsat8 satellite imagery has been used to estimate evapotranspiration using the SEBAL algorithm. Actual evapotranspiration is estimated at satellite overpass times during the growing season. To select hot and cold pixels in the SEBAL algorithm, the semi-automatic method proposed by Oldmo is used, which minimizes user participation in the selection of hot and cold pixels. To evaluate the accuracy of evapotranspiration estimation, the information of soil moisture sensors in the orchard has been used. 28 sensors measure soil moisture in different parts of the orchard. Using the soil moisture values, the actual evapotranspiration was estimated using the water balance method and used as a reference value.

Results and Discussion:

 A comparison of the results of the SEBAL algorithm and water balance method showed that the SEBAl algorithm was able to estimate the actual evapotranspiration in different parts of the orchard with an RMS error of 0.57. In addition, the correlation between the values estimated by the two methods was equal to 0.82, which indicates the appropriate capability of the SEBAL algorithm in estimating evapotranspiration values. The correlation between the actual evapotranspiration estimated from the SEBAL model and the reference evapotranspiration is 0.76. In addition, in the research, changes in the evapotranspiration in different parts of the garden and also gardens with different irrigation systems including flood, surface, and subsurface drips have been investigated. The results show that the orchard with subsurface irrigation had the lowest average of evapotranspiration on different dates. Considering that evapotranspiration is equal to the sum of evaporation from the soil surface and transpiration from the plant, this decrease can be attributed to the decrease in evaporation from the soil surface. In addition, evapotranspiration heterogeneity can be observed in all parts of orchards with the same irrigation system on all dates. For example, in the orchard with a flood irrigation system, parts of the garden show low evapotranspiration, which can be due to the lack of smoothing of the surface and lack of proper moisture in these areas. Obviously, the same amount of moisture accumulates in other parts of the garden and is inaccessible through deep percolation. This uneven distribution is also observed in the garden with a surface drip irrigation system. For example, the middle part of the garden with surface drip irrigation always shows a higher amount of evapotranspiration, which can indicate the loss of water in this part, due to the miss-operation of the dripper. To evaluate the difference in evapotranspiration in different irrigation systems, the average, minimum, maximum, and standard deviation values of evapotranspiration in orchards related to three different irrigation systems have been calculated. The results showed that in all dates, the ranges and standard deviation of evapotranspiration in the flood irrigation system were higher than in other systems, which indicates the lack of uniform irrigation in the orchard. Also, on all dates, the average amount of evapotranspiration in the orchard with a surface drip irrigation system has been more than flood irrigation system. Vegetation in orchards with drip irrigation systems (surface and subsurface) was denser compared to the flood irrigation systems.

Conclusion:

 In this study, the actual evapotranspiration of pistachio orchards has been estimated using satellite imagery and the SEBAL algorithm. The results of the study indicate the appropriate accuracy of the SEBAL algorithm in estimating the actual evapotranspiration of the orchards. Compared with the water balance method, the correlation coefficient was 0.82 and the root means the square error was 0.57. In addition, comparing the moisture situation in different parts of the orchard and in orchards with different irrigation systems has shown that by estimating the actual evapotranspiration using satellite imagery, appropriate information can be obtained on how to distribute moisture in the garden. This information provides valuable information on the optimal management of water resources and increases irrigation efficiency. Other results of this research include the significant difference between surface and subsurface drip irrigation methods. The results show that using subsurface irrigation methods can effectively reduce irrigation water loss due to evaporation from the soil surface. The results show that in areas where there is no access to information from soil moisture sensors or direct measurements of evapotranspiration, the use of the SEBAL algorithm and remote sensing methods can provide appropriate information for optimal water management.

Background and Objective:

 In recent decades, natural ecosystems have undergone fundamental changes due to increasing population growth and increasing demand in order to provide the necessary facilities for human welfare. Since these changes are generally associated with environmental degradation, one should always be concerned about the damage to the ecosystem that supports human life. Therefore, the maintenance and protection of ecosystems are critical to achieving balance, equilibrium and coordination between human society and the ecosystem and their functional services. Ecosystem services have the potential to be considered as a key tool for policy-making and decision-making at the global, national, regional and local levels. Using ecosystem services, several applications including sustainable management of natural resources, land use optimization, environmental protection, nature conservation and restoration, landscaping, basic nature solutions, water conservation and weather, disaster risk reduction, environmental education and environmental research can be pursued. However, the relationship between ecosystem processes and functions and human well-being is complex and a multifaceted and preventive approach must be taken to evaluate these relationships and value the benefits. The purpose of this study was to investigate the trend of changes in ecosystem services in urban growth and development, evaluation of ecosystem services and the consequences of urban growth on carbon storage ecosystem service in the study area of Karaj catchment area in the period (before and after the development of irrationality).

Materials and Methods:

 Ecosystem services mapping is an effective tool to improve land planning and land use. Valuation of these services can be an effective factor and a promising way to explain the relationship between services, society and the economy and can play an effective role in the cost-benefit system of policies to rehabilitate and manage the environment. In this study, carbon storage service was investigated in three catchments of Alborz, Karaj, Hashtgerd and Eshtehard provinces. Since the study area covers almost two-thirds of the province, so in the study of the characteristics of the study area, the same characteristics of the Alborz province have been mentioned. In line with this research, In the first step, land use maps of the study area using trust software and GIS and Landsat satellite imagery (Landsat 5TM sensor images 1988, Landsat 8 satellite ETM+ sensor 2018), and the Supervised classification have been applied for the two periods of 1988 and 2018 in four classes of vegetation, man-made space, the aquatic environment and non-man-made space. After preparing the land use map, the accuracy of the maps was checked using Google Earth software and field visits. Then, using the land use map, the carbon storage ecosystem service map was extracted using Invest software and finally evaluated using soil carbon, ground carbon, basement and dead tissue information.

Results and Discussion :

The results showed that the most changes in land use maps belong to non-constructed space and man-made space, which shows a decrease of 16% and an increase of 11%, respectively. Regarding carbon storage service, according to the plans obtained in two years, in the central part of the region, which has witnessed the growth of agricultural lands, the amount of carbon storage has also increased. The maximum carbon reserve, with a share of 78377470 tons per hectare in 1988 and 72618450 tons per hectare in 2018, belongs to the Tehran-Karaj basin, and the lowest with a share of 36078497 tons per hectare in 1988 and 34606913 tons per hectare per year. 2018, belongs to Eshtehard. In total, the value has increased from about 14163 billion rials per ton per hectare since 1988 to about 13163 billion rials in 2018, which has gone in a negative direction. Although the amount of carbon storage varied and changed in different places, the maximum amount of carbon storage remained unchanged during this period; because there is still unbuilt space.

Conclusion:

 In general, it can be stated that the use of ecosystem services valuation does not necessarily mean a reduction in the value of services in the development process, but it means that using this approach can be used to improve development planning to maintain and used to improve the quality of the ecosystem. In fact, it can be said that this approach can be used as an interface to link the anthropological and ecological sections under a single programmatic and managerial framework. and in order to; used instead of "to", especially after try, come, go More (Definitions, Synonyms, Translation).

Background and Objective :

Soil moisture is an important parameter in controlling many processes of the climate system, one of the basic parameters of the environment and its direct impact on the plant, animal and microorganisms, its importance in the global cycle of water, energy and carbon, the energy exchange between air and soil is known for its natural water cycle (especially in the distribution of rain between surface runoff and infiltration) and the management of water and soil resources. Soil moisture plays an important role in the interactive processes between the atmosphere and the earth and global climate change. Triangular and trapezoidal methods combining thermal and visible data are the most commonly used methods for determining the amount of soil surface moisture. The aim of this study is to estimate the surface moisture of the soil (TVDI), by the triangular method in the south of West Azerbaijan province using land temperature index (LST) and vegetation index (NDVI), during 2010, 2014 and 2018.

Materials and Methods:

 The present study using MODIS timing series images, NDVI index and LST index, to estimate the surface moisture index (Temperature–Vegetation Dryness Index, TVDI), in three time periods including; the first time period from 1 January 2010 to 30 December 2010 and the second period is from 1 January 2014 to 30 December 2014 and the third period is from 1 January 2018 to 30 December 2018. During each period, 12 images were used on the 15th day of each month. Also, surface moisture was estimated by two methods, one was to establish a high regression relationship and remove the minimum temperature, and the second method was to establish a high and low regression relationship of the pixels. To evaluate the accuracy of these two methods, a regression correlation between the results of these methods with the soil surface moisture content of the Agricultural Jihad (30 points) at a depth of 5 to 15 cm was used. The reason for choosing these three years is due to the difference in high rainfall in some months of the studied years. This study was conducted in the south of the province of West Azerbaijan, which is part of the western region of Iran.

Results and Discussion:

 The evaporative triangle diagram consisting of the vegetation index and the surface temperature of the earth in 2010 from January to December month has seen many temperature changes. These same changes in the Earth's surface temperature have caused that the graphs have many changes. During the 2010 year, according to the chart, the maximum temperature was August and the minimum was January, and the maximum vegetation was May and the minimum was December. In 2014, the maximum temperature in August and the minimum in January and the maximum vegetation in May and the minimum in January and also this year were relatively warmer and drier than in 2010. The evaporative triangle chart in 2018 is rainier than the other two years studied, and the amount of vegetation and according to the graphs in this year, the maximum temperature in July and the minimum is January and the maximum vegetation is May and the minimum in January. The surface moisture level of the soil in 2010 for the western region of Iran, which is the maximum moisture level in May and the minimum in August. In most of the 2010 moisture index maps, the maximum humidity in the west and the lowest in the South of this region. The results of the moisture index maps in 2014 this year have been relatively drier than in other years studied. In 2014 has little rainfall and vegetation. Humidity changes this year are lower than in 2010. The maximum and minimum humidity in 2014 was between 0 and 0.6. The maximum humidity is June and the minimum is August. The TVDI moisture index maps for 2018 have had more moisture indicators this year than in the other two years. In 2018, heavy rains caused the vegetation to increase and the ground temperature to decrease, and this has led to an increase in the moisture index compared to 2010 and 2014. In 2018, the vegetation reached 0.89. But in other years it has been studied up to 0.7. This year, the high humidity is in May and the lowest in August. The maximum humidity during this year is in the west and the lower is in the south. The results of the TVDI index for 2010, 2014 and 2018, using the second method, the general results of this method are similar to the first method. Based on the results obtained from the accuracy of both methods, we conclude that the accuracy of the first method is better and generally simpler than the second method. In 2018, in May, according to the first method, the amount of R2 = 0.67, and also according to the second method, the amount of R2 = 0.41.

Conclusion:

 Estimation of surface soil moisture is essential for optimal management of water and soil resources. Surface soil moisture is an important variable in the water cycle of nature, which plays an important role in the global balance of water and energy through its impact on hydrological, ecological and meteorological processes. Examination of the two methods used indicates that the first method, which was also used in this research in general, has higher accuracy than the terrestrial fields due to the results of image accuracy.  In 2010, the months of May and August, according to the first method are R2 = 0.61 and 0.57. In 2010, the amount of R2 according to terrestrial data and the use of the second method in May and August are R2 = 0.43 and 0.47. Also, in 2018, the value of R2 using the first method in May is 0.66. In 2018, the value of R2 using the second method in May is 0.41. The results of the soil surface moisture index, in this study, showed that this model is able to estimate the amount of soil moisture in large geographical areas with acceptable accuracy.

Land cover map show the spatial distribution of different landscapes such as agricultue, natural resources, water and man-made area. It is a valuable tool to managing and reducing risk in challenging issues such as drought and its effects, food security, flood control, and urban planning. In order to overcome the limitations of field work in the mapping of land cover, the use of satellite images due to the wide, multispectral and update data seems to be suitable. In the study area, the spatially heterogeneous landscapes also makes it difficult to classify features. Therefore, the main purpose of the study is accurate and high resolution land cover mapping using Sentinel-2A images in the Google Earth Engine platform. In this regard, three classification algorithms including RF, SVM and CART were evaluated and compared. Various indices were prepared using ratioing and transformation methods. The accuracy of the classifications was evaluated in comparison with ground reference data. Individual bands evaluation showed that the best overall accuracy (49%) was obtained using the CVI index.The best overall accuracy and kappa coefficient of 86% and 0.82 were obtained by RF algorithm. Therefore, while pointing to the advantages of the GEE including easily accessible data and the ability to process and quickly compare of data, it can be claimed that Sentinel-2A images for land cover mapping in terms of cost, time and accuracy, have high efficiency and the map can be very useful for the management and decision making in different natural and man-made resources for the successful implementation of sustainable development.