جستجوی مقالات مرتبط با کلیدواژه "sentinel-1 images" در نشریات گروه "جغرافیا"

Due to its environmental diversity, Iran ranks high in terms of crises caused by natural disasters. Flooding, as one of these disasters, is  causing severe social, economic, health, and environmental damage in many areas due to rapid urban growth and climate change. Therefore spatial forecasting of floods is crucial, as failure to identify flood risk areas in a catchment can exacerbate the destructive effects of floods. Recent advances in remote sensing, geographic information systems, machine learning, and statistical modelling have made it possible to produce highly accurate flood prediction maps. This study aims to predict flood risk areas in the Karun watershed using Sentinel satellite images and a novel ensemble approach with six machine learning models.

In this study, Synthetic Aperture Radar (SAR) data from Sentinel-1 images were used to identify areas affected by flooding.  First, the dates of heavy rainfall and flooding events in the study area were identified from various sources of information. Subsequently, Sentinel-1 images were obtained from the Copernicus database, representing the area before and after the flood events. The aforementioned data were processed using the SNAP platform. The identification of flood-affected areas was achieved through the application of the thresholding technique. For this purpose, the Normalized Difference Water Index (NDWI) generated from Sentinel-2 images and land cover classes indicating permanent water bodies were employed to determine the threshold for identifying flood-affected areas. The flood polygon layer was converted to a point layer, resulting in a total of 70 flood occurrence points. A review of previous studies and local characteristics identified seven main factors that significantly affect flood occurrence in the region. These factors include the Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), slope, flow direction, flow accumulation, distance from the river, and monthly rainfall. Additionally, the Digital Elevation Model (DEM) of the region was obtained from the SRTM database, and the spatial resolution of all factors was aligned with the DEM layer. Subsequently, various machine learning algorithms were employed to develop a combined model that provides more accurate predictions of flood-prone areas. The individual models include the Generalized Linear Model (GLM), Boosted Regression Tree (BRT), Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), and Maximum Entropy (MAXENT).                    

The results of this study indicate that the northeast of Aligudarz city, parts of Durud and Azna in Lorestan province, Khademmirza, Shahrekord, and Kiyar in Chaharmahal Bakhtiari province, Dana and Boyer Ahmad in Kohgiluyeh and Boyer Ahmad province, Semirom city in Isfahan province, and the southern border areas of Karun River in Khuzestan province have the highest flood potential in this basin. The performance evaluation of the models revealed that the Random Forest (RF) and Maximum Entropy (MaxEnt) models exhibited the highest accuracy among the individual models. These models, by combining environmental information and flood occurrence data, can produce highly accurate flood susceptibility maps. These maps can serve as crucial management tools to mitigate the adverse effects of floods and prevent development in vulnerable areas.

Overall, this study demonstrates that the use of an ensemble approach which combines machine learning models can provide more reliable results in the prediction of flood risk areas. The findings of this research are beneficial for managers and planners, as they can prevent development in vulnerable areas and consequently help reduce financial losses and human damages in the future.

Accurate assessment of forest above-ground biomass is essential for sustainable forest management. Estimation of forest biomass is necessary for studies such as estimation of greenhouse gases, carbon stored in forest resources and climate change models. Also, the forest biomass represents the production rate per unit area. The optical image data of Sentinel-2 satellite was used to estimate the above-ground biomass of the forest in the area of 285 hectares of the forests in Ilam province. 124 square-shaped sample plots with a 20×20 m dimension were located on the ground using a cluster method. Some characteristics of a total of 508 trees (both single stems and coppice forms), including the major and minor crown diameters were measured within each sample plot. Depending on whether the trees are single stem and multi-stem clumps, suitable allometric equations were used to calculate the above-ground biomass based on the measured characteristics. Finally, the total above-ground biomass was calculated for all trees in each sample plot. In order to estimate the above-ground biomass, MSI sensor images of Sentinel 2 satellite were used at the level of L2A corrections. Using spectral ratios, vegetation indices were calculated. In the next step, the corresponding spectral values of the sample plots were extracted from the main bands, and vegetation indices. A random forest regression model was used to estimate forest above-ground biomass. 70% of the samples were used for training the model, and the models were validated using the remaining 30% of the data. The results with R2=0.80 and RMSE=28.70 t/ha showed the acceptable performance of model for estimating the above-ground biomass of the forest. By using the Gini statistic, it was shown that RVI, GNDVI, NDVI, and DVI vegetatuin inices played a larger role in the estimation of biomass.

 Accurate assessment of forest above-ground biomass is essential for sustainable forest management. Estimation of forest biomass is necessary for studies such as the estimation of greenhouse gases, carbon stored in forest resources, and climate change models. Also, the forest biomass represents the production rate per unit area. Estimating forest biomass through direct measurements and cutting and weighing trees in the forests provides an accurate estimate of biomass, but it is a destructive, difficult, and time-consuming method. Therefore, the use of remote sensing methods is very important in the estimation of biomass.

The optical image data of the Sentinel-2 satellite was used to estimate the forest above-ground biomass in the area of 285 hectares of the forests in Ilam province. 124 square-shaped sample plots with a 20×20 m dimension were located on the ground using a cluster sampling strategy. Some characteristics of a total of 508 trees (both single stems and coppice forms), including the major and minor crown diameters were measured within each sample plot. Depending on whether the trees are single-stem or multi-stem clumps, suitable allometric equations were used to calculate the above-ground biomass based on the measured characteristics. Finally, the total above-ground biomass was calculated for all trees in each sample plot. In order to estimate the above-ground biomass, MSI sensor images of the Sentinel 2 satellite were used at the level of L2A corrections. Using spectral ratios, vegetation indices were calculated. In the next step, the corresponding spectral values of the sample plots were extracted from the original bands and vegetation indices. The correlation coefficient between the values of the original bands and vegetation indices with the amount of biomass calculated from the allometric equations in the sample plots was investigated. A random forest regression model was used to estimate forest above-ground biomass. 70% of the samples were used for training the model, and the models were validated using the remaining 30% of the data.

 The results of the descriptive statistics of above-ground forest biomass measured in 120 sample plots which were calculated using allometric equations showed that the lowest biomass in the sample plots is 0.61 and the highest is 268.88 tons per hectare. The average above-ground biomass per tree was measured as 657.6 and 231.2 kg in the single and multi-stemmed trees, respectively. The results of the correlation analysis of biomass with the investigated variables showed that among the main bands of the sensor, the red wavelength has the highest correlation (0.402) with biomass due to the high chlorophyll absorption of green plants in this wavelength. Among the vegetation indices investigated in the research, RVI and NDVI indices have the highest correlation with the forest above-ground biomass with a correlation coefficient of 0.529 and 0.525, respectively. The results of random forest regression analysis to estimate the forest above-ground biomass with R2=0.80, RMSE=28.70 t/ha show the acceptable performance of the model for estimating the above-ground biomass of the forest. Since in this research, the amount of forest above-ground biomass of the sample plots is calculated based on allometric equations in a part of Zagros forests; but these equations are not exactly related to the studied area, part of the model error can be due to this reason. By using the Gini statistic, it was shown that RVI, GNDVI, NDVI, and DVI vegetation indices played a larger role in the estimation of biomass. RVI, NDVI, and DVI indices are calculated using red and near-infrared bands, and since they are influenced by the photosynthetic activity of plants, they are very important in estimating the amount of biomass. GNDVI, which is calculated using green and near-infrared bands, is an indicator of the level of greenness or photosynthetic activity of the plant and is highly sensitive to changes in the chlorophyll content of plants.

 The results of forest above-ground biomass estimation using Sentinel 2 satellite images and random forest regression method showed that using the non-parametric method of the random forest regression model, which performs a large number of uncorrelated models; it has an acceptable ability to estimate forest biomass. Also, the findings showed that vegetation indices are more important in the process of forest above-ground biomass estimation model than Sentinel 2 original bands. The findings of the present research provide the possibility for the managers of Zagros forests to estimate the forest above-ground biomass and provide the basis for sustainable forest management strategies.

The studied area is located as a part of the Caspian Sea catchment with an area of ​​about 50083 hectares in the east of Kurdistan province, northwest of Iran. In this research, the data of 34 observation wells were used to investigate the condition of the underground water level and its depth fluctuations, and also to investigate the phenomenon of subsidence in region 8, the Sentinel-1 satellite image was used in the period (2014-2021). The research method includes statistical analysis of changes in the underground water level and interferometry of radar image. The results of the research showed that in the years 2014 to 2021, Dehgolan plain has subsided by -12 to -32 cm. In this way, an average of 6 centimeters of subsidence has occurred annually in these areas. The complete subsidence pattern of Dehgolan plain and the analysis of profiles and water level maps showed the center-west and southwest trends and the maximum subsidence is related to the central and western parts. Therefore, the conformity of the subsidence areas and the curves of the water table level drop, the use of underground water resources indicates that the withdrawal of more than the amount of aquifer nutrition has caused an increase in the effective stress in the sediments because the subsidence areas coincide with the areas of the groundwater level drop, or are located near them. In other words, this location adaptation in the high plains of Dehgolan shows that the cause of the subsidence of the high plains is the drop in the groundwater level, and as a result, the high plains are more vulnerable to the risk of subsidence.

Air pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) industries and the energy demand they satisfy have led to an increase in pollution in many Iranian metropolises. As one of the four Iranian industrial hubs, Arak has one of the worst air quality in this country. In addition to the presence of industries, having a relatively high population density (and consequently high traffic congestion level) and various climatic conditions affect the quality of air in Arak. It is essential to accurately measure air pollutants with a high spatial and temporal resolution, determine their distribution pattern and level of effectiveness, and provide provincial and national managers with applicable solutions. Unfortunately, air quality monitoring stations are not sufficiently and properly distributed in Iran.Many Iranian cities do not have even a single air monitoring station and many others have only one station. As the capital city of Markazi province and an industrial city, Arak has only four monitoring stations which are not simultaneously active in many cases.Failing to conduct proper site selection before the installation of ground-based monitoring stations results in local irregularities in the recorded concentration of pollutants. Furthermore, the stations are not usually calibrated on time and thus air quality monitoring observations are disrupted. In these cases, either this data is deleted from the final results or the station will be inactivated (for example, for a week or a month) by authorities. However, it seems that the observations made by these stations still include inaccurate data.

The present study has introduced a method based on composition and voting to validate the observations made by air quality monitoring stations using Sentinel-5 satellite images. Arak city was used as the study area. Level three images (L3) of the Sentinel-5 TROPOMI sensor received from the Google Earth Engine were used to monitor the concentration of pollutants in the present study. Sentinel-5 is a powerful atmospheric monitoring tool. Equipped with a spectrometer called TROPOMI, the satellite measures ultraviolet radiation reaching the Earth's surface in a high range. TROPOMI sensor is highly capable of imaging and monitoring a large number of pollutants. The present study has compared the concentration of NO2, SO2, CO and ozone pollutants monitored by ground-based stations in Arak city with Sentinel-5 images. Since the time resolution of ground-based observations is higher than satellite observations, a monthly average of pollutants' concentrations was calculated to increase the reliability of observations. In other words, the concentrations of pollutants were compared on a monthly basis. The proposed method has assumed that more accurate sets of ground observations show a higher linear correlation with satellite observations.
In order to select the appropriate set, the number of observations with an acceptable accuracy must be determined. To do so, a method based on a mixture of composition and voting has been used. As previously mentioned, each observation showed average pollutant concentration in a specific month of the study period. The process started with at least four monthly observations. As a result, assuming that all 19 monthly observations were available, 16 subsets were obtained with a maximum linear correlation between ground-based observations and their satellite correspondence which showed the accuracy of the observations. The second step was the proposed voting method which showed that the monthly ground-based observations (for example October 1398) were repeated several times. The high frequency of a monthly observation indicated its higher accuracy. The presence of this particular observation in different permutations has increased the linear correlation coefficient of the observations. Therefore, for an instance a frequency of 15 or 16 for the observation made by the ground-based station in October 2017 indicated high accuracy of the observation.

The present study has compared the concentration of NO2, SO2, CO and ozone pollutants Using the proposed method, some observations have been identified as outliers or errors. RMSE criterion was used to evaluate the accuracy of the proposed method. Some observations made by the ground-based station were not consistent with other ground-based and satellite observations, and removing them increased the correlation coefficient. Removing outliers from the observations, the RMSE (originally 2%) was improved and reached 47%.

Findings indicated that some observations made by ground-based monitoring stations were incorrect, or at least the stations had sometimes failed to exhibit the real general trend of environmental pollution correctly due to local irregularities caused by various reasons, such as improper location or lack of proper calibration.

Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, identification of thermal islands, monitoring of urban vegetation, and assessment of air pollution are just a few examples of areas related to urban management that remote sensing technology is the basis for indirect measurement of the related quantities. Maps of urban structures such as building blocks are commonly used in crisis management, urban design, and urban development studies.
 Materials

In this study, the production of urban building block maps using Sentinel 1 and 2 satellite images has been conducted. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index ( NDBI ) for three consecutive months, the slope feature derived from the 30-meter Shuttle Radar Topographic Mission (SRTM)Digital Elevation Model of the study area, along with two Vertical – Vertical (VV) and Vertical - Horizontal ( VH ) polarization in both ascending and descending orbits, form the set of input features.

The proposed method of this paper relies on the use of a generalizable trained classifier. Initially, the classifier is trained in 2015 using training samples obtained from a new rigorous refining process using different remote sensing and spatial products. This rigorous refining process uses a reference urban map of 2015. In the first step, the corresponding areas related to the ways and roads are removed using the OpenStreetMap data layer. Areas suspected of vegetation with NDVI greater than 0.2 are then discarded. Also, due to the high backscattering of buildings in Synthetic Aperture Radar images, areas with a value less than the average backscattering coefficient of the remaining areas are eliminated. Finally, the residual map is refined using the Mahalanabis distance and the Otsu automatic thresholding method. The trained classifier is then used to generate a map of building blocks at similar time intervals for the three target years (2018, 2019, and 2020). Due to the diversity of texture and density of building blocks in the metropolis of Tehran, the proposed method has been evaluated in this area. Due to the concentration of political, welfare, and social facilities, Tehran has experienced more unplanned and irregular expansion and urbanization than other cities in Iran, which has lead to changes in buildings and constructions. Also, due to the availability of free satellite images and various online processing operations, the Google Earth Engine platform has been used in this study. The performance of three different classifiers including Random Forest (RF), Minimum Mahalanabis Distance (MD), and Support Vector Machines (SVM) are examined in this process. In order to evaluate the proposed method, reference samples obtained from visual interpretation of high-resolution satellite images (Google Earth) in all three target years have been used.

The performance of the aforementioned classifiers has been investigated using 3 different criteria: overall accuracy, user accuracy, and F-score of building blocks. The RF method with an overall accuracy of over 93% in all three target years has shown the best performance. The SVM method ranks second with an accuracy of about 91% every three years. However, the MD method with an overall accuracy below 85% in all three target years has not performed well.

The results show better performance of the RF method in all three target years with an overall accuracy of over 93%. It should be noted that the MD classifier with higher user accuracy than other methods, has shown better performance in detecting the class of building blocks. However, the RF method is the best classifier in terms of the user accuracy of the background class. The effect of using two VV and VH polarization and also the slope derived from the SRTM Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a more than 10% reduction in overall classification accuracy.

Based on calculated overall accuracies which are above 80% in the majority of investigated cases, two different results can be concluded. First, the trained classifier has shown good temporal generalization and has achieved acceptable accuracy in the target years. Second, due to the different collection processes of training and evaluation data, the proposed rigorous refining method for the preparation of training data has shown good performance. The effect of using two VV and VH polarization and also the slope derived from the SRTM  Digital Elevation Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a tangible decreasein overall classification accuracy.

Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.

Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely. 

The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.

Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.

The danger of amplitude movements is considered as one of the hazards that has occurred in many areas in recent years. Identifying the areas exposed to amplitude movements and estimating its rate plays an important role in managing and controlling this phenomenon. Radar interference technique is an efficient method in measuring ground surface displacement which makes it possible to monitor small movements of the earth surface continuously, with high accuracy and in a wide range. This technology has become very common in the study of natural disasters of the earth, including slope displacement, subsidence, earthquakes and volcanic activity. This technique compares the phase taken from two radar datasets at two different times and. Besides, creating an interrogram, it is able to measure changes on the earth surface over time. In the current study, the radar images of 2015 and 2020 have been applied in order to identify and measure landslides. SARSCAPE software has been used to process information. The land-use map of the study area was extracted using Landsat 8 image and object-oriented classification method. The findings reveal that radar images have a good potential to detect the instability of slopes and to calculate their displacement. During the study period, the maximum amount of material movement has been recorded as 21 cm, indicating the area is active in terms of amplitude movements. The overlap of the landslide map with the land use layer also confirms the maximum occurrence of landslides in the use of vegetation and rainfed agriculture.

With their dynamic nature, water resources are essential fortheenvironment and play a vital role in human life, development of communities, and climate change. Water bodies have been declining over time due tothe rapid growth of urbanization, excessive abstraction of water, damming, increasing demand for agricultural products, pollution anddegradationofthe environment. Therefore, monitoring water bodies and retrievingrelated information are essential for management of environmental issues and decision making in this field. Accurate recognitionof water bodiesiscrucialin many applied fields, such as environmental monitoring, production of land cover and land use maps, flood risk assessing and monitoring, and drought monitoring.Modern methods such as object-oriented processing take advantage of remote sensing capabilities to make accurate and precise recognition of water bodies possible. Classical methods on the other hand, cannot accurately classify satellite imagery with similar spectral information merging into each other. This reduces the accuracy of pixel-based classification methods. Therefore, object-oriented processing of satellite images is used in the present study to obtain precise maps for the identification of waterbodies.

A part of Aji Chai River, near the city of Khajeh in Harris County, has been selected as the study area. The total study area included 28 square kilometers. Based on the aim of the present study, the study area was selected in a way to contain linear features, arable lands, and other topographical and human-madefeatures (shading factor) which interfere with the extraction of water bodies and reduce the classification accuracy. Object oriented methods (the closest neighbor and fuzzy object-oriented methods) were used in the present study to identify and extract water bodies from high resolution images (Sentinel 2A imagery).

Different functions used in OBIA techniques,such as GLCMtextual features, average number of bands in the image, geometric information (shape, compression and asymmetry), and normalized difference vegetation index(NDVI) were used in the present studyto precisely extract land cover. Moreover, algorithms with the highest membership degree in the class of water bodies were considered as effective factors in classification. Usual methods of extracting and monitoring water bodies use spectral information of pixels, and therefore, have limited ability in distinguishing water bodies from linear features, such as roads, clouds, shaded regions, and residential areas. These methods also have limited capabilities in mountainous areas, especially when they are required to separate water from snow. In other words, these methods cannot separate water bodies from regions with lower albedo. Therefore, the present study takes advantage of object-oriented methods (the nearest neighbor and fuzzy methods) and evaluate their effectiveness in the extraction of water bodies.

In this study, the nearest neighbor and fuzzy object-oriented methods were used to extract water bodies and their efficiencies were compared. To improve the results in the nearest neighbor method, the separation space between the samples was optimized using the FSO algorithm, then the water bodies were extracted with 95% accuracy and a Kappa coefficient of 93%. Findings of the present studyindicated that this method cannot distinguish water bodies from shaded regions, and linear featuressuch as roads, and residential areas, and categorizes these features as water bodies, which reduces the accuracy of the final results. In the next step, water bodies were once more extracted using object-oriented fuzzy model. In this method, membership degrees were first calculated for each sampleand then applied in the classification procedure. High accuracy of the results of this method (overall accuracy of 98% and a kappa coefficient of 96%) indicated the superiority of this method over the previous one (nearest neighbor). In this method, water bodies are completely distinguished from linear features such as roads, as well as shaded regions, clouds and residential areas. The results of this study can be generalized to other rivers and water bodies. Compared to classical methods, object-oriented methods are more time efficient and accurate.

A Digital Elevation Model or DEM is a physical representation of terrain and topography that is modeled by a digital 3D model. DEMs have various applications in many fields. Today, with respect to improvements in technology and importance of generating DEM from every region in our country, the importance of satellite remote sensing is more sensible.  One of the main topics in satellite remote sensing is radar remote sensing. In recent years, a number of satellites have been launched to capture SAR information from the surface of the Earth. The last project is Sentinel, and Sentinel-1generates SAR data. It generates images with medium spatial resolution from the Earth every 12 days. DEMs are generated through multiple methods, one of which is SAR interferometry.
Material and Methods: The area under study in this research for conducting experiments and generating the DEM is Iran and the city of Tehran. Tehran is located in the north of the country and south of the Alborz Mountains, 112 kilometers south of the Caspian Sea. Its elevation ranges from 2000 meters in the highest points of the north to1200 meters in the center and 1050 meters in the south. In this paper, the Sentinel-1 stereo images are used to generate DEM. Tehran is located on part of these images. These images are shown in Figure (1). In order to evaluate the digital model generated by these images, a reference digital model which has been prepared from the city of Tehran with an accuracy of 1 meter is used. This elevation data was collected using terrestrial surveying and aerial photogrammetry. In this paper, radar interferometry was used to generate digital elevation model from the Sentinel-1 images. In SAR interferometry, the phase of images taken from various imaging positions or various imaging times is compared pixel by pixel. The new image is produced by differentiating between these values which is called interferogram. Interferogram is a Fringe interference pattern. Fringes are lines with the equal phase differences similar to contours in topographic maps. The phase difference obtained from SAR interferometry is affected by several components. Some of the most important components are orbital paths, topographic, displacement and atmospheric components. By eliminating the major part of the orbital component (and calculating the effect of other components or assuming their insignificance effects comparing with orbital and topographic components), since the topographic radar observes the Earth from two different points, the stereoscopic effect is revealed. This topographic component leads to fringes which encompasses the topography like contours. These patterns are called topographic fringes.   In order to conduct the experiments considered in this paper, two mountainous and flat areas in Tehran are picked out and separated from the main image. The mountainous area is selected from the north and the flat one from the south of Tehran. The aforementioned technique is implemented and executed on these images. The generated DEM in these two areas is shown in Figure (2). After generating the Earth DEM using the Sentinel-1 images, and comparing it with the reference DEM having an elevation accuracy of 1 meter, the accuracy of the generated DEM was determined. As expected, the results in the flat area were more desirable compared to the mountainous area. The accuracy of the generated DEM was evaluated by creating a network with the dimensions of 138761 points from the flat area and a network with the dimensions of 78196 points from the mountainous area, from both generated and reference DEMs and comparing the corresponding elevations of the network points. Digital numbers of images represent the magnitude of error occurring in the generation of DEM. After testing the 3 error (blunder detection) and eliminating large errors occurred in DEM, a standard deviation error of 1.26 meters for the flat area (South of Tehran), and 10.32 meters for the mountainous area (North of Tehran) were obtained.  Considering the development of technology and the launch of new satellite imagery projects from the Earth and the importance of the existence of a digital elevation model from the country, it is possible to recognize the importance of studying these images more and more. One of the latest satellite remote sensing projects is the Sentinel project. The Sentinel-1 radar images with medium spatial resolution capabilities provide the possibility of generating a Digital Elevation Model (DEM) from the country.  This research is the first study on the accuracy of Digital Elevation Model resulted from the Sentinel-1 radar images in Iran. An elevation accuracy of 10.32 meters in the mountainous area, and 1.26 meters in the flat area were obtained. The results show that these satellite images have the capability of generating a relatively optimal DEM, particularly in non-mountainous area.