Synthetic aperture radar (SAR) systems have been widely used in the past two decades to produce high-resolution mapping and other remote sensing applications (Calabro et al. 2010; Sun et al. 2011). The ability of penetrating to the cloud , snow dry soil as well as day and-night operation made the SAR systems with more capability compared to optical imagery (after Karjalainen et al. 2012). SAR data are widely applied for several studies geophysical and geographical approach forestry and vegetation, biomass measurements, soil moisture, natural hazards and etc. (Lardeux et al. 2011,Herrera et al. 2013). The present study deal with morphological landform Identification over the area covered with dense forest. Where is the landform assessing and mapping almost appeared as big task due to difficulty of observing true the optical images. The high penetration potential of SAR signals through the vegetation cover can be obtained using the L band of the ALOS PALSAR satellite with nearly 24 cm signal wavelength (Herrera et al. 2013. Furuta et al. 2005) Furthermore, it is mention that ALOS PALSAR data is very useful for producing accurate digital elevation models (DEMs) and deformation monitoring, as well as disaster monitoring and hazard prevention. Topography is a key controlling factor in the operation of a variety of natural processes (Montgomery and Brandon 2002). Hence it needs to be quantitatively analyzed (Lague et al. 2003), to ascertain the relative efficacy of its constituents and operative mechanisms, and to gauge the response of geomorphic systems to different stimuli (Ahmed et al. 2010). Rivers are one of the most sensitive elements of the landscape (Smedberg et al. 2009). The systematic evaluation of land surfaces and drainage pattern characteristics remains as a main study object in geomorphology. Consequently in geo-morphometry (Prasannakumar et al. 2013). Digital elevation models (DEMs) have been frequently used for the above morphometric analysis of river basins through the extraction of topographic parameters and stream. The biggest advantage of DEMs over traditional topographical maps is the rehabilitee, coverage and data multiplying. Due to their wide applicability and rehabilitee, DEMs have been used in a variety of studies where terrain and drainage factors play prominent roles. The aim of this research is Extraction of Hillsides Drainage pattern in density forest area using Low-frequency radar data. Dense forests of Behshahr South are a rainforest which is geographically located at a latitude of 36° 25’ 30” up to 36° 43’ 30” N and longitude 53° 04’ 15” up to 53° 54’ 15” E. The Average elevation of the area is 704.58 m above mean sea level. The rainfall received over the basin area varies from 700 to 1000 mm annually. This paper provides a comparative study of different available or derived DEMs (SRTM, ASTER, ALOS-POLSAR), through extraction of stream networks and their eventual comparison. A flowchart schematically shows the methodology followed for the extraction of drainage networks from DEMs in a GIS environment (Fig. 2). The DEMs of the study area is first preprocessed through the operations of Flow Direction, sink dems and filling the data gaps, pit removal–depression filling, and finding outlet cells in an iterative manner. Pit removal and depression filling is a method of filtering the digital elevation data. This is done to overcome any data voids that may be present in the DEM tile and to also ensure proper channel network connectivity. Sometimes, there are some pixels in the continuous array of digital data where the value of the pixel is abnormally low or high in comparison to other neighbouring cells. These are known as data sinks or spikes respectively and these are inherent in any DEM. These need to be removed before carrying out any sort of analysis in the data. After extraction of drainage networks to use Digital Elevation Models, The ability of Digital Elevation Models were evaluated in the extraction of drainage networks. And in the last step, Drainage density was calculated in each of the land covers. The average elevation with a standard deviation of the study area extracted from the different DEMs have been calculated and subsequently presented in Table 2 and figure 3. The results showed that PALSAR digital elevation model has the lowest standard deviation. Figures 4,5 and tables 3,4 depict the comparisons of drainage networks derived from the different DEMs with respect to total stream lengths and Drainage density respectively. It is observed that the maximum lengths of streams and maximum area of high Drainage dendity are generated by PALSAR DEM. Integration of land use and drainage density map also showed that in PALSAR DEM, 73.62 percentage (520.97 K2) of dense forest area have been located in very high Drainage density class. While, in ASTER and SRTM DEMS only 0.033 and 0.672 percentage of dense forest area have been located in very high Drainage density class, respectively. Digital Elevation Models (DEMs) have been a subject of increasing attention and utilization in the last few decades because of the relative ease in delineation, extraction and calculation of various drainage and terrain morphometric parameters from them. The present study was carried out in order to find the best possible DEM for extraction the drainage pattern in density forest area. After analyzing the different parameters derived from these DEMs, it can be said that the DEM derived from the Low-frequency radar datasets is relatively more accurate and consistent than ASTER and SRTM DEMS. The results showed that Low-frequency radar datasets have High capability for penetration through the vegetation cover and extraction of drainage pattern

Morphometric study of a watershed plays a very important role in land structure. Remote sensing methods provide a good tool for studying and extracting stream networks. One of the common methods for extracting waterway networks and conducting a morphometric study of watersheds is the use of Digital Elevation Models (DEMs) with a high spatial resolution. The purpose of this study was to extract stream networks by using the DEMs of high spatial resolution, such as ALOS-1 and Sentinel-1, and those of medium spatial resolution like SRTM and TanDemX. To produce the DEM by using Sentinel-1 images, the InSAR method was applied. Finally, to validate the accuracy of this DEM for checking Sentinel-1 ability to extract stream networks, the ALOS-1 DEM with the spatial resolution of 12.5 m was used. The results revealed that the produced DEMs by using Sentinel-1 images had a high correlation of about 0.99 with the reference data of ALOS-1, thus showing the high capability of the DEM for extracting stream networks. The results of extracting the waterway networks demonstrated that each of the two DEMs of Sentinel-1 and ALOS-1 with high spatial resolutions could extract 9 waterway networks, while the digital models of SRTM and TanDemX with medium resolutions could only extract 7 and 6 stream networks, respectively. The studies indicated that the baseline parameters, as well as the time difference between the two Master and Slave images in InSAR, had to be highly considered by researchers to improve the quality of the Sentinel-1 DEM.

Generating and modifying spatial data for strategic areas of the country, especially boundary areas, is one of the key challenges in the defense technical information community. In this regard, digital elevation model (DEM) can be utilized to remove every ground operations in a direct process between the earth and overlapping satellite images. In this process, rational polynomial function (RPCs) are only used. However, these coefficients due to some errors in their gathering have some errors that must be removed in a manner. In proposed method of this study, the effects of RPC errors on IRS-P5 satellite images are reduced using the 2.5D matching procedure between IRS-P5 derived DEM and ASTER global DEM. Experimental results obtained by comparison between the resulting DEM and few available check points shows that the proposed method can achieve vertical accuracy of about 7 meter

Topographic maps show natural and artificial features. natural features such as rivers, lakes, mountains, etc., Man-made features such as cities, roads and bridges. Using the satellite images is a way to extract digital elevation models. In general, there are two types of resolution in digital ground elevation models.üArea resolution: The dimensions of the length and width of each cell in the pixel grid is a digital elevation model that shows the minimum dimensions of the topographic features taken on the ground.
ü Height resolution: represents the minimum elevation dimensions that the digital elevation model is able to display. For example, in the digital model of ground elevation with a resolution of 30 meters, elevation features less than 30 meters are not visible.
The digital elevation model can be prepared for a region with different accuracy. The high accuracy of the digital elevation map provides more accurate estimates of the physiographic characteristics of the basin, but the preparation of such maps is very costly. PRISM sensor from ALOS satellite with three cameras: 1- Forward 2- Vertical 3- Forward, which is captured earth surface with the characteristics of the earth (low and high). Therefore, an object that is high above the ground is shown with other points on a flat surface. As a result, by imaging points from different angles, the elevation of those points can be obtained through adaptive mathematical calculations. The purpose of this study is to evaluate the accuracy of the digital elevation model generated by the PRISM sensor of ALOS satellite in comparison with the digital elevation model of ASTER and SRTM for Sarakhs border region (between Iran and Turkmenistan).

The study area is located in north-eastern Iran in the range of 35 to 38 degrees north latitude and 56 to 60 degrees east longitude and on the border between Iran and Turkmenistan in the border region of Sarakhs. The research method in this research has an exploratory aspect that the production and extraction of digital elevation model from PRISM sensor stereo images from Alves satellite and its evaluation is with digital model extracted from ASTER image. The digital SRTM model has a spatial resolution of 90meters, the digital ASTER model has a spatial resolution of 15 meters and the digital elevation model obtained from the PRISM sensor from the ALOS satellite is 5 meters. In this study, elevation control points using Google Earth and GPS have been examined. The algorithms used in this method to extract elevation information are the same as the algorithms used in the photogrammetric method. Elevation digital models are made from satellite images taken in pairs. The accuracy of digital elevation models of this method is perfectly proportional to the scale or resolution of satellite images.

In this study, we evaluated the digital elevation model from stereo satellite images of ALOS/PRISM satellite and compared it with the digital model of ASTER elevation and ground observations in the Sarakhs border region located on the border between Iran and Turkmenistan. In this study, the ability to generate a digital elevation model prepared from stereo images extracted from a PRISM sensor with a file of rational polynomial coefficients has been investigated, and we compared it with digital models extracted from stereo ASTER satellite and digital models extracted from SRTM. The results obtained from the digital elevation model are the accuracy of the digital elevation model produced by the pair of ASTER satellite images using a correlation between the two images of 0.47 pixels. Due to the spatial accuracy of the image pixels, which is about 15 meters, the accuracy of the digital model is less than the size of pixels, i.e. less than 15 meters, 6 meters horizontally and 7 meters vertically, which is a total of 13 meters. The results show that RMSE as error index for digital model of elevation extracted from ASTER and PRISM and ground observations are 7.46, 8.77, 3.66 and 6.8 meters, respectively. The results obtained from the stereo images of the PRISM sensor are the standard deviation of the pixels in the longitudinal direction of 1.9 meters and in the transverse direction of 2.3 meters and the distance between the pixels of the digital model is 3 meters high. Therefore, the accuracy of the digital model extracted from PRISM sensor images is higher than SRTM and ASTER. It is recommended to use a high-precision digital elevation model in all borders of the country, which uses a digital elevation model produced from stereo PRISM images from ALOS satellite, which is accompanied by polynomial logical coefficient (RPC) files for geometric correction of images.

The higher the accuracy of the DEM, the more efficient it will be and give border commanders the ability to make better decisions in different situations. The elevation accuracy obtained from the stereo images of the PRISM sensor is 3 meters. The accuracy of the digital model of SRTM elevation in the plains is about 30 meters, which can be used for studies of phase zero and one of the projects, as well as reducing the huge costs of studies. The results of this paper, shows that the accuracy of the digital elevation model produced from the stereo images of the PRISM sensor is higher than the digital elevation and SRTM digital models, i.e. the RMSE error and standard deviation are relatively lower. As a result, it is recommended for border studies that require higher accuracy, and the entire borders of the country, to use the digital elevation model with accuracy.

Digital elevation models (DEMs) are one of the most important data for various applications such as hydrological studies, topography mapping, image ortho rectification, 3D images generation, extraction terrain parameters, disaster management, and etc. A digital elevation model can be derived from numerous techniques with different elevation accuracies. In photogrammetric techniques, a DEM can be extracted from stereo satellite images through many processing steps. A satellite imagery based digital elevation model is called ASTER GDEM ver2 was released on 2011 at a spatial resolution of 1 arc-second. This model was evaluated by differencing with other reference DEMs in order to investigate the quality and accuracy parameters over different land cover types. In this paper, an accuracy assessment of ASTER GDEM ver2 dataset with SRTM and local elevation model over the bare area of a port in southwest of IRAN is presented. This study investigates DEM’s characteristics such as systematic error (bias), vertical accuracy and outliers for these three datasets. The accuracy measures for the assessment of the height differences between each DEMs can be calculated based on the usual (Mean error, Root Mean Square Error, Standard Deviation) and the robust (Median, Normalized Median Absolute Deviation, Sample Quantiles) descriptor. The results demonstrated that there is a large negative elevation bias of approximately -4.5 m and -2.8 m of ASTER GDEM ver2 against the SRTM and local DEM. The median of the differences between GDEM and local DEM is about -3.7 m which is a robust measure to prove the existence of systematic shift between the two data. The RMSE measured for elevation differences between GDEM and two other DEMs is same, but the standard deviation GDEM and local DEM differences are higher than the value of this parameter between GDEM and SRTM. The accuracy measures NMAD of GDEM against the local DEM and SRTM are 5.5 m and 5.9 m, respectively. On the other hand, about 68% of the GDEM and local DEMelevation differences are in [0, 7.5] m, while this values are in [0, 9] m for GDEM and SRTM elevation differences.

Digital elevation model (DEM) is the raster representation of the ground surface so that the information of each cell on the image has a value equal to the altitude from the sea level corresponding to the same spot on the ground. DEM is an appropriate tool for the generation of topographic maps and contour lines, access to the information of surface roughness, three dimensional vision, etc. (Jacobsen, 2004). The accuracy of the digital elevation model is effective on the accuracy of the information from which it is obtained. This is why researchers are always looking for a way to increase the accuracy of digital elevation models. Among the information resources that are used to generate this model are ground mapping, aerial photography, satellite images, radar data, and Lidar. Some of these data generate the digital elevation model with little accuracy due to the insufficiency of the elevation information. The aim of this paper is to investigate the accuracy of DEMs derived from ASTER satellite images and SRTM data with 30 and 90-meter pixel dimensions and the digital elevation model derived from the topographic 1:25000-scale maps with Differential Global Positioning System (DGPS) in different landforms including plains, hills and mountains.  

The study area is a part of the project of dam and water transfer system from the Azad dam to the plain of Ghorve-Dehgolan (with the goal of transferring water from the catchments of Sirvan River into the country) in the province of Kurdistan and the city of Sanandaj. In this study, the Real-Time kinematic method (RTK) was used to locate the points. In this method, assuming that the coordinates of the reference station are known and comparing it with the location obtained from the GPS receiver, a correction value is obtained that is applied to the coordinates obtained for the Rover Station, which is known as the relative or differential method. In this method, the corrections are calculated asreal-time during the observations and are considered in the determination of the Rover location.The Leica GS10 GNSS receivers were used in this study. First, two reference stations were determined using the Fast Static method and then, the Real-Time kinematic (RTK) method was used. In order to investigate the extent of the data compliance and relation, the Pearson linear correlation analysis was used and the accuracy assessment of the extracted digital elevation models was carried out using the RMSE, mean error and standard deviation. 

The statistical parameters such as root mean square error (RMSE), bias (µ) and standard deviation () were used to assess the accuracy of each one of the investigated digital models. By comparing different sources that create DEMs, it can be seen that the minimum error is first related to the digital elevation model extracted from the contour lines of the 1:25000-scale map (27/6 = RMSE) and then to the ASTER digital elevation model with the pixel size of 30 meters (RMSE=7.43). The 30-meter pixel size DEM has always led to better results than the 90- meter pixel size DEM. Based on the mean error standard, the minimum bias is related to ASTER30 m (bias of 2 m) and then to the 1: 25,000 DEM (2.17). The maximum bias was related to 30-and 90-meter models extracted from the SRTM data. The results of standard deviation error were in compliance with the RMSE results, which confirmed the superiority of 1:25000-scale map and ASTER30 m DEMs. The results showed that the determination coefficient of relationship between the ground data and digital elevation models is between 97 and 99. The maximum compliance is related to the digital elevation model extracted from the 1:25000-scale topographic data and the ASTER30 m DEM, while the minimum compliance is related to the SRTM90 m data. In general, the compliance of the digital elevation models with the ground data decreased as the field's conditions became more difficult, i.e. from plain to mountain.

The results of DEMs accuracy assessment showed that the minimum error was primarily related to 1:25000 contour lines DEM (RMSE=6.27) and then, to the ASTER30 m DEM (RMSE=7.43). The pixel size of 30 meters has always been better than the pixels size of 90 meters. Based on the mean error standard, the minimum bias is related to the ASTER 30 m (bias of 2 m) and then, to the 1: 25,000 DEM (2.17). The maximum bias was related to 30-and 90-meter models extracted from the SRTM data. The results of the standard deviation error were consistent with the RMSE results, which confirmed the superiority of the digital elevation models extracted from the topographic 1:25000-scale maps and the ASTER30 m DEM.

This study aims to evaluate the quality and accuracy of the DEMs extracted from ASTER and topographic data of 1:25000 for morphometric analysis of stream patterns in different formations.
Matherials and To examine the DEM, first the map of drainage network of the studied area was prepared based on ASTER DEM and topographic map in Arc hydro medium.
In the next step, the Raster drainage network of the area was calculated in 30 thresholds values for the whole catchment area. Discussion and The results imply from the morphometry perspective the stream patterns extracted from ASTER and topography in all the formations are similar but different in number of stream patterns.
Although absolute accuracy of the topography DEM seems to be low, compared to the DEM data obtained from ArcGIS it is more valuable.
Also, the raster layers are not a reliable and precise source for comparison of the results obtained from the two extraction methods of stream patterns. In case of conversion to vector layers, the results will be clearer and more obvious.
The computer algorithms are not suitable and accurate sources for determination of the length and number of stream patterns. The reason lies in this fact that in the regions with a steep slope, there are many errors in estimation of the length of Grade 1 stream patterns when using manual layers and the precision is poor.
In contrary, in low slope regions the computer algorithms are unable to analyze the length of river and in mild slope regions this ability is improved.
Owing to the mountainous nature of the region, the results obtained from the Aster DEM are less accurate than those obtained from the topographic maps.
In addition, the Ilam-Sarvak formation (made of limestone with black shale limestone) with drainage density of 52.35, has the highest number of stream patterns versus other formations (Table 1).
The width of the grade 1 stream patterns in this formation, due to the higher amount of shale is larger than that in other formations and the RMSE magnitude of the Ilam-Sarvak formation is 192.82.
The Soormeh formation (made of limestone and dolomite limestone), containing the least number of stream patterns and the drainage density of 0.059, shows the lowest magnitude of RMSE (0.01). The morphometry obtained from ASTER DEM and topographic maps seems alike.
But, the results related to the number, length and drainage density of the stream patterns are discrepant. Furthermore, the results of RMSE in various formations (Table 5) verify their accuracy.
In mountainous regions, the accuracy of the number of stream patterns obtained from ASTER DEM is much lower than that obtained from topography.
The number of stream patterns depends on the material and slope of formations.
The results obtained from analysis show there is a direct correlation between drainage density and number of stream patterns and an indirect correlation between drainage density and RMSE.