جستجوی مقالات مرتبط با کلیدواژه « مدل رقومی ارتفاعی » در نشریات گروه « علوم انسانی »

There are many different types of the terrain visualization techniques, which are the result of the evolution over time and the development of technological innovations. Earth’s surface roughness plays a key role in controlling surface and the Earth's atmosphere processes. This relationship is so strong that understanding the nature of the terrain topography can directly lead to clarity in understanding of these processes, both analytically and computationally. Therefore, the analysis and visualization of the terrain topography has provided significant applications in many of the activities of the Geographic Information System and environmental Modeling. Geomorphologists and cartographers both use information derived from digital elevation models to quantify the shape and structure of the Earth's topographic surfaces. Cartographic purpose of terrain relief visualization is to display landforms and features of the earth’s surface which is done through drawing hachures and other methods that lead to more realistic terrain surface visualization. Cartographers have used a variety of mechanisms, including colors and shades to create a 3-dimensional appearance of topographical surface in 2-dimensional surface of the maps. The recent study tries to present different ideas and models in the section of terrain topography visualization. For this purpose, vector (point and line) and raster data structure, Digital elevation model with respecting to the concepts of geomorphometry and digital terrain modeling had been used. And python programming has been widely used to design and automating the algorithms of the models.

In the first part, the raster database was prepared and arranged. The raster database contains elevation data that includes digital surface model. Which is currently the most accurate elevation data on a free, global scale. digital Surface model released by the Japan Space Agency in May and October 2015 with a horizontal resolution of about 23 meters were used to model the visualization of terrain topography surfaces. This data is obtained from the ALOS satellite images which is extracted from a five-meter grid data with global coverage which is now the most accurate elevation data in the global scale. In the next step, slope calculated using four slope algorithms on a digital surface model grid structure. The aspect was calculated in the same way. The aspect layer with the results of these four slope algorithms, averaged and standard hill shade calculated using common formula in the geographic information system. Then, the visualization modeling of the terrain topographic surfaces followed in two parts. In the vector modeling section, the hachure modeling of the terrain surface carried out which the results in the field of linear hachure drawing includes: Max-Center-Min, Max-Min, Random Point – Min and multi-sector aspect hachuring models which coded in the python programming environment. In the Point hachure section, two random point models with slope weight and random point model with curvature as a weight were modeled, coded and executed. In the raster modeling section, three model approaches were considered. In the first approach, blended shading was followed, which included combining standard hill shading model with a variety of terrain curvature, radiation models (Morphoradiation), and ridgeline effect models.14 types of terrain curvature were used and combined with standard hill shading to create new models for visualization of terrain surface topography. In the second part, radiation models including direct radiation, direct radiation duration, diffuse radiation and global radiation were combined with standard hill shading to create new models. To add the effect of the ridgelines in the display of the terrain surface topography, the method provided by Solhi and Seif (1399) was used to identify the ridges of the terrain topography.

Various models were prepared and presented in the terrain surface topography visualization. Part of the modeling is related to the vector data structures and the other part is related to the raster data structures. In the vector modeling section, four algorithms including: Max-Center-Min, Max-Min, Random point-Min and Multi-Sector Aspect hachuring were configured and presented. These four algorithms have the ability to create linear hachures on the terrain topography surfaces in order to create a spatial dimension of the digital elevation model. All four algorithms are performed using the moving window technique and automatically apply linear hachuring with using digital elevation model. The second part is related to raster data structures, which includes hybrid models (combining standard hill shade with different terrain curvatures types, radiation models and ridgeline effect), hypsometric tinting, contour maquette, and colored contour maquette.

Terrain surface topography visualization has wide applications and plays important role in the cartography and preparation of base-maps, geological maps and topographic maps as well as thematic maps such as geomorphological maps and many other maps. Modeling terrain surface topography, using different ideas, methods, and techniques, can be effective in improving and the visualization of terrain surfaces in different applications. In this research, with emphasis on the subject of terrain visualization modeling, different methods and models were presented both in the section of vector data structures (linear and dotted) and raster data structures. Various methods and models have been proposed that can be used in a wide range of environmental studies as well as cartographic methods and techniques. The presented models are practiced only from the digital elevation model and do not require field and special data, which are considered as features and strengths of these models. Future researchers are advised to develop and evolve the models, methods and techniques of this field of study and try to create practical and creative models. The field of digital terrain modeling, in the analytical and demonstration sections, creates a suitable platform for environmental science studies and in the form of basic and fundamental research can lead to scientific creation and dynamism in this field.

global surface temperatures to a wide variety of scientific studies of climatology and meteorology and hydrology, ecology, geology, medical science, design and optimization of transport network and locating fires is required This study aimed to investigate the temperature on Earth Its digital elevation models using geostatistical environment (GIS) in Yazd province is the research of DEM (13M) and meteorological data were used to assess temperature with a 20-year period beginning on the regression equation for each May be then prepared for analysis of the collected data at the end of rastercalculator tool for disorders F Oscillation all months of the tool was used cellstatistics software used in this study include: Arc Gis and Excel are the results of this study revealed that parameter H in accordance with the temperature of the surface of the earth in order to agree with each other such that in areas with high temperature on low ground, and Balksh evaluated the results of this study showed that in the months months (June- July_ August) compared to the other months of the swing high temperature fluctuations belong to the city of Tabas, PA; Ardakan, Bafg, gets; Tabriz.

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.

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.

The existence of technical &theoretical knowledge for the assessment of gullies helps to the distinction of gully locales on topographical map& their digital model. Anyhow, the ability to use of topographical map (DEM) for the assessment of gullies situation needs to the necessary knowledge about the process of gully formation and the frequency of gullies. The present research has been carried out for the assessment of locales prone to gully formation in Dyereh.The substances& methods used in this research consist of the layers of DEM model& the secondary layers extracted from it, stream power index, land use, canals, lithology & the ways existent in the catchment& the collection of circular data. The above data has been analyzed in Arc GIS environment and its results have been scrutinized with chi-square test.According to the acquired results of this research in Dyereh catchment 5 factors (inclination, inclination’s horizontal curvature, land use, distance of the road, and lithology) among 8 factors have been recognized as the main factors in gully formation. Among these 5 factors the class of inclination 0-4/1 in which 33/22% of Dyereh from its total area has been located, has the most effect on the gully formation in this catchment.

The main goal of the study is evaluation of accuracy and precision of both versions of global digital elevation model (GDEM1, 2009; GDEM2, 20119) with 30m ground resolution and comparison of it with SRTM model (with 90 m resolution) and DEM produced by National Cartographic Center of Iran (or NIDEM with 10 m resolution). In order to achieve the goals mentioned, three different strips with various geomorphic characteristics have been selected and studied. After inland waters omission, all three models are resampled to models with one arc seconds resolution. In addition, differential layers of various DEM have been calculated and the distribution of errors in each strip has been investigated. Various types of errors and their spatial distributions in models are distinguished using scatter diagrams and scatter clouds. For visualizing of DEM's error, Hill shade models and topographic profile along selected errors has been prepared for all models. The results of the study showed that GDEM1 leads to significant error's respect to SRTM, which is related to the method of production. The results also showed that various types of anomalies such as "Step-anomalies", "Pit-anomalies", "Bump-anomalies" and "Pit-in-Bump anomalies" are clearly related to the stack number boundaries. The error ranges of the mentioned anomalies are from ten meters to more than 300. The mean of error in GDEM1 is 4.8 m with standard deviations equal to 13.8 m which its range in 95% confidence is between -22.3 to 31.9 m. Analysis of GDEM2 showed a significant improvement in quality of model respect to previous version. The mean of error in GDEM2 is less than of 3 m with standard deviations equal to 9.1 m which its range in 95% confidence is between -20.8 to 14.8 m. In comparison, the mean of error in SRTM is 2.0 m with standard deviations equal to 8.7 m which its range in 95% confidence is between -15.1 to 19.1 m. Moreover, the study showed GDEM1 was not as sharp as a standard 30 m resolution DEM and appeared to contain fewer spatial details. On the other hand, most of the errors in GDEM2 have been omitted and revealed an acceptable accuracy