mohammad amin ghannadi; Hamid Enayati; Elaheh Khesali
Abstract
Extended Abstract Introduction 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 ...
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Extended Abstract Introduction 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. Results and Discussion 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. Conclusion 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.
Marzieh Mokarram; Majid Hojjati; Abdol Rassoul Zareiee
Abstract
Extended Abstract Topography is a factor controlling the spatialdistribution of soil moisture, vegetation, soil salinity, soil texture andso on. It has an important role in changing thecharacteristics of the soil and hydrological processes. In recent yearsthe topographyhave been used as an important ...
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Extended Abstract Topography is a factor controlling the spatialdistribution of soil moisture, vegetation, soil salinity, soil texture andso on. It has an important role in changing thecharacteristics of the soil and hydrological processes. In recent yearsthe topographyhave been used as an important factor forpredicting the properties of soil, climate, geology, etc. According tothe importance of topography to extract different information, use ofsatellite images with high spatial resolution seems very necessary. Digitalelevation models (DEM) have become a widely used tool andproduct in the last 20 years. They provide a snapshot of the landscape and landscape features while also providingelevation values. They have allowed us to better visualize andinterrogate topographic features. In addition to increasing the spatial resolution, information of the digital elevation model (DEM) that isthe most important issues in quantitative geomorphology have increased. In orderto increase the spatial resolution several modelshave been proposed. Among the models, the attraction model as the newest modelhas very high accuracy. The sub-pixel attraction models convertthe pixel towards sub-pixels based on the fraction valuesin neighboring pixels that can be attracted only by centralpixel. Based on this approach only a maximum of eightneighboring pixels can be selected for the attraction. In themodel, other pixels are supposed to be far from thecentral pixel to have any attraction. In this study byusing sub-pixel attraction model, the spatial resolution of digitalelevation models (DEM) was increased (Sub-pixel mapping technology is apromising method of increasing the spatial resolution of the classificationresults derived from remote sensing imagery). The design of thealgorithm is accomplished by using digital elevation model (DEM) withspatial resolution of 30 m (Advanced Space borne Thermal Emissionand Reflection Radiometer (ASTER)) and 90 m (Shuttle Radar TopographyMission (SRTM)). This study was carried out in the EastMount Sahand, Iran is located at the longitude of N 37° 31َto 37° 30َand latitude of E 45° 55َto 45° 58َ. It is expected that usingattraction model increasesthe spatial resolution of DEM. The attraction model does not need any calibration and training similar to the machine learningalgorithms. So, to run the algorithm in the model, the computing time was reduced. In attraction model, scale factors of (2, 3 and 4) with two neighboring methods of touching andquadrant are applied to DEMs using Matlab software and thenusing RMSE (Root mean square error), determined the best model. The algorithm is evaluated using 2118 sample points that aremeasured by surveyors. As the result of Root mean squareerror (RMSE), it showed that the spatial attraction model withscale factor of (S=2 and T=2) for digitalelevation model (DEM) 30m and digital elevation model (DEM) 90mgives better results compared to scale factors that are greaterthan 2 and also touching neighborhood method proved to bemore accurate than quadrant. In fact, subtracting each pixel tomore than two sub-pixels caused to decrease the accuracyof resulted DEM which makes the value ofroot mean square error (RMSE)to increase and showed that attraction modelscould not be used for S which is greater than 2. So, according to the results, it is suggested that themodel to be used for increasing spatial resolution of DEM in the studiescatchment. Comparing the digital elevation model (DEM) as inputsin the attraction models determined that digital elevation model (DEM) 30 m (root mean square error < 5.54) has better spatialresolution than digital elevation model (DEM) 90 m (root meansquare error = 9.13) to find the best model for increasingspatial resolution. The results showed that by using the method, thespatial resolution of digital elevation model (DEM) with lower timeand cost could be increased. Digital elevation model (DEM) mapwith high resolution as a base can be used for findingmore information from the Earth surface. For different study such asamount of vegetation, temperature, rainfall and hydrological status the results of sub-pixel attractions on digital elevation model (DEM) can be used and more details of study area could be found. Therefore, it issuggested that the same researches should be done in other areas withdifferent topographic and geographical conditions in order to confirm theresults of this study.
Marzeyeh Mokarram; Ali Darvishi; Saeed Negahban
Abstract
Extended Abstract
Introduction
Watershed is an area of land that surface water of rain and melting snow conduct towards a single point, which is usually out of the basin. Check of watershed is one of the main strategies for integrated management of natural resources and sustainable development. Recently, ...
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Extended Abstract
Introduction
Watershed is an area of land that surface water of rain and melting snow conduct towards a single point, which is usually out of the basin. Check of watershed is one of the main strategies for integrated management of natural resources and sustainable development. Recently, the availability of remote sensing (RS) data and Geographical information system (GIS) technologies has allowed for improved understanding of the morphometric properties and surface drainage characteristics of many watersheds in different parts of the world (Parveenet al., 2012; Nayar& Natarajan, 2013). For example, Shrimaliet al. (2001) presented a case study of the 42 km Sukhana lake catchment in the Shiwalik hills for the delineation and prioritization of soil erosion areas. In addition, Srinivasaet al. (2004) used GIS techniques for morphometric analysis of subwatersheds in the Pawagada area, Tumkur district, Karnataka. Nookaratnamet al. (2005) carried out a study on dam positioning through prioritization of microwatersheds using the sediment yield index (SYI) model and morphometric analysis. Khan et al. (2001), used RS and GIS techniques for watershed prioritization in the Guhiya basin and sub-watersheds in Odisha, India respectively.
Materials & Methods
The study area is one of the subwatersheds of the river of Urmia (Nazloochaei) that is located in North West of Iran with an area of 948.75 km2. The study area was selected for detailed morphometric analysis using Geography information system (GIS). The input data for morphometric analysis was DEM with resolution of 30 m from ASTER satellite. The steps of stream extraction consist of:
1. Extraction of drainage networks from the DEM using the flow direction method, which consists of the following steps (O’Callaghan & Mark, 1984):
i. Fill Sinks: A sink is an uncompleted value lower than the values of its neighborhood. To ensure proper drainage mapping, these sinks were filled by increasing elevations of sink points to their lowest outflow point.
ii. Calculate Flow Direction: Using the filled DEM produced in Step1, the flow directions were calculated using the eight-direction flow model, which assigns flow from each grid cell to one of its eight adjacent cells in the direction with the steepest downward slope.
iii. Calculate Flow Accumulation: Using the output flow direction raster created in Step2, the number of upslope cells flowing to a location was computed.
iv. Define Stream Network: The next step is to determine a critical support area that defines the minimum drainage area that is required to initiate a channel using a threshold value.
v. Stream Segmentation: After the extraction of drainage networks, a unique value was given for each section of the network associated with a flow direction.
Morphometric analysis of the study area consist of:
Stream number (Nu)
Nu is number of segments in order U
Stream order (U)
Cumulative length of streams (L), L = ∑Nu, L is calculated as the number of streams in each order and total length of each order is computed at sub-watershed level (Horton, 1945).
Bifurcation ratio (Rb)
Rb=Nu/N (u+1) N (u+1) = Number of segments of the next higher order (Schumms, 1956),
Watershed relief (Bb), Bb = Hmax – Hmin, Bb is defined as the maximum vertical distance between the lowest and the highest points of a sub-watershed. Hmax and Hmin are maximum and minimum elevations respectively (Schumms, 1956)
Drainage density (Dd)
Dd=Lu/A, A=Watershed area (km2), L (u) is total stream length (Horton, 1932)
Stream frequency (Fs), Fs = Nu/A, Fs is computed as the ratio between the total number of streams and area of the watershed (Horton, 1932)
Form factor (Rf)
Rf =A/Lb2, Rf is computed as the ratio between the watershed area and square of the watershed length. 𝐿 is the watershed length (Horton, 1932)
Circularity ratio (Rc)
Rc= 4π*A/P2, P is the watershed perimeter (km)
Elongation ratio (Re)
Re= (2/Lb)*(A/π) 0.5
Results and discussion
The results showed that according to the high number of streams (489 waterways), the existence of first, second and third degree streams, the high length of the streams, the high proportion of length of the streams in relation to the basin area, high coefficient of relief which indicates high elevations and slopes, the area is erodible and requires more management. Also, Landform studies in the studied area showed that with the help of morphometric characteristics, the sensitivity of landforms to erosion can be determined in the area. So, after the mapping of landforms using topographic position index (TPI), and considering the erosion-sensitive areas through morphometric characteristics, erosion-sensitive landforms in the study area were determined, So that the increase in the number of waterways and their length in the watershed indicates an increase in erosion. Comparing the map of the landforms and the map of the streams in the studied area, it was determined that class 4 (U-shaped valleys) and class III (high drainage) landforms have the highest erodibility. The results showed that, with increasing drainage density, the erodibility increases and the highest erodibility was observed in Class 4 (U-shaped valleys) and Class 6 landforms due to the high drainage density.
Conclusion
Ridge landforms such as those in high altitude (landforms in class 9 and 10), had the highest erosion and were therefore the most sensitive landforms. The drainage density features as the most important factor for determination of erosion and its relation to landforms were used. The results showed that by increasing the amount of drainage density the erosion increases which were for landforms Class 4 and Class 6. This study has demonstrated that morphometric characteristics can be used to predict other watershed characteristics.
