Amir Reza Moradi; Mohammad Amin Ghannadi
Abstract
Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such ...
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Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such as natural resource management, engineering, and infrastructure projects, crisis management and risk analysis, archaeology, security, aviation industry, forestry, energy management, surveying and topography, landslide monitoring, subsidence analysis, and spatial information system (Makineci&Karabörk, 2016).
Satellite images are one of the main sources used to produce DEM. In satellite remote sensing, optical and radar imagery are often used to generate DEM. Compared to optical satellite images, the main advantage of using radar satellite images for DEM production is that they are available in different weather conditions and even at nights. Two strategies used to produce DEM from radar satellite images include radar interferometry and radargrammetry(Saadatseresht&Ghannadi, 2018).
Phase information of the images is used in radar interferometry, whereas domain information of the images is used in radargrammetry (Ghannadi, Saadatseresht, &Eftekhary, 2014). Moreover, short baseline image pairs are used in radar interferometry, while long baseline image pairs are useful in radargrammetry. These technologies both have their own advantages and disadvantages,which were investigated in previous studies (Capaldo et al., 2015).
With radar interferometry, it is possible to produce DEM forlarge areas. Sentinel is one of the recent projects in satellite remote sensing. Sentinel constellation collects multi-spectral imagery, radar imagery and thermal imagery from the earth. Sentinel-1 is the radar satellite of the constellation.
Recent studies have investigated the precision of radar interferometry using Sentinel-1 imagery (Yagüe-Martínez et al., 2016) and the precision of DEM produced using these images(Letsios, Faraslis, &Stathakis; Nikolakopoulos &Kyriou, 2015). Generally, DEMs generated through radar interferometry needs to be improved, mainly due tothe phase errors which in many cases turn into outlier points (Zhang, Wang, Huang, Zhou, & Wu, 2012). Various methods have been used to improve DEM generated from SAR imagery, one of which use the information obtained from SRTM DEM. For instance, a previous study used SRTM DEM to improve DEM generated from ESRI/2.Using the information obtained from SRTM, the interferometric phase of areas with lower coherency were improved (Zhang et al., 2012).
The present study proposed a method to improve the accuracy of DEMs generated by Sentinel-1 imagery. In this method, using ascending and descending Sentinel-1 image pairs from the study area, DEM is generated using radar interferometry process. Then, precision is improved using SRTM DEM and a method based on 2D wavelet transform.
Wavelet transform and 2D wavelet transform methods
As a spectral analysis tool, wavelet transform is based on expanding any function like f(t)
(1)
inwhichaiis the expansion coefficient and 𝜓iis the expansion function.
One of the interesting characteristics of discrete wavelet transform is that it can be used as a multi-resolution analysis tool. To do so, a series of scaling functions or are used along with the wavelets to determine coarse data of the signals. Signal detailsare also covered by different wavelets with different scales.
Separatingcoarsedataand details of the signal isthe actual basis of discrete wavelet transform algorithm which wasintroduced by Mallat (Mallat, 1989) and improved by Beylkin et al. (Beylkin, Coifman, &Rokhlin, 1991). As a fast and simple method for discrete wavelet transform,the process is performed based on the followingrecursive relationships betweenahighpass and a lowpass filters with the impulse responses h(n) and g(n), respectively (Primer et al., 1998):
(2)
and
(3)
Where the expansion coefficients h and g are scaling filter and wavelet respectively.
(4)
and
(5)
These formulas can be expanded to calculate 2D discrete wavelet transform.
Proposed Method
This section proposes a method of enhancing DEM generated from Sentinel-1 imagery using SRTM DEM and 2D wavelet transform. Considering the capability of wavelet transform as a multi-resolution analysis tool which can separate coarse data from details, figure 2 shows the proposed process of improving DEM. First, using discrete 2DWT, coarse information and details of each DEMs are separated using the ascending and descending conditions of Sentinel-1 images. Then, two stages are considered based on the nature of these models. First, filtering coefficients usingthresholding and considering the average as the detail or high frequency part of the enhanced model. Second, coarse information derived from wavelet transform method have a resolution of40m and thus data derived from SRTM (30m) has a higher quality. Therefore, inverse 2DWT will improve the results and reach a resolution of 20 m.
Experiments and Results
The study area is located in Northern areas of Tehran (Iran) at the UTM coordinates of (542450 ,3964590) northeast to (539010, 3962350) southwest. Two Sentinel-1 satellite image pairs (one ascending and one descending) are used in this study.
In addition, a SRTM DEM with a spatial resolution of30m is used to improve DEM generated from Sentinel-1 images. Sentinel-1 derived DEM is evaluated using the 1m resolutionreference DEM. RMSE values shows the effectiveness of the proposed method in enhancing the Sentinel-1 derived DEM, which means that using information obtained from the SRTM and 2D wavelet transform was reasonable. RMSE values are reduced from 24.2097m to 11.1749m which shows 54% improvement. The proposed method can enhance results to 30 - 82 percentapproximately.
Conclusion
The present study investigates methods used for generating DEM from satellite images especially Sentinel-1 radar imagery. DEM derived from Sentinel-1 data has a high spatial resolution.Yet, it has some outliers or errors in elevation points whichneeds to be modified. Therefore, the present study proposes a method based on 2D wavelet transformfor deriving elevation model witha spatial resolution (20m) equal to that of Sentinel-1 DEM and improved precision and accuracy. In this method, filtering the details of the model using discrete 2D wavelet transform and modifying coarse information using SRTM DEM results in an enhanced DEM with higher spatial resolution.
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.
narges fatholahi; Mehdi Akhoondzadeh Hanzaei; Abbas Bahroudi
Abstract
Extended Abstract
Land subsidence is a vertical movement of the earth surface relative to a stable reference level. It occurs as a result of plate tectonic and human activities. The common causes of subsidence from human activities are pumping under-ground water, oil and gas from overlying reservoirs. ...
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Extended Abstract
Land subsidence is a vertical movement of the earth surface relative to a stable reference level. It occurs as a result of plate tectonic and human activities. The common causes of subsidence from human activities are pumping under-ground water, oil and gas from overlying reservoirs. Withdrawal of fluids from hydrocarbon reservoirs causes their pressure to decrease. This pressure reduction rises the stress of reservoir’s overburden sediments which was previously controlled by the pressure of inside fluids before exploitation, and consequently increases the density of their porous surroundings. If the reservoir’s density exceeds a specific threshold, overburden rocks start to subside because of their weight. Therefore pressure drawdown leads to reservoir compaction, movement of the overburden and subsidence over the reservoir. This subsidence can prove costly for production and surface facilities. So study of the subsidence caused by hydrocarbon exploitation is an important task which needs precise considerations. Several methods are available to monitor land subsidence. Classical surveying such as Leveling and global positioning system (GPS) can produce some related data whereas they are expensive and cannot also produce the needed map at a particular period of time. Recent advances in satellite and Radar technology have made it possible to measure very small movements of the earth surface. Interferometric Synthetic Aperture Radar (InSAR) is a novel technology for measuring the surface deformation. Using the InSAR technique at relatively large subsidence areas can be monitored. The pros of InSAR are that it is not necessary to physically access the deformation areas and also the high spatial and temporal resolution of its data. Sub-centimeter accuracy has been reported for InSAR derived surface deformations. Interferometric Synthetic Aperture Radar relies on repeated imaging of a given geographic location by space-borne radar platforms. Synthetic Aperture Radar sensors measure both magnitude and phase of the transmitted electromagnetic signal that is backscattered from the earth surface. The phase measurement is used to derive information on heights and deformations of the terrain. This phase represents a combination of the distance scattering effect. If a second SAR data set is collected then from comparing the phase of the second image with the phase of the first, an interferogram can be formed. The basic principle of interferometric SAR is that if the surface characteristics are identical for both images, the phase differences are sensitive to topography and any intrinsic change in position of a given ground reflector. The interferogram can be corrected for topographic information using an external digital elevation model (DEM). The change in distance is along the line of sight to the satellite, preventing it from directly distinguishing vertical and horizontal movement. As geometrical and temporal baseline de-correlations and atmospheric noise are limitation factors to assess slow movements in subsidence areas, recent developments in multi temporal InSAR (MTI) algorithms have enabled the detection and monitoring of the slow deformation with millimetric precision. In this paper, Marun oil field; the second-largest oil field which is located in the south west of Iran has been studied. The Small Base Line Subset (SBAS) approach that is an (InSAR) algorithm has been performed for generating mean deformation velocity map and displacement time series from a data set of subsequently acquired SAR images. SBAS technique identifies coherent pixels with phase stability over a specific observation period which has been implemented in StaMPS software. This method which is based on multiple master interferograms, works with interferograms with small spatial baselines and short temporal intervals to overcome de-correlations by increasing spatial and temporal sampling and coherent areas. For this study, we have used 10 ASAR images acquired by the ENVISAT satellite from European Space Agency (ESA) during 2003 to 2006 and have generated 22 interferograms by the SBAS method. All interferometric processing were implemented using DORIS software. A SRTM Digital Elevation Model (DEM) with 3-arcsecond geographical resolution has been used to remove the topographic phase. SBAS processing was then implemented using the Stanford Method for Persistent Scatterers (StaMPS) software. As a result, the mean velocity map obtained through InSAR time series analysis which is in the Line-Of-Sight (LOS) direction of satellite to the ground. The time series analysis results of InSAR have been then compared with field production data. This sampled data allows us to evaluate potential of non-tectonic effects such as petroleum extraction on surface displacements and the relationship between both deformation and oil production rate. The results of InSAR analysis reveal the maximum subsidence on order of 13/5 mm per year over this field due to the extraction and geological characteristics in the time period of 2003-2006.
