Hekmatollah Mohammad Khanlu; Mahdi Modiri; Elahe Khesali; Hamid Enayati
Abstract
Introduction
Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal ...
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Introduction
Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal infrastructure of coastal countries. Providing proper hydrographic services ensures safe and efficient sailing. Thus, development of hydrographic services on the national level can improve safety of mariners, and protect people’s lives and belongings on the sea, while providing some facilities for the protection of marine environment. The advancement of space technologies in recent years has increased the speed of spatial information production and facilitated sea monitoring.
Materials and Methods
Different methods are used for bathymetry. Lyzanga et al (1978) used a linear combination of the logarithm of corrected radiance ratio. This method is based on the simplification of Beer's physical model in which a linear equation of five unknowns is obtained for two bands. In 2006, Lyzanga et al. presented an improved version of their model. Using Tow-Bands Reflection Ratio, Stampf et al (2003) not only reduced the number of unknown variables in Lyzenga method, but also decreased the sensitivity of depth determination to different substrates. In this method, the difference between absorption properties of green and blue bands is used. TCarta is a global supplier of geospatial products. The company generated Satellite Derived Bathymetry (SDB) dataset by accurately extracting water depth from multispectral imageries received from the European Space Agency’s Sentinel-2 Satellite. The resulting bathymetric data had a point spacing of 10 meters, while measuring up to a depth of 15 meters. Data covered a 30-square kilometer area around Preparis Island on the Bay of Bengal.
The present article used images received from Sentinel-2 in 7 different periods for depth determination, and 1: 25,000 ADMIRALTY Nautical Charts for accuracy evaluation. Following the assessment of water transparency in received images, the 12/15/2018 image was used for depth determination. Case study area contains around 130 km along the Port of Salalah, Oman.
Results and Discussion
In order to implement the model, it is necessary to separate land from water in images using NDVI, NDWI, MNDWI and AWEI indices. The NDVI index has been used in this project. NDVI is primarily used to estimate vegetation cover, but since this index exhibits a negative value in areas covered with water, this property is used to provide a mask for separating land from water. In this step, 68 control points and 68 check points were selected from the existing ADMIRALTY map. The DN values of the corresponding pixels of the selected points were extracted from four 10-meter bands of Sentinel-2 images. The control and checkpoints and the DN value of their corresponding pixels were extracted in 4 separate files, then these 4 files were logged into the Bathymetry software and the parameters of LMR and Stumpf methods were calculated. The root mean square error (RMSE) and correlation coefficient (CC) were used to assess geometric accuracy. In order to extract necessary parameters for each model, RMSE= 2.15 m and CC= 92.5% were calculated at depth distances of 0 to 20m. Results indicates higher accuracy and stronger correlation of LMR findings. Therefore, this method was used for depth determination between 0 to 20 meters. The 5 parameters extracted from the Bathymetry software and the corresponding pixel values of the four bands with 10-meter resolution extracted from the Sentinel-2 image (received from the on 12-15-2018) were used as input. Linear Regression Model was applied to transform 4 bands of Sentinel-2 image into depth. The output of the model (depth) was presented as the Substrate DEM of the coasts of Port of Salaleh, Oman.
Conclusion
Hence, it can be concluded that Remote Sensing technologies can be used for depth determination and sea monitoring at critical times (during wars or other periods of insecurity) for an acceptable time period. It also provides an appropriate context for bathymetry of inaccessible coastlines and monitoring of strategic widespread water zones. In this way, the depth of sea bed in shallow areas is extracted using spectral analysis of satellite data and different models.
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.
