عنوان مقاله [English]
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.
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.
2- Clark, R. K., Fay, T. H., & Walker, C. L. (1987). Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption. Applied optics, 26(19), 4036_4031-4038.
3- Gao, J. (2009). Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Progress in Physical Geography, 33(1), 103-116.
4- Green, E. P., Mumby, P. J., Edwards, A. J., & Clark, C. D. (2005). Remote sensing handbook for tropical coastal management.
5- Jupp, D. (1988). Background and extensions to depth of penetration (DOP) mapping in shallow coastal waters. Paper presented at the Proceedings of the Symposium on Remote Sensing of the Coastal Zone.
6- Lyzenga, D. R. (1978), Passive remote sensing techniques for mapping water depth and bottom features, Applied Optics, 17(3), 379, doi:10.1364/AO.17.000379.
7- Lyzenga, D. R. (1981). Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. International journal of remote sensing, 2(1), 71-82.
8- Lyzenga, D. R. (1985). Shallow-water bathymetry using combined lidar and passive multispectral scanner data. International journal of remote sensing, 6(1), 115-125.
9- Martin, S. (2014). An introduction to ocean remote sensing: Cambridge University Press.
10- Mishra, D.R., Narumalani, S., Rundquist, D., and M. Lawson. 2005. Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: Implications for water penetration by high resolution satellite data. Photogrammetry and Remote Sensing. 60: 48–64.
11- Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J.-M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution, 20(9), 503-510.
12- Safari, R., Homayouni, S., & Khazaee, S. (2014). Estimation of coastal waters depth using hyperspectral images. Surveying Engineering and spatial data Journal, 6.
13- Stumpf, R. P., Holderied, K., & Sinclair, M. (2003). Determination of water depth with high resolution satellite imagery over variable bottom types. Limnology and Oceanography, 48(1part2), 547-556
14- Su H., Liu H. and W. Heyman, 2008.Automated derivation for bathymetric information frommultispectral satellite imagery using a non-linear inversion model. Marine Geodesy, 31, pp. 281-298.