Abstract

The main objective of this research is to evaluate the effect of sentinel multispectral images on estimating shallow water depth using linear bathymetry model. Multispectral image data was integrated with available echo sounding and GPS data for the determination of the bathymetry after tide correction in three areas on Delta coast i.e., Kitchiner, Damietta and Rashid. Three visible and one near infrared band (Top of Atmospheric Reflectance level, with 10 m resolution) were used in models derivation. Sentinel image bands were geometrically and atmospherically corrected and sun glint was removed prior to bathymetry models estimation. Three models with ln function were the derivates using Ordinary Least Square (OLS) modeling tool under ArcGIS environment. The results indicated that Adjusted R-Squared for the three estimated bathymetry models were 0.68, 0.62 and 0.72 for Damietta, Rashid and Kitchiner areas respectively. About 75% of the residual values ranged from -1.63 to 1.3 m for Damietta points and 50% of the residual values were between -0.39 to 0.57 m for Rashid and about 65% between -0.76 to 0.97 m for Kitchiner. Hence it can be concluded that these predicted models provide time- and cost-effective solution for Shallow water depths estimation.

Keywords:

Bathymetry, delta coast of Egypt, ordinary least square, sentinel-2A images,

References

1. Benny, A.H. and G.J. Dawson, 1983. Satellite imagery as an aid to bathymetric charting in the Red Sea. Cartogr. J., 20(1): 5-16.
   CrossRef    
2. Bramante, J.F., D.K. Raju and S.T. Min, 2013. Derivation of bathymetry from multispectral imagery in the highly turbid waters of Singapore's south islands: A comparative study. Int. J. Remote Sens., 34(6): 2070-2088.
   CrossRef    
3. Conger, C.L., E.J. Hochberg, C.H. Fletcher and M.J. Atkinson, 2006. Decorrelating remote sensing color bands from bathymetry in optically shallow waters. IEEE T. Geosci. Remote, 44(6): 1655-1660.
   CrossRef    
4. Edwards, A.J., 2010. Lesson 5: Removing sun glint from Compact Airborne Spectrographic Imager (CASI) imagery. Bilko Module 7, UNESCO.
   
5. Fotiou, A. and C. Pikridas, 2006. GPS and Geodetic Applications. Ziti, Thessaloniki, Greece.
   
6. Green, E.P., P.J. Mumby, A.J. Edwards and C.D. Clark, 2000. Remote Sensing Handbook for Tropical Coastal Management. Ch. 8. Coastal Management Sourcebooks Series. UNESCO Publications, Paris, pp: 219-233.
   Direct Link
7. Hedley, J.D., A.R. Harborne and P.J. Mumby, 2005. Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos. Int. J. Remote Sens., 26(10): 2107-2112.
   CrossRef    
8. Hochberg, E.J., S. Andrefouet and M.R. Tyler, 2003. Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in near-shore environments. IEEE T. Geosci. Remote, 41(7): 1724-1729.
   CrossRef    
9. Kay, S., J.D. Hedley and S. Lavender, 2009. Sun glint correction of high and low spatial resolution images of aquatic scenes: A review of methods for visible and near-infrared wavelengths. Remote Sens., 1(4): 697-730.
   CrossRef    
10. Kerr, J.M., 2011. WorldView-02 offers new capabilities for the monitoring of threatened coral reefs. Digital Globe 8Band Challenge Winner.
    
11. Lyons, M., S. Phinn and C. Roelfsema, 2011. Integrating Quickbird multi-spectral satellite and field data: Mapping bathymetry, seagrass cover, seagrass species and change in Moreton Bay, Australia in 2004 and 2007. Remote Sens., 3: 42-64.
    CrossRef    
12. Lyzenga, D.R., 1978. Passive remote sensing techniques for mapping water depth and bottom features. Appl. Optics, 17(3): 379-383.
    CrossRef    PMid:20174418    
13. Lyzenga, D.R., 1981. Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data. Int. J. Remote Sens., 2(1): 71-82.
    CrossRef    
14. Lyzenga, D.R., 1985. Shallow-water bathymetry using combined lidar and passive multispectral scanner data. Int. J. Remote Sens., 6(1): 115-125.
    CrossRef    
15. Lyzenga, D.R., N.P. Malinas and F.J. Tanis, 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE T. Geosci. Remote, 44(8): 2251-2259.
    CrossRef    
16. Mishra, D., S. Narumalani, M. Lawson and D. Rundquist, 2004. Bathymetric mapping using IKONOS multispectral data. GISci. Remote Sens., 41(4): 301-321.
    CrossRef    
17. Negm, A.M., H. Mohamed, M. Zahran and S. Abdel-Fattah, 2016. Estimation of Bathymetry Using High-resolution Satellite Imagery: Case Study El-Burullus Lake, Northern Nile Delta. In: Negm, A. (Ed.), The Nile Delta. The Handbook of Environmental Chemistry. Springer, Cham, 55: 425-454.
    CrossRef    
18. Paredes, J.M. and R.E. Spero, 1983. Water depth mapping from passive remote sensing data under a generalized ratio assumption. Appl. Optics, 22(8): 1134-1135.
    CrossRef    PMid:20404863    
19. Stumpf, R.P., K. Holderied and M. Sinclair, 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr., 48(1): 547-556.
    CrossRef    
20. Su, H., H. Liu and W.D. Heyman, 2008. Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Mar. Geod., 31(4): 281-298.
    CrossRef    

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