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The Nile River pp 259–274Cite as

Nile River Bathymetry by Satellite Remote Sensing Case Study: Rosetta Branch

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Part of the book series: The Handbook of Environmental Chemistry ((HEC,volume 56))

Abstract

Remote sensing (RS) has many applications including bathymetry mapping in shallow water areas. It is considered a useful reconnaissance tool to save time and cost to be used in the preliminary survey. In many regions, natural water stream depth changes because of erosion and sedimentation processes and thus bathymetry must be updated regularly. There are several factors to be taken into account when derived water depth using satellite images in shallow water, especially rivers. These factors include the degree of water transparency, water turbidity, nature of river bottom, and reflections from surrounding areas. This chapter aims to assess the performance of three models to determine the bathymetry of Rosetta branch of the Nile River using Landsat-8. The models are tested on a study area that covers 5 km of Rosetta branch. In-situ measurements were acquired by the Nile Research Institute and data are registered to the satellite imagery spatial reference. Landsat-8 image bands are first pre-processed to carry out atmospheric corrections and to mask out land areas and remove scattering and sun specular effects. The tested models are the generalized linear model (GLM), 3rd order polynomial, and the artificial neural networks (ANN). The three models are applied to the pre-processed Landsat-8 image to derive the Rosetta branch bathymetry at the study area. Results showed that the ANN model results are more accurate than both GLM and nonlinear 3rd order polynomial models. However, the results of the three models are not well satisfactory as the root mean square error (RMSE) is about 2 m. The high turbidity of the Rosetta water is one of the main reasons affecting the performance of the three models.

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References

  1. Raj A, Sabu P (2013) Shallow water bathymetry using log-linear inversion technique: a case study at Vizhinjam. Int J Innovat Res Sci Eng Technol 2(1):223–230

    Google Scholar 

  2. Pattanaik A, Sahu K, Bhutiyani MR (2015) Estimation of shallow water bathymetry using IRS-multispectral imagery of Odisha Coast India. Aquat Procedia 4(0):173–181

    Article  Google Scholar 

  3. Su H, Liu H, Heyman WD (2008) Automated derivation of bathymetric information from multi-spectral satellite imagery using a non-linear inversion model. Mar Geod 31:281–298

    Article  Google Scholar 

  4. Ehses JS, Rooney JJ (2015) Depth derivation using multispectral WorldView-2 satellite imagery. Civ Environ Eng 24–46 https://www.pifsc.noaa.gov/library/pubs/tech/NOAA_Tech_Memo_PIFSC_46.pdf

  5. Melsheimer C, Chin LS (2001) Extracting bathymetry from multi-temporal SPOT images. Asian Conf Remote Sens 58(12):37–42

    Google Scholar 

  6. Camacho M (2006) Depth analysis of midway atoll using QuickBird multi-spectral imaging over variable substrates. Master’s thesis, Navel Postgraduate School, Monterey, CA

    Google Scholar 

  7. Gao J (2009) Bathymetric mapping by means of remote sensing: methods, accuracy and limitations. Prog Phys Geogr 33(1):103–116

    Article  Google Scholar 

  8. Jawak SD, Luis AJ (2016) High-resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes. Proceeding of the international society for optical engineering. p 987819

    Google Scholar 

  9. Doxani G, Papadopoulou M, Lafazani P, Pikridas C (2012) Shallow-water bathymetry over variable bottom types using multispectral Worldview-2 image. ISPRS—international archives of the photogrammetry, remote sensing and spatial information sciences. XXXIX (September), pp 159–164

    Google Scholar 

  10. Lyzenga DR (1978) Passive remote sensing techniques for mapping water depth and bottom features. Appl Opt 17:379–383

    Article  CAS  Google Scholar 

  11. Lyzenga DR (1985) Shallow-water bathymetry using combined lidar and passive multispectral scanner data. Int J Remote Sens 6(1):115–125

    Article  Google Scholar 

  12. Lyzenga DR (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

    Article  Google Scholar 

  13. Stumpf RP, Holderied K, Sinclair M (2003) Determination of water depth with high-resolution satellite imagery over variable bottom types. Limonol Oceanogr 48(1_part_2):547–556

    Article  Google Scholar 

  14. Ceyhun Ö, Yalçın A (2010) Remote sensing of water depths in shallow waters via artificial neural networks. Estuar Coast Shelf Sci 89(1):89–96

    Article  Google Scholar 

  15. Corucci L (2011) Approaching bathymetry estimation from high resolution multispectral satellite images using a neuro-fuzzy technique. J Appl Remote Sens 5(1):053515

    Article  Google Scholar 

  16. Sari E, Gholamalifard M, Abkar A, Naimi B (2013) Principal components analysis (PCA) in Southern Caspian Sea. Int J Environ 7(4):877–886

    Google Scholar 

  17. Green EP, Mumby PJ, Edwards AJ, Clark C (1996) A review of remote sensing for the assessment and management of tropical coastal resources. Coast Manag 24(1):1–40

    Article  Google Scholar 

  18. Green EP, Mumby PJ, Edwards AJ, Claek CD (2000) Remote senseing handbook tropical coastal management. Coastal management sourcebook 3, vol X. UNESCO, Paris, p 316

    Google Scholar 

  19. Mishra DR, Narumalani S, Rundquist D, Lawson M (2005) Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: implications for water penetration by high resolution satellite data. ISPRS J Photogramm Remote Sens 60(1):48–64

    Article  Google Scholar 

  20. USGS (2015) Landsat 8 (L8) data users handbook. Department of Interior, U.S. Geological Survey

    Google Scholar 

  21. Mohamed H, Negm A, Zahran M, Saavedra OC (2016) Bathymetry determination from high resolution satellite imagery using ensemble learning algorithms in Shallow Lakes: case study El-Burullus Lake. Int J Environ Sci Dev 7(4):295–301

    Article  Google Scholar 

  22. Chander G, Markham BL, Helder DL (2009) Remote sensing of environment summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM +, and EO-1 ALI sensors. Remote Sens Environ 113(5):893–903

    Article  Google Scholar 

  23. ENVI (2009) ENVI atmospheric correction module: QUAC and FLAASH User’s guide. ITT Visual Information Solutions

    Google Scholar 

  24. Hochberg EJ, Andréfouët S, Tyler MR (2003) Sea surface correction of high spatial resolution ikonos images to improve bottom mapping in near-shore environments. IEEE Tran Geosci Remote Sens 41(7-PART II):1724–1729

    Article  Google Scholar 

  25. Hedley JD, Harborne AR, Mumby PJ (2005) Technical note: simple and robust removal of sun glint for mapping shallow water benthos. Int J Remote Sens 26(10):2107–2112

    Article  Google Scholar 

  26. Graupe D (2013) Principles of artificial neural networks, 3rd edn. World Scientific Publishing Co. Pte. Ltd, Singapore

    Google Scholar 

  27. Mohamed H, Negm A, Zahran M, Saavedra OC (2015) Assessment of artificial neural network for bathymetry estimation using High Resolution Satellite imagery in Shallow Lakes: case study El Burullus Lake. In: International water technology conference. pp 12–14

    Google Scholar 

  28. Walker JM (2015) Artificial neural networks, vol 1260, 2nd edn. Springer, New York

    Google Scholar 

  29. Yanjiao W, Peiqun Z, Wenjie D, Ying Z (2007) Study on remote sensing of water depths based on BP artificial neural network. Mar Sci Bull 9(1):26–35

    Google Scholar 

  30. Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32(14–15):2627–2636

    Article  CAS  Google Scholar 

  31. Yu H, Wilamowski BM (2011) Levenberg–Marquardt training, chapter 12. In: Intelligent systems. Industrial electronics handbook, vol. 5, 2nd edn. CRC Press, p 12–1 to 12–15

    Google Scholar 

  32. Elhaddad E, Al-Zyoud S (2017) The quality assessment of pollution of Rosetta branch, Nile River, Egypt. Arab J Geosci 10:97. doi:10.1007/s12517-017-2870-y

    Article  Google Scholar 

  33. Alawy AE, El-tras WF, Khater DF, El Raiy HR (2015) Impact of industrial wastewater on water and fish quality of Nile River in Kafr El-Zayat, Egypt. BENHA Vet Med J 78:87

    Google Scholar 

  34. Ezzat SM, Mahdy HM, Abo-State MA, Abd El Shakour EH, El-Banhnasawy MA (2012) Water quality assessment of River Nile at Rosetta Branch: impact of drains discharge. Middle-East J Sci Res 12(4):413–423

    CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt

    Abdelazim Negm

  2. Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

    Saleh Mesbah & Omar Makboul

  3. Nile Research Institute, NWRC, MWRI, El Qanater, 13621, Egypt

    Tarek Abdelaziz

Authors
  1. Abdelazim Negm
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  2. Saleh Mesbah
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  3. Tarek Abdelaziz
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  4. Omar Makboul
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Correspondence to Abdelazim Negm .

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Editors and Affiliations

  1. Faculty of Engineering, Zagazig University , Zagazig, Egypt

    Abdelazim M. Negm

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Negm, A., Mesbah, S., Abdelaziz, T., Makboul, O. (2017). Nile River Bathymetry by Satellite Remote Sensing Case Study: Rosetta Branch. In: Negm, A. (eds) The Nile River. The Handbook of Environmental Chemistry, vol 56. Springer, Cham. https://doi.org/10.1007/698_2017_17

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