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Automatic detection of shoreline change: case of North Sinai coast, Egypt

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Abstract

This paper introduces an appropriate visualization of how to effectively digitize, quantify and predict shoreline kinematics changes. The method relies mainly on the coupling of Geographic Information System (GIS) with Digital Shoreline Analysis System (DSAS). The North Sinai coast in Egypt is selected as a case study. The proposed technique is applied over a quarter-centennial period of 27 years (1989–2016). However, the years 2025, 2035, and 2050 are used for prediction purpose. Histogram threshold of band 5, Histogram threshold of band ratio, and Tasselled Cap Transformation (TCT) are initially tempted as semi-automatic shoreline extraction techniques for Landsat ETM 2010 imagery. Among of them, the TCT is found superior as a digitizing technique that attains the least normalized root mean square errors with the corresponding field data in 2010. Meanwhile, the shoreline change rates in the form of erosion/accretion patterns are automatically quantified by four statistical parameters functioned in DSAS coding. Those, namely end point rate (EPR), net shoreline movement (NSM), linear regression rate (LRR), and least median of squares (LMS). On the basis of the LRR and EPR results, this study offers to the coastal managers a highly reliable decision-support-algorism that can dynamically assist in elaborating strategies to curtail the non-affirmative consequences due to the erosion/accretion of the shoreline.

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Acknowledgments

The authors would like to thank the Egyptian Ministry of Higher Education (MoHE) for providing the financial support for the first author Ph.D. scholarship. Sincere appreciation to the institute of oceanography and fisheries for providing us the shoreline field data in 2010 along the North Sinai coast.

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Authors and Affiliations

  1. Environmental Engineering Department, School of Energy and Environmental Engineering, Egypt-Japan University of Science and Technology, E-JUST, Alexandria, Egypt

    Karim Nassar, Hassan Fath & Wael Elham Mahmod

  2. Irrigation & Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Karim Nassar & Ali Masria

  3. Civil Engineering Department, Faculty of Engineering, Assiut University, 71515, Assiut, Arab Republic of Egypt

    Wael Elham Mahmod

  4. Mechanical and Environmental Informatics Department, Tokyo Institute of Technology, Ookayama 2-12-1-W8-13, Meguro-Ku, Tokyo, 152-8552, Japan

    Kazuo Nadaoka

  5. Water Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt

    Abdelazim Negm

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  1. Karim Nassar
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  2. Hassan Fath
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  3. Wael Elham Mahmod
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  4. Ali Masria
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  5. Kazuo Nadaoka
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  6. Abdelazim Negm
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Correspondence to Karim Nassar.

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Nassar, K., Fath, H., Mahmod, W.E. et al. Automatic detection of shoreline change: case of North Sinai coast, Egypt. J Coast Conserv 22, 1057–1083 (2018). https://doi.org/10.1007/s11852-018-0613-1

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  • DOI: https://doi.org/10.1007/s11852-018-0613-1

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