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

  • Karim Nassar
  • Hassan Fath
  • Wael Elham Mahmod
  • Ali Masria
  • Kazuo Nadaoka
  • Abdelazim Negm
Article

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.

Keywords

North Sinai coast Shoreline change DSAS Satellite imagery Remote sensing and GIS Shoreline prediction 

Notes

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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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Karim Nassar
    • 1
    • 2
  • Hassan Fath
    • 1
  • Wael Elham Mahmod
    • 1
    • 3
  • Ali Masria
    • 2
  • Kazuo Nadaoka
    • 4
  • Abdelazim Negm
    • 5
  1. 1.Environmental Engineering Department, School of Energy and Environmental EngineeringEgypt-Japan University of Science and Technology, E-JUSTAlexandriaEgypt
  2. 2.Irrigation & Hydraulics Department, Faculty of EngineeringMansoura UniversityMansouraEgypt
  3. 3.Civil Engineering Department, Faculty of EngineeringAssiut UniversityAssiutArab Republic of Egypt
  4. 4.Mechanical and Environmental Informatics DepartmentTokyo Institute of TechnologyTokyoJapan
  5. 5.Water Engineering Department, Faculty of EngineeringZagazig UniversityZagazigEgypt

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