Multispectral satellite imagery and machine learning for the extraction of shoreline indicators

Emma McAllister*, Andres Payo, Alessandro Novellino, Tony Dolphin, Encarni Medina-Lopez

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Analysis of shoreline change is fundamental to a broad range of investigations undertaken by coastal scientists, coastal engineers, and coastal managers. Multispectral Satellite Imagery (MSI) provides high resolution datasets that allow coastlines to be monitored more frequently and on a global scale. The Landsat and Sentinel-2 MSI datasets are free for public use, which has increased the frequency of studies focusing on coastal change using satellite imagery. However, despite access to global and free satellite imagery, a method has yet to be developed to monitor different shoreline types and indicators globally, as not all shorelines are sandy beaches, and the waterline cannot be representative of all shoreline changes. The review paper introduces different techniques used currently to extract shoreline features, including water indexing, Machine Learning (ML) and segmentation methods. We presented here a comprehensive review of range of the methods available for shoreline extraction from MSI and discuss why some shoreline features have been identified using multispectral satellite imagery and others not. This approach helps to signal where the gaps are on the current methods for shoreline extraction and provides a roadmap of the key challenges that prevents MSI to be used for understanding shoreline changes at a global scale.

Original languageEnglish
Article number104102
JournalCoastal Engineering
Early online date28 Feb 2022
Publication statusPublished - Jun 2022

Keywords / Materials (for Non-textual outputs)

  • Machine learning
  • Remote sensing
  • Shoreline extraction
  • Shoreline indicators


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