Benchmarking satellite-derived shoreline mapping algorithms

Journal Article (2023)
Author(s)

K. Vos (University of New South Wales)

K. D. Splinter (University of New South Wales)

J. Palomar-Vázquez (Universitat Politécnica de Valencia)

J. E. Pardo-Pascual (Universitat Politécnica de Valencia)

J. Almonacid-Caballer (Universitat Politécnica de Valencia)

C. Cabezas-Rabadán (Universitat Politécnica de Valencia, Université de Bordeaux)

E. C. Kras (Deltares)

A. P. Luijendijk (Deltares, TU Delft - Coastal Engineering)

F. Calkoen (TU Delft - Coastal Engineering, Deltares)

undefined More Authors (External organisation)

DOI related publication
https://doi.org/10.1038/s43247-023-01001-2 Final published version
More Info
expand_more
Publication Year
2023
Language
English
Issue number
1
Volume number
4
Article number
345
Downloads counter
441
Collections
Institutional Repository
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.