Benchmarking satellite-derived shoreline mapping algorithms

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.

Can Satellite‐Derived Beach Images Resolve the Responses to Human Activities?

“…The greater the water‐level fluctuation is, the flatter the beach is, and the larger the random error is. This explains why the random position errors for the high‐water line at Lingbei Beach are comparable to those in other steep beaches and are slightly better than those at beaches with moderate slopes (Castelle et al., 2021; Hagenaars et al., 2018; Pardo‐Pascual et al., 2018; Vitousek et al., 2023; Vos et al., 2019, 2023; Zhang et al., 2021). The same principle applies when explaining why the random position errors for the low‐water line at Lingbei Beach are relatively large.…”

“…Vos et al. (2023) evaluated the reliability of five shoreline identification algorithms using measured data from four beaches and reported that these algorithms produced shoreline positions with random errors ranging from 6.9 to 48.3 m. In particular, in Truc Vert Beach, the results were generally unsatisfactory, with random errors ranging from 20.1 to 48.3 m.…”

“…Castelle et al (2021) and Vitousek et al (2023) used the program shared by Vos et al (2019) to recognize the waterlines of Truc Vert Beach and Ocean Beach, respectively, and the random position errors of these waterlines were 12.8-14 m. Castelle et al (2021) and Vitousek et al (2023) also found that wave runup is critical for accurately estimating waterline positions. Vos et al (2023) evaluated the reliability of five shoreline identification algorithms using measured data from four beaches and reported that these algorithms produced shoreline positions with random errors ranging from 6.9 to 48.3 m. In particular, in Truc Vert Beach, the results were generally unsatisfactory, with random errors ranging from 20.1 to 48.3 m. Zhang et al (2021Zhang et al ( , 2022 proposed the use of a transect-focused approach that integrates automatic and manual subpixel shoreline recognition methods to directly identify the positions of multiple shorelines on transects. Therefore, this approach avoids the heavy workload of identifying all shorelines on large numbers of satellite images, which is beneficial for making full use of satellite imagery.…”

On the shoreline positioning via remote sensing imagery: an isoradiometric approach

“…The shoreline serves as the boundary line separating terrestrial sediments from adjacent water bodies, such as seas, lakes, and rivers. A variety of shoreline indicators can act as a proxy for the land-water boundary, 1 with algorithms or approaches, 2 materials, software, and indices available for shoreline monitoring or identification 3 leading to the creation of coastline products with varying strengths and weaknesses 4 . A significant amount of research has been conducted on coastal erosion and shoreline change, 5 and vulnerability assessments can be performed based on the physical and socio-economic conditions of the coast 6 …”

Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

“…The challenge lies in accurately defining the water boundary, considering the limitations imposed by the pixel size of the satellite images. Satellite-derived shoreline (SDS) algorithms such as CoastSat [10], SHOREX [11], and CASSIE [12] have tackled this challenge by devising efficient approaches to overcome such limitations (see review and benchmark comparison in Vos et al [13]). Sustained in the subpixel solution proposed by Pardo-Pascual et al [14] and in the workflow followed by SHOREX, the tool SAET [15] has appeared as a new alternative focused on offering high autonomy, efficiency and robustness in the extraction.…”