Search

The steepness of the beach face is a fundamental parameter for coastal morphodynamic research. Despite its importance, it remains extremely difficult to obtain reliable estimates of the beach‐face slope over large spatial scales (thousands of km of coastline). In this letter, a novel approach to estimate this slope from time series of satellite‐derived shoreline positions is presented. This new technique uses a frequency domain analysis to find the optimum slope that minimizes high‐frequency tidal fluctuations relative to lower‐frequency erosion/accretion signals. A detailed assessment of this new approach at eight locations spanning a range of tidal regimes, wave climates, and sediment grain sizes shows strong agreement (R2 = 0.93) with field measurements. The automated technique is then applied across thousands of beaches in eastern Australia and California, USA, revealing similar regional‐scale distributions along these two contrasting coastlines and highlights the potential for new global‐scale insight to beach‐face slope spatial distribution, variability, and trends. Plain Language Summary: How steep a beach is can dictate the way the beach interacts with the incoming ocean waves and therefore is of paramount importance for coastal scientists and engineers, coastal flood modelers, and swim safety officers. However, despite its importance, it is impractical to obtain reliable estimates of the "typical" beach‐face slope along large lengths of sandy coastlines (hundreds to thousands of km) because of the logistics that would be necessary to visit many sites repeatedly to obtain these measurements. This letter describes a new technique to estimate the beach‐face slope in the absence of field observations, relying instead on long‐term publicly available satellite observations and a global tide model. This technique is then applied to thousands of beaches along the coastlines of eastern Australia and California in the United States. Key Points: A novel remote sensing technique to estimate beach‐face slopes from satellite imagery and modeled tides is presentedTime series of shoreline change are transformed into frequency domain to find the slope that minimizes high‐frequency tidal fluctuationsValidation against in situ data shows high accuracy across sites ranging in grain size, tidal range, and wave climate [ABSTRACT FROM AUTHOR]

The ability to repeatedly observe and quantify the changing position of the shoreline is key to present-day coastal management and future coastal planning. This study evaluates the capability of satellite remote sensing to resolve at differing temporal scales the variability and trends in shoreline position along sandy coastlines. Shorelines are extracted from 30 + years of publicly available satellite imagery and compared to long-term in-situ measurements at 5 diverse test sites in Europe, Australia, the USA and New Zealand. These sites span a range of different beach characteristics including wave energy and tide range as well as timescales of observed shoreline variability, from strongly seasonal (e.g., Truc Vert, France), to storm-dominated (e.g., Narrabeen-Collaroy, Australia), to only minor annual to multi-annual signals (e.g., Duck, USA). For the 5 sites, the observed typical horizontal errors varied between a root-mean-squared error (RMSE) of 7.3 m and 12.7 m. An analysis of the typical magnitudes of shoreline variability at temporal scales ranging from a single month up to 10 years indicates that, by the implementation of targeted image pre-processing then the application of a robust sub-pixel shoreline extraction technique, the resulting satellite-derived shorelines are generally able to resolve (signal-to-noise ratio > 1) the observed shoreline variance at timescales of 6 months and longer. The only exception to this is along coastlines where minimal annual to multi-annual shoreline variability occurs (e.g. Duck, USA); at these sites decadal-scale variations are successfully captured. The results of this analysis demonstrate that satellite-derived shorelines spanning the past 30 years as well as into the future can be used to explore and quantify intra- and inter-annual shoreline behaviour at a wide range of beaches around the world. Moreover, it is demonstrated that present-day satellite observations are also capable of capturing event-scale shoreline changes (e.g. individual storms) that occur at timescales shorter than 6 months, where this rapid response exceeds the typical magnitude of shoreline variability. Finally, several practical coastal engineering applications are presented, demonstrating the use of freely-available satellite imagery to monitor inter-annual embayed beach rotation, rapid storm-induced shoreline retreat and a major sand nourishment. • A refined sub-pixel resolution shoreline detection technique capable of mapping the sand/water interface is presented. • Satellite-derived shoreline accuracy is assessed at diverse sandy beach sites in Europe, Australia, the USA and New Zealand. • The observed RMSE in shoreline positions for the sites tested ranged between 7.3 m and 12.7 m. • Rapid (e.g., storm erosion, beach nourishment), seasonal and inter-annual shoreline changes are able to be captured. [ABSTRACT FROM AUTHOR]

CoastSat is an open-source software toolkit written in Python that enables the user to obtain time-series of shoreline position at any sandy coastline worldwide from 30+ years (and growing) of publicly available satellite imagery. The toolkit exploits the capabilities of Google Earth Engine to efficiently retrieve Landsat and Sentinel-2 images cropped to any user-defined region of interest. The resulting images are pre-processed to remove cloudy pixels and enhance spatial resolution, before applying a robust and generic shoreline detection algorithm. This novel shoreline detection technique combines a supervised image classification and a sub-pixel resolution border segmentation to map the position of the shoreline with an accuracy of ~10 m. The purpose of CoastSat is to provide coastal managers, engineers and scientists a user-friendly and practical toolkit to monitor and explore their coastlines. The software is freely-available on GitHub (https://github.com/kvos/CoastSat) and is accompanied by guided examples (Jupyter Notebook) plus step-by-step README documentation. • Global shoreline mapping toolbox from publicly available Landsat and Sentinel-2 satellite imagery. • Open-source Python toolkit that enables users to obtain 30+ years of satellite-derived shorelines at any beach worldwide. • Satellite images of the user-defined region of interest are retrieved efficiently with Google Earth Engine. • The sand/water interface is automatically mapped using a robust sub-pixel resolution shoreline detection technique. [ABSTRACT FROM AUTHOR]