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Satellite remote sensing is transforming coastal science from a ‘data-poor’ field into a ‘data-rich’ field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change has outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in-situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, i.e., (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer-vision to fully leverage this massive treasure-trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

Sandy beaches are unique environments composed of unconsolidated sediments that are constantly reshaped by the action of waves, tides, currents, and winds. The most seaward region of the dry beach, referred to as the beach face, is the primary interface between land and ocean and is of fundamental importance to coastal processes, including the dissipation and reflection of wave energy at the coast, and the exchange of sediment between the land and sea. The slope of the beach-face is a critical parameter in coastal geomorphology and coastal engineering, necessary to calculate the total elevation and excursion of wave run-up at the shoreline. However, datasets of the beach-face slope remain unavailable along most of the world’s coastlines. This study presents a new dataset of beach-face slopes for the Australian coastline derived from a novel remote sensing technique. The dataset covers 13,200 km of sandy coast and provides an estimate of the beach-face slope at every 100 m alongshore, accompanied by an easy to apply measure of the confidence of each slope estimate. The dataset offers a unique view of large-scale spatial variability in beach-face slope and addresses the growing need for this information to predict coastal hazards around Australia. The beach-face slope dataset and relevant metadata are available at https://doi.org/10.5281/zenodo.5606217 (Vos et al., 2021)

Wildfire and post-fire rainfall have resounding effects on hillslope processes and sediment yields of mountainous landscapes. Yet, it remains unclear how fire-flood sequences influence downstream coastal littoral systems. It is timely to examine terrestrial-coastal connections because climate change is increasing the frequency, size, and intensity of wildfires, altering precipitation rates, and accelerating sea-level rise; and these factors can be understood as contrasting accretionary and erosive agents for coastal systems. Here we provide new satellite-derived shoreline measurements of Big Sur, California and show how river sediment discharge significantly influenced shoreline positions during the past several decades. A 2016 wildfire followed by record precipitation increased sediment discharge in the Big Sur River and resulted in almost half of the total river sediment load of the past 50 years (~ 2.2 of ~ 4.8 Mt). Roughly 30% of this river sediment was inferred to be littoral-grade sand and was incorporated into the littoral cell, causing the widest beaches in the 37-year satellite record and spreading downcoast over timescales of years. Hence, the impact of fire-flood events on coastal sediment budgets may be substantial, and these impacts may increase with time considering projected intensification of wildfires and extreme rain events under global warming.

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.

Shoreline change information is critical for effective management of the coastal zone. This study presents a low-cost method for mapping shoreline change that harnesses smartphone images collected by the community and uploaded to social media platforms. A smartphone camera cradle installed overlooking a coastal region is used to constrain the crowd-sourced camera extrinsic parameters and accompanying signage instructs participants on how to share their image to social media using a site-specific hashtag identifier. Surveyed ground control points solve for the focal length of the smartphone lens and more-accurately resolve the camera extrinsic parameters. Shoreline position is subsequently mapped on georectified images using an edge detection technique based on the red and blue colour channels. A validation of the method was conducted at two sandy beaches in SE Australia and resulted in strong community participation (400 images submitted over 7 months by 198 individual contributors). Concurrent shoreline surveys using RTK-GNSS indicated that shoreline accuracy using this crowd-sourced approach is comparable to that of established coastal imaging systems, with cross-shore shoreline accuracy best for these two elevated validation sites (camera elevation = 17.3 m–27.1 m above MSL) in the camera nearfield (RMSD ≈ 1.4 m) and RMSD ranging between 2.6 and 3.9 m over coastal stretches spanning up to 1 km. Minimal differences in shoreline accuracy were observed between low resolution images characteristic of those uploaded to social media and higher resolution images sourced from the smartphone. The successful application of this low-cost approach, combined with the proliferation of smartphones and social media usage, open up new possibilities for crowd-sourced shoreline change mapping at suitable coastal locations worldwide.

In the Pacific Basin, El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability and drives substantial changes in oceanographic forcing, likely having a significant impact on Pacific coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 35 years of Landsat imagery to map shoreline variability around the Pacific Rim (72,000 beach transects) and identify coherent patterns of beach erosion and accretion controlled by ENSO. We find that approximately one third of all beaches experience significant erosion during El Niño phases, with the Eastern Pacific particularly vulnerable to widespread erosion (most notably during the large 1997/1998 event). In contrast, La Niña events coincide with significant accretion for approximately one quarter of all beaches, although conversely drives substantial erosion in south-east Australia and other localized regions. The significant regional variability in coastal response to ENSO should be considered in light of future projected intensification and shifts in ENSO amplitudes and flavors.

Coral reef islands are among the most vulnerable landforms to climate change. However, our understanding of their morphodynamics at intermediate (seasonal to interannual) timescales remains poor, limiting our ability to forecast how they will evolve in the future. Here, we applied a semi-automated shoreline detection technique (CoastSat.islands) to 20 years of publicly available satellite imagery to investigate the evolution of a group of reef islands located in the eastern Indian Ocean. At interannual timescales, island changes were characterized by the cyclical re-organization of island shorelines in response to the variability in water levels and wave conditions. Interannual variability in forcing parameters was driven by El Niño Southern Oscillation (ENSO) cycles, causing prolonged changes to water levels and wave conditions that established new equilibrium island morphologies. Our results present a new opportunity to measure intermediate temporal scale changes in island morphology that can complement existing short-term (weekly to seasonal) and long-term (decadal) understanding of reef island evolution.

The slope of the beach face is a critical parameter for coastal scientists and engineers studying sandy coastlines. However, despite its importance for coastal applications (engineering formulations, coastal flood modelling, swimming safety), it remains extremely difficult to obtain reliable estimates of the beachface slope over large spatial scales (hundreds to thousands of km of coastline). This presentation describes a new method to estimate the beach-face slope exclusively from space-borne observations: shoreline positions derived from publicly available optical imaging satellites and tide heights from satellite altimetry. This new technique is first validated against field measurements and then applied across hundreds of beaches in eastern Australia and California, USA (data available at http://coastsat.wrl.unsw.edu.au/).Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/U9zMbFX4gPk

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 ...

International audience; Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.

International audience; Predictions of shoreline change are of great societal importance, but models tend to be tested and tuned for the specific site of interest. To overcome this issue and test the ability of numerical models to simulate shoreline change over the medium scale (order of years) we have organized a non-competitive competition where participants were given data to train their model (1999-2014) and data to predict seasonal to inter-annual future changes (2014-2017). Participants were shown the observed shoreline changes only after submission of their modelling results. Overall, 19 numerical models were tested, the vast majority falling in the broad categories of "hybrid models" or "machine learning". Models were able to reproduce the mean characteristics of shoreline change but often failed to reproduce the observed rapid changes induced by storms.

Despite their global abundance and high ecological and socio-economic significance, the dynamics of coastal inlets often remain poorly quantified at multi-decadal time scales. Here, we introduce InletTracker ( https://github.com/VHeimhuber/InletTracker ), a new tool that reconstructs the time-evolving state of dynamic coastal inlets over the last 30+ years from publicly available Landsat 5, 7 and 8 and Sentinel-2 satellite imagery. InletTracker is a Google Earth Engine enabled python toolkit that uses a novel least-cost pathfinding approach to trace inlets along and across the berm (i.e., barrier, bar), and then analyses the resulting transects to infer whether an inlet is open or closed. To evaluate the performance of InletTracker, we applied the tool at 12 intermittent coastal inlets with different maximum inlet widths (≤30-200 m), geomorphological setting and opening frequency located across Southeastern and Southwestern Australia. This exercise involved 6363 unique binary inlet state predictions (i.e., open vs. closed) that were validated against visually inferred inlet states (from the satellite imagery itself), on-ground observational records, and in-situ water levels from inside the inlets. InletTracker reproduced the visually inferred inlet states with an average accuracy across all sites of 89% for the combined Landsat and Sentinel-2 record (15-30 m resolution) and 94% for the Sentinel-2 record only (10 m resolution). Overall, we found good agreement between the predictions of the tool and the three independent validation datasets for all but the smallest sites. Our results demonstrate that InletTracker will enable coastal engineers, managers, and researchers to gain new insights into the dynamics and drivers of coastal inlets or similar shallow water landforms such as river mouths, tidal flats, floodplains, wetlands or delta channel networks. Further, the high spatial (i.e., 10 m) and temporal (i.e., 5-daily) resolution provided by Sentinel-2 makes InletTracker a viable option for near real-time monitoring of even relatively small inlets with a minimum channel width of around 10 m and frequent, short-duration, openings.