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Satellite altimetry water level measurements are valuable in episodic and climate change related hydrodynamic impact studies, despite their sparse temporal distribution over the global ocean. This study presents the spatiotemporal characteristics of the open-ocean satellite derived water level measurements globally for the period 31/12/1992-15/10/2019 and evaluates their efficacy to represent the water level even during intense atmospheric conditions. Water level measurements from 23 different satellite missions are compared with tide gauge records and hydrodynamic simulations. The satellite measurements reproduce the water-level variations with good to excellent skill for ~60% of the areas considered. Additionally, satellite measurements and local atmospheric conditions are utilized in order to examine whether statistical data driven models can contribute to decreasing the temporal sparseness of the water level data over the global ocean. The suitability of this low computational-cost method is demonstrated by deriving a 63-year hindcast of the daily maximum water level for the global ocean, and for a medium-term 15-day ensemble forecast. The publicly available long-term water-level hindcast and the parameters of the data-driven statistical model derived can serve as a tool for designing and facilitating local and global coastal risk-assessment studies.

Abstract The central-eastern tropical Pacific is currently significantly warmer than normal, and the likelihood of a strong El Niño developing by early 2024 is 75–85%, according to the National Weather Service’s Climate Prediction Center. Disruptions in ecosystem services and increased vulnerability, in particular in the coastal zones, are expected in many parts of the world. In this comment, we review the latest seasonal forecasts and showcase the potential for predicting the world’s coastlines based on data-driven modeling.

Abstract Global coastal areas are at risk due to geomorphological issues, climate change-induced sea-level rise, and increasing human population, settlements, and socioeconomic activities. Here, the study examines the vulnerability of the West African (WA) coast using six satellite-derived geophysical variables and two key socioeconomic parameters as indicators of coastal vulnerability index (CVI). These geophysical and socioeconomic variables are integrated to develop a CVI for the WA coast. Then, the regional hotspots of vulnerability with the main indicators that could influence how the WA coast behaves and can be managed are identified. The results indicate that 64, 17 and 19% of WA coastal areas had high to very high CVI, moderate CVI, and low to very low CVI, respectively. The study reveals that while geophysical variables contribute to coastal vulnerability in WA, socioeconomic factors, particularly high population growth and unsustainable human development at the coast, play a considerably larger role. Some sections of the WA coast are more vulnerable and exposed than others, particularly those in the region's northwestern and Gulf of Guinea regions. Climate change and human presence may amplify the vulnerability in these vulnerable areas in the future. Hence, future coastal economic development plans should be based on a deep understanding of local natural conditions, resource status, and geophysical parameters to prevent negative coastal ecosystem transformation. It is also essential to establish a coastal management plan that would facilitate the development of desired actions and stimulate sustainable management of West African coastal areas.

Understanding beach dynamics, both in time and in space, is paramount to better understand how and when to intervene to improve coastal management strategies. Beach morphodynamics is expressed in a variety of ways. As indicators of beach change, we can measure the shoreline, the beach topography, and the bathymetry; e.g., in situ measurements rarely cover large extents, are often collected on a local scale (beach), and rarely cover a sufficient time span with a sufficient surveying frequency or a simultaneous measurement of the beach and bar system. Regular-revisit satellites, such as the ESA’s Sentinel-2 mission, provide the opportunity to regularly monitor both shoreline and sandbar dynamics, and the time span is increasing and likely to continue for the decades to come. Using the satellite-derived shoreline and bar position, here, we show that the shoreline and bar are intrinsically coupled. Using Sentinel-2 satellite imagery, we show that the actual erosion/accretion status of the beach at Saint Louis (Senegal) is strongly influenced by the sandbar dynamics. There is a coupled behavior in their seasonal evolution and trend. Our results show that a very large accretive wave of about 50 m observed on the beach is driven by a local welding of the inner sandbar to the beach. Finally, we conclude that this type of event could be anticipated by an analysis of the sandbar.

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Abstract Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards.

Abstract Sea level rise will exacerbate the vulnerability of low-lying coastal regions around the world in the coming decades, posing a severe threat to coastal populations. Here, we assess the future population and asset exposure of West Africa (WA) to normal and extreme coastal flooding based on the projected sea level rise scenarios reported in the IPCC Sixth Assessment Report using a bathtub modeling approach, MERIT DEM and gridded population gross domestic product datasets that are consistent with the Shared Socioeconomic Pathways. We find that socioeconomic development will be responsible for the maximum increase in future coastal flooding along the WA coast towards the end of the century. While contributions from climate-induced sea level rise will dominate and be responsible for changes in coastal flooding events in some countries, exposure to these events is likely to dominate in many countries if the ongoing horizontal infrastructural development and economic-oriented transformation continue. These results have important implications for both sustainable coastal planning and flooding risk mitigation for WA’s coastal areas and should be considered as a cautionary tale for managing increasing socioeconomic development and coastward migration at the expense of the region’s coastal ecosystems.

In a context of global warming characterized by a mean sea level rise and extreme meteorological events, the study of the causes for coastal flooding is essential to protect communities and ecosystems. Densely urbanized, and rather unprotected cities in developing countries such as the historic Saint Louis city in Senegal are particularly vulnerable to coastal flooding and sea hazards. From satellite-derived high resolution DEM and global ocean reanalyses, here we quantify the extreme coastal water level in order to identify the neighborhoods and places of particular socio-economical interest of Saint-Louis potentially vulnerable to flooding. The results reveal that the most severe levels have the potential to flood up to almost half of this low-lying river mouth plain. Social, economic and heritage stakes are exposed, and the artisanal fisherman district of Gueth Ndar, is particularly vulnerable to coastal flooding because of its low elevation and situation on the sand barrier. The co-occurrence of high tides and wave-induced runup contributes most to ECWL but they do not present a significant trend over the study period. The results show that over the period 1994-2015, potential flood risk increased by nearly one day per year, primarily due to sea level rise, sounding a warning signal to take countermeasures to protect communities and infrastructure.

Satellite sensors now provide low-cost global monitoring, with relatively high resolution with frequent revisits [...]

As sea levels rise, coasts are being increasingly threatened by overtopping caused by the combination of sea level rise, storm surge and wave runup. Here the authors find that global coastal overtopping has increased by over 50% in the last two decades, and under a RCP 8.5 scenario this could increase up to 50 times by 2100 compared to today.

Like low-lying sandy coasts around the world, the Ghanaian coast is experiencing increasingly frequent coastal flooding due to climate change, putting important socioeconomic infrastructure and people at risk. Our study assesses the major factors contributing to extreme coastal water levels (ECWLs) from 1994 to 2015. ECWLs are categorized into low, moderate, and severe levels corresponding to the 30th, 60th, and 98th percentiles, respectively. Using these three levels over the Pleiades satellite-derived digital elevation model topography, potential flood extent zones are mapped. ECWLs have the potential to flood more than 40% of the study area, including socioeconomically important sites such as tourist beach resorts, Cape St. Paul lighthouse, and Fort Prinzenstein. In this study, all coastal flooding events recorded by the municipality of Keta fall within the 98th percentile category. Our results show a gradual increase in the frequency of flooding over the years. Flooding events are caused by a compound effect of the tide, sea level anomaly, waves, and atmospheric conditions. Finally, while wave run-up is the major contributor to coastal flooding, the tide is the one varying most, which facilitates a simple early warning system based on waves and tide but adds uncertainty and complicates long-term predictability.

Measurement(s) surf zone waves • swash hydrodynamics • surf zone flow velocity • morphology • sediment Technology Type(s) Gauge or Meter Device • Lidar • velocimetry • mechanical profiler • optical backscatter sensor • Pressure transducers Factor Type(s) Wave height • Wave period • beach morphology Sample Characteristic - Environment beach • shingle beach Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13194872

Increasingly, it is reported that the coastline of the Dakar region is affected by coastal flooding due to extreme water levels during wave events. Here, we quantify the extreme coastal water levels as well as the different factors contributing to coastal flooding during the period 1994–2015. Severe water levels reach values of 1.78 m and increase by 8.4 mm/year. The time spent above this threshold has already increased by 1.7 over the study period and will increase by 2100 to 8 times with 0.4 m mean sea level rise and up to 20 times with 0.8 m in the IPCC low and high greenhouse gas emission scenarios, respectively. Tide is the main contributor to the extremes when combined with large wave runup, due to wave breaking which contributes to 38% of the increase in extreme events while sea level rises to 44%. Our results show that because of its prominent location, Dakar region is affected by waves coming from the Northern and Southern Hemispheres with contrasted evolutions: wave runup events increase faster (7 mm/year) during austral winter due to a maximum of the South Atlantic storm activity, and have a decreasing trend (−3 mm/year) during boreal winter (December, January, February) driven by the evolution of corresponding climate modes.

Faced with sea level rise and the intensification of extreme events, human populations living on the coasts are developing responses to address local situations. A synthesis of the literature on responses to coastal adaptation allows us to highlight different adaptation strategies. Here, we analyze these strategies according to the complexity of their implementation, both institutionally and technically. First, we distinguish two opposing paradigms – fighting against rising sea levels or adapting to new climatic conditions; and second, we observe the level of integrated management of the strategies. This typology allows a distinction between four archetypes with the most commonly associated governance modalities for each. We then underline the need for hybrid approaches and adaptation trajectories over time to take into account local socio-cultural, geographical, and climatic conditions as well as to integrate stakeholders in the design and implementation of responses. We show that dynamic and participatory policies can foster collective learning processes and enable the evolution of social values and behaviors. Finally, adaptation policies rely on knowledge and participatory engagement, multi-scalar governance, policy monitoring, and territorial solidarity. These conditions are especially relevant for densely populated areas that will be confronted with sea level rise, thus for coastal cities in particular.

Low-lying coastal zones are home to around 10% of the world’s population and to many megacities. Coastal zones are largely vulnerable to the dynamics of natural and human-induced changes. Accurate large-scale measurements of key parameters, such as bathymetry, are needed to understand and predict coastal changes. However, nearly 50% of the world’s coastal waters remain unsurveyed and for a large number of coastal areas of interest, bathymetric information is unavailable or is often decades old. This lack of information is due to the high costs in time, money and safety involved in collecting these data using conventional echo sounder on ships or LiDAR on aircrafts. Europe is no exception, as European seas are not adequately surveyed according to the International Hydrographic Organisation. Bathymetry influences ocean waves and currents, thereby shaping sediment transport which may alter coastal morphology over time. This paper discusses state-of-the-art coastal bathymetry retrieval methods and data, user requirements and key drivers for many maritime sectors in Europe, including advances in Satellite-Derived Bathymetry (SDB). By leveraging satellite constellations, cloud services and by combining complementary methods, SDB appears as an effective emerging tool with the best compromise in time, coverage and investment to map coastal bathymetry and its temporal evolution.

There was an error in the original article [...]

Optical satellite images of the nearshore water surface offer the possibility to invert water depths and thereby constitute the underlying bathymetry. Depth inversion techniques based on surface wave patterns can handle clear and turbid waters in a variety of global coastal environments. Common depth inversion algorithms require video from shore-based camera stations, UAVs or Xband-radars with a typical duration of minutes and at framerates of 1–2 fps to find relevant wave frequencies. These requirements are often not met by satellite imagery. In this paper, satellite imagery is augmented from a sequence of 12 images of Capbreton, France, collected over a period of ∼1.5 min at a framerate of 1/8 fps by the Pleiades satellite, to a pseudo-video with a framerate of 1 fps. For this purpose, a recently developed method is used, which considers spatial pathways of propagating waves for temporal video reconstruction. The augmented video is subsequently processed with a frequency-based depth inversion algorithm that works largely unsupervised and is openly available. The resulting depth estimates approximate ground truth with an overall depth bias of −0.9 m and an interquartile range of depth errors of 5.1 m. The acquired accuracy is sufficiently high to correctly predict wave heights over the shoreface with a numerical wave model and to find hotspots where wave refraction leads to focusing of wave energy that has potential implications for coastal hazard assessments. A more detailed depth inversion analysis of the nearshore region furthermore demonstrates the possibility to detect sandbars. The combination of image augmentation with a frequency-based depth inversion method shows potential for broad application to temporally sparse satellite imagery and thereby aids in the effort towards globally available coastal bathymetry data.

At the interface between land and sea, the shoreface of sandy coasts extends from the dune (up to tens of meters above the sea level) to below the depth of the closure (often tens of meters below sea level). This is a crucial zone to monitor in order to reduce the uncertainty associated with forecasting the impact of storms and climate change on the coastal zone. At the same time, monitoring the dynamic interface between land and sea presents a traditional challenge for both in situ and remote sensing techniques. Here, we show the potential of using a video from a metric optical satellite sensor to estimate the emerged topography and submerged bathymetry over a single-pass. A short sequence (21 s, 10 Hz) of satellite-images was acquired with the Jilin-1/07 satellite covering the area in the vicinity of the Field Research Facility (FRF) at Duck (North Carolina, USA). The FRF site is regularly monitored with traditional surveys. From a few satellite images, the topography is reconstructed using stereo-photogrammetry techniques, while the bathymetry is inversed using incident waves through time-series spatio-temporal correlation techniques. Finally, the topography and bathymetry are merged into a seamless coastal digital elevation model (DEM). The satellite estimate shows a good agreement with the in situ survey with 0.8 m error for the topography and 0.5 m for the bathymetry. Overall, the largest discrepancy (more than 2 m) is obtained at the foreshore land–water interface due to the inherent problems of both satellite methods. A sensitivity analysis shows that using a temporal approach becomes beneficial over a spatial approach when the duration goes beyond a wave period. A satellite-based video with a duration of typically tens of seconds is beneficial for the bathymetry estimation and is also a prerequisite for stereo-based topography with large base-over-height ratio (characterizes the view angle of the satellite). Recommendations are given for future missions to improve coastal zone optical monitoring with the following settings: matricial sensors (potentially in push-frame setting) of ∼100 km2 scenes worldwide; up to a monthly revisit to capture seasonal to inter-annual evolution; (sub)meter resolution (i.e., much less than a wavelength) and burst of images with frame rate >1 Hz over tens of seconds (more than a wave period).

The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.

At first order, wind-generated ocean surface waves represent the dominant forcing of open-coast morpho-dynamics and associated vulnerability over a wide range of time scales. It is therefore paramount to improve our understanding of the regional coastal wave variability, particularly the occurrence of extremes, and to evaluate how they are connected to large-scale atmospheric regimes. Here, we propose a new “2-ways wave tracking algorithm” to evaluate and quantify the open-ocean origins and associated atmospheric forcing patterns of coastal wave extremes all around the Pacific basin for the 1979–2020 period. Interestingly, the results showed that while extreme coastal events tend to originate mostly from their closest wind-forcing regime, the combined influence from all other remote atmospheric drivers is similar (55% local vs. 45% remote) with, in particular, ~22% coming from waves generated remotely in the opposite hemisphere. We found a strong interconnection between the tropical and extratropical regions with around 30% of coastal extremes in the tropics originating at higher latitudes and vice-versa. This occurs mostly in the boreal summer through the increased seasonal activity of the southern jet-stream and the northern tropical cyclone basins. At interannual timescales, we evidenced alternatingly increased coastal wave extremes between the western and eastern Pacific that emerge from the distinct seasonal influence of ENSO in the Northern and SAM in the Southern Hemisphere on their respective paired wind-wave regimes. Together these results pave the way for a better understanding of the climate connection to wave extremes, which represents the preliminary step toward better regional projections and forecasts of coastal waves.

The seafloor—or bathymetry—of the world’s coastal waters remains largely unknown despite its primary importance to human activities and ecosystems. Here we present S2Shores (Satellite to Shores), the first sub-kilometer global atlas of coastal bathymetry based on depth inversion from wave kinematics captured by the Sentinel-2 constellation. The methodology reveals coastal seafloors up to a hundred meters in depth which allows covering most continental shelves and represents 4.9 million km2 along the world coastline. Although the vertical accuracy (RMSE 6–9 m) is currently coarser than that of traditional surveying techniques, S2Shores is of particular interest to countries that do not have the means to carry out in situ surveys and to unexplored regions such as polar areas. S2Shores is a major step forward in mitigating the effects of global changes on coastal communities and ecosystems by providing scientists, engineers, and policy makers with new science-based decision tools.

Coasts are areas of vitality because they host numerous activities worldwide. Despite their major importance, the knowledge of the main characteristics of the majority of coastal areas (e.g., coastal bathymetry) is still very limited. This is mainly due to the scarcity and lack of accurate measurements or observations, and the sparsity of coastal waters. Moreover, the high cost of performing observations with conventional methods does not allow expansion of the monitoring chain in different coastal areas. In this study, we suggest that the advent of remote sensing data (e.g., Sentinel 2A/B) and high performance computing could open a new perspective to overcome the lack of coastal observations. Indeed, previous research has shown that it is possible to derive large-scale coastal bathymetry from S-2 images. The large S-2 coverage, however, leads to a high computational cost when post-processing the images. Thus, we develop a methodology implemented on a High-Performance cluster (HPC) to derive the bathymetry from S-2 over the globe. In this paper, we describe the conceptualization and implementation of this methodology. Moreover, we will give a general overview of the generated bathymetry map for NA compared with the reference GEBCO global bathymetric product. Finally, we will highlight some hotspots by looking closely to their outputs.

The present study focuses on the long-term multi-year evolution of the shoreline position of the Nha Trang sandy beach. To this end an empirical model which is a combination of longshore and cross-shore models, is used. The Nha Trang beach morphology is driven by a tropical wave climate dominated by seasonal variations and winter monsoon intra-seasonal pulses. The combined model accounts for seasonal shoreline evolution, which is primarily attributed to cross-shore dynamics but fails to represent accretion that occurs during the height of summer under low energy conditions. The reason is in the single equilibrium Dean number Ωeq of the ShoreFor model, one of the components of the combined model. This equilibrium Dean number cannot simultaneously account for the evolution of strong intra-seasonal events (i.e., winter monsoon pulses) and the annual recovery mechanisms associated with swash transport. By assigning a constant value to Ωeq, when the surf similarity parameter is higher than 3.3 (occurrence of small surging breakers in summer), we strongly improve the shoreline position prediction. This clearly points to the relevance of a multi-scale approach, although our modified Ωeq retains the advantage of simplicity.

Coastal zones are highly dynamical systems affected by a variety of natural and anthropogenic forcing factors that include sea level rise, extreme events, local oceanic and atmospheric processes, ground subsidence, etc. However, so far, they remain poorly monitored on a global scale. To better understand changes affecting world coastal zones and to provide crucial information to decision-makers involved in adaptation to and mitigation of environmental risks, coastal observations of various types need to be collected and analyzed. In this white paper, we first discuss the main forcing agents acting on coastal regions (e.g., sea level, winds, waves and currents, river runoff, sediment supply and transport, vertical land motions, land use) and the induced coastal response (e.g., shoreline position, estuaries morphology, land topography at the land–sea interface and coastal bathymetry). We identify a number of space-based observational needs that have to be addressed in the near future to understand coastal zone evolution. Among these, improved monitoring of coastal sea level by satellite altimetry techniques is recognized as high priority. Classical altimeter data in the coastal zone are adversely affected by land contamination with degraded range and geophysical corrections. However, recent progress in coastal altimetry data processing and multi-sensor data synergy, offers new perspective to measure sea level change very close to the coast. This issue is discussed in much detail in this paper, including the development of a global coastal sea-level and sea state climate record with mission consistent coastal processing and products dedicated to coastal regimes. Finally, we present a new promising technology based on the use of Signals of Opportunity (SoOp), i.e., communication satellite transmissions that are reutilized as illumination sources in a bistatic radar configuration, for measuring coastal sea level. Since SoOp technology requires only receiver technology to be placed in orbit, small satellite platforms could be used, enabling a constellation to achieve high spatio-temporal resolutions of sea level in coastal zones.

In this paper, a new approach to model wave-driven, cross-shore shoreline change incorporating multiple timescales is introduced. As a base, we use the equilibrium shoreline prediction model ShoreFor that accounts for a single timescale only. High-resolution shoreline data collected at three distinctly different study sites is used to train the new data-driven model. In addition to the direct forcing approach used in most models, here two additional terms are introduced: a time-upscaling and a time-downscaling term. The upscaling term accounts for the persistent effect of short-term events, such as storms, on the shoreline position. The downscaling term accounts for the effect of long-term shoreline modulations, caused by, for example, climate variability, on shorter event impacts. The multi-timescale model shows improvement compared to the original ShoreFor model (a normalized mean square error improvement during validation of 18 to 59%) at the three contrasted sandy beaches. Moreover, it gains insight in the various timescales (storms to inter-annual) and reveals their interactions that cause shoreline change. We find that extreme forcing events have a persistent shoreline impact and cause 57–73% of the shoreline variability at the three sites. Moreover, long-term shoreline trends affect short-term forcing event impacts and determine 20–27% of the shoreline variability.

A Correction to this paper has been published: https://doi.org/10.1038/s41597-021-00874-2.

Sea state is a key variable in ocean and coastal dynamics. The sea state is either sparsely measured by wave buoys and satellites or modelled over large scales. Only a few attempts have been devoted to sea state measurements covering a large domain; in particular its estimation from optical images. With optical technologies becoming omnipresent, optical images offer incomparable spatial resolution from diverse sensors such as shore-based cameras, airborne drones (unmanned aerial vehicles/UAVs), or satellites. Here, we present a standalone methodology to derive the water surface elevation anomaly induced by wind-generated ocean waves from optical imagery. The methodology was tested on drone and satellite images and compared against ground truth. The results show a clear dependence on the relative azimuth view angle in relation to the wave crest. A simple correction is proposed to overcome this bias. Overall, the presented methodology offers a practical way of estimating ocean waves for a wide range of applications.

In this Special Issue “Application of Remote Sensing Methods to Monitor Coastal Zones” nine original research papers were published, with topics covering a wide range of ranging of remote sensing applications including coastal topography, bathymetry, land cover, and nearshore hydrodynamics [...]

A field experiment was conducted at a tropical microtidal intermediate sandy beach with a low tide terrace (Nha Trang, Vietnam) to investigate the short-term swash-zone hydrodynamics and morphodynamics under variable wave conditions. Continuous 2D Lidar scanner observations of wave height at the lower foreshore, subsequent run-up and swash-induced topographic changes were obtained. These data were complemented by detailed real-time kinematic GPS topographic surveys. Variable wave and tide conditions were experienced during the field experiment with relatively large swell waves (offshore significant wave height, Hs = 0.9 m to 1.3 m; peak wave period, Tp = 8 to 12 s) concomitant with spring tides at the beginning of the period, followed by mild wind waves (offshore Hs under 0.5 m and Tp 5 s) and neap tides. This resulted in the following morphological sequence: berm erosion followed by rapid neap berm reformation and beach recovery within a few days. New insights into the link between intra-tidal swash dynamics and daily beach profile evolution were found using the Lidar dataset. While waves directly cause morphology changes on a wave-by-wave basis, tidal levels were found to be a key factor in determining the morphological wave-effect (accretive or erosive) due to modulated interaction between surf and swash hydro-morphodynamics.

Video camera systems have been used over nearly three decades to monitor coastal dynamics. They facilitate a high-frequency analysis of spatiotemporal shoreline mobility. Video camera usage to measure beach intertidal profile evolution has not been standardized globally and the capacity to obtain accurate results requires authentication using various techniques. Applications are mostly site specific due to differences in installation. The present study examines the accuracy of intertidal topographic data derived from a video camera system compared to data acquired with unmanned aerial vehicle (UAV, or drone) surveys of a reflective beach. Using one year of 15-min video data and one year of monthly UAV observations, the intertidal profile shows a good agreement. Underestimations of intertidal profile elevations by the camera-based method are possibly linked to the camera view angle, rectification and gaps in data. The resolution of the video-derived intertidal topographic profiles confirmed, however, the suitability of the method in providing beach mobility surveys matching those required for a quantitative analysis of nearshore changes. Beach slopes were found to vary between 0.1 and 0.7, with a steep slope in May to July 2018 and a gentle slope in December 2018. Large but short-scale beach variations occurred between August 2018 and October 2018 and corresponded to relatively high wave events. In one year, this dynamic beach lost 7 m. At this rate, and as also observed at other beaches nearby, important coastal facilities and infrastructure will be prone to erosion. The data suggest that a low-cost shore-based camera, particularly when used in a network along the coast, can produce profile data for effective coastal management in West Africa and elsewhere.

With high anthropogenic pressure and the effects of climate change (e.g., sea level rise) on coastal regions, there is a greater need for accurate and up-to-date information about the topography of these systems. Reliable topography and bathymetry information are fundamental parameters for modelling the morpho-hydrodynamics of coastal areas, for flood forecasting, and for coastal management. Traditional methods such as ground, ship-borne, and airborne surveys suffer from limited spatial coverage and temporal sampling due to logistical constraints and high costs which limit their ability to provide the needed information. The recent advancements of spaceborne remote sensing techniques, along with their ability to acquire data over large spatial areas and to provide high frequency temporal monitoring, has made them very attractive for topography and bathymetry mapping. In this review, we present an overview of the current state of spaceborne-based remote sensing techniques used to estimate the topography and bathymetry of beaches, intertidal, and nearshore areas. We also provide some insights about the potential of these techniques when using data provided by new and future satellite missions.

Climatological changes occur globally but have local impacts. Increased storminess, sea level rise and more powerful waves are expected to batter the coastal zone more often and more intense. To understand climate change impacts, regional bathymetry information is paramount. A major issue is that the bathymetries are often non-existent or if they do exist, outdated. This sparsity can be overcome by space-borne satellite techniques to derive bathymetry. Sentinel-2 optical imagery is collected continuously and has a revisit-time around a few days depending on the orbital-position around the world. In this work, Sentinel-2 imagery derived wave patterns are extracted using a localized radon transform. A discrete fast-Fourier (DFT) procedure per direction in Radon space (sinogram) is then applied to derive wave spectra. Sentinel-2 time-lag between detector bands is employed to compute the spectral wave-phase shift and depth using the gravity wave linear dispersion. With this novel technique, regional bathymetries are derived at the test-site of Capbreton, France with an root mean squared (RMS)-error of 2.58 m and a correlation coefficient of 0.82 when compared to the survey for depths until 30 m. With the proposed method, the 10 m Sentinel-2 resolution is sufficient to adequately estimate bathymetries for a wave period of 6.5 s or greater. For shorter periods, the pixel resolution does not allow to detect a stable celerity. In addition to the wave-signature enhancement, the capability of the Radon Transform to augment Sentinel-2 20 m resolution imagery to 10 m is demonstrated, increasing the number of suitable bands for the depth inversion.

Although coastal morphology results essentially from underwater sediment transports, the evolution of underwater beach profiles along the diverse coastlines of the world is still poorly documented. Bathymetry inversion from shore-based video cameras set forth a more systematic evaluation and is becoming more commonly used. However, there are limitations to this profiling method that are insufficiently assessed, undermining confidence in operational applications. In this paper, we investigate the daily evolution of a low tide terrace (LTT) in Nha Trang beach, Vietnam, under strong seasonal forcing: from weak wind waves during summer monsoon to moderate waves during winter monsoon. A new error estimation for depth inversion is presented based on tidal evaluation. The method compares video-based estimate and direct measurement of tidal amplitudes to provide a quality criterion. It reveals three types of errors, the main one being a deep water error associated with physical limits—loss of celerity-bathymetry relationship in deep water. This error is dependent on wave period and thus has a strong seasonal pattern in Vietnam. It is generally detrimental to depth inversion where wind waves are dominant (in summer here). On the contrary, the second error type is larger for larger waves and is located at breakpoint, altering wave detection. The last error type is due to nonlinear effects and wave setup in shallow water. After removing the faulty data, we finally present the first reliable three-year time-series of a beach profile in Nha Trang, Vietnam. A main result is the overall stability demonstrated for the LTT beach, with rapid exchange of sediment between the terrace and the upper beach during typhoons, monsoon events or seasonal cycles. These tropical environments may provide faster beach recovery compared with mid-latitude configurations.

High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection and Ranging System (LiDAR). The derived 2-m Pleiades DEM shows an overall good agreement with concurrent methods (RTK-GPS and LiDAR; correlation coefficient of 0.9), with a vertical Root Mean Squared Error (RMS error) that ranges from 0.35 to 0.48 m, after absolute coregistration to the LiDAR dataset. The largest errors (RMS error > 0.5 m) occurred in the steep dune faces, particularly at shadowed areas. This work shows that DEMs derived from sub-meter satellite imagery capture local morphological features (e.g., berm or dune shape) on a sandy beach, over a large spatial domain.

Coastal evolution occurs on a wide range of time-scales, from storms, seasonal and inter-annual time-scales to longer-term adaptation to changing environmental conditions. Measuring campaigns typically either measure morphological evolution on a short-time scale (days) with high frequency (hourly) or long-time scales (years) but intermittently (monthly). This leaves an important observational gap that limits morphological variability assessments. Traditional echo sounding measurements on this long time-scale and high-frequency sampling require a significant financial injection. Shore-based video systems with high spatiotemporal resolution can bridge this gap. For the first time, hourly Kalman filtered video-derived bathymetries covering 1.5 years of morphological evolution with an hourly resolution obtained at Porhtowan, UK are presented. Here, the long-term hourly dataset is used and aims to show its added value for, and provide an in-depth, morphological analyses with unprecedented temporal resolution. The time-frame includes calm and extreme (storm) wave conditions in a macro-tidal environment. The video-derived bathymetries allow hourly beach state classification while before this was not possible due to the dependence on foam patterns of wave breaking (e.g., saturation during storms). The study period covers extreme storm erosion during the most energetic winter season in 60 years (2013–2014). Recovery of the beach takes place on several time-scales: (1) an immediate initial recovery after the storm season (first 2 months), (2) limited recovery during low energetic summer conditions and (3) accelerated recovery as the wave conditions picked up in the subsequent fall—under wave conditions that are typically erosive. The video-derived bathymetries are shown to be effective in determining bar-positions, outer-bar three-dimensionality and volume analyses with an unprecedented hourly temporal resolution.

This work analyzes the components of the total water level (TWL) that cause flooding in a tropical coastal area (Nha Trang beach, Southeast of Vietnam), and examines their link with global mean sea level rise (GMSLR). Interactions between the wave induced run-up (R) and astronomical tide (AT) were responsible for 43% of the 35 flooding events identified between 1993 and 2015. Most of these events (97%) took place during the winter monsoon season, when long-lasting extreme R and positive non-tidal residual (NTR) are likely to occur. Removal of the GMSLR trend from the NTR was found to affect the flood occurrence of 17% of these events, while the trend in wave height did not have any detectable impact. Our research highlights the direct connection between global climate changes and coastal flooding events.

Une série d'images satellitaires Spot couvrant la période 1997-2009 (base Kalideos Littoral) est exploitée dans l'optique de caractériser l'évolution interannuelle du littoral sableux au sud du Bassin d'Arcachon. Un algorithme semi-analytique reposant sur les propriétés optiques des eaux et des petits fonds a été calibré et validé par des données in situ et appliqué à la série d'images pour produire des spatiocartes bathymétriques. Les observations indiquent une très forte connectivité entre les grands bancs de l'embouchure (Banc d'Arguin et Banc du Pineau) et les barres sous-marines des plages de par la relation « amont-aval » induite par la dérive littorale. Une séquence de 5 ans (1999-2004) a été constatée débutant par la dislocation du Banc d'Arguin, la migration d'énormes bancs de sable à travers la Passe Sud, conduisant à une alimentation massive du Banc du Pineau et des structures sous-marines des plages. En outre, la fréquence et l'incidence des houles de tempête contrôlent l'intensité et la direction des déplacements des barres sous-marines (migration cross-shore/longshore). Sur la période récente, cette mobilité se traduit notamment par la progression vers le sud de structures sableuses solidaires de la barre externe mais de plus faible profondeur, se déplaçant face à la plage de Biscarrosse actuellement suivie par des caméras vidéo.