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.@en

The aim of this study is to innovate wave-based depth-inversion towards smarter and faster algorithms to be used with various remote sensing instruments for broad community use. Wave-based depth inversion describes a branch of coastal remote sensing, which uses video recordings of a wave-field to derive depths and thereby create digital maps of coastal bathymetries. The technique utilizes the fact that waves react to the underlying bathymetry by changing their length and celerity, respectively getting shorter and slower as the water depth gets shallower. Waves may also change their direction due to refraction. Depth inversion techniques using surface wave patterns can handle clear and turbid waters and thereby a variety of global coastal environments. The idea to use observed wave characteristics as a proxy for the underlying bathymetry already came up during the time of the second world war, when the aim was to acquire bathymetry information for military landing operations. Starting around the 1980’s, the idea received more attention among the coastal engineering community, as increased computational power enabled easier analysis of wave-field recordings through spectral decompositions. Since then, different depth inversion algorithms (DIA) have been developed in pursuit of getting increasingly accurate bathymetry maps. Besides estimating depths, some DIAs also incorporate functionalities to map wave propagation directions and wave celerity, and even near-surface currents from wave-field video. While the video recording instruments resemble the hardware, DIAs resemble the software needed for wavebased coastal remote sensing (WCRS). Yet, WCRS is a specialistic branch within the coastal engineering and -user community. The technique typically requires a certain amount of user-expertise and it has mostly been applied in research settings. While data can be retrieved on kilometre scale with XBand-radars and cameras, it was historically difficult to scale up WCRS to entire coasts, which was a reason to discontinue its application in the Netherlands. Besides land-based instruments (i.e., XBand-radars, fixed camera stations) in the meantime also airborne UAVs, and space-borne satellites can be used to record a wave field, making WCRS more flexible and scalable. These recording instruments have also become more accessible. Moreover, DIAs – the software required to analyse the wave recordings – can be used interchangeably on data of these different instruments. This means that WCRS becomes potentially attractive to a broad user-community of coastal managers, the industry and the coast guard. However, DIAs still restrict broad usage of WCRS: while an important step has been taken in the open accessibility of DIAs, much is still to be gained in their handling and computational speed. This study aims to improve upon that, by building towards operational, self-adaptive and intelligent algorithms, which can provide maps of depth, near-surface currents and wave hydrodynamics on-the-fly. For this purpose, video data from a variety of instruments (fixed camera station, UAV, XBandradar, satellite) on different spatial scales 𝑂(100 m2,1 km2,10 km2,100 km2) and field-sites around the world (Netherlands, UK, USA, Australia, France) are analysed. Combining rapid processing capabilities with a broad applicability this study forms a stepping stone for a potentially broad WCRS user community. The analyses are presented going from land-based to air-borne to space-borne WCRS. This is done in three stages from (1) applying an operational DIA on XBand radar data, to (2) applying an on-the-fly DIA on camera and UAV data, to finally (3) applying a DIA on temporally sparse satellite data. First, a DIA named XMFit (X-Band Matlab Fitting) is introduced, which is robust, accurate and fast enough for operational use. This is achieved through an iterative procedure that selects the best result among a series of depth and near-surface current estimates. For this study, video data from XBand-radars are analysed. Focusing on depth estimates, XMFit is validated for two case studies in the Netherlands: (1) the “Sand Engine”, a beach mega nourishment at a uniform open coast, and (2) the tidal inlet of the Dutch Wadden Sea island Ameland, characterizing a more complex coast. Considering both sites, the algorithm performance is characterized by a spatially averaged depth bias of −0.9 m at the Sand Engine (corresponding to an 18 h snapshot of the field site) and a time-varying bias of approximately −2–0 m at the Ameland Inlet (corresponding to a one-year time evolution with varying hydrodynamic conditions). When compared to in-situ depth surveys the accuracy is lower, but the time resolution higher. Dutch in-situ surveys typically occur annually, while depth estimates from the Ameland tidal inlet are produced every 50 min by an operational system using a navigational X-Band radar. It enables to monitor the placement of a 5 Mm3 ebb-tidal delta nourishment – a pilot measure for coastal management. Volumetric changes in the nourishment area over the year 2018, occurring at 7 km distance from the radar, are estimated with an error of 7 %. Depth errors statistically correlate with the direction and magnitude of simultaneous near-surface current estimates. Additional experiments on Sand Engine data demonstrate that depth errors may be significantly reduced using an alternative spectral approach and/or by using a Kalman filter. Having demonstrated the potential of DIAs for operational application, the next step is to design an algorithm that can self-adapt to video from any field-site and can process it on-the-fly. To do so, a DIA is designed whose code architecture for the first time includes the Dynamic Mode Decomposition (DMD) to reduce the data complexity of wavefield video. The DMD is paired with loss-functions to handle spectral noise, and a novel spectral storage system and Kalman filter to achieve fast converging measurements. The algorithm is showcased for videos from ARGUS stations and drones recorded at fieldsites in the USA, UK, Netherlands, and Australia. The performance with respect to mapping bathymetry is validated using ground truth data. It is demonstrated that merely 32 s of video footage is needed for a first mapping update with average depth errors of 0.9–2.6 m. These further reduce to 0.5–1.4 m as the videos continue and more mapping updates are returned. Simultaneously, coherent maps for wave direction and -celerity are achieved as well as maps of local near-surface currents. The algorithm is capable of mapping the coastal parameters on-the-fly and thereby offers analysis of video feeds, such as from drones or operational camera installations. Hence, the innovative application of analysis techniques like the DMD enables both accurate and unprecedentedly fast coastal reconnaissance. With a skilled, intelligent DIA at hand, the question remains whether it can also be used on satellite imagery, as that would further broaden the application range. DIAs commonly analyse video from shore-based camera stations, UAVs or XBandradars with durations of minutes and at framerates of 1–2 fps to find relevant wave frequencies. However, these requirements are typically not met by raw, temporally sparse satellite imagery. To overcome this problem a preprocessing step is utilized. Here, 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, is augmented 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 resulting video is subsequently processed with the self-adaptive DIA. 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 broad usage of WCRS for mapping coastal bathymetry data around the globe. By improving DIAs and their application to different instruments, this study has helped to increase the technological readiness of WCRS and its potential to be adopted by end-users. It was shown that WCRS can be performed on wave field records of land-based, airborne and space-born instruments and therewith on scales ranging from 𝑂(100 m2)(fixed camera) to 𝑂(100 km2)(X-band radar,satellite). The cost of WCRS is minor, as existing navigational X-band radars can be used, affordable UAVs and cameras, and accessible satellite data. X-band radars can operationally monitor complex coastal environments and recognize morphological trends, UAVs and cameras can be used for fast lean-and-mean mapping of coastal bathymetry, and by estimating depths from satellite imagery valuable data can be collected in otherwise data-poor environments. Yet, further steps should be taken in the accessibility, multifunctionality, quality, robustness and user-friendliness of WCRS. The key takeaway for effective WCRS monitoring is that future developments should strive towards integrated, self-adaptive software, which gives prompt visual response and requires little user-expertise. These measures reduce the difficulty to learn WCRS, increase its compatibility with data from different instruments (Xband-radars, cameras, UAVs, satellites) and thereby enable relatively easy coastal measurements. As a consequence WCRS becomes more adoptable by the coastal remote sensing community. With the exponential growth of data volumes worldwide, future data clouds may facilitate storage and offer future perspectives for online integration of data with numerical models and modern data science techniques like neural networks. This may create new possibilities for understanding system dynamics and thereby further aid decision makers in coastal management, the industry and the coast guard.@en

Mapping coastal bathymetry from remote sensing becomes increasingly more attractive for the coastal community. It is facilitated by a rising availability of drone and satellite data, advances in data science, and an open-source mindset. Coastal bathymetry, but also wave directions, celerity and near-surface currents can simultaneously be derived from aerial video of a wave field. However, the required video processing is usually extensive, requires skilled supervision, and is tailored to a fieldsite. This study proposes a video-processing algorithm that resolves these issues. It automatically adapts to the video data and continuously returns mapping updates and thereby aims to make wave-based remote sensing more inclusive to the coastal community. The code architecture for the first time includes the dynamic mode decomposition (DMD) to reduce the data complexity of wavefield video. The DMD is paired with loss-functions to handle spectral noise and a novel spectral storage system and Kalman filter to achieve fast converging measurements. The algorithm is showcased for fieldsites in the USA, the UK, the Netherlands, and Australia. The performance with respect to mapping bathymetry was validated using ground truth data. It was demonstrated that merely 32 s of video footage is needed for a first mapping update with average depth errors of 0.9–2.6 m. These further reduced to 0.5–1.4 m as the videos continued and more mapping updates were returned. Simultaneously, coherent maps for wave direction and celerity were achieved as well as maps of local near-surface currents. The algorithm is capable of mapping the coastal parameters on-the-fly and thereby offers analysis of video feeds, such as from drones or operational camera installations. Hence, the innovative application of analysis techniques like the DMD enables both accurate and unprecedentedly fast coastal reconnaissance. The source code and data of this article are openly available.@en

Coastal management in the Netherlands has the aim to defend coastal zones by preventing flooding and mitigating erosion. To that end, large-scale nourishments are placed in the nearshore, which are supposed to dynamically preserve the coastal zone over a timescale of years. To assess their effectiveness, these nourishments are monitored over large areas and long durations. As repetitive, in-situ measurements become too expensive, remote sensing offers an attractive alternative, mapping depth and near-surface current fields via depth inversion algorithms (DIA). However, the information that can be derived from remotely-sensed data is subject to improvement. In this study a 3D-FFT based DIA named XMFit (X-Band Matlab Fitting) is introduced, which is robust, accurate and fast enough for operational use. Focusing on depth estimates, the algorithm was validated for two case studies in the Netherlands: (1) the “Sand Engine”, a beach mega nourishment at a uniform open coast, and (2) the tidal inlet of the Dutch Wadden Sea island Ameland, characterizing a more complex coast. Considering both sites, the algorithm performance was characterized by a spatially averaged depth bias of −0.9 m at the Sand Engine and a time-varying bias of approximately -2 – 0 m at the Ameland Inlet. When compared to in-situ depth surveys the accuracy was lower, but the time resolution higher. Depth estimates from the Ameland tidal inlet were produced every 50 min by an operational system using a navigational X-Band radar to monitor the placement of a 5 million m³ ebb-tidal delta nourishment – a pilot measure for coastal management. Volumetric changes in the nourishment area over the year 2018, occurring at 7 km distance from the radar, were estimated with an error of 7%. Depth errors statistically correlated with the direction and magnitude of simultaneous near-surface current estimates. Additional experiments on Sand Engine data demonstrated that depth errors may be significantly reduced using an alternative spectral approach and/or by using a Kalman filter.

@en

Monitoring of beach and nearshore environments is essential for obtaining better insights into the functioning of the coastal zone. It has driven the understanding of these environments and worked beneficially alongside modelling studies. Hydrodynamics, water quality, and sedimentological and morphological processes can be observed and quantified through field measurements. A successful monitoring programme has a well-considered design, reflecting the interests of all parties involved and balancing scientific requirements (such as measuring scales and resolutions in time and space) against available budgets and resources. The key to utilizing the monitoring result is a data management system that accommodates the FAIR principles – Findable, Accessible, Interoperable and Reusable – for data handling. For the future of coastal monitoring we foresee that recent technological developments will help define the way; particularly miniaturized sensors, data transmission advances, and remote sensing techniques. These developments, especially if embedded in high-profile, open-access coastal observatories, can pave the way towards now-casting of coastal systems.@en

A large-scale field campaign was carried out on the ebb-tidal delta (ETD) of Ameland Inlet, a basin of the Wadden Sea in the Netherlands, as well as on three transects along the Dutch lower shoreface. The data have been obtained over the years 2017-2018. The most intensive campaign at the ETD of Ameland Inlet was in September 2017. With this campaign, as part of KustGenese2.0 (Coastal Genesis 2.0) and SEAWAD, we aim to gain new knowledge on the processes driving sediment transport and benthic species distribution in such a dynamic environment. These new insights will ultimately help the development of optimal strategies to nourish the Dutch coastal zone in order to prevent coastal erosion and keep up with sea level rise. The dataset obtained from the field campaign consists of (i) single-and multi-beam bathymetry; (ii) pressure, water velocity, wave statistics, turbidity, conductivity, temperature, and bedform morphology on the shoal; (iii) pressure and velocity at six back-barrier locations; (iv) bed composition and macrobenthic species from box cores and vibrocores; (v) discharge measurements through the inlet; (vi) depth and velocity from X-band radar; and (vii) meteorological data. The combination of all these measurements at the same time makes this dataset unique and enables us to investigate the interactions between sediment transport, hydrodynamics, morphology and the benthic ecosystem in more detail. The data provide opportunities to calibrate numerical models to a high level of detail. Furthermore, the open-source datasets can be used for system comparison studies. The data are publicly available at 4TU Centre for Research Data at https://doi.org/10.4121/collection:seawad (Delft University of Technology et al., 2019) and https://doi.org/10.4121/collection:kustgenese2 (Rijkswaterstaat and Deltares, 2019). The datasets are published in netCDF format and follow conventions for CF (Climate and Forecast) metadata. The http://data.4tu.nl (last access: 11 November 2020) site provides keyword searching options and maps with the geographical position of the data.

@en