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In this paper, coastal dune data are collected at Truc Vert, SW France, using photogrammetry via Unmanned Aerial Vehicles (UAVs). A low-cost GoPro-equipped DJI Phantom 2 quadcopter and a 20 MPix camera-equipped DJI Phantom 4 Pro quadcopter UAVs were used to remotely sense the coastal dune morphology over large spatial scales (4 km alongshore, i.e., approximately 1 km2 of beach-dune system), within a short time (less than 2 h of flight). The primary objective of this paper is to propose a low-cost and replicable approach which, combined with simple and efficient permanent Ground Control Point (GCP) set-up, can be applied to routinely survey upper beach and coastal dune morphological changes at high frequency (after each storm) and high resolution (0.1 m). Results show that a high-resolution and accurate Digital Surface Model (DSM) can be inferred with both UAVs if enough permanent GCPs are implemented. The more recent DJI Phantom 4 gives substantially more accurate DSM with a root-mean-square vertical error and bias of 0.05 m and −0.03 m, respectively, while the DSM inferred from the DJI Phantom 2 still largely meets the standard for coastal monitoring. The automatic flight plan procedure allows replicable surveys to address large-scale morphological evolution at high temporal resolution (e.g., weeks, months), providing unprecedented insight into the coastal dune evolution driven by marine and aeolian processes. The detailed morphological evolution of a 4-km section of beach-dune system is analyzed over a 6-month winter period, showing highly alongshore variable beach and incipient foredune wave-driven erosion, together with wind-driven inland migration of the established foredune by a few meters, and alongshore-variable sand deposition on the grey dune. In a context of widespread erosion, this photogrammetry approach via low-cost flexible and lightweight UAVs is well adapted for coastal research groups and coastal dune management stakeholders, including in developing countries where data are lacking.

In the context of sea levels rising, monitoring spatial and temporal topographic changes along coastal dunes is crucial to understand their dynamics since they represent natural barriers against coastal flooding and large sources of sediment that can mitigate coastal erosion. Different technologies are currently used to monitor coastal dune topographic changes (GNSS, UAV, airborne LiDAR, etc.). Satellites recently emerged as a new source of topographic data by providing high-resolution images with a rather short revisit time at the global scale. Stereoscopic or tri-stereoscopic acquisition of some of these images enables the creation of 3D models using stereophotogrammetry methods. Here, the Ames Stereo Pipeline was used to produce digital elevation models (DEMs) from tri-stereo panchromatic and high-resolution Pleiades images along three 19 km long stretches of coastal dunes in SW France. The vertical errors of the Pleiades-derived DEMs were assessed by comparing them with DEMs produced from airborne LiDAR data collected a few months apart from the Pleiades images in 2017 and 2021 at the same three study sites. Results showed that the Pleiades-derived DEMs could reproduce the overall dune topography well, with averaged root mean square errors that ranged from 0.5 to 1.1 m for the six sets of tri-stereo images. The differences between DEMs also showed that Pleiades images can be used to monitor multi-annual coastal dune morphological changes. Strong erosion and accretion patterns over spatial scales ranging from hundreds of meters (e.g., blowouts) to tens of kilometers (e.g., dune retreat) were captured well, and allowed to quantify changes with reasonable errors (30%). Furthermore, relatively small averaged root mean square errors (0.63 m) can be obtained with a limited number of field-collected elevation points (five ground control points) to perform a simple vertical correction on the generated Pleiades DEMs. Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography.

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.

Fixed video camera systems are consistently prone to importune motions over time due to either thermal effects or mechanical factors. Even subtle displacements are mostly overlooked or ignored, although they can lead to large geo-rectification errors. This paper describes a simple and efficient method to stabilize an either continuous or sub-sampled image sequence based on feature matching and sub-pixel cross-correlation techniques. The method requires the presence and identification of different land-sub-image regions containing static recognizable features, such as corners or salient points, referred to as keypoints. A Canny edge detector ( C E D ) is used to locate and extract the boundaries of the features. Keypoints are matched against themselves after computing their two-dimensional displacement with respect to a reference frame. Pairs of keypoints are subsequently used as control points to fit a geometric transformation in order to align the whole frame with the reference image. The stabilization method is applied to five years of daily images collected from a three-camera permanent video system located at Anglet Beach in southwestern France. Azimuth, tilt, and roll deviations are computed for each camera. The three cameras showed motions on a wide range of time scales, with a prominent annual signal in azimuth and tilt deviation. Camera movement amplitude reached up to 10 pixels in azimuth, 30 pixels in tilt, and 0.4° in roll, together with a quasi-steady counter-clockwise trend over the five-year time series. Moreover, camera viewing angle deviations were found to induce large rectification errors of up to 400 m at a distance of 2.5 km from the camera. The mean shoreline apparent position was also affected by an approximately 10–20 m bias during the 2013/2014 outstanding winter period. The stabilization semi-automatic method successfully corrects camera geometry for fixed video monitoring systems and is able to process at least 90% of the frames without user assistance. The use of the C E D greatly improves the performance of the cross-correlation algorithm by making it more robust against contrast and brightness variations between frames. The method appears as a promising tool for other coastal imaging applications such as removal of undesired high-frequency movements of cameras equipped in unmanned aerial vehicles (UAVs).

Although geologically constrained sandy beaches are ubiquitous along wave-exposed coasts, there is still a limited understanding of their morphological response, particularly under storm conditions, which is mainly due to a critical lack of nearshore bathymetry observations. This paper examines the potential to derive bathymetries from video imagery under challenging wave conditions in order to investigate headland control on morphological beach response. For this purpose, a video-based linear depth inversion algorithm is applied to three consecutive weeks of frames collected during daylight hours from a single fixed camera located at La Petite Chambre d’Amour beach (Anglet, SW France). Video-derived bathymetries are compared against in situ topo-bathymetric surveys carried out at the beginning and end of the field experiment in order to assess the performance of the bathymetric estimates. The results show that the rates of accretion/erosion within the surf zone are strongly influenced by the headland, whereas the beach morphological response can be classified into three main regimes depending on the angle of wave incidence θp: (1) under deflection configuration (θp>0°), the alongshore sediment transport was trapped at the updrift side of the headland, promoting sand accretion. (2) Under shadowed configuration (θp<0°), the interruption of the longshore current drove a deficit of sand supply at the downdrift side of the headland, leading to an overall erosion in the surf zone. (3) Under shore-normal configuration (θp=0°), rip channels developed, and up-state beach transition was observed. A comparison between video-derived bathymetries and surveys shows an overall root mean square error (RMSE) around 0.49 to 0.57 m with a bias ranging between −0.36 and −0.29 m. The results show that video-derived bathymetries can provide new insight into the morphological change driven by storm events. The combination of such inferred bathymetry with video-derived surface current data is discussed, showing great potential to address the coupled morphodynamics system under time-varying wave conditions.

Radar altimetry provides unique information on water stages of inland hydro-systems. In this study, the performance of seven altimetry missions, among the most commonly used in land hydrology (i.e., European Remote-Sensing Satellite-2 (ERS-2), ENVIronment SATellite (ENVISAT), Satellite with Argos and ALtika (SARAL), Jason-1, Jason-2, Jason-3 and Sentinel-3A), are assessed using records from a dense in situ network composed of 19 gauge stations in the Inner Niger Delta (IND) from 1995 to 2017. Results show an overall very good agreement between altimetry-based and in situ water levels with correlation coefficient (R) greater than 0.8 in 80% of the cases and Root Mean Square Error (RMSE) lower than 0.4 m in 48% of cases. Better agreement is found for the recently launched missions such as SARAL, Jason-3 and Sentinel-3A than for former missions, indicating the advance of the use of the Ka-band for SARAL and of the Synthetic-aperture Radar (SAR) mode for Sentinel-3A. Cross-correlation analysis performed between water levels from the same altimetry mission leads to time-lags between the upstream and the downstream part of the Inner Niger Delta of around two months that can be related to the time residence of water in the drainage area.

Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, waves, tide, and time (e.g., day hour, weekday). This approach is applied to data collected from Biscarrosse Beach during the summer of 2023, where beach attendance varied significantly (from 0 to 2031 individuals). Results indicate that the optimal XGBoost model achieved high predictive accuracy, with a coefficient of determination (R2) of 0.97 and an RMSE of 70.4 users, using daily mean weather data, tide and time as input variables, i.e., disregarding wave data. The model skilfully captures both day-to-day and hourly variability in attendance, with time of day (hour) and daily mean air temperature being the most influential variables. An XGBoost model using only daily mean temperature and hour of the day even shows good predictive accuracy (R2 = 0.90). The study emphasises the importance of daily mean weather data over instantaneous measurements, as beach users tend to plan visits based on forecasts. This model offers reliable, computationally inexpensive, and high-frequency (e.g., every 10 min) beach user predictions which, combined with existing surf-zone hazard forecast models, can be used to anticipate life risk at the beach.

Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management.

Monitoring changes in coastal sea levels is necessary given the impacts of climate change. Information on the sea level and its changes are important parameters in connection to climate change processes. In this study, radar altimetry data from successive satellite missions, European Remote Sensing-2 (ERS-2), Jason-1, Envisat, Jason-2, and Satellite with ARgos and ALtiKa (SARAL), were used to measure sea surface heights (SSH). Altimetry-derived SSH was validated for the southern Bay of Biscay, using records from seven tide gauges located along the French Atlantic coast. More detailed comparisons were performed at La Rochelle, as this was the only tide gauge whose records covered the entire observation period for the different radar altimetry missions. The results of the comparison between the altimetry-based and in-situ SSH, recorded from zero to five kilometers away from the coast, had root mean square errors (RMSE) ranging from 0.08 m to 0.21 m, 0.17 m to 0.34 m, 0.1 m to 0.29 m, 0.18 m to 0.9 m, and 0.22 m to 0.89 m for SARAL, Jason-2, Jason-1, ENVISAT, and ERS-2, respectively. Comparing the missions on the same orbit, ENVISAT had better results than ERS-2, which can be accounted for by the improvements in the sensor mode of operation, whereas the better results obtained using SARAL are related to the first-time use of the Ka-band for an altimetry sensor. For Jason-1 and Jason-2, improvements were found in the ocean retracking algorithm (MLE-4 against MLE-3), and also in the bi-frequency ionosphere and radiometer wet troposphere corrections. Close to the shore, the use of model-based ionosphere (GIM) and wet troposphere (ECMWF) corrections, as applied to land surfaces, reduced the error on the SSH estimates.

Headland and groyne sand bypassing greatly influences embayment dynamics at medium to long timescales, but is often disregarded or partially included in reduced-complexity shoreline models. This study explores how accounting for subaqueous sediment bypassing in a shoreline model affects mean embayed beach planshape and spatial variability. We implement a generic parametrization of sand bypassing in the LX-Shore model, with simulations on a synthetic embayment in two configurations: “full bypassing” (FB) where the sediments bypass the obstacle in the surfzone and beyond, and “shoreline bypassing” (SB) where bypassing occurs only when the shoreline extends beyond the obstacle. Time-invariant wave simulations show significant differences in updrift shoreline position between FB and SB. Simulations with time-varying wave angles and fixed wave height and period reveal that FB significantly impacts the embayment mean planform and spatial variability: FB reduces beach rotation by about 1/3, particularly under slightly oblique and slightly asymmetrical wave climates, and decreases shoreline curvature, especially under highly oblique wave climates. Downdrift shoreline erosion may be overestimated by up to 20% under SB. Our simulations provide new insight into the influence of subaqueous sand bypassing on embayed beach dynamics and emphasize the importance of including this process when modelling shoreline evolution in coastal embayments.