*1.2. Photogrammetric Workflow Optimisation*

A common limitation to achieving efficient surveys mentioned in previous work, and finding important echo in time-constrained (e.g., tidal) environments, is the need for a dense and well-measured network of GCPs for the accurate scaling and georeferencing of photogrammetric results (e.g., Ref. [43]). In the absence of other external controls, this requirement is particularly important, shown by observations of decreasing DEM quality with distance increases from the nearest GCP [44–46]. Relief can be procured through the installation of fixed (i.e., permanent) targets, although it is generally only partial and, in application to the coast, implies that fixed targets are limited to dry zones [37].

The uptake of high-precision RTK-GNSS technology for drone navigation can further improve survey efficiency. With RTK drones, image geolocations serve as airborne controls, which, despite a generally lower precision compared to GCPs and a repartition outside the measurement volume, can compensate for these caveats due to the comparatively very-large number of controls provided [47]. Previous research showed that results of a similar quality or even better can be achieved using a reduced number of GCPs when the camera positions are used as external controls [48]. Using the camera information as the only external control during photogrammetric processing forms the basis behind direct georeferencing (DG) approaches (e.g., Refs. [49–51]). The DG of photogrammetry is particularly advantageous when access to the field site and the measurement of GCPs are problematic. However, previous studies showed that DG generally comes at the cost of reduced measurement quality (e.g., lower accuracy and precision).

The commercialization of RTK drones at competitive prices (e.g., the Phantom 4 RTK quadcopter) has reinstated large interest in determining the capability of DG approaches across different settings, including building facades, urban and rural landscapes as well as river and coastal environments. Previous studies investigated the differences between drone positioning corrections provided either in real time (RTK) from a nearby base station managed by the field operators or through RTK networks, or post-processed (PPK) using observations at known base stations [52,53]. They also assessed the effect of the image viewing angle (nadir vs. oblique [54]), as well as changes in measurement quality with and without the provision of GCPs (e.g., Refs. [52–56]).

These studies concluded that, in the absence of GCPs, systematic error mainly through vertical bias can impact the measurement quality, which was related to the imprecise calibration of internal orientation parameters (IOP). Yet, contrasting results were obtained when it comes to the number of GCPs to introduce to correct this effect. Some studies reported that one GCP was enough, although the GCP position may be important, while other studies recommended using at least three GCPs. With the exception of the effort to survey 109 points with a theodolite on a building's facades by Taddia et al. [52], error evaluation is generally limited to a few check points (ChkPts), whose number and repartition may not be adequate for reliable error characterization. This, in turn, limits the generalization of the findings such that the measurement capabilities of RTK-assisted UAV photogrammetry over large and GCP-poor coastal tracts are not fully understood.

#### *1.3. Paper Overview*

In this study, we explored the feasibility of using an RTK quadcopter (Phantom 4 RTK) and SfM photogrammetry for fine-scale (e.g., submeter) monitoring of water-worked coastal topography. We carried out a variety of tests of interest for the application because of their influences on data collection time and measurement quality (e.g., resolution and precision). More specifically, we were interested in enhancing the survey efficiency to surpass tide-related constraints while ensuring the results' effectiveness through rigorous error characterization at two macrotidal coastal field sites with contrasting survey areas and morphologies. The DEM error evaluation at localized check points (ChkPts) was supplemented where possible by full DEM comparisons and the collection of over 2000 survey points with RTK-GNSS, whilst the availability of repeat surveys and invariant features enabled the assessment of the complete workflow replicability.

Our results show that at least five GCPs, in addition to camera information, were necessary to achieve the optimal model quality that minimized vertical bias and random errors. Under this configuration, the standard deviation of error representing measurement precision (~1 GSD) was approximately two times better than that when camera information was unused. First, the geomorphic analyses are presented, highlighting the potential of the method for submeter bedform characterization over entire beach systems.

#### **2. Materials and Methods**

#### *2.1. Field Sites*

The two coastal field sites presented in this study are located in Brittany in northwest France (Figure 1a,b). The field site at La Palue and Lostmarc'h beaches, hereafter generally referred to as "La Palue" for concision, is situated at the western end of Crozon Peninsula. Facing due west, La Palue and Lostmarc'h are adjacent beaches totaling ~2.5 km alongshore separated by a rocky promontory (Kerdra point) at high tide, backed by granitic cliffs (20–50 m high) at each end point and a mostly consolidated and vegetated dune elsewhere. It is a macrotidal environment with mean neap and spring tidal ranges of 2.25 and 5.60 m [57], respectively. Waves originate from Atlantic depressions generating SW through NW swells, but also shorter period seas from locally generated wind [58]. Based on samples collected on the intertidal beach and processed using traditional size-sieving, sediment belongs to fine to medium sand with a D50 (the median sediment size for which 50% of the sediment distribution is finer) of ~0.25 mm and a sorting coefficient (√D84/D16) of 1.35 (i.e., well-sorted). The upper beach abutting the dune is composed of cobbles, especially on the northern parts (Figure 2b). The morphodynamics are akin to intermediate beaches according to the Masselink and Short classification [59], covering a range of beach states depending on ongoing hydrodynamic conditions. Generally, a flatter profile is observed over winter, contrasting with marked bar and rip channel morphologies developing across calmer months. During spring tides, the intertidal beach is approximately 300–400 m wide (Figure 1c). Large patches of ripples and megaripples can often be observed. Bedrock may locally appear near the low-tide line, depending on sand levels. Outside a few of channelized access points and small blockhouses remnant of World War 2, human encroachments are limited. Man-made features, at the condition they are relatively flat, were useful for preparing fixed ground targets for photogrammetry. Depending on survey date, up to six red-painted crosses approximately 40 cm in size were used (Table 1).

**Table 1.** Overview of the field surveys and data collection for UAV–SfM photogrammetry. The tidal coefficient (20–120) represents the tide magnitude in relation to its mean.


**Figure 1.** Survey sites. (**a**) Map of France; (**b**) bathymetric DEM of western Brittany with green stars representing field sites at La Palue (left) and Sillon de Talbert (top right); (**c**,**d**) orthophotographs (source: Ortho Littorale V2, Ministère en charge de l'environnement, 2013) of La Palue and Sillon de Talbert, respectively, showing the (yellow enclosed) survey area and depth contours (source: Litto3D Finistère 2014—Shom). Black and pink lines represent MSL and MLWS/MHWS, respectively.

The second field site corresponds to the proximal section of the Sillon de Talbert gravel spit. This 3.5-km-long swash-aligned barrier is located north of Brittany. The sediment volume is estimated at 1.23 × 106 m3 [60]. According to the barrier morphology and sedimentology, the gravel accumulation can be classed as a "composite gravel beach" [61,62]. The beach face shows a break slope point at the mean water level, which delimitates the gravel barrier from an otherwise flat rocky platform (slope = 0.01%) covered by thin and patchy periglacial deposits and/or recent sandy sheets. The upper part of the beach face is characterized by steeper slopes of between 5% and 15%. Back-barrier areas include a large sand-flat and salt-marshes. The site is located in a macrotidal to megatidal setting (maximum tidal range of 10.95 m) [57]. Dominant swell comes from the WNW, which means that waves break with a slight angle according to the shoreline orientation, generating a longshore drift oriented to the NE. Until the end of the 17th century, the barrier was connected to the islets of the Olone archipelago located to the NE. The disconnection of

the barrier occurred in the early 18th century [63]. In the 1970s and 1980s, several coastal defenses were installed to prevent the retreat of the barrier, especially in the proximal section where a groin was installed. Annual topographic surveys realized with RTK-GNSS since 2002 revealed a strong erosion of the beach face downdrift of the groin [64]. The loss of sediment was estimated to ca. 11,000 m<sup>3</sup> between 2002 and 2017, which caused significant barrier lowering and narrowing locally. These morphological changes led to the opening of a breach at the beginning of March 2018, which has experienced a rapid enlargement (35 vs. 15 m) and deepening (3.4 vs. 1.25 m) over the following months [65]. For monitoring breach evolution, we have put in place trimestral UAV photogrammetry surveys.

**Figure 2.** Field surveys. Orthophotos showing camera locations (black dots), DRTK-2 drone mobile station (green square), ground photogrammetric targets (triangles; the five fixed targets present at La Palue during the September survey are shown in red), GNSS base station (yellow square) and geodetic marks (yellow star) at (**a**,**b**) La Palue, and (**c**) Sillon de Talbert field sites, respectively. RTK-GNSS survey points used for the vertical evaluation of photogrammetry at La Palue are shown in red. At Sillon de Talbert, the GNSS base station and DRTK-2 drone mobile station were installed one after the other using the same geodetic mark.

#### *2.2. Field Operation and Data Collection*

Five surveys altogether covering a seven-month period are considered in this study: one at Sillon de Talbert and four at La Palue (Table 1). For the latter, only the first survey (17 September 2020) is described and analyzed in detail, the other three (repeat) surveys being used for assessing measurements' replicability (cf. Section 2.6).

Data collection starts with centimetric-accuracy RTK-GNSS (Topcon Hyper V) measurements of geodetic marks (cast metal disks installed on stable ground) and photogrammetric targets (red plastic plates 30 cm in diameter fixed to the ground using tent poles) for data georeferencing and validation (cf. Figure 2). Between 20 and 30 photogrammetric targets, including fixed targets, were deployed to be used either as GCPs or ChkPts (cf. Tables 1 and 2). Measurements are carried out in rapid-static mode (10 s average) using GNSS rovers mounted on a 2-m pole equipped with a bubble level. To provide RTK corrections, a GNSS base station was materialized at both sites using a geodetic mark. Fixed coordinates were obtained using long-static averaging with a tripod (~240 min), over several periods of time, and postprocessed in comparison with nearby IGN (Institut Géographique National) GNSS stations. Repeated measurements of other geodetic marks provide insights on georeferencing quality. For example, using between three and seven survey marks, the standard error (SE) was estimated to be 0.004 m horizontally and 0.002 m vertically on average at La Palue across four consecutive surveys. Geographic coordinates are referenced to the French legal systems for this region, i.e., RGF93-Lambert 93 for planimetry and NGF-IGN69 for altimetry. Elevation zero (m NGF) corresponds to approximately 0.5 m below mean sea level (MSL).

**Table 2.** Overview of the processing parameters and scenarios used for DEM reconstruction with Agisoft Metashape.


N is the number of photogrammetric targets available, and - stands for standard parameters.

Aerial image acquisition for photogrammetry was carried out using DJI's Phantom 4 RTK quadcopter (P4 RTK) and D-RTK2 mobile station. The latter is used for drone RTK positioning. According to the manufacturer, this system allows image georeferencing precisions of 1 cm + 1 ppm (parts per million, i.e., 1 mm per 1000 m) and 1.5 cm + 1 ppm along the horizontal and vertical directions, respectively, traducing to absolute accuracies of around 0.05 m at a flying height of 100 m. During surveys, the D-RTK2 is positioned atop a geodetic mark with known coordinates (explanations above). The P4 RTK is equipped with a 20-megapixel (2.41 × 2.41-pixel size) complementary metal-oxide-semiconductor (CMOS) camera (FC6310R) with a mechanical global shutter and an 8.8 mm focal length. Besides precise positioning, the drone attitude (pitch, roll and yaw angles) is recorded automatically for each image using an onboard inertial measurement unit (IMU). For collecting images, we used a single photogrammetric block arrangement with a front and side overlap of 80% and 70%, respectively (Table 1, Figure 2) and a forward-looking camera angle of 6◦ off-nadir. The flying height, and, thus, the GSD, was adjusted depending on the field site in order to complete surveys within two hours around low tide. The flying height (relative to take-off elevation) was maintained at approximately 58 m and 106 m for the Sillon de Talbert and La Palue field sites, traducing to a GSD of 0.014 m and 0.026 m, respectively. To satisfy local drone regulations active at the sites, five flights, from five different take-off and landing spots, were necessary at La Palue, whereas two flights were enough to cover the breach at Sillon de Talbert. Flight pattern was designed to include an overlap between each image block (cf. Figure 2) while ensuring that flight duration remained below the 20-min mark. Before performing the flights, camera settings were tentatively adjusted to the environmental conditions.

Additional topographic measurements were carried out at La Palue using RTK-GNSS (Figure 2a) to serve as vertical ground truths for the evaluation of photogrammetry. To make for an efficient survey covering as much of the beach length as possible, a GNSS rover was securely mounted onto a bike, using a pannier rack above the rear wheel. The rover was mounted with the antenna pointing downwards (vertically) and aligned with the rear wheel axis. The approximate height of the antenna center above the ground was measured, using a tape ruler, to be ~0.60 m. Determination of the exact height was completed using a geodetic mark, with the bike positioned atop and loaded to replicate survey conditions. The height of the antenna center was used to reference the data into the same coordinate system as other surveys presented above. Point sampling along the beach using the "bike GNSS" was carried out in dynamic mode at 1 Hz. The survey was limited to the intertidal zone close to the low-tide water line with packed sand and where photogrammetric quality was expected to be lower due to the difficulty of deploying GCPs and more generally because of the presence of water, the latter making for more challenging tie point detection. Surveying was done by riding the bike alongshore (cf. Figure 2a) to limit as much as possible introducing a pitch angle between the GNSS antenna and the vertical. Roll minimization was made possible by a bubble level mounted onto the bike handlebar. Strategies to account for pitch and roll-related errors on point coordinates were implemented and will be presented in Section 2.5.

#### *2.3. Photogrammetric Data Processing: Standard Workflow*

Drone images were processed using the SfM method implemented in Agisoft Metashape (version 1.70). For producing the final data, the same "standard" workflow was implemented allowing results' comparison (Table 2). In the following, we present the standard workflow used before presenting variations to this workflow (Section 2.4), which was meant to assess controls on measurement quality and to validate our approach.

After importing images from all flights into a single chunk, image coordinates are converted from initially WGS84 ellipsoid to RGF93-Lambert 93 and NGF-IGN69. Automatic identification of low-quality images is performed using the "estimate image quality" tool, whereby each image is associated to a score between 0 and 1 representing sharpness (0 means totally blurred). Following recommendations, images with quality below 0.5 were

not processed in order not to negatively influence image alignment. Image alignment is carried out using the "High" accuracy setting, a reference pair preselection set to "source", key point (tie point) limits set to 60,000 (10,000). After initial image alignment and the reconstruction of a sparse point cloud, photogrammetric targets' coordinates are imported. Each target was manually tagged in at least eight images, progressively improving model georeferencing. To retain only the most reliable tie points, sparse point clouds were systematically cleaned using the "gradual selection" tool with the following parameters: minimum image count of 3, maximum reprojection error of 0.4 pixel and maximum reconstruction uncertainty of 5.

During photogrammetric model optimization, model georeferencing and 3D geometry are adjusted through self-calibrating bundle block adjustment (BBA) requiring external information. Using the standard workflow, BBA is carried out using all camera information available (i.e., position, attitude and associated precisions provided by the drone) and all targets as ChkPts but five (i.e., five targets used as GCPs). For all scenarios tested (explanations below), adaptive camera model fitting was used for camera self-calibration, allowing the complete set of calibration parameters (f, cx, cy, k1-k4, p1-p2 and b1-b2) to be automatically adjusted given user-decided weighting of the external information provided. Following recommendations in Ref. [43], the marker and tie point accuracy were adjusted to match the error in pixel on marker coordinates and the RMS reprojection error (pixel) on tie point coordinates, respectively.

For producing the final dense models, point cloud densification is carried out using a reconstruction quality set to "High" with "aggressive" depth filtering. Using "High" reconstruction quality setting, dense matching is applied to images resampled at half their resolution, speeding up the process and reducing data size. With this setting, the optimum DEM resolution (i.e., minimum grid spacing) is capped at 2 GSD, equaling 0.028 and 0.052 m at Sillon de Talbert and La Palue, respectively. It was considered sufficient for our application since DEMs analyzed have resolutions of 0.1 m and 1 m. Before producing the DEMs, dense point clouds were cleaned using the "Filter by confidence" tool, whereby each model point is graded (1–255) according to how many depth maps the point in question appears in. For this application, points with confidence between 0 and 3 were systematically filtered. Finally, DEMs and orthophotos with a minimum planimetric resolution of 0.1 m are created (interpolation enabled) and exported using same orthogonal grids with integer bounding values.
