## 4 Approach for Data Processing  
The approach of this study is based on the fact that changing the flight scenario modifies the image network geometry, which can impact the quality of reconstruction by changing the spatial distribution of tie points detected during image matching. We compare thereafter the results obtained for different flight plans (i.e., Flight 1, Flight 2 and Flight 3) to assess to what extent the flight scenario can limit or emphasize the geometrical distortion effects, particularly in case of restricted GCPs distribution. The restricted GCPs distribution does not seek to be realistic or to minimise the topographic modelling error, the purpose is to assess the extent of the predictable \"bowl\" effect after more than hundred meters without GCP and how the flight scenario can influence this effect. In the same way, using images with geolocation information encoded in their EXIF would limit the geometric distortions, but our aim is rather to enhance the impact of the flight scenario on the \"bowl effect\". \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Flight 1** & **Flight 2** & **Flight 3** \\ \hline  & “Classical” &
\begin{tabular}{c} Oblique Pointing \\ Camera (40\({}^{\circ}\)) \\ \end{tabular} & Varying Altitude \\ \hline Number of images & 83 & 73 & 93 \\ Flight altitude (m) & 50 m & 50 m & 40 m/60 m \\ Image resolution & 1.68 & 2.12 [1.64; 2.95] & 1.55 [1.33; 1.77] \\ (cm/pix) & & & 49.4 \(\times\) 32.9 m \\ Mean image footprint & 53.5 \(\times\) 35.6 m & 67.5 \(\times\) 44.9 m & [42.3 \(\times\) 28.2 m; 56.4 \(\times\) 37.5 m] \\ Mean distance between camera positions & 7.4 m & 7.7 m & 7.0 m \\ Coverage area (m\({}^{2}\)) & 22,100 & 31,700 & 18,200 \\ Images overlap & & & \\ \hline  & **PhotoScan** & **MicMac** & **PhotoScan** & **MicMac** \\ \hline Number of tie points & 57,781 & 129,478 & 65,382 & 129,977 & 56,376 & 202,890 \\ \hline Average density of tie points in \(r\) = 1 m & 5.1 & 22.5 & 3.9 & 12.1 & 6.5 & 36.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of the parameters for the different flight plans (PS: PhotoScan; MM-F15P7: MicMac with F15P7 distortion model; MM-Fra. : MicMac with Fraser distortion model). For each flight (Table 1), the mean flight altitude is kept around 50 m. As the flight plan and the footprint of images vary from one flight to another, the overlap between images is also shown. For Flight 2 and Flight 3, the viewing angle and the scale significantly change from one flight line to another. That implies less resemblance between images and therefore keypoints identification is likely to be more difficult prior to image matching. Figure 5 shows an example of tie points detection in PhotoScan(r) for images acquired from different flight lines for Flight 2 (Figure 5a) and Flight 3 (Figure 5b). The beach surface is sufficiently textured (coral debris, footprints, depressions or bumps in the sand) to have detectable features for image matching. Nevertheless, considering the environmental constraints inherent to the study area, only a small proportion of the photos is taken into account in the image matching (Figure 5). This restricted spatial distribution of the tie points can affect the quality of the SfM reconstruction. The images being processed by different operators with PhotoScan(r) and MicMac(r), to avoid variations in the delineation of masks, it has been decided to not use mask. Indeed, as very few valid tie points are situated in water (Figure 5), the noise they would introduce in bundle adjustment is considered negligible. Comparing the total number of valid tie points from one flight to another (Table 1), it can be noticed that for PhotoScan(r) the number of tie points is \(\approx\)12-13% higher for Flight 2 than for Flights 1 and 3. On the contrary, for MicMac(r), the number of tie points is 36% higher for Flight 3 than for Flight 1 and 2.