Electronic Supplementary Material

Camera used

As camera, we used a GoPro HERO4 Black with a modified lens. The modified lens

substitutes the original GoPro one, and has the following characteristics:

Focal length 5.4mm

Horizontal opening angle 60°

Aperture range F 2.5

Agisoft Photoscan workflow

Workflow, parameters and algorithms used in Agisoft Photoscan are shown in Table S1. The

parameters used in the automatic classification on the dense point cloud in Agisoft Photoscan

(function “Classify Ground Point”) are shown at the bottom of the table. In the first step of the

classification, the dense cloud is divided into cells of a dimension of the parameter “cell size”.

In each cell, the lowest point is detected and a first approximation of the bottom is given by a

triangulation of these points. In the second step a new point is added to the bottom point class

providing that it satisfies two conditions: it lies within a certain distance from the bottom

model (max. distance parameter) and that the angle between the bottom model and the line

connecting the new point with the bottom is less than the “max. angle” parameter. This second

step is repeated while there are still points to be checked. The results of the classification are

shown in Fig. 3f.

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Table S1 Workflow and parameters used in Agisoft Photoscan to process drone imagery.

First step: Align photoAccuracy HighPair preselection DisabledKey point limit 40000

Build mesh (preliminary step to insert GCPs)Surface type ArbitraryFace count Medium (30000)Source data Sparse cloudInterpolation EnabledPoint classes AllSecond step: Locate and place GCPs in the scene and

import GCPs coordinatesMeasurement accuracy (for GCPs)

Camera accuracy (m)

10

Marker accuracy (m)

0.05

Scale bar accuracy (m)

0.001

Projection accuracy (pix)

0.1

Tie point accuracy (pix)

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Third step: Build dense cloudQuality HighDepth filtering Aggressive

Fourth step: Build meshSurface type ArbitraryFace count Medium (136012)Source data Dense cloudInterpolation EnabledPoint classes All

Generate Orthophoto – Export OrthophotoGenerate 3D Model – Export DEM

Classification of ground points(geometrical filtering)

Parameter ValueMax angle (deg) 0.5Max distance (m) 0.1

Cell size (m) 1

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LiDAR data

The LiDAR point cloud was derived from a topobathymetric campaign conducted between 10

and 26 June 2015 by the joint collaboration of Fugro LADS corporation, the French

Polynesian Service de l’urbanisme (SAU), the French Service hydrographique et

océanographique de la marine (SHOM) and the MooreaIDEA consortium funded by the US

National Science Foundation Long Term Ecological Research Program. Moorea’s coastal

fringe was surveyed by the RIEGL VQ-820-G hydrographic airborne laser scanner operating

at 532 nm and 251 kHz with a nominal swath width of 375 m, which leads to a sounding

density of nominally four points m–2. Data were post-processed relative to the International

Terrestrial reference Frame 2008 and delivered to the projection system UTM 6S associated

with the geodetic system RGPF and altimetric system NGPF. The horizontal and vertical

accuracies of 1 m and 0.25 m were computed based on fifty benchmarks.

SfM-derived bathymetry–LiDAR alignment

The SfM-derived bathymetric DEM was aligned to the LiDAR on the basis of the position of

four conspicuous coral heads, shown in Figure S1. The XYZ distances between the points in

Figure S2 are shown in Table S2.

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Fig. S1 Left panel: the bathymetric DEM obtained from the Agisoft Photoscan workflow.

Right panel: comparison between the DEM in the left panel and the LiDAR dataset (dots, in

tones of grey). The color scale of the DEM is equivalent to that used in Fig. 1f and Fig. 3b–e.

The color scale used for the LiDAR point cloud is a simple grayscale where white represents

deeper areas and black represents shallower ones. The green and orange dots in the right panel

represent the georeferencing points identified respectively in the DEM (green) and in the

LiDAR (orange) for alignment. The results of the alignment process are shown in Table S2.

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The latitude-longitude differences between the points in the SfM-derived bathymetry obtained

in this study and the LiDAR are shown in Table S2. We used the points in Table S2 to align

the SfM-derived bathymetry to the LiDAR with the ArcMap Georeferencing tool, 1st order

polynomial transformation.

Table S2 Points used to align the SfM-derived bathymetry and the LiDAR cloud point.

Point ID

Longitude DEM (deg)

Latitude DEM (deg)

Longitude LiDAR (deg)

Latitude LiDAR (deg)

Delta X (m)

Delta Y (m)

Delta distance

(m)

1 -149.899896 -17.486611 -149.899887 -17.486606 -1.0 -0.5 1.12 -149.89994 -17.486472 -149.899928 -17.486466 -1.3 -0.6 1.53 -149.89995 -17.487035 -149.899945 -17.487034 -0.6 -0.1 0.64 -149.899807 -17.485864 -149.899788 -17.485853 -2.1 -1.2 2.4

Averages -1.2 -0.6 1.4

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Agisoft Photoscan report

The flowing information has been extracted from the Agisoft Photoscan processing report.

Figure S2 Camera locations (dots) and image overlap (colors)

Table S3 Details of the flight and on the processing results

Number of images: 306 Camera stations: 306

Flying altitude: 29.4 m Tie points: 4911

Ground resolution: 7.84 mm/pix Projections: 86,269

Coverage area: 8.38×103 square meters Reprojection error: 1.11 pixels

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Figure S3 Image residuals

Table S3 Details of the flight and the processing results

Type: Frame Skew: -3.12523

Fx: 3481.7 Cx: 2029.53

Fy:3479.07 Cy: 1520.39

K1: -0.0927356 P1: -0.00105114

K2: 0.142992 P2: -0.000219611

K3: -0.0259784 P3: -2.74474

K4: -0.00238544 P4: 0.744745

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Table S4 Internal error associated with each Ground Control Point, and total internal errors.

Label XY error (m)

Z error (m) Error (m) Projections Error (pix)

GCP_green 0.143012 -0.112925 0.182222 70 0.696GCP_blue 0.182276 -0.0976561 0.206788 37 0.457GCP_pink 0.0292783 0.127223 0.130548 41 0.408Sand_end 0.652693 0.0664985 0.656072 20 0.011Sand_center_1 0.0799207 0.0110363 0.0806791 24 0.024Sand_center_2 0.386935 -0.0638596 0.392169 21 0.021Left_sand 0.322584 0.226539 0.394183 7 0.088Coral_left 0.941053 0.333445 0.998382 13 0.017Near_boat 0.475707 0.176596 0.507428 5 0.009GCP_green 0.143012 -0.112925 0.182222 70 0.696TOTAL 0.453699 0.163567 0.482283 0.452

Table S5 Internal error associated with each Scale Bar, and total internal errors.

Label Distance (m) Error (m)Pink_diagonal 0.594472 -0.0655278Blue_diagonal 0.598206 -0.0617939Green_diagonal 0.594201 -0.0657986TOTAL 0.0643993

Processing parameters

GeneralCameras 306Aligned cameras 306Markers 15Scale bars 3Coordinate system WGS 84 (EPSG::4326)Point CloudPoints 4,911 of 18,067RMS reprojection error 0.712232 (1.11005 pix)Max reprojection error 12.418 (15.0632 pix)Mean key point size 1.63915 pixEffective overlap 30.5845Alignment parametersAccuracy HighestPair preselection DisabledKey point limit 40,000Tie point limit 1,000Constrain features by mask NoMatching time 6 hours 30 minutesAlignment time 3 minutes 48 secondsOptimization parameters

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Parameters fx, fy, cx, cy, skew, k1-k4, p1, p2, p3, p4Optimization time 8 secondsDense Point CloudPoints 55,506,938Reconstruction parametersQuality HighDepth filtering AggressiveProcessing time 6 hours 43 minutesDEMSize 6,275 x 16,621Coordinate system WGS 84 (EPSG::4326)Reconstruction parametersSource data Dense cloudInterpolation EnabledOrthomosaicSize 8,794 x 26,786Coordinate system WGS 84 (EPSG::4326)Channels 3, uint8Blending mode MosaicReconstruction parametersSurface DEMEnable color correction No

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