## 3 Materials and Methods
### Data Processing  
Geolocated photographs were processed using SfM photogrammetry integrated in the popular software Agisoft PhotoScan(r) Pro v1.2.3 (Figure 3c). The first step before data processing consists in creating and formatting a camera position file compatible with PhotoScan(r). This file is composed of four columns: a label (corresponding to the name of each photograph) and the easting, northing, and altitude coordinates of the camera measured by the RTK GNSS. The altitude provided by the RTK GNSS must be corrected from the vertical offset D (as explained in Section 3.1). As several photos were taken from the same position, they share the same coordinates.  
All datasets are processed using the same parameters in the processing software to avoid biases in the comparison between Reflex and smartphone camera datasets (cf. below). The main steps of the image matching and stereorestitution process are:  
* Image orientation by bundle adjustment (detection and matching of homologous keypoints in overlapping photographs in order to compute the external parameters for each camera). The accuracy is set to \"high\", which means that the software works with the photos of the original size. Key point limit and tiepoint limit are set to their default values.
* Refinement of camera calibration parameters (internal parameters) using the camera positions (parametrized with an accuracy of 5 cm in the Reference pane in PhotoScan(r), i.e., the RTK GNSS-measured positions are taken as initial values and their variations during bundle block adjustment process are constrained in a radius of 5 cm).
* Dense image matching to produce a dense point cloud using the estimated external and internal camera parameters. For this step, the accuracy parameter is set to \"high\" to obtain more detailed and accurate geometry and the depth filtering mode is set to \"aggressive\".  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Camera Model** & \
\begin{tabular}{c} **Reflex** \\ **Camera–Nikon D800** \\ \end{tabular} &
\begin{tabular}{c} **Smartphone–Huawei** \\ **Y5 2016** \\ \end{tabular} \\ \hline Number of camera positions & 9 & 10 \\ Mean distance between successive camera positions & \(\approx\)10 m & \(\approx\)10 m \\ Height of the camera above the ground & \(\approx\)1.5 m & \(\approx\)1.5 m \\ Mean distance to the cliff face & \(\approx\)45–50 m & \(\approx\)50 m \\ Mean pixel size & 1.60 cm/pix & 1.93 cm/pix \\ Number of images & 76 & 63 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image acquisition carried out with the Nikon D800 reflex camera (test 1) and with the Huawei Y5 Smartphone (test 2).  
The colored dense point cloud is exported as the final result (Figure 4). Indeed, georeferenced DEMs and orthophotographs are not as interesting, as the study focuses on sub-vertical objects (cliffs).  
The dense point cloud obtained with the Huawei Smartphone covers a larger area than the Nikon point cloud, including stable sections of the cliff that are not concerned by the monitoring, as shown in Figure 4. The reason is image size, which, as mentioned before, is more panoramic for the smartphone and, therefore, provides a higher overlap on external parts of the survey area.  
Two processing methods are performed for the survey with the Smartphone camera (Test 2): one with the camera position file created from positions measured by RTK GNSS (Test 2a), another using the geotag of the photographs and so the position measured by the Smartphone internal GNSS (Test 2b). For Test 2a, the camera position accuracy was set to 5 cm, whereas it was set to 5 m for Test 2b. Comparing the camera positions measured by the RTK GNSS and by the Smartphone's internal GNSS (single frequency GNSS) provided a Root Mean Square Error (RMSE) of 3.44 m.