## 3 Methods
### Photogrammetry Processing  
UAV images have been processed using Agisoft METASHAPE ([[http://www.agisoft.com/](http://www.agisoft.com/)]([http://www.agisoft.com/](http://www.agisoft.com/))), a commercial SfM software, and Agisoft Cloud beta service for data processing. The software was chosen for its user-friendly interface, spontaneous workflow, the good quality of the outputs [77; 78; 79] and the reasonable price of the educational license. These qualities have made it a broadly used application by the earth science community. SfM photogrammetric workflow, described in detail by [80; 81], consists in a sequence of steps that from a set of overlapping images lead to the creation of a sparse and dense cloud, a 3-D tiled model, an orthomosaic and a DSM as final products (Table 2).  
Figure 3: Examples of artificial and natural targets used in the present work to collect ground control points (GCPs). (**a**) Operator collecting a GCP in the field using RTK GPS receiver. (**b**) Artificial target as large as 20 cm, we collected the uppermost black band. (**c**) Corner of drone gray box (that is as large as 35 cm). (**d**) Five slices of bread adjusted to form a X, we collected the corner of the central one (as large as 10 cm). (**e**) Lower right corner of massive rocks (the rock is as large as 1 m). (**f**) Lava flow margin where it forms a 90\({}^{\circ}\) angle.  
The processing approach can be divided into four main stages:  
1. Photo alignment: an initial low-quality photo alignment using common key points in each image generated a sparse 3-D point cloud (Figure 4a). The focal length and photo dimensions were automatically computed by the software and then used for the subsequent calibration of the core parameters of the camera (principal point coordinates, lens distortion coefficients).
2. Georeferencing: GCPs were identified into the photos and markers have been assigned with the surveyed coordinates. This process allowed to scale and georeference the point cloud and thus to improve the accuracy of the final models. Then, the images were realigned using high accuracy settings and default values for Tie and Key points settings, 40,000 and 4000 respectively, and using Generic and Reference preselection settings.
3. Dense Point Cloud Building: a 3-D dense point cloud was generated, using a mild depth filtering and medium quality settings, from the sparse point cloud (Figure 4b).
4. DSM and orthomosaic generation: a DSM was generated from the dense point cloud, and successively the orthomosaic was produced using the corresponding DSM (Figure 5a,b).
5. The software also allows to generate a high-quality 3-D tiled model, both including the mesh and the texture (Figure 5c). The latter can be used as a scenario for immersive virtual reality [31]; here we suggest a Medium quality set-up with a tile size of \(4096\times 4096\) pixels.  
All outputs refer to the geographic coordinate system (WGS84).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Alignment processing accuracy & High \\ \cline{2-3}  & Dense Cloud processing accuracy & Medium \\ \cline{2-3}  & Dense Cloud Points & 257,803,308 \\ \cline{2-3}  & 3-D Tiled Model Resolution & 2.6 cm/pix \\ \cline{2-3}  & DSM Resolution & 10 cm/pix \\ \cline{2-3}  & Ortomosaic Resolution & 2.6 cm/pix \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of resolution and time processing for DSM, orthomosaic and 3-D tiled models derived using photogrammetry.  
Figure 4: Snapshots of the steps performed with the SfM software to obtain the final outputs of our workflow, consisting in the calculation of sparse point clouds (**a**) and dense point clouds (**b**).  
Figure 5: SfM-derived orthomosaic (**a**) and DSM (**b**) of the study area, with corresponding resolution indicated in the lower right corner. Altitudes are shown as a colour range (legend in figure); reference system: UTM28-WGS84. (**c**) The final 3-D tiled model.