## 3 Methods
### Drone Surveys and Data Collection
Also, we considered the wind speed and direction, being always parallel to the main and longer paths during each drone survey, and we maintained a constant path direction in each subarea in order to be sure to obtain the entire coverage of the whole big area. Furthermore, a crucial step in order to georeference the resulting models (orthomosaics and DSMs) and thus to allow the collection of high-precision quantitative measurements, was to collect World Geodetic System (WGS84) coordinates of ground control points (GCPs) in each drone-surveyed area. The placement and acquisition of GCPs was planned considering the specificities of the study area. We collected 37 GCPs (Figure 2c) distributed both at the corners and in the central part of each subarea to reduce the doming effect resulting from SfM processing and reduce systematic error [69; 70; 71; 72]. Such GCPs can be artificial or natural objects, both must be easily recognizable on drone-captured photos (Figure 3). Artificial GCPs deployment and recovery, on an extensive area, is really time-consuming since targets must be placed before starting the survey and retrieved afterward. For this reason, we used both artificial and natural targets as GCPs: artificial targets include objects with bright colours (e.g., Figure 3b), the corner of the backpack case of our drone (Figure 3c), and series of slices of white bread arranged to form an X on the ground (Figure 3d), whereas natural targets include massive rocks with defined geometric shapes (e.g., Figure 3e) and geometric lava flow margins (e.g., Figure 3f). Slices of white bread and natural targets were easily recognisable from UAV images and they have been used since the unnecessity to retrieve them could significantly speed up the survey. The collection of GCPs has been performed with the Emlid Reach RS(c) single frequency receivers in RTK configuration (with centimetre-level accuracy), linked in real time by NTRIP to the Icelandic CORS network (ICECRS--[[https://www.lmi.is/en/icecors-network/](https://www.lmi.is/en/icecors-network/)]([https://www.lmi.is/en/icecors-network/](https://www.lmi.is/en/icecors-network/))), used as reference station. Because of the use of RTK/GNSS GPS information, we expect to obtain a model with centimetric accuracy, as already highlighted by several authors; for a more in-depth reading about the resulting accuracy we suggest the work proposed by the following authors: [73; 74; 75; 76]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **AREA 1** & **AREA 2** & **AREA 3** & **AREA 4** & **AREA 5** & **AREA 6** & **AREA 7** & **AREA 8** & **AREA 9** \\ \hline \multirow{6}{*}{**Synthetic**} & Flying Date & 2019/6/23 & 2019/6/23 & 2019/6/23 & 2019/6/24 & 2019/6/24 & 2019/6/24 & 2019/6/24 & 2019/6/23 & 2019/6/23 \\ \cline{2-10}  & Flying Height (m) & 95 & 85 & 90 & 85 & 80 & 85 & 85 & 85 & 85 \\ \cline{2-10}  & Frontal Overlap (\%) & 85 & 85 & 85 & 85 & 85 & 85 & 85 & 85 & 85 \\ \cline{2-10}  & Lateral Overlap (\%) & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 & 80 \\ \cline{2-10}  & Flight Speed (m/s) & 7 & 7 & 7 & 7 & 6 & 6.4 & 6.4 & 6.4 & 6.4 \\ \cline{2-10}  & Shutter Interval (s) & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ \cline{2-10}  & Ground Resolution (cm/pix) & 2.6 & 2.3 & 2.4 & 2.3 & 2.2 & 2.3 & 2.3 & 2.3 & 2.3 \\ \cline{2-10}  & Flying Time (mins) & 27 & 53 & 38 & 37 & 43 & 22 & 22 & 22 & 17 \\ \cline{2-10}  & Covered Area (Ha) & 30 & 43 & 30 & 31.72 & 29.12 & 18.54 & 18.67 & 18.14 & 14.25 \\ \cline{2-10}  & Images Numbers & 676 & 1275 & 945 & 922 & 1088 & 543 & 548 & 544 & 427 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: UAV survey dates and detailed flight acquisition parameters.