## 3 Methodology
### Point Cloud generation and Accuracy Assessment  
The University of Tasmania's farm was used as the study area to assess the accuracy of the point cloud generated by the UAV platform (Figure 3). The 100 \(\times\) 100 m area provides significant variation in slope as well as an area of significant canopy coverage in the south west. Furthermore, five different planar man-made features are present within the area. This area was surveyed using 8 different flight transects during 4 different flights at an average flying height of 50 \(\mathrm{m}\) and an approximate horizontal velocity of 3.3 \(\mathrm{m}/\mathrm{s}\). As previously mentioned, only scan angles of between \(\pm\)30\({}^{\circ}\) were used for mapping. Under these conditions the resultant point cloud has a swath width of 57 \(\mathrm{m}\) and a point density of approximately 50 points per \(\mathrm{m}^{2}\). The laser footprint at nadir is 0.69 \(\mathrm{m}\) along track and 0.07 \(\mathrm{m}\) across track. This results in significant overlap of laser footprints along the track and a gap of approximately 0.06 \(\mathrm{m}\) across track. The footprint size increases towards the edge of the swath depending on the range and the angle of incidence with the terrain. Each of the four flights was flown using the OktoKopter's on-board autopilot and are summarised in Table 2. All flights were flown in very similar conditions, indicating the on-board autopilot provides an adequate solution for following predefined flight paths. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Flight**} & **Flight** & **Mean** & **Mean Horizontal** & **Primary** & \multirow{2}{*}{**Key Frames/s**} \\  & **Time (s)** & **Height (m)** & **Velocity (\(\mathrm{m}/\mathrm{s}\))** & **Heading (deg)** & \\ \hline
1 & 161 & 48 & 3.77 & 180 & 2.3 \\
2 & 137 & 54 & 3.27 & 90 & 1.8 \\
3 & 130 & 46 & 3.16 & 135 & 1.9 \\
4 & 195 & 44 & 3.33 & 225 & 1.8 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of the four flights flown over the study area. Figure 3: The test field (red) used for both the calibration and the determination accuracy of the UAV-platform showing the flight paths (yellow). Each flight was processed twice to assess the effect of the inclusion or otherwise of SfM observations. To achieve this assessment, 32 high reflectivity targets were placed across the study site. The 0.3 \(\mathrm{m}^{2}\) target size allowed a minimum of 4 direct observations to be made of each target under the targeted flying conditions. The design of these targets allowed them to be identified within the point cloud based on the return pulse width, which is reported by the Ibee LUX and can be considered analogous to intensity for this study. A threshold at which these target could be identified was found by examining a histogram of return pulse widths produced for each point cloud. As trees were the only other features within the study producing pulse widths above this threshold, ground control strikes were identified as ground points (see Section 3.5) with a pulse width above this threshold. The location of ground control targets as measured by the laser scanner was determined to be the mean of all identifiable strike positions. This location was compared to the location of the center point of each target measured using dual frequency differential GPS (\(\pm\)0.02 m horizontal and \(\pm\)0.05 m vertical) to determine the accuracy of the UAV-borne LiDAR system. Only ground control with 5 or more identifiable strikes were used in this comparison, resulting in a small number of target from the edge of the scan being excluded. The mean difference and standard deviation in the North, East and up values were calculated for each transect as well as the absolute horizontal error statistics. Finally, the points obtained over planar surfaces (e.g., building roofs) were extracted from each of the 8 point clouds. After applying a least squares fit of a plane to the extracted data, the standard deviation was calculated.