## 3 Results
### Analysis of the Point Cloud Horizontal and Vertical Positional Precision for Flight A  
The slow horizontal velocity experienced by the APX-18 sensor during flight A allowed for the best analysis of the impact of heading precision on the positional precision of a UAS-lidar point cloud. As the horizontal velocity of the UAS was the slowest of all flights, the APX-18 sensor was closest to being in a level orientation during this mission. The total tilt angle of the sensor, calculated with respect to the nadir direction, was typically less than 10\({}^{\circ}\) during each N-S flight line. A point observed near the edge of the circular field of view (FoV) of the Mid-40 lidar sensor is most affected by the precision of the trajectory heading estimate. The observable horizontal angle (\(\theta\)) and vertical angle (\(\alpha\)) for a point at the edge of a circular FoV are related by \(\cos(\theta)\cos(\alpha)=\cos(\mathrm{W}/2)\), where W is the total FoV angle (e.g., \(\mathrm{W}=38.4^{\circ}\) for the Mid-40). As a result, the two georeferenced point clouds produced from the single-antenna and dual-antenna trajectories for flight A were filtered to only include points that were observed within one of four narrow edge regions of the Mid-40's FoV. For comparison, an additional narrow region of points least affected by the precision of the heading estimate, located at the middle of the FoV, were also included for analysis (Table 6 and Figures 11 and 12). Each point within the single-antenna generated point cloud was matched to its corresponding point within the dual-antenna generated point cloud and the horizontal and vertical distances between the matched points were calculated. Figure 13 illustrates a north-west looking-perspective view of the point cloud for flight A, colored by gray-scale intensity values. The analysis presented here focuses on relative differences, although a higher accuracy reference point cloud (e.g., produced from a terrestrial laser scanner) may allow an absolute horizontal accuracy assessment of each respective point cloud and will thus be the focus of future research. The calculated positional differences between the matched points were used to estimate the impact of the precision of the heading estimate on the positional precision of a UAS-lidar point cloud to compare with theoretical expectations. Figure 14 illustrates the horizontal position differences between the single and dual-antenna generated point clouds based on the points within the five regions of the Mid-40's FoV. In the figure, note that the heading precisions for the single-antenna GNSS/INS trajectory are shown as the top dashed line and reference the secondary vertical axis. Figure 12: Views of a point cloud containing 5 s of collected data from the five FoV regions of the Mid-40 sensor. Figure 13: North–west looking-perspective view of the point cloud for flight A, colored by gray-scale intensity values. An inspection of Figure 14 indicates varying correlations between the horizontal position differences, the single-antenna heading precisions, and the direction each flight line was flown. Excluding the first flight line, every flight line flown in the south direction unexpectedly yielded smaller horizontal position differences than flight lines flown in the north direction. Table 7 presents quantitative measures for the horizontal position differences between the point clouds within the five FoV regions for flight A. Further analysis of the north (Y) and east (X) coordinate differences of the horizontal position differences within the four edge regions suggested the presence of a bias within the heading estimates for the single-antenna GNSS/INS trajectory, relative to the dual-antenna GNSS/INS trajectory. Computing the differences between the heading estimates from the single and dual-antenna GNSS/INS trajectories produced a mean difference of 0.27\({}^{\circ}\).