## 5 Conclusions and Future Work  
This paper has outlined the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system. This included the development of a Sigma Point Kalman Smoother (SPKS) with the aim of optimally combining observations from a Micro-Electromechanical System (MEMS)-based Inertial Measurement Unit (IMU), a GPS receiver and the observations of orientation using High Definition (HD) video and a Structure from Motion (SfM) algorithm to determine an accurate estimate of aircraft position and orientation. This system was assessed with application to forest inventory to determine the spatial accuracy of the resultant point clouds and the repeatability in the measurement of individual tree height, location and canopy width.  
We demonstrated that with the inclusion of observations of orientation from video, this system is capable of producing point clouds with RMS errors of 0.34 \(\mathrm{m}\) horizontally and 0.14 \(\mathrm{m}\) vertically for a nominal flying height of 50 \(\mathrm{m}\). This represents a 68% reduction in variance within the horizontal component when compared to point clouds generated with the same SPKS without video observations. This accuracy combined with the very high resolution point clouds (with densities up to 62 points per \(\mathrm{m}^{2}\)) allowed measurements of tree height, location, and canopy width to be made with standard deviations as small as 0.05 \(\mathrm{m}\) for height, 0.44 \(\mathrm{m}\) for location and 0.25 \(\mathrm{m}\) for canopy width.  
Future research is aimed at performing forest surveys to further evaluate the UAV's potential within the forestry industry. These surveys will be designed to assess the UAV as a tool which allows forest managers to make more informed decisions on pruning and thinning regimes, monitoring tree health and defoliation, as well as improving the accuracy of allometric forest growth models. To achieve this goal, further research is required to analyse the full effects of the reduced flying heights and increased  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Tree**} & \multirow{2}{*}{**Measurement Count**} & **Location** & \multicolumn{2}{c}{**Height (m)**} & \multicolumn{2}{c}{**Width (m)**} \\  & & \(\boldsymbol{\sigma}\) (m) & **Mean (m)** & \(\boldsymbol{\sigma}\) (m) & **Mean (m)** & \(\boldsymbol{\sigma}\) (m) \\ \hline
1 & 6 & 0.47 & 10.85 & 0.10 & 7.77 & 0.32 \\
2 & 4 & 0.73 & 11.25 & 0.25 & 11.21 & 0.54 \\
3 & 3 & 0.74 & 12.56 & 0.35 & 12.92 & 1.18 \\
4 & 5 & 1.14 & 14.47 & 0.31 & 14.10 & 0.36 \\ \hline \hline \end{tabular} (b)  
\end{table}
Table 5: _Cont._point densities in comparison to full-scale LiDAR on the derivation of the metrics required to make these assessments.  
Although future developments of this system (including hardware and battery technology) will increase flight endurance, the current system has been shown to be capable of flying multiple transects over an individual forest plot in a single flight. These results have confirmed that our UAV-LiDAR system is a suitable platform for the generation of high resolution point clouds for assessing forest structure at the individual tree level.