## 1 Introduction  
### Background  
Airborne LiDAR remote sensing has become a powerful tool in the management of modern forest inventories [1]. Ongoing research into the processing and analysis of LiDAR data has allowed for the development of an extensive range of LiDAR data products from which a wide range of forest metrics can be derived [2; 3; 4]. Stand metrics and tree-level statistics have provided forest managers with significantly richer information about their forests [5; 6]. It is, however, evident that the full potential of airborne LiDAR technology for forest measurement and management is yet to be reached. Prohibitive factors, including high survey costs and short flying seasons in many areas, have limited the ongoing application of multi-temporal studies. As such, the assessment of factors such as forest health, defoliation, and rate of canopy closure are not feasible from the current intermittent LiDAR surveys utilised by forest managers. Recently, improvements in small-scale technology have enabled the use of Unmanned Aerial Vehicles (UAVs) as an alternative remote sensing platform offering a distinctive combination of very high resolution data capture at a significantly lower survey cost. Current research into the use of UAVs as a 3D data-capture platform includes archaeological surveys [7; 8] and vegetation monitoring [9; 10; 11], for example. These studies use image matching and photogrammetric techniques, which allow high density point clouds to be generated from the very high resolution imagery collected by UAVs. These point clouds have also been evaluated for forest monitoring and management by [PERSON] _et al._[12] and [PERSON] and [PERSON] [13], both showing the advantages of significantly higher densities point clouds in comparison to those commonly collected with full-scale LiDAR platforms. The drawback of point clouds generated from imagery is that few points will be measured from within the canopy and from the underlying surface in densely vegetated areas. This within canopy information is vital for many of the techniques which have been developed for deriving stand level and tree level metrics from point clouds, such as above ground biomass. [PERSON] _et al._[14] provided the first example of the potential of UAV-borne LiDAR for use in the forestry research. With the deployment of a rotor wing UAV equipped with a number of navigation sensors, in combination with two on-board LiDAR sensors, high-resolution data sets were produced offering improved individual tree level mapping. [PERSON] _et al._[14], and more recently [PERSON] _et al._[15], have shown that due to the improved density of a UAV LiDAR point cloud, several metrics can be measured at a finer scale and with higher precision when compared to traditional LiDAR platforms. Because of their high spatial and temporal resolution, together with low operational costs, UAVs can provide a more targeted approach to forest monitoring and allow for the use of multi-temporal surveys such as forest health and canopy closure monitoring. Studies such as these suggest that through the combination of low-cost, high resolution data capture, UAV platforms are likely to be the next tool of choice for optimising detailed small area surveys within forests. Several other UAV-platforms have also been developed for the purpose of collecting LiDAR data [14, 16, 17, 18]. The majority of these examples have been designed for government or military purposes [16, 17] or as proof of concept platforms to show that LiDAR data can be collected from UAVs [18]. The drawback of such platforms is that the size and budget is significantly larger than what could be considered useful as an operational tool in forest management. A key reason for this is that the derivation of a spatially accurate point cloud requires careful consideration to be given to the determination of aircraft position and orientation. Even small errors in the observation of orientation and position result in substantial on ground displacements in point measurements [19].