@article {bnh-5481, title = {Assessing the ability of image based point clouds captured from a UAV to measure the terrain in the presence of canopy cover}, journal = {forests}, volume = {10}, year = {2019}, month = {04/2019}, abstract = {

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5\% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.

}, keywords = {drones, Fire, forest measurement, image based point clouds, RPAS, structure from motion, UAS}, doi = {https://doi.org/10.3390/f10030284}, url = {https://www.mdpi.com/1999-4907/10/3/284}, author = {Luke Wallace and Chris Bellman and Bryan Hally and Jaime Hernandez and Simon Jones and Samuel Hillman} } @article {bnh-6222, title = {A Method for Validating the Structural Completeness of Understory Vegetation Models Captured with 3D Remote Sensing}, journal = {Remote Sensing}, volume = {11}, year = {2019}, month = {09/2019}, abstract = {

Characteristics describing below canopy vegetation are important for a range of forest ecosystem applications including wildlife habitat, fuel hazard and fire behaviour modelling, understanding forest recovery after disturbance and competition dynamics. Such applications all rely on accurate measures of vegetation structure. Inherent in this is the assumption or ability to demonstrate measurement accuracy. 3D point clouds are being increasingly used to describe vegetated environments, however limited research has been conducted to validate the information content of terrestrial point clouds of understory vegetation. This paper describes the design and use of a field frame to co-register point intercept measurements with point cloud data to act as a validation source. Validation results show high correlation of point matching in forests with understory vegetation elements with large mass and/or surface area, typically consisting of broad leaves, twigs and bark 0.02 m diameter or greater in size (SfM, MCC 0.51{\textendash}0.66; TLS, MCC 0.37{\textendash}0.47). In contrast, complex environments with understory vegetation elements with low mass and low surface area showed lower correlations between validation measurements and point clouds (SfM, MCC 0.40 and 0.42; TLS, MCC 0.25 and 0.16). The results of this study demonstrate that the validation frame provides a suitable method for comparing the relative performance of different point cloud generation processes

}, keywords = {3D remote sensing, biomass, forest measurement, structure from motion, terrestrial laser scanning, validation, vegetation structure}, doi = {https://doi.org/10.3390/rs11182118}, url = {https://www.mdpi.com/2072-4292/11/18/2118}, author = {Samuel Hillman and Luke Wallace and Karin Reinke and Bryan Hally and Simon Jones and Daisy Saldias} } @article {bnh-4424, title = {Efficiency of Individual Tree Detection Approaches Based on Light-Weight and Low-Cost UAS Imagery in Australian Savannas}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {01/2018}, abstract = {
The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds{\textemdash}obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera{\textemdash}can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70\%) for dominant and co-dominant trees, but yielded low detection rates (\<35\%) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.
}, keywords = {biomass, canopy height, low-cost UAS, segmentation, single tree detection, structure from motion}, doi = {10.3390/rs10020161 }, url = {http://www.mdpi.com/2072-4292/10/2/161/htm}, author = {Grigorijs Goldbergs and Stefan Maier and Shaun R Levick and Andrew C. Edwards} }