Accurate tree metrics is essential for forest management. Quantitative Structure Model (QSM) which can reconstruct an accurate 3D model of trees, has been used with Terrestrial Laser Scanning (TLS) point cloud as input. Indeed, image-based Structure from Motion (SfM) can produce point cloud as well. Unmanned Aerial Vehicle (UAV), which can collect images of a large scale in a short period, seems like a good choice for forest study. This study aims to investigate the feasibility of UAV point cloud for QSM of individual trees. Flights were carried out during the leaf-on and leaf-off seasons with an inclined camera onboard. Four oblique camera angles were used during the leaf-on season to obtain the optimal angle for UAV data collection. The Diameter at Breast Height (DBH) derived from UAV point cloud and QSM were compared with field measured data. The accuracy of QSM-biomass estimations was assessed with reference, which was calculated using field measured DBH through the allometry. In this study, it was found that the point density of the whole scene increased with the increase of oblique camera angle. DBH extracted from the UAV-generated point cloud versus reference showed no significant difference (p > 0.05), while a significant difference was found between QSM-estimated DBH and the reference DBH. The QSM-based biomass showed 49.16% underestimation for leaf-off season. Although the QSM did not behave well with UAV data, it was found that the UAV point cloud could be used for accurate tree parameter extraction and could be a useful tool for forest management.
|Number of pages||11|
|Journal||International Journal of Applied Earth Observation and Geoinformation|
|Early online date||20 May 2019|
|Publication status||Published - 1 Sep 2019|
Ye, N., Van Leeuwen, L. M., & Nyktas, P. (2019). Analysing the potential of UAV point cloud as input in quantitative structure modelling for assessment of woody biomass of single trees. International Journal of Applied Earth Observation and Geoinformation, 81, 47-57. https://doi.org/10.1016/j.jag.2019.05.010