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Published:
Sep 4, 2022DOI:
10.4314/sajg.v11i1.6Keywords:
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Main Article Content
Estimating the performance of multi-rotor unmanned aerial vehicle structure-from-motion (UAV<sub>sfm</sub>) imagery in assessing homogeneous and heterogeneous forest structures: a comparison to airborne and terrestrial laser scanning
Abstract
The implementation of Unmanned Aerial Vehicles (UAVs) and Structure-from-Motion (SfM) photogrammetry in assessing forest structures for forest inventory and biomass estimations has shown great promise in reducing costs and labour intensity while providing relative accuracy. Tree Height (TH) and Diameter at Breast Height (DBH) are two major variables in biomass assessment. UAV-based TH estimations depend on reliable Digital Terrain Models (DTMs), while UAV-based DBH estimations depend on reliable dense photogrammetric point cloud. The main aim of this study was to evaluate the performance of multi-rotor UAV photogrammetric point cloud in estimating homogeneous and heterogeneous forest structures, and their comparison to more accurate LiDAR data obtained from Aerial Laser Scanners (ALS), Terrestrial Laser Scanners (TLS), and more conventional means like manual field measurements. TH was assessed using UAVSfM and LiDAR point cloud derived DTMs, while DBH was assessed by comparing UAVSfM photogrammetric point cloud to LiDAR point cloud, as well as to manual measurements. The results obtained in the study indicated that there was a high correlation between UAVSfM TH and ALSLiDAR TH (R2 = 0.9258) for homogeneous forest structures, while a lower correlation between UAVSfM TH and TLSLiDAR TH (R2 = 0.8614) and UAVSfM TH and ALSLiDAR TH (R2 = 0.8850) was achieved for heterogeneous forest structures. A moderate correlation was obtained between UAVSfM DBH and field measurements (R2 = 0.5955) for homogenous forest structures, as well as between UAVSfM DBH and TLSLiDAR DBH (R2 = 0.5237), but a low correlation between UAVSfM DBH and UAVLiDAR DBH (R2 = 0.1114). The study demonstrated that UAV acquired imagery can be used to accurately estimate TH in both forest types, but has challenges estimating DBH. The research does not suggest that UAVSfM serves as a replacement for more high-cost and accurate LiDAR data, but rather as a cheaper adequate alternative in forestry management depending on accuracy requirements.
