Annals of Forest Science (May 2025)
Extraction of individual tree attributes using ultra-high-density point clouds acquired by low-cost UAV-LiDAR in Eucalyptus plantations
Abstract
Abstract Key message In this paper, we first introduced a novel method for directly measuring tree diameters from UAV-LiDAR point clouds utilizing the χ 2-filtering technique and a technique for measuring tree heights using pseudo-waveforms. Context Eucalyptus plantation forests constitute the largest expanse of planted broad-leaved forests worldwide. Detailed and accurate individual tree attributes are essential for precision forestry. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) are frequently employed to acquire information on individual trees. However, both technologies suffer from low efficiency. Therefore, the challenge remains how to access this information efficiently. Aims Consequently, this paper investigated a novel technical approach to automatically extract individual tree attributes using low-cost UAV-LiDAR technology. Methods The framework consists of three independent yet interrelated approaches. Firstly, the tree trunks were detected using an approach based on the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm. It utilized 3D point clouds to achieve precise tree counts and their approximate locations. These locations then enabled cylindrical segmentation of the point clouds at the trunk level, facilitating diameter measurement. Secondly, stem diameters were directly measured using the probability density function of the chi-square distribution. This process produced precise stem diameters, trunk positions, and growth directions, which were subsequently used to determine the center of the crown top for tree height extraction. Lastly, the tree height was estimated based on the pseudo-waveforms. We validated this framework by acquiring ultra-high-density UAV-LiDAR data in an Eucalyptus plantation. Results The result indicated a precision of 91.1% for individual tree detection, with an F-score of 0.916. The root mean square errors (RMSEs) for direct measurements of diameter at breast height (DBH) and tree height were 14.60% (2.18 cm) and 2.69% (0.31 m), respectively. Furthermore, this study suggested that the classical circle-fitting method might not be suitable for directly measuring tree diameter using low-cost UAV-LiDAR data. Conclusion The proposed framework facilitates automated inventory and monitoring in Eucalyptus plantation forests. However, more trials are needed to verify the framework’s applicability in other planted and natural forests.
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