Terrain and individual tree vertical structure-based approach for point clouds co-registration by UAV and Backpack LiDAR

Abstract

Tree-level structural parameters estimation plays a key role in the researches and practice in sustainable forest management, carbon storage estimation, as well as ecological function evaluation. However, single Light Detection and Ranging (LiDAR) platform exhibits limitations when acquiring complete (i.e., including over-story and under-story) point cloud data for forest stands, e.g., UAV LiDAR systems tend to overlook details of the tree trunk or the lower ground, while Backpack LiDAR systems struggle to capture the treetop, etc. The limited shared features of point clouds from UAV and Backpack LiDAR sensors also pose challenges in the accurate registration and merging of these datasets. In this study, we proposed a marker free automatic registration framework for multi-platform forest point clouds with terrain features. The framework comprised three key stages: first, a curvature-adaptive weighting mechanism was adapted to optimized the Fast Point Feature Histogram (FPFH) descriptors for initial coarse registration, utilizing terrains features. Second, individual tree positions were extracted from each platform’s LiDAR dataset and employed as key feature points for matching. Third, a similarity function was constructed to evaluate the most geometrically consistent point correspondences across platforms, which were subsequently refined through an Iterative Closest Point (ICP) algorithm. Furthermore, a voxel-based denoising algorithm that integrated point density with vertical connectivity was developed to identify and filter out noise from the backpack LiDAR data—specifically, non-structural elements such as branches and shrubs. This denoising process laid a robust foundation for accurately locating individual tree centers. Additionally, a layer-wise adaptive circular fitting method was introduced for determining trunk positions. By clustering trunk point clouds at successive vertical layers, this method yielded precise estimates of straight, individual tree trunk centers for use in subsequent registration steps. The proposed framework achieved a registration accuracy of RMSE = 0.098–0.134 m across diverse forest types and terrain conditions, demonstrating its robustness and applicability in complex environments. This facilitated the integration of UAV and backpack LiDAR technologies in forestry resource monitoring. Using the fused point cloud data, tree-level structural parameters estimation of diameter at breast height (RMSE = 1–1.2 cm), tree height (RMSE = 0.29–0.55 m).