Accurate forest mapping is essential for understanding and managing forest ecosystems. In recent years, Light Detection and Ranging (LiDAR) technology has become a powerful tool for forest monitoring. However, registering UAV-LiDAR strips remains challenging in GNSS-degraded forest environments due to sparse ground features, canopy occlusion, and limited availability of reliable control points. Existing registration methods often rely on surface-based or feature-plane matching, which can be suboptimal in structurally heterogeneous forests. To address this gap, we present a robust and target-less registration framework for UAVLS data that leverages tree localization as structural anchors. Each detected tree is encoded into a graph using three spatial descriptors: (a) planimetric distance, (b) vertical distance, and (c) height difference relative to neighboring trees. Initial correspondences between trees across overlapping strips are established using the Hungarian algorithm, which maximizes a similarity function derived from these descriptors. Subsequently, a three-dimensional rigid transformation (rotation and translation) is estimated via Particle Swarm Optimization (PSO) to refine the spatial alignment. The method was validated on eight circular forest plots (30 m in diameter) derived from two overlapping UAVLS strips. The root mean square error (RMSE) of residual distances between registered tree pairs ranged from ±17.6 cm to ±27.4 cm for poplar plots and from ±17.5 cm to ±24.3 cm for dawn blackwood plots. Furthermore, the proposed method improved the tree matching ratio by 17%–27% compared to a baseline approach, demonstrating higher alignment accuracy. By integrating graph-based correspondence with swarm-based optimization, this study contributes a scalable, accurate, and GNSS-independent registration solution tailored to the complexities of forest environments. The framework has strong potential to support UAV-LiDAR-based forest mapping, monitoring, and inventory tasks in both research and operational settings. |
