QuadrantSearch: A Novel Method for Registering UAV and Backpack LiDAR Point Clouds in Forested Areas

Unmanned aerial vehicle (UAV) laser scanning (ULS) and backpack laser scanning (BLS) are two commonly employed technologies in precision forestry. However, data acquired by these two types of light detection and ranging (LiDAR) are distinct, with one capturing point clouds beneath the canopy and the other above. Consequently, there is minimal overlap in the point clouds collected by both methods, especially in dense forests, presenting significant challenges for data registration. Furthermore, many trees in forests (particularly broadleaf trees) have the tree tops and trunk centers not aligned vertically, which greatly increases the difficulty of the data registration methods based on tree position. To solve the above-mentioned problems, we here propose a novel and robust method to register ULS and BLS point clouds in forested areas. Our method consists of three key steps, that is, tree location extraction, quadrant search-based minimum spanning tree (MST) matching, and registration. The quadrant searching strategy dynamically searches for potential candidates in four quadrants centered on the initial tree locations. By constructing MSTs for the potential tree locations, triangle constraints require only four topologically similar tree locations to find one-to-one correspondences during the stepwise MST matching process. The proposed method was evaluated in five urban forest sample plots and one natural forest sample plot located in China, covering both coniferous and broadleaf forests. The results show that our method obtained good registration results on all six sample plots, with an averaged rotation error, translation error, pointwise error, and root-mean-square error (RMSE) of 0.012 rad, 0.354, 0.378, and 0.379 m, respectively. Comparative studies indicate that our method outperformed existing registration methods, demonstrating its effectiveness and robustness. Our method allows for the creation of a more complete picture of forest vertical structure and holds great potential for informing sustainable forest management practices and supporting critical ecological assessments.