文献类型：期刊文献

英文题名：A Registration Method for ULS-MLS Data in High-Canopy-Density Forests Based on Feature Deviation Metric

作者：Liang, Houyu[1,2] Zhou, Xiang[2] Lv, Tingting[1] Liu, Qingwang[3] Tao, Zui[1] Zhang, Hongming[1]

第一作者：Liang, Houyu

通信作者：Zhou, X[1]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, State Key Lab Efficient Prod Forest Resources, Beijing 100091, Peoples R China

年份：2025

卷号：17

期号：20

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20254419427808);Scopus(收录号：2-s2.0-105020263677);WOS:【SCI-EXPANDED(收录号:WOS:001603028700001)】；

基金：This research was funded by the National Key Research and Development Program of China, grant number 2023YFB3907705.

语种：英文

外文关键词：forest; LiDAR; registration; ULS; MLS

摘要：Highlights What are the main findings? Currently, the registration of ULS (Unmanned Laser Scanning) and MLS (Mobile Laser Scanning) point clouds in high-canopy-density forests faces challenges such as significant differences in point cloud structure, uneven density, and limited overlap areas, making it difficult to achieve accurate registration. Our method achieves high-precision ULS-MLS registration in stratified mixed tropical forests with an average canopy density of 0.93 and pure forest plots in northern China with an average canopy density of 0.75. What is the implication of the main finding? We proposed a solution to address registration obstruction from structural complexity and ULS-MLS discrepancies in dense forests. We developed a new feature extraction and correspondence relationship construction methodology for coarse registration.Highlights What are the main findings? Currently, the registration of ULS (Unmanned Laser Scanning) and MLS (Mobile Laser Scanning) point clouds in high-canopy-density forests faces challenges such as significant differences in point cloud structure, uneven density, and limited overlap areas, making it difficult to achieve accurate registration. Our method achieves high-precision ULS-MLS registration in stratified mixed tropical forests with an average canopy density of 0.93 and pure forest plots in northern China with an average canopy density of 0.75. What is the implication of the main finding? We proposed a solution to address registration obstruction from structural complexity and ULS-MLS discrepancies in dense forests. We developed a new feature extraction and correspondence relationship construction methodology for coarse registration.Abstract The integration of unmanned aerial vehicle-based laser scanning (ULS) and mobile laser scanning (MLS) enables the detection of forest three-dimensional structure in high-density canopy areas and has become an important tool for monitoring and managing forest ecosystems. However, MLS faces difficulties in positioning due to canopy occlusion, making integration challenging. Due to the variations in observation platforms, ULS and MLS point clouds exhibit significant structural discrepancies and limited overlapping areas, necessitating effective methods for feature extraction and correspondence establishment between these features to achieve high-precision registration and integration. Therefore, we propose a registration algorithm that introduces a Feature Deviation Metric to enable feature extraction and correspondence construction for forest point clouds in complex regional environments. The algorithm first extracts surface point clouds using the hidden point algorithm. Then, it applies the proposed dual-threshold method to cluster individual tree features in ULS, using cylindrical detection to construct a Feature Deviation Metric from the feature points and surface point clouds. Finally, an optimization algorithm is employed to match the optimal Feature Deviation Metric for registration. Experiments were conducted in 8 stratified mixed tropical rainforest plots with complex mixed-species canopies in Malaysia and 6 structurally simple, high-canopy-density pure forest plots in anorthern China. Our algorithm achieved an average RMSE of 0.17 m in eight tropical rainforest plots with an average canopy density of 0.93, and an RMSE of 0.05 m in six northern forest plots in China with an average canopy density of 0.75, demonstrating high registration capability. Additionally, we also conducted comparative and adaptability analyses, and the results indicate that the proposed model exhibits high accuracy, efficiency, and stability in high-canopy-density forest areas. Moreover, it shows promise for high-precision ULS-MLS registration in a wider range of forest types in the future.