Work

Automatic Registration of Terrestrial and UAV LiDAR Forest Point Clouds Through Canopy Shape Analysis

Year: 2025

Type: article

Abstract: Accurate registration of multi-platform light detection and ranging (LiDAR) point clouds is essential for detailed forest structure analysis and ecological monitoring. In this study, we developed a novel two-stage method for aligning terrestrial and unmanned aerial vehicle LiDAR point clouds in forest environments. The method first performs coarse alignment using canopy-level digital surface models and Fast Point Feature Histograms, followed by fine registration with Iterative Closest Point. Experiments conducted in six forest plots achieved an average registration accuracy of 0.24 m within 5.14 s, comparable to manual registration but with substantially reduced processing time and human intervention. In contrast to existing tree-based methods, the proposed approach eliminates the need for individual tree segmentation and ground filtering, streamlining preprocessing and improving scalability for large-scale forest monitoring. The proposed method facilitates a range of forest applications, including structure modeling, ecological parameter retrieval, and long-term change detection across diverse forest types and platforms.

Source: Forests

DOI: 10.3390/f16081347

Authors: Sisi Yu, Zhanzhong Tang, Beibei Zhang, Jie Dai, Shangshu Cai

Institutions: Chinese Academy of Forestry, Institute of Forest Ecology, Environment and Protection, Xingtai University, Institute of Forest Resource Information Techniques, State Forestry and Grassland Administration

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,137