Individual tree species classification using low-density airborne LiDAR data via attribute-aware cross-branch transformer

Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping reliable individual tree attributes. Although deep learning techniques have shown promise in tree species sification, they are not sufficiently explored for individual tree-level classification using low-density (less than point/m2) 2 ) Airborne Multispectral LiDAR (AML) data. This study therefore explores the feasibility of using a learning (DL) framework for processing low-density AML point clouds to enhance tree species classification challenging forest environments. A point-based deep learning network with a dual-branch mechanism combined Cross-Branch Attention modules named Attribute-Aware Cross-Branch (AACB) Transformer is designed for data to better differentiate tree species from delineated individual trees. In addition, a channel merging approach is introduced, which is suited to prepare the training samples of deep learning networks and reduces computational costs. This study was tested with an average 9 points/m2 2 AML point cloud for 6 tree species including Populus tremuloides, , Larix laricina, , Acer saccharum, , Picea abies, , Pinus resinosa, , and Pinus strobus from Canadian mixed forest. The overall accuracies achieved 83.1 %, 85.8 %, and 95.3 % at species, genus, and type levels, respectively. The comparison between the proposed method and other widely used tree species classification methods demonstrates the effectiveness of the proposed approach in enhancing tree species sification accuracy. We discuss potentials and remaining challenges, and our findings allow to further improve tree species classification of low-density AML point clouds by DL technology.