Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer,Remote Sensing of Environment

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Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2024-10-05 , DOI: 10.1016/j.rse.2024.114456
Lanying Wang, Dening Lu, Linlin Xu, Derek T. Robinson, Weikai Tan, Qian Xie, Haiyan Guan, Michael A. Chapman, Jonathan Li

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 with reliable individual tree attributes. Although deep learning techniques have shown promise in tree species classification, they are not sufficiently explored for individual tree-level classification using low-density (less than 30 point/m²) Airborne Multispectral LiDAR (AML) data. This study therefore explores the feasibility of using a deep learning (DL) framework for processing low-density AML point clouds to enhance tree species classification in 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 AML 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 the computational costs. This study was tested with an average 9 points/m² AML point cloud for 6 tree species including Populus tremuloides, Larix laricina, Acer saccharum, Picea abies, Pinus resinosa, and Pinus strobus from a Canadian mixed forest. The overall accuracies achieved 83.1 %, 85.8 %, and 95.3 % at species, genus, and leaf-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 classification 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.

更新日期：2024-10-05

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