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.

中文翻译：

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通过属性感知交叉分支变压器使用低密度机载多光谱 LiDAR 数据对单个树种进行分类


传统的森林清查为森林监测和管理提供必要的数据，包括树种，但获取单个树木级别的信息越来越重要。具有多光谱观测功能的机载光探测和测距 （LiDAR） 为改进具有可靠单棵树木属性的森林清查制图提供了丰富的信息。尽管深度学习技术在树种分类方面显示出前景，但对于使用低密度（小于 30 点/m2）机载多光谱 LiDAR （AML） 数据的单个树级分类，它们并未得到充分探索。因此，本研究探讨了使用深度学习 （DL） 框架处理低密度 AML 点云以增强具有挑战性的森林环境中树种分类的可行性。一个名为 Attribute-Aware Cross-Branch （AACB） Transformer 的基于点的深度学习网络具有双分支机制组合的跨分支注意力模块，专为 AML 数据而设计，以更好地区分树种和描绘的单个树木。此外，还引入了一种通道合并方法，该方法适用于制备深度学习网络的训练样本，降低了计算成本。本研究对来自加拿大混交林的 6 种树种进行了平均 9 点/m2 AML 点云测试，包括 Populus tremuloides、Larix laricina、Acer saccharum、Picea abies、Pinus resinosa 和 Pinus strobus。在物种、属和叶型水平上，总体准确度分别达到 83.1 % 、 85.8 % 和 95.3 %。所提出的方法与其他广泛使用的树种分类方法之间的比较证明了所提出的方法在提高树种分类准确性方面的有效性。 我们讨论了潜力和仍然存在的挑战，我们的发现允许通过 DL 技术进一步改进低密度 AML 点云的树种分类。