AUTHOR=Zhang Hanyu , Liu Bingjie , Yang Bin , Guo Jiachang , Hu Zhenhua , Zhang Mengtao , Yang Zhaohui , Zhang Jianshuang 

TITLE=Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China

JOURNAL=Frontiers in Forests and Global Change

VOLUME=Volume 8 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2025.1431603

DOI=10.3389/ffgc.2025.1431603

ISSN=2624-893X

ABSTRACT=IntroductionThe unmanned aerial vehicle -based light detection and ranging (UAV-LiDAR) can quickly acquire the three-dimensional information of large areas of vegetation, and has been widely used in tree species classification.MethodsUAV-LiDAR point clouds of Populus alba, Populus simonii, Pinus sylvestris, and Pinus tabuliformis from 12 sample plots, 2,622 tree in total, were obtained in North China, training and testing sets were constructed through data pre-processing, individual tree segmentation, feature extraction, Non-uniform Grid and Farther Point Sampling (NGFPS), and then four tree species were classified efficiently by two machine learning algorithms and two deep learning algorithms.ResultsResults showed that PointMLP achieved the best accuracy for identification of the tree species (overall accuracy = 96.94%), followed by RF (overall accuracy = 95.62%), SVM (overall accuracy = 94.89%) and PointNet++(overall accuracy = 85.65%). In addition, the most suitable number of point cloud sampling of single tree is between 1,024 and 2048 when using the NGFPS method in the two deep learning models. Furthermore, feature value of elev_percentile_99th has an important influence on tree species classification and tree species with similar crown structures may lead to a higher misidentification rate.DiscussionThe study underscores the efficiency of PointMLP as a robust and streamlined solution, which offers a novel technological support for tree species classification in forestry resource management.