Sharing multi-platform light detection and ranging (LiDAR) point clouds of forests is of great significance for LiDAR remote sensing research and applications in forestry. To this end, the Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, has constructed a multi-platform LiDAR point cloud dataset for forest plots in subtropical regions, featuring airborne laser scanning, unmanned aerial vehicle laser scanning, and terrestrial laser scanning (TLS) point clouds, along with forest inventory data. The dataset was collected at Gaofeng Forest Farm in Guangxi, China, covering 25 plots with three tree species: Eucalyptus, Chinese fir, and Pinus massoniana. The field forest inventory data include plot locations, tree positions, diameter at breast height, tree height, height to the first live branch, and crown width. The dataset enables analysis of forest three-dimensional structural information captured by LiDAR from various platforms, evaluating automated processing algorithms like point cloud registration and tree segmentation. It provides important references for forest research at regional, plot, and tree levels. Additionally, this study developed a ground survey method guided by TLS data. This method utilizes tree stem point clouds to mark tree positions and measures individual trees according to tree maps, improving operational efficiency.

subtropical forest;light detection and ranging;airborne;unmanned aerial vehicle;terrestrial;multi-platform;forest plot;point cloud;reference dataset;field forest inventory