Three-Dimensional Reconstruction of Forest Scenes with Tree-Shrub-Grass Structure Using Airborne LiDAR Point Cloud

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground-grass points from an airborne LiDAR point cloud. For ground-grass points, a ground-grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D alpha-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D alpha-shape algorithm. Finally, tree, shrub, and ground-grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-W & uuml;rttemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE < 0.15 m, the volume accuracy of shrub and tree models is assessed with R-2 > 0.848 and R-2 > 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes.