The spatial pattern and driving factor analysis for 3D architecture of Quercus mongolica using terrestrial LiDAR technology

Three-dimensional (3D) tree architecture is the external response of the interaction between tree internal genetic information and environmental conditions. Effective and accurate extraction of 3D tree architecture in different forest environments is the prerequisite for understanding its large-scale spatial pattern and driving factors. Currently, field-based manual measurement is one of the most frequently used methods for tree architecture parameter extraction. However, taking field measurements is very labor-intensive and time-consuming. Near-surface light detection and ranging (LiDAR) technology has a strong capability to penetrate forest canopy and obtain 3D tree structures, which provides a new way to obtain tree architecture parameters. In this study, Quercus mongolica, a species that can transit from arbor under moisture environment to shrub under arid environment, was used as an example to analyze the plasticity of tree architecture. Terrestrial LiDAR data and field measurements were collected at 12 study sites across northern China with various precipitation and temperature conditions, and 15 tree architecture parameters were extracted. The results show that the terrestrial LiDAR data can be used to extract 3D tree architecture parameters at the individual tree level and branch level accurately (R²>0.9). Most of Quercus mongolica architecture parameters were significantly different among populations (p<0.05). Leaf-level parameters (such as leaf area, leaf length and leaf width) showed the most significant variations among populations and had relatively high plasticity (relative distances plasticity index = 0.41-0.61). From west to east, leaf area, leaf length, and specific leaf area increased significantly (p<0.05); tree height and DBH also had an increasing pattern, but insignificant; and leaf area index was the only parameter that had a significant decreasing pattern (p<0.05). From north to south, all architecture parameters had no significant changing patterns. The Mixed Effect Model analysis indicated that precipitation was the most important factor controlling the variation in tree height and leaf area; while the interactions between genetic populations and the length of growing season had the largest contribution to leaf area index and specific leaf area variations.