Research Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China.
Key Laboratory of Forest Ecology and Environment, State Forestry and Grassland Administration, Beijing, China.
PLoS One. 2019 Aug 29;14(8):e0221734. doi: 10.1371/journal.pone.0221734. eCollection 2019.
Living vegetation volume (LVV), one of the most difficult tree parameters to calculate, is among the most important factors that indicates the biological characteristics and ecological functions of the crown. Obtaining precise LVV estimates is, however, challenging task because the irregularities of many crown shapes are difficult to capture using standard forestry field equipment. Terrestrial light detection and ranging (T-LiDAR) can be used to record the three-dimensional structures of trees. The primary branches of Larix olgensis and Quercus mongolica in the Qingyuan Experimental Station of Forest Ecology at the Chinese Academy of Sciences (CAS) were taken as the research objects. A new rapid LVV estimation method called the filling method was proposed in this paper based on a T-LiDAR point cloud. In the proposed method, the branch point clouds are divided into leaf points and wood points. We used RiSCAN PRO 1.64 to manually separate the leaf points and wood points under careful visual inspection, and calculated that leaf points and wood points accounted for 91% and 9% of the number of the point clouds of branches. Then, the equation LVV = V1N (where N is the number of leaf points, and V1 is cube size) is used to calculate LVV. When the laser transmission frequency is 300,000 points/second and the point cloud is diluted to 30% using the octree method, the point cloud can be replaced by a cube (V1) of 6.11 cm3 to fill the branch space. The results showed that good performance for this approach, the measuring accuracy for L. olgensis and Q. mongolica at the levels of α = 0.05 and α = 0.01, respectively (94.35%, 90.01% and 91.99%, 85.63%, respectively). The results suggest that the proposed method can be conveniently used to calculate the LVV of coniferous and broad-leaf species under specific scanning settings. This work is explorative because hypotheses or a theoretical framework have not been previously defined. Rather, we would like to contribute to the formation of hypotheses as a background for further studies.
活体植被体积（LVV）是最难计算的树木参数之一，是树冠生物特性和生态功能的最重要指标之一。然而，由于许多树冠形状的不规则性很难用标准的林业现场设备来捕捉，因此获得精确的 LVV 估算值是一项具有挑战性的任务。地面激光探测和测距（T-LiDAR）可用于记录树木的三维结构。本研究以中国科学院森林生态研究所清源实验站的落叶松（Larix olgensis）和蒙古栎（Quercus mongolica）的主枝为研究对象。提出了一种基于 T-LiDAR 点云的新的快速 LVV 估算方法，称为填充法。在该方法中，将分支点云分为叶点和木点。我们使用 RiSCAN PRO 1.64 在仔细的视觉检查下手动分离叶点和木点，并计算出叶点和木点分别占分支点云数量的 91%和 9%。然后，使用 LVV = V1N（其中 N 是叶点的数量，V1 是立方大小）来计算 LVV。当激光传输频率为 300,000 点/秒，并且使用八叉树方法将点云稀释 30%时，可以使用 6.11 cm3 的立方体（V1）来填充分支空间。结果表明，该方法具有良好的性能，落叶松和蒙古栎的测量精度分别在α=0.05 和α=0.01 水平上达到 94.35%、90.01%和 91.99%、85.63%。结果表明，在特定扫描设置下，该方法可方便地用于计算针叶树种和阔叶树种的 LVV。这项工作是探索性的，因为以前没有定义假设或理论框架。相反，我们希望为进一步研究形成假设提供背景。
