Data underlying the publication: Improving the 3D Representation of Plant Architecture and Parameterization Efficiency of Functional-Structural Tree Models using Terrestrial LiDAR Data

Data repository for all materials for the paper<em> "Improving the 3D Representation of Plant Architecture and Parameterization Efficiency of Functional-Structural Tree Models using Terrestrial LiDAR Data".</em> For this paper we demonstrated the use of TLS data for FSP model inputs of a general tropical tree model and Scots Pine model. This dataset constrains the raw tropical tree point clouds collected in Guyana, and scots pine trees collected in Loobs, the Netherlands. These were processed by removing the leaves using the LeWoS algorithm and fitting a Quantitative Structural Model (QSM) to derive tree architecture information. The information from the QSM was processed and used as FSPM model inputs. The dataset contains the QSM output results from the processed point clouds. Additionally the field data used to assess the accuracy are provided, and the python scripts to process, analyse, and visualise the data.

本数据集为论文《基于地面激光雷达数据优化功能结构树木模型的植物构型三维表征与参数化效率》的全部配套材料仓库。本研究在该论文中验证了将地面激光雷达（Terrestrial LiDAR, TLS）数据应用于通用热带树木模型与苏格兰松模型的功能结构树木模型输入的可行性。本数据集包含在圭亚那采集的原始热带树木点云数据，以及在荷兰卢布斯（Loobs）采集的苏格兰松树木点云数据。上述点云数据均经过预处理：通过LeWoS算法去除叶片点云，并拟合定量结构模型（Quantitative Structural Model, QSM）以提取树木构型信息。从QSM提取的构型信息经进一步处理后，将作为功能结构树木模型的输入参数。本数据集包含经预处理点云生成的QSM输出结果。此外，数据集还提供了用于模型精度评估的野外实测数据，以及用于数据处理、分析与可视化的Python脚本。