Data and code for the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models"

This is the repository for the data and code to reproduce the article "Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models". Summary: · Background and Aims Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate aboveground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (e.g. cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from e.g. beam footprint compared to the structure diameter. · Methods We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the aboveground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis, and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory. · Key Results The model accurately predicted the biomass with 1.6% nRMSE at the segment scale from a k-fold cross-validation. It also gave satisfactory results when up-scaled to the branch level with a significantly lower error (13% nRMSE) and bias (-5%) compared to conventional cylinder fitting to the point cloud (nRMSE: 92%, bias: 82%), or using the pipe model theory (nRMSE: 31%, bias: -27%). The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7km of structures on average and that 96% of that length was coming from the twigs (i.e. <5 cm diameter). Our results showed that neglecting twigs can lead to a significant underestimation of tree aboveground woody biomass (-21%). · Conclusions The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass.本仓库用于复现论文《Advancing Fine Branch Biomass Estimation with LiDAR and Structural Models》（《基于激光雷达与结构模型提升细枝生物量估算精度》）的数据与代码。 摘要： · 背景与研究目标 激光雷达（LiDAR）是实现树木快速精准测量的极具潜力的工具。目前已有多种基于激光雷达点云估算地上木质生物量的方法，其中应用最为广泛的一类方法是将几何基元（如圆柱体）拟合至点云，以此重建树木的几何形态与拓扑结构。然而，当前算法难以实现细枝生物量的精准估算，原因在于相较于枝条结构直径，激光雷达波束足迹（beam footprint）会导致点云分布不可靠，进而影响细枝体积的估算精度。 · 研究方法 我们提出一种新方法，将基于点云的骨架化与基于结构数据的多线性统计建模相结合，构建可精准估算树木地上木质生物量的结构模型（structural model），该模型可处理包括细枝在内的高质量激光雷达点云数据。本研究在片段级、轴级与枝条层级对该结构模型进行测试，并将其与圆柱体拟合算法（cylinder fitting algorithm）及管道模型理论（pipe model theory）进行对比。 · 关键结果 在k折交叉验证（k-fold cross-validation）下，该模型在片段级实现了1.6%的归一化均方根误差（nRMSE），精准预测生物量。当将模型扩展至枝条级时，其表现同样优异：相较于传统点云圆柱体拟合方法（归一化均方根误差：92%，偏差：82%）与管道模型理论（归一化均方根误差：31%，偏差：-27%），该模型的误差显著更低（13% nRMSE），偏差为-5%。随后将模型应用至整树尺度，结果显示采样树木的平均结构长度超过1.7千米，其中96%的长度来自直径小于5厘米的细枝（twigs）。研究结果表明，忽略细枝会导致树木地上木质生物量被显著低估（低估幅度达21%）。 · 研究结论 本结构模型方法可有效实现激光雷达点云中小枝条体积的精准估算。该方法具备通用性，但需要对枝条进行人工测量以完成模型校准。尽管如此，一旦模型完成校准，即可实现无偏且大规模的树木结构体积估算，为精准的树木三维重建与立木生物量估算提供了极佳的解决方案。