Abstract
Estimating forest above-ground biomass (AGB) is crucial for assessing carbon storage and supporting sustainable forest management. Quantitative Structural Model (QSM) offers a non-destructive approach to AGB estimation through 3D tree structural reconstruction. However, current QSM methods face significant limitations, as they are primarily designed for individual trees,depend on high-quality point cloud data from terrestrial laser scanning (TLS), and also require multiple pre-processing steps that hinder scalability and practical deployment. This study presents a novel unified framework that enables end-to-end processing of large-scale point clouds using an innovative graph-based pipeline. The proposed approach seamlessly integrates tree segmentation,leaf-wood separation and 3D skeletal reconstruction through dedicated graph operations including pathing and abstracting for tree topology reasoning. Comprehensive validation was conducted on datasets with varying leaf conditions (leaf-on and leaf-off), spatial scales (tree- and plot-level), and data sources (TLS and UAV-based laser scanning, ULS). Experimental results demonstrate strong performance under challenging conditions, particularly in leaf-on scenarios (~20% relative error) and low-density ULS datasets with partial coverage (~30% relative error). These findings indicate that the proposed framework provides a robust and scalable solution for large-scale, non-destructive AGB estimation. It significantly reduces dependency on specialized pre-processing tools and establishes ULS as a viable alternative to TLS. To our knowledge, this is the first method capable of enabling seamless, end-to-end 3D tree reconstruction at operational scales. This advancement substantially improves the feasibility of QSM-based AGB estimation, paving the way for broader applications in forest inventory and climate change research.
Abstract (translated)
估算森林地上生物量（AGB）对于评估碳储存和支持可持续森林管理至关重要。定量结构模型（QSM）通过3D树木结构重建提供了一种非破坏性的AGB估算方法。然而，现有的QSM方法面临重大限制，因为它们主要是为单一树木设计的，依赖于地面激光扫描（TLS）提供的高质量点云数据，并且需要多个预处理步骤，这阻碍了其可扩展性和实际部署。本研究提出了一种新颖的一体化框架，该框架能够使用创新的基于图的流水线对大规模点云进行端到端处理。所提出的这种方法通过专门的图操作（包括路径规划和抽象）无缝地集成了树木分割、叶木分离以及3D骨架重建。在具有不同叶片条件（带叶和无叶）、空间尺度（单树级和地块级）及数据来源（TLS和基于无人机的激光扫描，ULS）的数据集上进行了全面验证。 实验结果显示，在挑战性条件下性能强大，特别是在带叶场景中相对误差约为20%，以及在低密度ULS数据集中部分覆盖情况下的相对误差约为30%。这些发现表明，所提出的框架为大规模、非破坏性的AGB估算提供了稳健且可扩展的解决方案，大大减少了对专门预处理工具的依赖，并确立了ULS作为TLS的有效替代方案。据我们所知，这是第一个能够在操作规模上实现无缝端到端3D树木重建的方法。 这一进步极大地提高了基于QSM的AGB估算的实际可行性，为森林调查和气候变化研究中的更广泛应用铺平了道路。
URL
https://arxiv.org/abs/2506.15577
