Data underlying the publication: 'Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana'

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R2 = 0.92–0.93) than traditional pantropical models (R2 = 0.85–0.89), and were especially accurate for large trees (diameter &gt; 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees. <br>

树木与森林碳储量估算中存在的显著不确定性，削弱了各国依托国家监测、计量、报告与核查系统精准估算地上生物量（aboveground biomass, AGB）的工作成效。用于估算生物量的异速生长方程虽已有所改进，但仍存在诸多局限：此类方程依赖破坏性采样；构建方程所用数据集内大型树木占比不足；且无法直接推广应用至不同区域。上述因素均会导致生物量估算产生不确定性与系统误差。本研究针对圭亚那地区的树木地上生物量开发了异速生长模型，模型基于72株热带树木的地面激光扫描（terrestrial laser scanning, TLS）点云获取的树木属性（胸径、树高、冠幅）以及木材密度构建。我们利用额外26株经破坏性采样获取的树木数据，对所提出的方法与模型进行了验证。研究结果表明，纳入冠幅参数的最优地面激光扫描衍生异速生长模型，其地上生物量估算精度（决定系数R²=0.92~0.93）优于传统泛热带模型（R²=0.85~0.89），尤其对胸径大于70cm的大型树木具备更高的估算准确性。所评估的泛热带模型对地上生物量存在4%~13%的低估情况。不过，其中一款泛热带模型（Chave等人2005年的研究，未纳入树高参数）在所有测试的泛热带模型中表现始终最优（R²=0.89），且可在所有树木径级范围内实现精准的地上生物量预测——若无破坏性采样或地面激光扫描衍生的验证数据，我们无法获知这一结论。本研究同时发现，实地测量树高存在较大难度，且在异速生长模型中纳入树高参数反而会持续降低地上生物量的估算精度。我们验证得出，基于地面激光扫描得到的地上生物量估算结果不存在偏倚。本研究提出的方法无需开展破坏性采样即可完成异速生长模型的开发、测试与筛选，为相关研究提供了方法学层面的改进。