Calibration and validation of aboveground biomass (AGB) (AGB) products retrieved from satellite-borne sensors require accurate AGB estimates across hectare scales (1 to 100 ha). Recent studies recommend making use of non-destructive terrestrial laser scanning (TLS) based techniques for individual tree AGB estimation that provide unbiased AGB predictors. However, applying these techniques across large sites and landscapes remains logistically challenging. Unoccupied aerial vehicle laser scanning (UAV-LS) has the potential to address this through the collection of high density point clouds across many hectares, but estimation of individual tree AGB based on these data has been challenging so far, especially in dense tropical canopies. In this study, we investigated how TLS and UAV-LS can be used for this purpose by testing different modelling strategies with data availability and modelling framework requirements. The study included data from four forested sites across three biomes: temperate, wet tropical, and tropical savanna. At each site, coincident TLS and UAV-LS campaigns were conducted. Diameter at breast height (DBH) and tree height were estimated from TLS point clouds. Individual tree AGB was estimated for ≥170 trees per site based on TLS tree point clouds and quantitative structure modelling (QSM), and treated as the best available, non-destructive estimate of AGB in the absence of direct, destructive measurements. Individual trees were automatically segmented from the UAV-LS point clouds using a shortest-path algorithm on the full 3D point cloud. Predictions were evaluated in terms of individual tree root mean square error (RMSE) and population bias, the latter being the absolute difference between total tree sample population TLS QSM estimated AGB and predicted AGB. The application of global allometric scaling models (ASM) at local scale and across data modalities, i.e., field-inventory and light detection and ranging LiDAR metrics, resulted in individual tree prediction errors in the range of reported studies, but relatively high population bias. The use of adjustment factors should be considered to translate between data modalities. When calibrating local models, DBH was confirmed as a strong predictor of AGB, and useful when scaling AGB estimates with field inventories. The combination of UAV-LS derived tree metrics with non-parametric modelling generally produced high individual tree RMSE, but very low population bias of ≤5% across sites starting from 55 training samples. UAV-LS has the potential to scale AGB estimates across hectares with reduced fieldwork time. Overall, this study contributes to the exploitation of TLS and UAV-LS for hectare scale, non-destructive AGB estimation relevant for the calibration and validation of space-borne missions targeting AGB estimation.

从星载传感器检索的地上生物量 (AGB) (AGB) 产品的校准和验证需要跨公顷规模（1 到 100 哈）。最近的研究建议使用基于非破坏性地面激光扫描 (TLS) 的技术进行单个树 AGB 估计，从而提供无偏的 AGB 预测器。然而，在大型场地和景观中应用这些技术在后勤方面仍然具有挑战性。无人飞行器激光扫描 (UAV-LS) 有可能通过收集多公顷的高密度点云来解决这个问题，但迄今为止，基于这些数据估计单个树的 AGB 一直具有挑战性，尤其是在茂密的热带树冠中。在这项研究中，我们通过测试具有数据可用性和建模框架要求的不同建模策略来研究如何将 TLS 和 UAV-LS 用于此目的。该研究包括来自三个生物群落的四个森林地点的数据：温带、湿热带、和热带稀树草原。在每个站点，同时进行了 TLS 和 UAV-LS 活动。从 TLS 点云估计胸径 (DBH) 和树高。基于 TLS 树点云和定量结构建模 (QSM) 估计每个站点 ≥ 170 棵树的单个树 AGB，并在没有直接、破坏性测量的情况下被视为 AGB 的最佳可用、非破坏性估计。使用全 3D 点云上的最短路径算法从 UAV-LS 点云中自动分割出单个树。根据单个树的均方根误差 (RMSE) 和总体偏差来评估预测，后者是总树样本总体 TLS QSM 估计的 AGB 和预测的 AGB 之间的绝对差。全局异速生长缩放模型 (ASM) 在局部尺度和跨数据模式（即现场库存和光检测以及测距 LiDAR 指标）的应用导致报告研究范围内的个体树预测误差，但人口偏差相对较高。应考虑使用调整因子在数据模式之间进行转换。在校准本地模型时，DBH 被确认为 AGB 的强预测因子，并且在使用现场清单缩放 AGB 估计时很有用。UAV-LS 派生的树指标与非参数建模的组合通常会产生较高的个体树 RMSE，但从 55 个训练样本开始，跨站点的总体偏差非常低，≤5%。UAV-LS 有可能在减少现场工作时间的情况下在公顷范围内扩展 AGB 估计。全面的，