Abstract Tropical forests are characterized by large carbon stocks and high biodiversity, but they are increasingly threatened by human activities. Since structure strongly influences the functioning and resilience of forest communities and ecosystems, it is important to quantify it at fine spatial scales. Here, we propose a new simulation-based approach, the “Canopy Constructor”, with which we quantified forest structure and biomass at two tropical forest sites, one in French Guiana, the other in Gabon. In a first step, the Canopy Constructor combines field inventories and airborne lidar scans to create virtual 3D representations of forest canopies that best fit the data. From those, it infers the forests' structure, including crown packing densities and allometric scaling relationships between tree dimensions. In a second step, the results of the first step are extrapolated to create virtual tree inventories over the whole lidar-scanned area. Across the French Guiana and Gabon plots, we reconstructed empirical canopies with a mean absolute error of 3.98 m [95% credibility interval: 3.02, 4.98], or 14.4%, and a small upwards bias of 0.66 m [−0.41, 1.8], or 2.7%. Height-stem diameter allometries were inferred with more precision than crown-stem diameter allometries, with generally larger heights at the Amazonian than the African site, but similar crown-stem diameter allometries. Plot-based aboveground biomass was inferred to be larger in French Guiana with 400.8 t ha−1 [366.2–437.9], compared to 302.2 t ha−1 in Gabon [267.8–336.8] and decreased to 299.8 t ha−1 [275.9–333.9] and 251.8 t ha−1 [206.7–291.7] at the landscape scale, respectively. Predictive accuracy of the extrapolation procedure had an RMSE of 53.7 t ha−1 (14.9%) at the 1 ha scale and 87.6 t ha−1 (24.2%) at the 0.25 ha scale, with a bias of −17.1 t ha−1 (−4.7%). This accuracy was similar to regression-based approaches, but the Canopy Constructor improved the representation of natural heterogeneity considerably, with its range of biomass estimates larger by 54% than regression-based estimates. The Canopy Constructor is a comprehensive inference procedure that provides fine-scale and individual-based reconstructions even in dense tropical forests. It may thus prove vital in the assessment and monitoring of those forests, and has the potential for a wider applicability, for example in the exploration of ecological and physiological relationships in space or the initialisation and calibration of forest growth models.

中文翻译：

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一种从机载激光扫描和森林清单推断树木异速生长和森林结构的模拟方法

摘要 热带森林具有碳储量大、生物多样性高等特点，但日益受到人类活动的威胁。由于结构强烈影响森林群落和生态系统的功能和恢复力，因此在精细空间尺度上对其进行量化非常重要。在这里，我们提出了一种新的基于模拟的方法，即“Canopy Constructor”，我们用它量化了两个热带森林地点的森林结构和生物量，一个在法属圭亚那，另一个在加蓬。第一步，Canopy Constructor 结合现场库存和机载激光雷达扫描，创建最适合数据的森林冠层虚拟 3D 表示。从这些，它推断森林的结构，包括树冠堆积密度和树木尺寸之间的异速生长比例关系。第二步，第一步的结果被推断为在整个激光雷达扫描区域内创建虚拟树木库存。在法属圭亚那和加蓬地块中，我们重建了平均绝对误差为 3.98 m [95% 可信区间：3.02, 4.98] 或 14.4% 以及 0.66 m [-0.41, 1.8] 的小向上偏差的经验冠层，或 2.7%。高度 - 茎直径异变比冠 - 茎直径异变更精确，亚马逊河的高度通常比非洲更高，但冠 - 茎直径异变相似。推断法属圭亚那基于地块的地上生物量较大，为 400.8 t ha-1 [366.2-437.9]，而加蓬为 302.2 t ha-1 [267.8-336.8]，并下降至 299.8 t ha-1 [275.9-1] 333.9] 和 251.8 t ha-1 [206.7–291.7] 在景观尺度上，分别。外推程序的预测准确度在 1 公顷规模下为 53.7 t ha-1（14.9%），在 0.25 公顷规模下为 87.6 t ha-1（24.2%），偏差为 -17.1 t ha-1 (-4.7%)。这种准确性类似于基于回归的方法，但 Canopy Constructor 显着改善了自然异质性的表示，其生物量估计范围比基于回归的估计大 54%。Canopy Constructor 是一种综合推理程序，即使在茂密的热带森林中也能提供精细尺度和基于个体的重建。因此，它可能在这些森林的评估和监测中被证明是至关重要的，并且具有更广泛的适用性，例如在空间生态和生理关系的探索或森林生长模型的初始化和校准中。