Many studies have established the strong connections between aboveground biomass and lidar height metrics; however, these relationships are site-specific. Field data required to derive these relationships are not readily available in many cases. We developed a model to estimate plot-level aboveground biomass density (AGBD) directly from large-footprint lidar waveform measurements. An individual tree-based aboveground biomass (AGB)-height allometric relationship was scaled up to the plot level using lidar-waveform sensed tree height and crown size distribution characteristics. The AGBD was estimated based on a waveform/foliage profile-weighted height-based allometric equation. The AGBD-height scaling exponent was then built on the allometric relationships of tree height with stem diameter and crown volume with tree height. Global vegetation structure data analysis demonstrated that one general model (scaling exponent ~ 1.6–1.8) works reasonably well across all global forest biomes except boreal forests (scaling exponent ~ 0.9). We applied the model to estimate aboveground biomass in two distinct geographic regions: temperate deciduous/conifer forests in the northeastern USA and a montane conifer forest in Sierra National Forest in California. Local vegetation structural data analysis leads to a consistent height scaling exponent for these two distinct biomes, slightly different from the global data analysis results. This model produced optimal AGBD estimates using the local height scaling exponent value. Adequate AGBD estimates with the general height scaling exponent value were also provided by our model. Our analysis suggests one general allometric relationship between plot-level AGBD and large-footprint lidar waveforms. Integrating local structure allometric relationships improve the predictive accuracy of the model. Our model outperformed the lidar height metrics-based approach for AGBD estimates and overcame the biomass underestimation problem using height metrics for high biomass regions. This model could potentially serve as a general and robust model for monitoring forest carbon stocks using large-footprint lidar waveform measurements such as the Global Ecosystem Dynamics Investigation (GEDI) mission at the continental and global scales. The model could be a framework for integrating a demography-based terrestrial ecosystem model and GEDI global mission measurements to improve global carbon stock and flux estimates.

许多研究已经建立了地上生物量和激光雷达高度指标之间的密切联系；但是，这些关系是特定于站点的。在许多情况下，导出这些关系所需的现场数据并不容易获得。我们开发了一个模型来直接从大面积激光雷达波形测量中估计地块级地上生物量密度 (AGBD)。使用激光雷达波形感测的树高和树冠尺寸分布特征，将单个基于树的地上生物量 (AGB)-高度异速生长关系按比例放大到样地级别。AGBD 是根据波形/树叶轮廓加权的基于高度的异速生长方程估算的。然后将 AGBD 高度比例指数建立在树高与茎直径和树冠体积与树高的异速生长关系上。全球植被结构数据分析表明，一个通用模型（尺度指数~1.6-1.8）在除北方森林（尺度指数~0.9）之外的所有全球森林生物群系中都相当有效。我们应用该模型来估计两个不同地理区域的地上生物量：美国东北部的温带落叶/针叶林和加利福尼亚塞拉国家森林的山地针叶林。局部植被结构数据分析导致这两个不同生物群落的高度比例指数一致，与全球数据分析结果略有不同。该模型使用局部高度缩放指数值产生最佳 AGBD 估计。我们的模型还提供了具有一般高度缩放指数值的充分 AGBD 估计值。我们的分析表明，绘图级 AGBD 和大尺寸激光雷达波形之间存在一种普遍的异速生长关系。整合局部结构异速生长关系提高了模型的预测精度。我们的模型在 AGBD 估计方面优于基于激光雷达高度指标的方法，并使用高生物量区域的高度指标克服了生物量低估问题。该模型有可能成为一个通用且稳健的模型，用于使用大型激光雷达波形测量来监测森林碳储量，例如大陆和全球尺度的全球生态系统动力学调查 (GEDI) 任务。该模型可以是一个框架，用于整合基于人口学的陆地生态系统模型和 GEDI 全球任务测量，以改进全球碳储量和通量估计。