We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual tree level employing a Gaussian process regressor. In order to validate the proposed model, a set of 8,342 samples on tree height, trunk diameter, and biomass has been assembled. It covers seven biomes globally present. We reference our model to four other models based on both, the Jucker data and our own dataset. Although our approach deviates from standard biomass-height-diameter models, we demonstrate the Gaussian process regression model as a viable alternative. In addition, we decompose the uncertainty of tree biomass estimates into the model- and fitting-based contributions. We verify the Gaussian process regressor has the capacity to reduce the fitting uncertainty down to below 5%. Exploiting airborne LiDAR measurements and a field inventory survey on the ground, a stand-level (or plot-level) study confirms a low relative error of below 1% for our model. The data used in this study are available at https://github.com/zhu-xlab/BiomassUQ .

翻译：我们提出了一种基于LiDAR数据的树级生物量估算模型，该模型近似异速生长方程。由于星载LiDAR测量难以准确估算树冠直径，我们开发了一个采用高斯过程回归器将单木树高与生物量关联的模型。为验证该模型，我们整理了包含树高、树干直径及生物量的8,342个样本数据集，涵盖全球七个生物群系。我们将本模型与基于Jucker数据集及自身数据集的其他四个模型进行了参照比较。尽管我们的方法偏离了标准的生物量-树高-直径模型，但我们证明了高斯过程回归模型是可行的替代方案。此外，我们将树木生物量估算的不确定性分解为模型贡献与拟合贡献两部分。验证结果表明，高斯过程回归器能将拟合不确定性降低至5%以下。利用机载LiDAR测量与地面实地勘察数据进行的林分（样地）水平研究证实，本模型的相对误差低于1%。本研究所用数据可从https://github.com/zhu-xlab/BiomassUQ获取。