  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 .
