  Quantification of forest biomass stocks and their dynamics is important for
implementing effective climate change mitigation measures. The knowledge is
needed, e.g., for local forest management, studying the processes driving af-,
re-, and deforestation, and can improve the accuracy of carbon-accounting.
Remote sensing using airborne LiDAR can be used to perform these measurements
of vegetation structure at large scale. We present deep learning systems for
predicting wood volume, above-ground biomass (AGB), and subsequently
above-ground carbon stocks directly from airborne LiDAR point clouds. We devise
different neural network architectures for point cloud regression and evaluate
them on remote sensing data of areas for which AGB estimates have been obtained
from field measurements in the Danish national forest inventory. Our adaptation
of Minkowski convolutional neural networks for regression gave the best
results. The deep neural networks produced significantly more accurate wood
volume, AGB, and carbon stock estimates compared to state-of-the-art approaches
operating on basic statistics of the point clouds. In contrast to other
methods, the proposed deep learning approach does not require a digital terrain
model. We expect this finding to have a strong impact on LiDAR-based analyses
of biomass dynamics.

Computer Vision and Pattern Recognition;Computers and Society;Machine Learning