Time series of national biomass maps from deep learning applied to airborne laser scanning point cloud data

Abstract
More accurate local estimates of biomass and other forest attributes translate
into more accurate national-level estimates, improving forest monitoring and
informing forest policy. Higher-resolution local estimates facilitate more precise
monitoring of forest growth and harvest, allowing for better forest management
planning, and can also be used for verification of forest carbon storage, such as
for tree-based carbon credit programs and afforestation projects.

We present the first time series of high-resolution national maps of tree biomass,
carbon, volume, canopy height, and basal area produced using deep learning
methods applied to 3D point cloud LiDAR data. With hexagonal tiles of a 30
m diameter, the maps enable direct observation of stock change of aboveground
biomass, carbon, and other forest attributes at high resolution, in contrast to
inventory based estimates or coarser resolution remote sensing-based products.
We verify that our approach provides reliable estimates at the national and local
scales by comparing it to additional ground truth plot data from a time series
of local inventories.

The model was trained and validated on ground-truth data from the Danish Na-
tional Forest Inventory (DNFI) by combining field measurements aligned with
more than 20,000 sample plots extracted from two complete national LiDAR
scans. Based on [1], we apply a 3D convolutional neural network (CNN) using
the SENet50 architecture. We extended the approach to perform quantile re-
gression for uncertainty quantification. Our best model achieves an R2 of 0.83
for biomass and carbon, 0.84 for volume, 0.91 for canopy height, and 0.78 for
basal area on validation data.

We find that our model outperforms other state-of-the-art methods, which are
either based on passive 2D imagery or depend on using point cloud data indi-
rectly by extracting summary statistics. By using active LiDAR, we can derive
information from beneath tree canopies, and using the full point cloud enables
the model to learn from detailed information on forest structure, which may be
a key advantage.

The high resolution and accuracy of our method offer unprecedented potential
for time series analysis. The model is sensitive to changes at the individual tree
level, allowing for the detection of individual tree removals or growth. While
large scale forest cover change is easily detected with aerial imagery, thinnings
or partial removals are more difficult to uncover with most methods; however,
our analysis of independent repeated local inventory plots shows that our model
successfully detects smaller scale thinnings and tree growth.

References
[1] Stefan Oehmcke et al. “Deep point cloud regression for above-ground forest
biomass estimation from airborne LiDAR”. In: Remote Sensing of Environ-
ment 302 (2024).