  There are numerous emerging applications for digitizing trees using
terrestrial and aerial laser scanning, particularly in the fields of
agriculture and forestry. Interpretation of LiDAR point clouds is increasingly
relying on data-driven methods (such as supervised machine learning) that rely
on large quantities of hand-labelled data. As this data is potentially
expensive to capture, and difficult to clearly visualise and label manually, a
means of supplementing real LiDAR scans with simulated data is becoming a
necessary step in realising the potential of these methods. We present an open
source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point
clouds which simulate scanning with user-defined sensor, trajectory, tree shape
and layout parameters. Upon simulation, material classification is kept in a
pointwise fashion so leaf and woody matter are perfectly known, and unique
identifiers separate individual trees, foregoing post-simulation labelling.
This allows for an endless supply of procedurally generated data with similar
characteristics to real LiDAR captures, which can then be used for development
of data processing techniques or training of machine learning algorithms. To
validate our method, we compare the characteristics of a simulated scan with a
real scan using similar trees and the same sensor and trajectory parameters.
Results suggest the simulated data is significantly more similar to real data
than a sample-based control. We also demonstrate application of SimTreeLS on
contexts beyond the real data available, simulating scans of new tree shapes,
new trajectories and new layouts, with results presenting well. SimTreeLS is
available as an open source resource built on publicly available libraries.
