LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR

1. Leaf-wood separation in terrestrial LiDAR data is a prerequisite for
non-destructively estimating biophysical forest properties such as
standing wood volumes and leaf area distributions. Current methods have
not been extensively applied and tested on tropical trees. Moreover, their
impacts on the accuracy of subsequent wood volume retrieval were rarely
explored. 2. We present LeWoS, a new fully automatic tool to automate the
separation of leaf and wood components, based only on geometric
information at both the plot and individual tree scales. This data-driven
method utilizes recursive point cloud segmentation and regularization
procedures. Only one parameter is required, which makes our method easily
and universally applicable to data from any LiDAR technology and forest
type. 3. We conducted a two-fold evaluation of the LeWoS method on an
extensive data set of 61 tropical trees. We first assessed the point-wise
classification accuracy, yielding a score of 0.91 ± 0.03 in average.
Secondly, and for the first time, we evaluated the impact of the proposed
method on 3D tree models by cross-comparing estimates in wood volume and
branch length with those based on manually separated wood points. This
comparison showed similar results, with relative biases of less than 9%
and 21% on volume and length, respectively. 4. LeWoS allows an automated
processing chain for non-destructive tree volume and biomass estimation
when coupled with 3D modelling methods. The average processing time on a
laptop was 90s for 1 million points. We provide LeWoS as an open source
tool with an end-user interface, together with a large data set of
labelled 3D point clouds from contrasting forest structures. This study
closes the gap for stand volume modelling in tropical forests where leaf
and wood separation remain a crucial challenge.