Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
Climate-smart and biodiversity-preserving forestry demands precise
information on forest resources, extending to the individual tree level.
Multispectral airborne laser scanning (ALS) has shown promise in automated
point cloud processing and tree segmentation, but challenges remain in
identifying rare tree species and leveraging deep learning techniques. This
study addresses these gaps by conducting a comprehensive benchmark of machine
learning and deep learning methods for tree species classification. For the
study, we collected high-density multispectral ALS data (>1000 pts/m$^2$) at
three wavelengths using the FGI-developed HeliALS system, complemented by
existing Optech Titan data (35 pts/m$^2$), to evaluate the species
classification accuracy of various algorithms in a test site located in
Southern Finland. Based on 5261 test segments, our findings demonstrate that
point-based deep learning methods, particularly a point transformer model,
outperformed traditional machine learning and image-based deep learning
approaches on high-density multispectral point clouds. For the high-density ALS
dataset, a point transformer model provided the best performance reaching an
overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065
segments and 92.0% (85.1%) with 5000 training segments. The best image-based
deep learning method, DetailView, reached an overall (macro-average) accuracy
of 84.3% (63.9%), whereas a random forest (RF) classifier achieved an overall
(macro-average) accuracy of 83.2% (61.3%). Importantly, the overall
classification accuracy of the point transformer model on the HeliALS data
increased from 73.0% with no spectral information to 84.7% with single-channel
reflectance, and to 87.9% with spectral information of all the three channels.
