Lidar-based Norwegian tree species detection using deep learning
Martijn Vermeer Jacob Alexander Hay David Völgyes Zsófia Koma Johannes Breidenbach Daniele Stefano Maria Fantin
Background: The mapping of tree species within Norwegian forests is a
time-consuming process, involving forest associations relying on manual
labeling by experts. The process can involve both aerial imagery, personal
familiarity, or on-scene references, and remote sensing data. The
state-of-the-art methods usually use high resolution aerial imagery with
semantic segmentation methods. Methods: We present a deep learning based tree
species classification model utilizing only lidar (Light Detection And Ranging)
data. The lidar images are segmented into four classes (Norway Spruce, Scots
Pine, Birch, background) with a U-Net based network. The model is trained with
focal loss over partial weak labels. A major benefit of the approach is that
both the lidar imagery and the base map for the labels have free and open
access. Results: Our tree species classification model achieves a
macro-averaged F1 score of 0.70 on an independent validation with National
Forest Inventory (NFI) in-situ sample plots. That is close to, but below the
performance of aerial, or aerial and lidar combined models.