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Detailed forest inventories are critical for sustainable and flexiblemanagement of forest resources, to conserve various ecosystem services. Modernairborne laser scanners deliver high-density point clouds with great potentialfor fine-scale forest inventory and analysis, but automatically partitioningthose point clouds into meaningful entities like individual trees or treecomponents remains a challenge. The present study aims to fill this gap andintroduces a deep learning framework that is able to perform such asegmentation across diverse forest types and geographic regions. From thesegmented data, we then derive relevant biophysical parameters of individualtrees as well as stands. The system has been tested on FOR-Instance, a datasetof point clouds that have been acquired in five different countries usingsurveying drones. The segmentation back-end achieves over 85% F-score forindividual trees, respectively over 73% mean IoU across five semanticcategories: ground, low vegetation, stems, live branches and dead branches.Building on the segmentation results our pipeline then densely calculatesbiophysical features of each individual tree (height, crown diameter, crownvolume, DBH, and location) and properties per stand (digital terrain model andstand density). Especially crown-related features are in most cases retrievedwith high accuracy, whereas the estimates for DBH and location are lessreliable, due to the airborne scanning setup.

Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning