Detailed forest inventories are critical for sustainable and flexible
management of forest resources, to conserve various ecosystem services. Modern
airborne laser scanners deliver high-density point clouds with great potential
for fine-scale forest inventory and analysis, but automatically partitioning
those point clouds into meaningful entities like individual trees or tree
components remains a challenge. The present study aims to fill this gap and
introduces a deep learning framework that is able to perform such a
segmentation across diverse forest types and geographic regions. From the
segmented data, we then derive relevant biophysical parameters of individual
trees as well as stands. The system has been tested on FOR-Instance, a dataset
of point clouds that have been acquired in five different countries using
surveying drones. The segmentation back-end achieves over 85% F-score for
individual trees, respectively over 73% mean IoU across five semantic
categories: ground, low vegetation, stems, live branches and dead branches.
Building on the segmentation results our pipeline then densely calculates
biophysical features of each individual tree (height, crown diameter, crown
volume, DBH, and location) and properties per stand (digital terrain model and
stand density). Especially crown-related features are in most cases retrieved
with high accuracy, whereas the estimates for DBH and location are less
reliable, due to the airborne scanning setup.

通过介绍一种深度学习框架，本研究旨在自动地将高密度点云分割为有意义的树木实体，以及树组件，并从分割数据中获得相关的生物物理参数。通过测试，该系统在个体树木和五个语义类别的分割上取得了较高的准确性，在树冠相关特征方面表现尤其突出，而对于树直径和位置的估计相对不太可靠，这是由于航空扫描设置的原因。