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

• URL: http://arxiv.org/abs/2312.15084v2
• Date: Fri, 23 Feb 2024 07:44:00 GMT
• Title: Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
• Authors: Binbin Xiang and Maciej Wielgosz and Theodora Kontogianni and Torben Peters and Stefano Puliti and Rasmus Astrup and Konrad Schindler
• Abstract summary: ForAINet is able to perform a segmentation across diverse forest types and geographic regions. System has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones.
• Score: 16.071397465972893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, termed ForAINet, 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.

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