Lößl, Clara

Machine Learning for Automated Forest and Plantation Inventory from LiDAR Point Clouds

Accurate and efficient tree monitoring is increasingly needed for comprehensive forest inventory, structural assessment, and carbon stock estimation. Moreover, the growing demand for sustainable energy production drives the need for efficient monitoring of fast-growing tree plantations, such as Short Rotation Coppices used for biomass and bioenergy.

Machine learning and geometric deep learning methods for analyzing LiDAR-derived 3D point clouds offer promising approaches for these applications. This project encompasses the complete pipeline from field data acquisition using aerial laser scanning (ALS) to method development, requiring interdisciplinary expertise bridging agricultural sciences and computer science. The aim is to investigate, explain, and improve machine learning approaches, with a focus on modern geometric deep learning architectures that learn directly from 3D point cloud representations. The research addresses individual tree segmentation from large-scale, high-resolution point clouds and automated regression of biometric parameters such as trunk diameter at breast height (DBH) and above-ground biomass (AGB). Both self-collected plantation data and publicly available forestry benchmark datasets are utilized to ensure generalizability.

The outcomes are expected to enhance inventory practices, reduce labor-intensive manual measurements, and contribute to sustainable forest management, biodiversity conservation, and climate change mitigation efforts.

Project team: Clara Lößl (Ph.D. Student), Dr.-Ing. Ralf Pecenka (ATB), Dr. Thomas Jarmer (UOS), Prof. Dr. Kai-Uwe Kühnberger (UOS), Prof. Dr. Martin Atzmüller (UOS), Prof. Dr. Tim Römer (UOS)