Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests

• Verfasst von: Weiser, Hannah [VerfasserIn]
• Verfasst von: Schäfer, Jannika [VerfasserIn]
• Verfasst von: Winiwarter, Lukas [VerfasserIn]
• Verfasst von: Krašovec, Nina [VerfasserIn]
• Verfasst von: Fassnacht, Fabian E. [VerfasserIn]
• Verfasst von: Höfle, Bernhard [VerfasserIn]
• Titel: Individual tree point clouds and tree measurements from multi-platform laser scanning in German forests
• Verf.angabe: Hannah Weiser, Jannika Schäfer, Lukas Winiwarter, Nina Krašovec, Fabian E. Fassnacht, and Bernhard Höfle
• E-Jahr: 2022
• Jahr: 05 Jul 2022
• Umfang: 23 S.
• Fussnoten: Gesehen am 29.07.2022
• Titel Quelle: Enthalten in: Earth system science data
• Ort Quelle: Katlenburg-Lindau : Copernics Publications, 2009
• Jahr Quelle: 2022
• Band/Heft Quelle: 14(2022), 7, Seite 2989-3012
• ISSN Quelle: 1866-3516
• DOI: doi:10.5194/essd-14-2989-2022
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Forschungsdaten: Weiser, Hannah: Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation
• K10plus-PPN: 1811994415

Abstract

Laser scanning from different acquisition platforms enables the collection of 3D point clouds from different perspectives and with varying resolutions. These point clouds allow us to retrieve detailed information on the individual tree and forest structure. We conducted airborne laser scanning (ALS), uncrewed aerial vehicle (UAV)-borne laser scanning (ULS) and terrestrial laser scanning (TLS) in two German mixed forests with species typical of central Europe. We provide the spatially overlapping, georeferenced point clouds for 12 forest plots. As a result of individual tree extraction, we furthermore present a comprehensive database of tree point clouds and corresponding tree metrics. Tree metrics were derived from the point clouds and, for half of the plots, also measured in the field. Our dataset may be used for the creation of 3D tree models for radiative transfer modeling or lidar simulation studies or to fit allometric equations between point cloud metrics and forest inventory variables. It can further serve as a benchmark dataset for different algorithms and machine learning tasks, in particular automated individual tree segmentation, tree species classification or forest inventory metric prediction. The dataset and supplementary metadata are available for download, hosted by the PANGAEA data publisher at https://doi.org/10.1594/PANGAEA.942856 (Weiser et al., 2022a).