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

Weiser, Hannah; Schäfer, Jannika; Winiwarter, Lukas; Krašovec, Nina; Fassnacht, Fabian E.; Höfle, Bernhard

doi:https://doi.org/10.5194/essd-14-2989-2022

Articles | Volume 14, issue 7

https://doi.org/10.5194/essd-14-2989-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-2989-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 7

Data description paper

|

05 Jul 2022

Data description paper |

| 05 Jul 2022

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

Hannah Weiser, Jannika Schäfer, Lukas Winiwarter, Nina Krašovec, Fabian E. Fassnacht, and Bernhard Höfle

Supplement

https://doi.org/10.5194/essd-14-2989-2022-supplement

Data sets

Terrestrial, UAV-borne, and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements Hannah Weiser, Jannika Schäfer, Lukas Winiwarter, Nina Krašovec, Christian Seitz, Marian Schimka, Katharina Anders, Daria Baete, Andressa Soarez Braz, Johannes Brand, Denis Debroize, Paula Kuss, Lioba Lucia Martin, Angelo Mayer, Torben Schrempp, Lisa-Maricia Schwarz, Veit Ulrich, Fabian E. Fassnacht, and Bernhard Höfle https://doi.org/10.1594/PANGAEA.942856

Model code and software

3dgeo-heidelberg/syssifoss: Version 1.0.0 Hannah Weiser https://doi.org/10.5281/zenodo.6759913

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Short summary

3D point clouds, acquired by laser scanning, allow us to retrieve information about forest structure and individual tree properties. We conducted airborne, UAV-borne and terrestrial laser scanning in German mixed forests, resulting in overlapping point clouds with different characteristics. From these, we generated a comprehensive database of individual tree point clouds and corresponding tree metrics. Our dataset may serve as a benchmark dataset for algorithms in forestry research.