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A large dataset of labelled single tree point clouds, QSMs and tree graphs

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Abstract

High-resolution data of individual trees are critical for advancing forest monitoring, inventory development, and ecological research. This dataset, BioDiv-3DTrees, comprises 4,952 individual tree point clouds of 19 species, captured using Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS), along with 3,386 Quantitative Structure Models (QSMs) and graph representations of the 14 broadleafed species in the dataset. The trees were sampled across the three research areas of the Biodiversity Exploratories in Germany. Each tree is linked to an existing open-access forest inventory dataset, which includes species identity, diameter at breast height (DBH), and tree height. The dataset is suitable for various research applications, including biomass estimation, algorithm development, tree structure analysis, and data fusion with traditional inventory methods. All QSMs were generated using TreeQSM 2.4.1 and have been validated for tree height, diameter at breast height and crown projection area against their underlying point clouds to ensure consistency. The dataset provides a reliable and scalable resource for forest science and remote sensing communities.

Data availability

The complete dataset is available on GROdata (https://doi.org/10.25625/8PB1IF).

Code availability

The code of the used histogram-based outlier removal algorithm, as well as the code to reverse the coordinate normalization is available at the dataset’s GitLab repository (https://gitlab.gwdg.de/griese1/biodiv-3dtrees/). The repository also includes the code that creates and cleans up the graph representation.

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Acknowledgements

We thank Tim Geis, Muluken Bazezew, Tao Jiang and Hans Fuchs for their help during the data acquisition and point cloud post processing. We thank the authors of the forest inventory dataset Peter Schall, Christian Ammer, as well the data collector Andreas Parth for their work, which made it possible to add tree species labels to this dataset. We thank the managers of the three Exploratories, Max Müller, Robert Künast, Franca Marian, and all former managers for their work in maintaining the plot and project infrastructure; Victoria Grießmeier for giving support through the central office, Andreas Ostrowski for managing the central data base, and Markus Fischer, Eduard Linsenmair, Dominik Hessenmöller, Daniel Prati, Ingo Schöning, François Buscot, Ernst-Detlef Schulze, Wolfgang W. Weisser, and the late Elisabeth Kalko for their role in setting up the Biodiversity Exploratories project. We thank the administration of the Hainich national park, the UNESCO Biosphere Reserve Swabian Alb, and the UNESCO Biosphere Reserve Schorfheide-Chorin as well as all land owners for the excellent collaboration. Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen, and Brandenburg. The work has been partly funded by the DFG Priority Program 1374 “Biodiversity-Exploratories” (DFG project numbers 433273584 and 193957772). Funding for Nils Griese was provided by the German Research Foundation (DFG project number 496533645). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation program (Grant agreement No. 101041669).

Funding

Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations

  1. Department of Forest Inventory and Remote Sensing, University of Göttingen, Göttingen, Germany

    Nils Griese & Nils Nölke

  2. Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany

    Martin Ritzert

Authors

  1. Nils Griese
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  2. Martin Ritzert
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  3. Nils Nölke
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Contributions

N.G. collected the data. N.G. developed the methods described apart from the Graph derivation. M.R. derived the Graphs from the data and contributed to the data validation. N.N. supervised the project. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to
Nils Griese.

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Competing interests

The authors declare no competing interests.

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Griese, N., Ritzert, M. & Nölke, N. A large dataset of labelled single tree point clouds, QSMs and tree graphs.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06421-7

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