Publications

2024

• Schiefer, F., Schmidtlein, S. and Hartmann, H. and Schnabel, F. and Kattenborn, T. (2024). Large-scale remote sensing reveals that tree mortality in Germany appears to be greater than previously expected. bioRxiv. https://doi.org/10.1101/2024.11.10.622853

• Sierra, E., Gillespie, L.E., , Soltani, S., Exposito-Alonso, M. , Kattenborn, T. (2024). DivShift: Exploring Domain-Specific Distribution Shift in Volunteer-Collected Biodiversity Datasets. arXiv. https://doi.org/10.48550/arXiv.2410.19816

• Mosig, C., Vajna-Jehle, J., Mahecha, M. D., Cheng, Y., Hartmann, H., Montero, D., Junttila, S., Horion, S., Adu-Bredu, S., Al-Halbouni, D., Allen, M., Altman, J., Angiolini, C., Astrup, R., Barrasso, C., Bartholomeus, H., Brede, B., Buras, A., Carrieri, E., Göritz, A., Gassilloud, M., Fabi, M., … Kattenborn, T. (2024). deadtrees.earth – An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics. bioRxiv. https://doi.org/10.1101/2024.10.18.619094

• Kattenborn, T., Wieneke, S., Montero, D. et al. Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations. Commun Earth Environ 5, 550 (2024). https://doi.org/10.1038/s43247-024-01712-0

• Dechant, B., Kattge, J., Pavlick, R., Schneider, F. D., Sabatini, F. M., Moreno-Martínez, Á., … Kattenborn, T…. & Townsend, P. A. (2024). Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches. Remote Sensing of Environment, 311, 114276. doi: https://doi.org/10.1016/j.rse.2024.114276

• Montero, D., Kraemer, G., Anghelea, A., Aybar, C., Brandt, G., Camps-Valls, G., … Kattenborn, T… & Mahecha, M. D. (2024). Earth System Data Cubes: Avenues for advancing Earth system research. arXiv preprint arXiv:2408.02348. doi: https://doi.org/10.48550/arXiv.2408.02348

• Mora, K., Rzanny, M., Wäldchen, J., Feilhauer, H., Kattenborn, T., Kraemer, G., … & Mahecha, M. D. (2024). Macrophenological dynamics from citizen science plant occurrence data. Methods in Ecology and Evolution. doi: https://doi.org/10.1111/2041-210X.14365

• Soltani, S., Ferlian, O., Eisenhauer, N., Feilhauer, H., & Kattenborn, T. (2024). From simple labels to semantic image segmentation: leveraging citizen science plant photographs for tree species mapping in drone imagery. Biogeosciences, 21(11), 2909-2935. doi: https://doi.org/10.5194/bg-21-2909-2024

• Mahecha, M. D., Bastos, A., Bohn, F. J., Eisenhauer, N., Feilhauer, H., Hickler, T., … Kattenborn, T., … & Quaas, J. (2024). Biodiversity and climate extremes: known interactions and research gaps. Earth’s Future, 12(6), e2023EF003963. doi: https://doi.org/10.1029/2023EF003963

• Scheiter, S., Wolf, S., & Kattenborn, T. (2024). Crowd-sourced trait data can be used to delimit global biomes. EGUsphere, BG, 21, 4909–4926. doi: https://bg.copernicus.org/articles/21/4909/2024/

• Winter, C., Mueller, S., Kattenborn, T., Stahl, K., Szillat, K., Weiler, M., & Schnabel, F. (2024). Forest dieback in drinking water protection areas–a hidden threat to water quality. bioRxiv, 2024-08. doi: https://doi.org/10.1101/2024.08.07.606951

• Werner, C., Wallrabe, U., Christen, A., Comella, L., Dormann, C., Göritz, A., … & Wöllenstein, J. (2024). ECOSENSE-Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks. Research Ideas and Outcomes, 10, e129357. https://doi.org/10.3897/rio.10.e129357

2023

• Müllerová, J., Brundu, G., Große-Stoltenberg, A., Kattenborn, T., & Richardson, D. M. (2023). Pattern to process, research to practice: remote sensing of plant invasions. Biological Invasions, 25(12), 3651-3676. doi: https://doi.org/10.1007/s10530-023-03150-z

• Ouaknine, A., Kattenborn, T., Laliberté, E., & Rolnick, D. (2023). OpenForest: A data catalogue for machine learning in forest monitoring. arXiv preprint arXiv:2311.00277. doi: https://doi.org/10.48550/arXiv.2311.00277

• Harkema, M. R., Nijland, W., de Jong, S. M., Kattenborn, T., & Eichel, J. (2023). Monitoring solifluction movement in space and time: A semi-automated high-resolution approach. Geomorphology, 433, 108727. doi: https://doi.org/10.1016/j.geomorph.2023.108727

• Loaiza, D. M., Kraemer, G., Anghelea, A., Camacho, C. L. A., Brandt, G., Camps-Valls, G., … Kattenborn, T., … & Mahecha, M. (2023). Data Cubes for Earth System Research: Challenges Ahead. doi: https://doi.org/10.31223/X58M2V

• Cherif, E., Feilhauer, H., Berger, K., Dao, P. D., Ewald, M., Hank, T. B., … & Kattenborn, T. (2023). From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data. Remote Sensing of Environment, 292, 113580. doi: https://doi.org/10.1016/j.rse.2023.113580

• Schiefer, F., Schmidtlein, S., Frick, A., Frey, J., Klinke, R., Zielewska-Büttner, K., … & Kattenborn, T. (2023). UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series. ISPRS Open Journal of Photogrammetry and Remote Sensing, 8, 100034. doi: https://doi.org/10.1016/j.ophoto.2023.100034

• Kattenborn, T., Mosig, C., Pratima, K., Frey, J., Perez-Priego, O., Schiefer, F., … & Mahecha, M. (2024). deadtrees. earth-an open, dynamic database for accessing, contributing, analyzing, and visualizing remote sensing-based tree mortality data (No. EGU24-15502). Copernicus Meetings.

• Lusk, D., Wolf, S., Moreno Martínez, Á., Kattge, J., Sabatini, F. M., & Kattenborn, T. (2024, April). Combining citizen science and Earth observation data to produce global maps of 31 plant traits. In EGU General Assembly Conference Abstracts (p. 2477).

• Gassilloud,M., Haidarian, S., Kattenborn, T., Koch, B., Reiterer, A., Schmitt, A., Wallrabe, U., Zakaryapour, H., Göritz, A. (2024), Assessing forest ecosystems with UAV based remote sensing – from laser scanning towards an active Chlorophyll fluorescence LiDAR. Workshop on UAV-based Remote Sensing Methods for Monitoring Vegetation 30.09. – 1.10.2024, University of Cologne, Germany

• Sotomayor, L., Kattenborn, T., Poux, F., Turner, D., & Lucieer, A. (2024). Investigating Deep Learning Techniques to Estimate Fractional Vegetation Cover in the Australian Semi-arid Ecosystems combining Drone-based RGB imagery, multispectral Imagery and LiDAR data (No. EGU24-5766). Copernicus Meetings.

• Cherif, E., Feilhauer, H., Berger, K., Ewald, M., Hank, T. B., Kovach, K. R., … & Kattenborn, T. (2023, May). From spectra to functional plant traits: Transferable multi-trait models from heterogeneous and sparse data. In EGU General Assembly Conference Abstracts (pp. EGU-10901).

• Wolf, S., Mahecha, M., Sabatini, F. M., Wirth, C., Bruelheide, H., Kattge, J., … & Kattenborn, T. (2023, May). Citizen science observations capture global patterns of plant traits. In EGU General Assembly Conference Abstracts (pp. EGU-2415).

• Kattenborn, T., Richter, R., Guimarães-Steinicke, C., Feilhauer, H., & Wirth, C. (2023, May). AngleCam-Tracking leaf angle distributions through time with image series and deep learning. In EGU General Assembly Conference Abstracts (pp. EGU-16063).

• Soltani, S., Feilhauer, H., Duker, R., & Kattenborn, T. (2023, May). Transfer learning from citizen science photos enables plantspecies identification in UAV imagery. In EGU General Assembly Conference Abstracts (pp. EGU-17308).

• M. Gassilloud, A. Göritz, B. Koch (2023): Forest volume exploration with UAV-based laserscanning: Investigating effects of acquisition parameters on canopy occlusion in a mixed European forest. Event: SilviLaser 2023 conference, 6 – 8 September 2023, UCL, London, UK

• Sharma, A., & Kattenborn, T. (2024). PlantTraits2024. Kaggle. https://kaggle.com/competitions/planttraits2024

• Kattenborn, T. (2023). PlantTraits2023. Kaggle. https://kaggle.com/competitions/planttraits2023

2024: Invited seminar hosted by the International Tree Mortality Network (ITMN) deadtrees.earth – a database of centimeter-scale aerial imagery for mapping global tree mortality (available on YouTube).

2024: Invited Blogpost at Springer Nature´s Earth & Environmental Sciences Community: Behind the Paper: Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations.

2024: Press release at uni-freiburg.de: Bewegungen von Pflanzenblättern verzerren satellitengestütztes Vegetationsmonitoring.

2023: Press release at uni-freiburg.de: Transparent Forests through Augmented Reality: XR Future Forest Lab launches at the University of Freiburg.

2023: FROM THE PERSPECTIVE OF THE SAFETY NET. Documentation of the art residency and exhibition 2023 organised by iDiv, UFZ and NFDI4Biodiversity, curated by Sophie Wolf, Robert Köpke and Thore Engel https://doi.org/10.5281/zenodo.8263198