Publications

2024

• 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, 2024, 1-25. doi: https://doi.org/10.5194/egusphere-2024-276

• 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 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.