Publications

2026

• Lusk, D., Wolf, S., Svidzinska, D., Dormann, C. F., Kattge, J., Bruelheide, H., Sabatini, F. M., Damasceno, G., Martínez, Á. M., Violle, C., Hending, D., Hähn, G. J. A., Tabeni, S., Phartyal, S., Gonçalves, F., Kreft, H., Schmidt, M., Chen, H., Güler, B., Dolezal, J., Pielech, R., Guido, A., Dwyer, C., Napoleone, F., Willie, J., Gasper, A. L., Macía, M. J., Chytry, M., Lenoir, J., Thakur, D., Dengler, J., Świerszcz, S., Altman, J., Mucina, L., Nerlekar, A. N., Kakinuma, K., Rawat, P., Stančić, Z., Testolin, R., Hatim, M. Z., Rodrigues, F., Homeier, J., Marques, M. C. M., McCarthy, J. K., El-Sheikh, M. A., Gerberding, K. & Kattenborn, T. (2025). Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping. Nature Communications. https://doi.org/10.1038/s41467-026-68996-y

• M.D. Mahecha, G. Kraemer, M. Reinhardt, D. Montero, F. Gans, A. Bastos, H. Feilhauer, I. Flik, C. Ji, T. Kattenborn, M. Migliavacca, M. Mönks, J. Quaas, S. Sippel, S. Walther, S. Wieneke, C. Wirth, & G. Camps-Valls (2026), Accelerated north–east shift of the global green wave trajectory, PNAS. 123 (9) e2515835123, https://doi.org/10.1073/pnas.2515835123

• Mosig, C., Kattenborn, T., Montero Loaiza, D., Vanja-Jehle, J., Brandt, J., Jacobs, N., Khanal, S., Xing, E., Schwartz, M., Muller-Landau, H. C., Beloiu, M., Bozzini, A., Cheng, Y., Ganz, K., Grüning, B., Hartmann, H., Hempel, J., Horion, S., Junttila, S., Korznikov, K., Kraemer, G., Mönks, M., Nardi, D., Neumeier, P., Schmid, J., Soltani, S., Therese-Schmehl, M., Veitch-Michaelis, J., & Mahecha, M. (2026). Sub-pixel mapping of disturbance and tree mortality dynamics from Sentinel-2 time series around the globe. EarthArXiv. https://doi.org/10.31223/X5B18W

• Brzozon, J., Schwarzkopf, P., Kattenborn, T., Frey, J., Lang, F., & Schack-Kirchner, H. (2026). Controls of spatio-temporal patterns of soil respiration in a mixed forest. bioRxiv, 2026-01. https://doi.org/10.64898/2026.01.21.700858

• Winter, C., Kattenborn, T., Stahl, K., Szillat, K., Weiler, M., & Schnabel, F. (2026). Wie Waldverlust zu einem Problem für unser Trinkwasser werden kann. In Umweltimpulse–19 Wege in eine lebenswerte Zukunft: Vernetztes Wissen–konkrete Lösungen–Handlungsoptionen (pp. 115-119). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-72198-8_11

• Zou, Y., Liu, X., Pfefferle, A., Feigl, J., Daniel, N. M., Kattenborn, T., Koch, B., & Munteanu, C. (2026). Reconstructing past landscapes: Transformer-based deep learning for land use and land cover mapping from historical spy satellite imagery. SSRN preprint. https://doi.org/10.2139/ssrn.6075181

2025

• Lee, H., Stippich, T., Petersen, J., Meine, D., Brzozon, J., Sulzer, M., Deeded, L., Christen, A., Kattenborn, T., Werner, C., & Kreuzwieser, J. (2025). Strong toluene and p-cymene emission from waterlogged hotspots of a temperate mixed-forest soil. EGUsphere, 2025, 1-29. https://doi.org/10.5194/egusphere-2025-5426

• Kremer, L., Pisek, J., Richter, R., Frey, J., Lusk, D., Werner, C., Wirth, C., & Kattenborn, T. (2025). AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos. bioRxiv. https://doi.org/10.1101/2025.09.17.676742

• Mosig, C., Vajna-Jehle, J., Mahecha, M. D., Cheng, Y., Hartmann, H., Montero, D., Junttila, S., Horion, S., Beloiu Schwenke, M., Koontz, M. J., Abdul Maulud, K. N., Adu-Bredu, S., Al-Halbouni, D., Ali, M., Allen, M., Altman, J., Amorós, L., Angiolini, C., Astrup, R., Awada, H., … Kattenborn, T. (2026). deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics. Remote Sensing of Environment, 332, 115027. https://doi.org/10.1016/j.rse.2025.115027

• Sharma, A., Trost, J., Lusk, D., Dollinger, J., Schrader, J., Rossi, C., Lopatin, J., Laliberté, E., Haberstroh, S., Eichel, J., Mederer, D., Cerda-Paredes, J. M., Phartyal, S. S., Schwarz, L.-M., Linstädter, A., Caldeira, M. C., & Kattenborn, T. (2025). PlantTraitNet: An uncertainty-aware multimodal framework for global-scale plant trait inference from citizen science data (Version 1). arXiv. https://doi.org/10.48550/arXiv.2511.06943

• Mederer, D., Kattenborn, T., Cherif, E., Guimaraes-Steinicke, C., Joswig, J. S., Schneider, F. D., & Feilhauer, H. (2025). Unraveling the seasonality of functional diversity through remote sensing. Communications Earth & Environment, 6, 790. https://doi.org/10.1038/s43247-025-02646-x

• Möhring, J., Kattenborn, T., Mahecha, M. D., Cheng, Y., Beloiu Schwenke, M., Cloutier, M., Denter, M., Frey, J., Gassilloud, M., Göritz, A., Hempel, J., Horion, S., Jucker, T., Junttila, S., Khatri-Chhetri, P., Korznikov, K., Kruse, S., Laliberté, E., Maroschek, M., Neumeier, P., Pérez-Priego, O., Potts, A., Schiefer, F., Seidl, R., Vajna-Jehle, J., Zielewska-Büttner, K., & Mosig, C. (2025). Global, multi-scale standing deadwood segmentation in centimeter-scale aerial images. ISPRS Open Journal of Photogrammetry and Remote Sensing, 18, 100104. https://doi.org/10.1016/j.ophoto.2025.100104

• Kükenbrink, D., Gassilloud, M., Brede, B., Bornand, A., Calders, K., Cherlet, W., Eichhorn, M. P., Frey, J., Gretler, C. M., Höfle, B., Kattenborn, T., Klinger, L., Mokros, M., Pitkänen, T., Saarinen, N., Terryn, L., Weiser, H., & Göritz, A. (2025). Occlusion in forest laser scanning: Causes, types, and implications for structural complexity assessments. EarthArXiv. https://doi.org/10.31223/X5N16X

• Tesch, J., Kühnhammer, K., Wagner, D., Christen, A., Dormann, C., Frey, J., Grote, R., Kattenborn, T., Sulzer, M., Wallrabe, U., Weiler, M., Werner, C., Baghbani, S., Brzozon, J., Comella, L. M., Dedden, L., Dumberger, S., Frey, Y., Gassilloud, M., Gerach, T., Göritz, A., Haberstroh, S., Klüppel, J., Kremer, L., Kreuzwieser, J., Lee, H., Maack, J., Müller, J., Prucker, O., Rajak, S. K., Rühe, J., Rupitsch, S. J., Schack-Kirchner, H., Scharinger, C., Shinde, U., Steinmann, T., Stock, C., and Strack, J.: The ECOSENSE forest: A distributed sensor and data management system for real-time monitoring of ecosystem processes and stresses, EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2025-4979

• Sotomayor, L. N., Lucieer, A., Turner, D., Lewis, M., & Kattenborn, T. (2025). Mapping fractional vegetation cover in UAS RGB and multispectral imagery in semi-arid Australian ecosystems using CNN‑based semantic segmentation. Landscape Ecology, 40, Article 169. https://doi.org/10.1007/s10980-025-02193-y

• Mederer, D., Kattenborn, T., Cherif, E., Guimaraes-Steinicke, C., Joswig, J., Schneider, F. D., & Feilhauer, H. (in press). Unraveling the seasonality of functional diversity through remote sensing. Communications Earth & Environment.

• Lusk, D., Wolf, S., Svidzinska, D., Dormann, C. F., Kattge, J., Bruelheide, H., Sabatini, F. M., Damasceno, G., Martínez, Á. M., Violle, C., Hending, D., Hähn, G. J. A., Tabeni, S., Phartyal, S., Gonçalves, F., Kreft, H., Schmidt, M., Chen, H., Güler, B., Dolezal, J., Pielech, R., Guido, A., Dwyer, C., Napoleone, F., Willie, J., Gasper, A. L., Macía, M. J., Chytry, M., Lenoir, J., Thakur, D., Dengler, J., Świerszcz, S., Altman, J., Mucina, L., Nerlekar, A. N., Kakinuma, K., Rawat, P., Stančić, Z., Testolin, R., Hatim, M. Z., Rodrigues, F., Homeier, J., Marques, M. C. M., McCarthy, J. K., El-Sheikh, M. A., & Kattenborn, T. (2025). From smartphones to satellites: Uniting crowdsourced biodiversity monitoring and Earth observation to fill the gaps in global plant trait mapping. bioRxiv. https://doi.org/10.1101/2025.03.10.641660

• Umlauft, J., Mora, K., Limberger, F., Gerberding, K., Wirth, C., Werner, C., & Kattenborn, T. (2025). The Seismic Fingerprint of Tree Sway. EarthArXiv. https://doi.org/10.1111/2041-210X.70173

• Cherif, E., Ouaknine, A., Brown, L. A., Dao, P. D., Kovach, K. R., Lu, B., Kattenborn, T., & Rolnick, D. (2025). GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction. arXiv preprint arXiv:2507.06806. https://doi.org/10.48550/arXiv.2507.06806

• Lotz, S., Kattenborn, T., Frey, J., Soltani, S., Göritz, A., Jakszat, T., & Katal, N. (2025). Litter vs. Lens: Evaluating LAI from Litter Traps and Hemispherical Photos Across View Zenith Angles and Leaf Fall Phases. EGUsphere, 2025, 1-20. https://doi.org/10.5194/egusphere-2025-1496

• Sierra, E., Gillespie, L. E., Soltani, S., Exposito-Alonso, M., & Kattenborn, T. (2025, April). DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 27, pp. 28386-28396). https://doi.org/10.1609/aaai.v39i27.35060
  (awarded with the outstanding paper award)

• Seiler, J., Gerberding, K., Schmitt, A., Kattenborn, T., & Reiterer, A. (2025, August). A UAV-mounted dual-wavelength LiDAR for leaf water content retrieval. In Optical Measurement Systems for Industrial Inspection XIV (Vol. 13567, pp. 236-242). SPIE. https://doi.org/10.1117/12.3061188

• Pärtel, M., Tamme, R., Carmona, C. P., Riibak, K., Moora, M., Bennett, J. A., … Katal, N., … & Oja, T. (2025). Global impoverishment of natural vegetation revealed by dark diversity. Nature, 1-8. https://doi.org/10.1038/s41586-025-08814-5

• Winter, C., Müller, S., Kattenborn, T.,Stahl, K., Szillat, K., Weiler, M., &Schnabel, F. (2025). Forest dieback indrinking water protection areas—a hiddenthreat to water quality. Earth’s Future, 13,e2025EF006078. https://doi.org/10.1029/2025EF006078
  (awarded with the Helmut-Lieth-Preis for Environmental Research)

• International Tree Mortality Network (2025). Towards a global understanding of tree mortality. New Phytol. https://doi.org/10.1111/nph.20407

• Möhring, J., Kattenborn, T., Mahecha, M. D., Cheng, Y., Schwenke, M. B., Cloutier, M., Denter, M., Frey, J., Gassilloud, M., Göritz, A., Hempel, J., Horion, S., Jucker, T., Junttila, S., Khatri-Chhetri, P., Korznikov, K., Kruse, S., Laliberté, E., Maroschek, M., Vajna-Jehle, J., … Mosig, C. (2025). Global, multi-scale standing deadwood segmentation in centimeter-scale aerial images. TechRxiv. https://doi.org/10.36227/techrxiv.174137781.13803217/v1

• Gassilloud, M., Koch, B., & Göritz, A. (2025). Occlusion mapping reveals the impact of flight and sensing parameters on vertical forest structure exploration with cost-effective UAV based laser scanning. International Journal of Applied Earth Observation and Geoinformation, 139, 104493. https://doi.org/10.1016/j.jag.2025.104493

• Ouaknine A, Kattenborn T, Laliberté E, Rolnick D. OpenForest: a data catalog for machine learning in forest monitoring. Environmental Data Science. 2025;4:e15. https://doi.org/10.1017/eds.2024.53

• Soltani, S., Gillespie, L. E., Exposito-Alonso, M., Ferlian, O., Eisenhauer, N., Feilhauer, H., and Kattenborn, T. (2025, preprint): From Ground Photos to Aerial Insights: Automating Citizen Science Labeling for Tree Species Segmentation in UAV Images, EGUsphere. https://doi.org/10.5194/egusphere-2025-662

• Bonato Asato, A. E., Guimarães-Steinicke, C., Stein, G., Schreck, B., Kattenborn, T., Ebeling, A., Posch, S., Denzler, J., Büchner, T., Shadaydeh, M., Wirth, C., Eisenhauer, N., & Hines, J. (2025). Seasonal shifts in plant diversity effects on above-ground–below-ground phenological synchrony. Journal of Ecology, 00, 1–13. https://doi.org/10.1111/1365-2745.14470

• Mederer, D., Feilhauer, H., Cherif, E., Berger, K., Hank, T. B., Kovach, K. R., Dao, P. D., Lu, B., Townsend, P. A., & Kattenborn, T. (2025). Plant trait retrieval from hyperspectral data: Collective efforts in scientific data curation outperform simulated data derived from the PROSAIL model. ISPRS Open Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.ophoto.2024.100080

• Montero, D., Kraemer, G., Anghelea, A., Aybar, C., Brandt, G., Camps-Valls, G., … Kattenborn, T… & Mahecha, M. D. (2025). Earth System Data Cubes: Avenues for advancing Earth system research. Environmental Data Science. https://doi.org/10.1017/eds.2024.22

2024

• Schiefer, F., Schmidtlein, S., Hartmann, H., Schnabel, F., Kattenborn, T. (2024) Large-scale remote sensing reveals that tree mortality in Germany appears to be greater than previously expected, Forestry: An International Journal of Forest Research, cpae062, https://doi.org/10.1093/forestry/cpae062

• Sierra, E., Gillespie, L., Soltani, S., Expósito-Alonso, M., & Kattenborn, T. (2024). DivShift: Exploring domain-specific distribution shift in large-scale, volunteer-collected biodiversity datasets. Proceedings of the NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning. https://www.climatechange.ai/papers/neurips2024/43

• 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

• 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

2025: Best Paper Award within the AAAI conference for AI for Social Impact: Sharma, A., Trost, J., Lusk, D., Dollinger, J., Schrader, J., Rossi, C., … & Kattenborn, T. (2025). PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data. arXiv preprint arXiv:2511.06943. https://doi.org/10.48550/arXiv.2511.06943

2025: Highlight paper & Editors Choice in EGU Biogeosciences for Soltani, S., Gillespie, L. E., Exposito-Alonso, M., Ferlian, O., Eisenhauer, N., Feilhauer, H., and Kattenborn, T.: Automated mask generation in citizen science smartphone photos and their value for mapping plant species in drone imagery, Biogeosciences, 22, 6545–6561, https://doi.org/10.5194/bg-22-6545-2025, 2025.

2024: Outstanding Paper Award within the AAAI conference for Sierra, E., Gillespie, L. E., Soltani, S., Exposito-Alonso, M., & Kattenborn, T. (2025, April). DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 27, pp. 28386-28396). https://doi.org/10.1609/aaai.v39i27.35060

2023: Emmy Noether Fellowship (German Research Foundation, DFG) for Teja Kattenborn. Related project: PANOPS – Revealing Earth´s plant functional diversity with citizen science.

• 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).

• Cheng, Y., Oehmcke, S., Mosig, C., Mirela, B., Kattenborn, T., Abel, C., … & Horion, S. (2024). High-resolution mapping of tree mortality in European forests (No. EGU24-20213). Copernicus Meetings. https://doi.org/10.5194/egusphere-egu24-20213, 2024

• Möhring, J.; Mosig, C.; Cheng, Y.; Mahecha, M.; Perez Priego, O.; Beloiu, M.; Volpi, M.; Horion, S.; Latifi, H.; Shafeian, E.; Fassnacht, F.; Montero, D.; Zielewska-Büttner, K.; Laliberté, E.; Cloutier, M.; Schmehl, M.-T.; Frick, A.; Müller-Landau, H.; Cushman, K.C.; Hupy, J.; Ma, Q.; Su, Y.; Khatri-Chhetri, P.; Kruse, S.; Frey, J.; Schiefer, F.; Junttila, S.; Potts, A.; Uhl, A.; Rossi, C.; Kattenborn, T. (2024): Delineating standing deadwood in high-resolution RGB drone imagery (No. EGU24-19025). Copernicus Meetings. https://doi.org/10.5194/egusphere-egu24-19025, 2024

• Mosig, C.; Mahecha, M.; Montero, D.; Cheng, Y.; Perez Priego, O.; Beloiu, M.; Volpi, M.; Horion, S.; Latifi, H.; Shafeian, E.; Fassnacht, F.; Ganz, S.; Zielewska-Büttner, K.; Laliberté, E.; Cloutier, M.; Schmehl, M.-T.; Frick, A.; Müller-Landau, H.; Cushman, K.C.; Hupy, J.; Ma, Q.; Su, Y.; Khatri-Chhetri, P.; Kruse, S.; Frey, J.; Schiefer, F.; Junttila, S.; Potts, A.; Uhl, A.; Rossi, C.; Kattenborn, T. (2024): Mapping tree mortality at global scale using Sentinel-2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18230, https://doi.org/10.5194/egusphere-egu24-18230

• 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

2026: Press article at SPIEGEL: Wie der Klimawandel die »grüne Welle« verändert.

2026: Press article at Tagesspiegel: Grüne Welle: Die Vegetation wandert Richtung Norden – und Osten.

2026: Press release at Universitat de Valencia: Un nuevo método constata que la vegetación de la Tierra se deplaza hacia el noreste.

2026: Press release at uni-freiburg.de: Wie KI, Smartphones und Satelliten gemeinsam helfen, globale Ökosysteme besser zu verstehen.

2026: Press release at uni-freiburg.de: Deadtrees.earth – From drones to AI: cross-border teaching on digital forest monitoring

2025: Invited Blogpost at AAAI (Association for the Understanding of Artificial Intelligence) on the outstanding Paper Award on DivShift: Exploring domain-specific distribution shift in large-scale, volunteer-collected biodiversity datasets.

2025: Comment about TreeAI: A Database for annotating tree species using high-resolution aerial imagery in Schweizerische Zeitschrift für Forstwesen (SZF).

2025: Public lecture (Samstags Uni series on Biological Diversity, University of Freiburg) on citizen science and tree mortality: Tote Bäume aus dem All erkennen – Wie Künstliche Intelligenz, Drohnen und Satelliten beim Monitoring von Waldsterben helfen.

2025: Radio and TV interviews (radioeins, ARD) of Carolin Winter on the effect of tree mortality on water quality: Link to Radio interview. Link to TV-interview (at minute 15:00)

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