Institute of Geography and Geoecology (IFGG)

Dr. Teja Kattenborn

  • Karlsruher Institut für Technologie (KIT)
    Institut für Geographie und Geoökologie
    Kaiserstr. 12
    76131 Karlsruhe

Teja Kattenborn

Remote sensing of vegetation, UVA-based remote sensing, hyperspectral data


  • Deep Learning and Convolutional Neural Networks (CNN)
  • Radiative transfer modelling of plant canopies
  • Remote Sensing of plant functions and strategies
  • Hyperspectral remote sensing
  • UAV-based remote sensing applications

Curriculum vitae

Since 2018 PostDoc at the IfGG
2018 Visiting Scientist at the Bio-Economy Unit, Directorate D - Sustainable Resources, Joint Research Centre (JRC), European Commission, Ispra, Italy.
2017 Research Stay at the Cavender-Bares Lab, College of Biological Sciences, University of Minnesota, Minnesota, USA.
2015 - 2018 PhD dissertation at the IfGG Combination of empirical and physical based models for habitat and vegetation monitoring.
Since 2014 Co-CEO at GeoCopter
2012 - 2014 M.Sc. Environmental Sciences (major) / GIS & Environmental Modelling (minor), Albert-Ludwigs University Freiburg.
2012 - 2014 Research Assistant at the Chair of Remote Sensing and Landscape Information Systems (FeLis), Albert-Ludwigs University Freiburg.
2009 - 2012 B.Sc. Environmental Sciences (major) / Hydrology (minor), Albert-Ludwigs University Freiburg / Stellenbosch University, South Africa.


Data analysis, Introduction to R, Scientific Research Skills

Grants & Awards

2019       Young Scientist Oral Presentation Award (1st prize) at the IAVS Annual Symposium 2019, Bremen, Germany for the presentation Combining Convolutional Neural Networks and high resolution UAV imagery – a powerful tool for vegetation mapping.

2021       DLR/BMWi-funded research project UAVforSAT - Operationalization of Vegetation Mapping through UAV-based Reference Data Acquisitions and Cloudbased Analysis of Earth Observation Data.

2019       ARCADIS prize for geo- and environmental research for the PhD thesis Linking Plant functioning and Canopy Reflectance with Radiative Transfer Modelling.

2019       Award for the best oral presentation at the EARSel SIG Imaging Spectroscopy Workshop Brno, Czech Republic, for the presentation After this Talk You will always map Leaf Pigment Content and not Concentration.

2014       Fellowship for the project Development of a UAV-Based beach-profile monitoring system on outer islands, Freunde der Universität, Albert-Ludwigs-University Freiburg, Germany.

2014       Karl-Steinbuch-fellowship 2013 for the project Development of a UAV-based Monitoring Workflow for the detection of growth deficiencies and loss on agricultural areas.

2012       DAAD-fellow (PROMOS pogramme) in the cooperation project Assessing Utilization of Lesser Known Species in Colombia, Facultad de Medio Ambiente (Universidad Distrital Francisco José de Caldas, Bogotá / Albert-Ludwigs-University, Freiburg).




Fassnacht, F. E.; Schmidt-Riese, E.; Kattenborn, T.; Hernández, J. (2021). Explaining Sentinel 2-based dNBR and RdNBR variability with reference data from the bird’s eye (UAS) perspective. International journal of applied earth observation and geoinformation, 95, Article no: 102262. doi:10.1016/j.jag.2020.102262Full textFull text of the publication as PDF document
Kattenborn, T.; Leitloff, J.; Schiefer, F.; Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS journal of photogrammetry and remote sensing, 173, 24–49. doi:10.1016/j.isprsjprs.2020.12.010
Eichel, J.; Draebing, D.; Kattenborn, T.; Senn, J. A.; Klingbeil, L.; Wieland, M.; Heinz, E. (2020). Unmanned aerial vehicle‐based mapping of turf‐banked solifluction lobe movement and its relation to material, geomorphometric, thermal and vegetation properties. Permafrost and periglacial processes, 31 (1), 97–109. doi:10.1002/ppp.2036Full textFull text of the publication as PDF document
Kattenborn, T.; Eichel, J.; Wiser, S.; Burrows, L.; Fassnacht, F. E.; Schmidtlein, S. (2020). Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. Remote sensing in ecology and conservation, 6 (4), 472–486. doi:10.1002/rse2.146
Nutrient Network; Kattge, J.; Bönisch, G.; Díaz, S.; Lavorel, S.; Prentice, I. C.; Leadley, P.; Tautenhahn, S.; Werner, G. D. A.; Kattenborn, T.; et al. (2020). TRY plant trait database – enhanced coverage and open access. Global change biology, 26 (1), 119–188. doi:10.1111/gcb.14904Full textFull text of the publication as PDF document
Poblete, T.; Camino, C.; Beck, P. S. A.; Hornero, A.; Kattenborn, T.; Saponari, M.; Boscia, D.; Navas-Cortes, J. A.; Zarco-Tejada, P. J. (2020). Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis. ISPRS journal of photogrammetry and remote sensing, 162, 27–40. doi:10.1016/j.isprsjprs.2020.02.010
Schiefer, F.; Kattenborn, T.; Frick, A.; Frey, J.; Schall, P.; Koch, B.; Schmidtlein, S. (2020). Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks. ISPRS journal of photogrammetry and remote sensing, 170, 205–215. doi:10.1016/j.isprsjprs.2020.10.015
Fassnacht, F. E.; Schiller, C.; Kattenborn, T.; Zhao, X.; Qu, J. (2019). A Landsat-based vegetation trend product of the Tibetan Plateau for the time-period 1990-2018. Scientific data, 6 (1), Art. Nr.: 78. doi:10.1038/s41597-019-0075-9Full textFull text of the publication as PDF document
Kattenborn, T.; Fassnacht, F. E.; Schmidtlein, S. (2019). Differentiating plant functional types using reflectance: which traits make the difference?. Remote sensing in ecology and conservation, 5 (1), 5–19. doi:10.1002/rse2.86Full textFull text of the publication as PDF document
Kattenborn, T.; Lopatin, J.; Förster, M.; Braun, A. C.; Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote sensing of environment, 227, 61–73. doi:10.1016/j.rse.2019.03.025
Kattenborn, T.; Schiefer, F.; Zarco-Tejada, P.; Schmidtlein, S. (2019). Advantages of retrieving pigment content [μg/cm 2 ] versus concentration [%] from canopy reflectance. Remote sensing of environment, 230, Art. Nr.: 111195. doi:10.1016/j.rse.2019.05.014
Lopatin, J.; Dolos, K.; Kattenborn, T.; Fassnacht, F. E. (2019). How canopy shadow affects invasive plant species classification in high spatial resolution remote sensing. Remote sensing in ecology and conservation, 5 (4), 302–317. doi:10.1002/rse2.109Full textFull text of the publication as PDF document
Lopatin, J.; Kattenborn, T.; Galleguillos, M.; Perez-Quezada, J. F.; Schmidtlein, S. (2019). Using aboveground vegetation attributes as proxies for mapping peatland belowground carbon stocks. Remote sensing of environment, 231, 111217. doi:10.1016/j.rse.2019.111217
Wagner, A.; Hilgert, S.; Kattenborn, T.; Fuchs, S. (2019). Proximal VIS-NIR spectrometry to retrieve substance concentrations in surface waters using partial least squares modelling. Water science & technology / Water supply, 19 (4), 1204–1211. doi:10.2166/ws.2018.177
Zarco-Tejada, P. J.; Hornero, A.; Beck, P. S. A.; Kattenborn, T.; Kempeneers, P.; Hernández-Clemente, R. (2019). Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline. Remote sensing of environment, 223, 320–335. doi:10.1016/j.rse.2019.01.031Full textFull text of the publication as PDF document
Zarco-Tejada, P. J.; Camino, C.; Beck, P. S. A.; Calderon, R.; Hornero, A.; Hernández-Clemente, R.; Kattenborn, T.; Montes-Borrego, M.; Susca, L.; Morelli, M.; et al. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature plants, 4 (7), 432–439. doi:10.1038/s41477-018-0189-7
Fassnacht, F. E.; Mangold, D.; Schäfer, J.; Immitzer, M.; Kattenborn, T.; Koch, B.; Latifi, H. (2017). Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications?. Forestry, 90 (5), 613–631. doi:10.1093/forestry/cpx014
Kattenborn, T.; Fassnacht, F. E.; Pierce, S.; Lopatin, J.; Grime, J. P.; Schmidtlein, S. (2017). Linking plant strategies and plant traits derived by radiative transfer modelling. Journal of vegetation science, 28 (4), 717–727. doi:10.1111/jvs.12525
Lopatin, J.; Faßnacht, F. E.; Kattenborn, T.; Schmidtlein, S. (2017). Mapping plant species in mixed grassland communities using close range imaging spectroscopy. Remote sensing of environment, 201, 12–23. doi:10.1016/j.rse.2017.08.031
Kattenborn, T.; Maack, J.; Faßnacht, F.; Enßle, F.; Ermert, J.; Koch, B. (2015). Mapping forest biomass from space - Fusion of hyperspectralEO1-hyperion data and Tandem-X and WorldView-2 canopy heightmodels. International Journal of Applied Earth Observation and Geoinformation, 35 (PB), 359–367. doi:10.1016/j.jag.2014.10.008
Maack, J.; Kattenborn, T.; Fassnacht, F. E.; Enssle, F.; Hernandez, J.; Corvalan, P.; Koch, B. (2015). Modeling forest biomass using very-high-resolution data - combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images. European journal of remote sensing, 48, 245–261. doi:10.5721/EuJRS20154814
Kattenborn, T.; Sperlich, M.; Bataua, K.; Koch, B. (2014). Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds. The international archives of photogrammetry, remote sensing and spatial information sciences, XL-3, 139–144. doi:10.5194/isprsarchives-XL-3-139-2014Full textFull text of the publication as PDF document
Koch, B.; Kattenborn, T.; Straub, C.; Vauhkonen, J. (2014). Segmentation of forest to tree objects. Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies. Ed.: M. Maltamo, 89–112, Springer Netherlands.
Koch, B.; Kattenborn, T.; Fritz, A. (2013). UAV-based photogrammetric point clouds - Tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. The international archives of photogrammetry, remote sensing and spatial information sciences, 40, 141–146. doi:10.5194/isprsarchives-xl-1-w2-141-2013Full textFull text of the publication as PDF document