Teja Kattenborn

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

Topics

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.

Teaching

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).

Publications

2021

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.2020.102262

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

Schiefer, F.; Schmidtlein, S.; Kattenborn, T. (2021). The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology. Ecological indicators, 121, Art.-Nr.: 107062. doi:10.1016/j.ecolind.2020.107062

2020

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.2036

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.; u. a. (2020). TRY plant trait database – enhanced coverage and open access. Global change biology, 26 (1), 119–188. doi:10.1111/gcb.14904

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

2019

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/s41597-019-0075-9

Kattenborn, T. (2019). Linking Canopy Reflectance and Plant Functioning through Radiative Transfer Models. Dissertation. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000089168

Kattenborn, T.; Eichel, J.; Fassnacht, F. E. (2019). Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Scientific reports, 9 (1), Article no: 17656. doi:10.1038/s41598-019-53797-9

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/rse2.86

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.; Schmidtlein, S. (2019). Radiative transfer modelling reveals why canopy reflectance follows function. Scientific reports, 9 (1), Article: 6541. doi:10.1038/s41598-019-43011-1

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/rse2.109

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.2019.01.031

2018

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.; u. a. (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

2017

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

2015

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

2014

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-3-139-2014

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.

2013

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-w2-141-2013