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  1. UAV-based reference data for the prediction of fractional cover of standing deadwood from Sentinel time series

    Article • ISPRS Open Journal of Photogrammetry and Remote Sensing, April 2023, Elsevier

    Dr Teja Kattenborn

  2. AngleCam - Tracking leaf angle distributions through time with image series and deep learning

    Article • February 2023, Copernicus GmbH

    Dr Teja Kattenborn

  3. From spectra to functional plant traits: Transferable multi-trait models from heterogeneous and sparse data

    Article • February 2023, Copernicus GmbH

    Dr Teja Kattenborn

  4. Transfer learning from citizen science photos enables plantspecies identification in UAV imagery

    Article • February 2023, Copernicus GmbH

    Dr Teja Kattenborn

  5. Citizen science observations capture global patterns of plant traits

    Article • February 2023, Copernicus GmbH

    Dr Teja Kattenborn

  6. Citizen science plant observations encode global trait patterns

    Article • Nature Ecology & Evolution, October 2022, Springer Science + Business Media

    Dr Teja Kattenborn

  7. AngleCam : Predicting the temporal variation of leaf angle distributions from image series with deep learning

    Article • Methods in Ecology and Evolution, October 2022, Wiley

    Dr Teja Kattenborn, Claudia Guimaraes-Steinicke

  8. Spatially autocorrelated training and validation samples inflate performance assessment of convolutional neural networks

    Article • ISPRS Open Journal of Photogrammetry and Remote Sensing, June 2022, Elsevier

    Dr Teja Kattenborn

  9. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing

    Article • ISPRS Journal of Photogrammetry and Remote Sensing, March 2021, Elsevier

    Dr Teja Kattenborn

  10. The retrieval of plant functional traits from canopy spectra through RTM-inversions and statistical models are both critically affected by plant phenology

    Article • Ecological Indicators, February 2021, Elsevier

    Dr Teja Kattenborn

  11. Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks

    Article • ISPRS Journal of Photogrammetry and Remote Sensing, December 2020, Elsevier

    Dr Teja Kattenborn

  12. Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery

    Article • Remote Sensing in Ecology and Conservation, February 2020, Wiley

    Dr Teja Kattenborn

  13. Unmanned aerial vehicle-based mapping of turf-banked solifluction lobe movement and its relation to material, geomorphometric, thermal and vegetation properties

    Article • Permafrost and Periglacial Processes, January 2020, Wiley

    Dr Teja Kattenborn

  14. Deep Learning enables to identify plant species in aerial imagery

    Article • Scientific Reports, November 2019, Springer Science + Business Media

    Dr Teja Kattenborn

  15. Advantages of retrieving pigment content [μg/cm2] versus concentration [%] from canopy reflectance

    Article • Remote Sensing of Environment, September 2019, Elsevier

    Dr Teja Kattenborn

  16. Using aboveground vegetation attributes as proxies for mapping peatland belowground carbon stocks

    Article • Remote Sensing of Environment, September 2019, Elsevier

    Dr Teja Kattenborn

  17. UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data

    Article • Remote Sensing of Environment, June 2019, Elsevier

    Dr Teja Kattenborn

  18. A Landsat-based vegetation trend product of the Tibetan Plateau for the time-period 1990–2018

    Article • Scientific Data, May 2019, Springer Science + Business Media

    Dr Teja Kattenborn

  19. The functioning of plants determines how they reflect light

    Article • Scientific Reports, April 2019, Springer Science + Business Media

    Dr Teja Kattenborn

  20. Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline

    Article • Remote Sensing of Environment, March 2019, Elsevier

    Dr Teja Kattenborn

  21. Proximal VIS-NIR spectrometry to retrieve substance concentrations in surface waters using partial least squares modelling

    Article • Water Science & Technology Water Supply, October 2018, IWA Publishing

    Dr Teja Kattenborn

  22. PILOT STUDY ON THE RETRIEVAL OF DBH AND DIAMETER DISTRIBUTION OF DECIDUOUS FOREST STANDS USING CAST SHADOWS IN UAV-BASED ORTHOMOSAICS

    Article • ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, September 2018, Copernicus GmbH

    Dr Teja Kattenborn

  23. Differentiating plant functional types using reflectance: which traits make the difference?

    Article • Remote Sensing in Ecology and Conservation, July 2018, Wiley

    Dr Teja Kattenborn

  24. Modis-Based Grassland Trends Within and Around the Kekexili Core Protection Zone of the Sanjiangyuan Nature Reserve

    Article • July 2018, Institute of Electrical & Electronics Engineers (IEEE)

    Dr Teja Kattenborn

  25. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations

    Article • Nature Plants, June 2018, Springer Science + Business Media

    Dr Teja Kattenborn

  26. Mapping plant species in mixed grassland communities using close range imaging spectroscopy

    Article • Remote Sensing of Environment, November 2017, Elsevier

    Dr Teja Kattenborn

  27. Detecting the spread of invasive species in central Chile with a Sentinel-2 time-series

    Article • June 2017, Institute of Electrical & Electronics Engineers (IEEE)

    Dr Teja Kattenborn

  28. Linking plant strategies and plant traits derived by radiative transfer modelling

    Article • Journal of Vegetation Science, April 2017, Wiley

    Dr Simon Pierce, Dr Teja Kattenborn

  29. Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications?

    Article • Forestry An International Journal of Forest Research, March 2017, Oxford University Press (OUP)

    Dr Teja Kattenborn

  30. Linking plant strategies (CSR) and remotely sensed plant traits

    Article • August 2016, Institute of Electrical & Electronics Engineers (IEEE)

    Dr Teja Kattenborn

  31. Corrigendum to “Mapping forest biomass from space – Fusion of hyperspectralEO1-hyperion data and Tandem-X and WorldView-2 canopy heightmodels” [Int. J. Appl. Earth Obs. Geoinf. Issue no. 35 (2015) 359-367]

    Article • International Journal of Applied Earth Observation and Geoinformation, September 2015, Elsevier

    Dr Teja Kattenborn

  32. Building a hybrid land cover map with crowdsourcing and geographically weighted regression

    Article • ISPRS Journal of Photogrammetry and Remote Sensing, May 2015, Elsevier

    Dr Teja Kattenborn

  33. Mapping forest biomass from space – Fusion of hyperspectral EO1-hyperion data and Tandem-X and WorldView-2 canopy height models

    Article • International Journal of Applied Earth Observation and Geoinformation, March 2015, Elsevier

    Dr Teja Kattenborn

  34. Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

    Article • European Journal of Remote Sensing, January 2015, Taylor & Francis

    Dr Teja Kattenborn

  35. Automatic Single Tree Detection in Plantations using UAV-based Photogrammetric Point clouds

    Article • August 2014, Copernicus GmbH

    Dr Teja Kattenborn

  36. Segmentation of Forest to Tree Objects

    Article • March 2013, Springer Science + Business Media

    Dr Teja Kattenborn