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Scientific Reports (3)
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Showing 1–8 of 8 results

Advanced filters: Author: Teja Kattenborn Clear advanced filters

Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping

Plant traits drive ecosystem dynamics yet are challenging to map globally due to sparse measurements. Here, the authors combine crowdsourced biodiversity observations with Earth observation data to accurately map 31 plant traits at 1 km² resolution.

Daniel Lusk
Sophie Wolf
Teja Kattenborn
ResearchOpen Access30 Jan 2026
Nature Communications

Volume: 17, P: 1-17
Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Teja Kattenborn
Jana Eichel
Fabian Ewald Fassnacht
ResearchOpen Access27 Nov 2019
Scientific Reports

Volume: 9, P: 1-9
Unraveling the seasonality of functional diversity through remote sensing

Plant functional diversity is highly dynamic over time, driven by seasonal cycles and wet–dry periods and varying across biomes, based on an analysis of over 4,000 hyperspectral satellite scenes at the global scale.

Daniel Mederer
Teja Kattenborn
Hannes Feilhauer
ResearchOpen Access06 Oct 2025
Communications Earth & Environment

Volume: 6, P: 1-9
Citizen science plant observations encode global trait patterns

Comparing global vegetation trait patterns derived from citizen science data versus those from scientific survey plots, the authors reveal high correlations between the two approaches and improvements over previously published trait maps.

Sophie Wolf
Miguel D. Mahecha
Teja Kattenborn
Research20 Oct 2022
Nature Ecology & Evolution

Volume: 6, P: 1850-1859
Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations

Temporal variation of vertical leaf angles can strongly alter widely used vegetation indices, with the effect dependent on both environment and species, according to an analysis of time series leaf photos and radiative transfer model simulations.

Teja Kattenborn
Sebastian Wieneke
Benjamin Dechant
ResearchOpen Access02 Oct 2024
Communications Earth & Environment

Volume: 5, P: 1-11
Deep learning and citizen science enable automated plant trait predictions from photographs

Christopher Schiller
Sebastian Schmidtlein
Teja Kattenborn
ResearchOpen Access12 Aug 2021
Scientific Reports

Volume: 11, P: 1-12
Radiative transfer modelling reveals why canopy reflectance follows function

Teja Kattenborn
Sebastian Schmidtlein
ResearchOpen Access25 Apr 2019
Scientific Reports

Volume: 9, P: 1-10
A Landsat-based vegetation trend product of the Tibetan Plateau for the time-period 1990–2018

Fabian Ewald Fassnacht
Christopher Schiller
Jiapeng Qu
ResearchOpen Access31 May 2019
Scientific Data

Volume: 6, P: 1-11