Articles | Volume 23, issue 5
https://doi.org/10.5194/bg-23-1949-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-1949-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Litter vs. Lens: Evaluating LAI from Litter Traps and Hemispherical Photos Across View Zenith Angles and Leaf Fall Phases
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
Teja Kattenborn
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
Julian Frey
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
Chair of Forest Growth and Dendroecology, University of Freiburg, Freiburg, Germany
Salim Soltani
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
Anna Göritz
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
Tom Jaksztat
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
Negin Katal
Chair of Sensor-based Geoinformatics, University of Freiburg, Freiburg, Germany
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To adapt to changing environmental conditions, plants can adjust their leaf angles. We developed AngleCam V2, an AI method that estimates leaf inclination angles from photos taken during both day and night. Trained on thousands of images from about 200 species, it monitors daily changes in leaf angle, aligns with laser-scanning data, and detects systematic shifts under water limitation. AngleCam V2 provides an open-source tool for monitoring leaf angle dynamics over time, taxa, and environments.
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We introduce an automated approach for generating segmentation masks for citizen science plant photos, making them applicable to computer vision models. This framework effectively transforms citizen science data into a data treasure for segmentation models for plant species identification in aerial imagery. Using automatically labeled photos, we train segmentation models for mapping tree species in drone imagery, showcasing their potential for forestry, agriculture, and biodiversity monitoring.
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This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
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Eya Cherif, Teja Kattenborn, Luke A. Brown, Michael Ewald, Katja Berger, Phuong D. Dao, Tobias B. Hank, Etienne Laliberté, Bing Lu, and Hannes Feilhauer
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Biomes are widely used to map vegetation patterns at large spatial scales and to assess impacts of climate change, yet there is no consensus on a generally valid biome classification scheme. We used crowd-sourced species distribution data and trait data to assess whether trait information is suitable for delimiting biomes. Although the trait data were heterogeneous and had large gaps with respect to the spatial distribution, we found that a global trait-based biome classification was possible.
Salim Soltani, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn
Biogeosciences, 21, 2909–2935, https://doi.org/10.5194/bg-21-2909-2024, https://doi.org/10.5194/bg-21-2909-2024, 2024
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In this research, we developed a novel method using citizen science data as alternative training data for computer vision models to map plant species in unoccupied aerial vehicle (UAV) images. We use citizen science plant photographs to train models and apply them to UAV images. We tested our approach on UAV images of a test site with 10 different tree species, yielding accurate results. This research shows the potential of citizen science data to advance our ability to monitor plant species.
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Short summary
Digital hemispherical photography(DHP) is a valuable tool for monitoring leaf area index(LAI), a key factor in ecosystem productivity and climate interactions. We compared DHP with litter traps in a temperate forest and found that at a view zenith angle around 20°, both methods aligned best. We applied a calibration model to assess site variability which significantly improved accuracy. Our findings enhance the reliability of ground-based LAI monitoring, supporting better ecosystem assessments.
Digital hemispherical photography(DHP) is a valuable tool for monitoring leaf area index(LAI), a...
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