Teja Kattenborn

The paper introduces MillionTrees, a global benchmark dataset for airborne tree detection, showing that current models struggle to generalize across diverse data and that training on MillionTrees improves performance.

AngleCam V2 estimates leaf inclination angles from daytime and nighttime photos across species, showing improved accuracy and generalization compared to its predecessor.

The paper predicts spatio-temporal patterns of soil respiration in a mixed forest using downscaled meteorological data, finding they outperform local parameters in explaining variability.

The paper quantifies global shifts in plant functional trait composition due to human introductions, finding convergence toward smaller, more acquisitive, and shorter-lived assemblages.

The paper evaluates a trait-based vegetation model's ability to predict global specific leaf area (SLA) and finds that improving leaf phenology abundance accuracy enhances its performance.

The paper assesses forest dieback in drinking water protection areas and finds it correlates with increased nitrate concentrations in groundwater.

This paper combines crowdsourced biodiversity data with Earth observation to model plant traits at high resolution, achieving strong predictive power and improving spatial coverage for global trait mapping.

Germany-wide 10 m remote sensing maps put 2018–2021 standing deadwood at 978 ± 529 kha, almost twice earlier satellite estimates; late frost, water deficit and heat were top predictors.