Dr. Henning Buddenbaum
- Scion Pheno (2021-2026): Seeing the forest for the trees: transforming phenotyping for future forests: Development of a remote sensing-based phenotyping platform for New Zealand.
- PapyHyp (2022-2023): Recording the Trier Papyri with Hyperspectral cameras. Article in KonzenTRIERt
- Urban Green View (2022-2023): Satellite data for more Green in cities. Article at Raumfahrer.net
- HyperEcos (2022-2025): Hyperspectral Prisma Data for Ecosystem functions, habitats, and diversity characterization. Eurac
- BrandSat (2020-2022) - Mapping Fire Risk using remote sensing and meteorological data (with HU Berlin)
- TreeCop (2020-2022) - Development of a Sentinel-based tool to detect drought stress in city trees in Essen
- EnMAP Core Science Team (2010-2019): Algorithms for using hyperspectral satellite data for forest applications with EnMAP.
M.S. Watt, T. Poblete, D. de Silva, H.J.C. Estarija, R.J.L. Hartley, E.M.C. Leonardo, P. Massam, H. Buddenbaum & P.J. Zarco-Tejada (2023): Prediction of the severity of Dothistroma needle blight in radiata pine using plant based traits and narrow band indices derived from UAV hyperspectral imagery. Agricultural and Forest Meteorology, 330, 109294. DOI
P. Kaiser, H. Buddenbaum, S. Nink & J. Hill (2022): Potential of Sentinel-1 Data for Spatially and Temporally High-Resolution Detection of Drought Affected Forest Stands. Forests, 13, 2148. DOI
M.S. Watt, H. Buddenbaum, E.M.C. Leonardo, H.J. Estarija, H.E. Bown, M. Gomez-Gallego, R.J.L. Hartley, G.D. Pearse, P. Massam, L. Wright & P.J. Zarco-Tejada (2020): Monitoring biochemical limitations to photosynthesis in N and P-limited radiata pine using plant functional traits quantified from hyperspectral imagery. Remote Sensing of Environment, 248, 112003. DOI
J. Hill, H. Buddenbaum & P.A. Townsend (2019): Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems. Surveys in Geophysics, 40 (3), 631-656. DOI