Current knowledge of global patterns of plant traits and functional diversity is limited by geographic, taxonomic, and functional gaps, hindering our understanding of biodiversity and Earth system dynamics. Studies attempting to extrapolate insitu trait data using environmental predictors have faced significant uncertainties due to data sparsity, while globally available satellite observations are constrained due to their limited perspective ‘from above’. PANOPS aims to fill these gaps by using crowdsourced plant photographs and deep learning to predict and map global plant traits and functional diversity. This approach offers a promising toolset to advance macroecological research and our understanding of biodiversity-environment relationships.Funding bodies: German Research Foundation (DFG)
| Associated researchers | Ayushi Sharma, Daniel Lusk, Teja Kattenborn, Jonanna Trost |
| Collaborators | Sophie Wolf, Simon Scheiter, Hannes Feilhauer, Miguel Mahecha, Jens Kattge, Francesco Maria Sabatini, Helge Bruelheide, Ben Dechant, Alvaro Moreno Martinez and others. |
| Duration | 2023-2029 |
| Funding | German Research Foundation – DFG (Project no. 504978936) |