Florian Männer
Florian Männer Contact: Karlrobert-Kreiten-Str. 13 Office: Nußallee 1, 3rd floor, room 3.010 Tel: (+49) 228 73-3695 2nd Affiliation: 3rd Affiliation: | ||
Research Group: | Grassland Ecology & Grassland Management https://www.ipe.uni-bonn.de/arbeitsgruppen/grassland/forschung-1 | |
Project: | NamTip – Understanding and Managing Desertification Tipping Points in Dryland Social-Ecological Systems – A Namibian Perspective https://www.namtip.uni-bonn.de/ I am currently pursuing a PhD as part of the BMBF-funded transdisciplinary project NamTip, which aims to understand ecological processes and societal and management issues associated with desertification tipping points (DTPs) in the semi-arid savannah grasslands of Namibia. My work in this project is taking place in Namibia and focuses on hyperspectral sensing and ecological analyses of forage production, quality, and vegetation cover in a semi-arid savannah grassland under management and climatic pressure. SEBAS – SEnsing Biodiversity Across Scales (SEBAS). Land-use effects on the biodiversity-ecosystem functioning (BEF) and the biodiversity-ecosystem service (BES) relationship in central European grasslands https://www.geographie.uni-bonn.de/Pressemitteilungen/sensing-biodiversity-across-scales-sebas I work on hyperspectral canopy sensing of grassland traits, such as biomass, forage quality, plant functional types and plant functional traits | |
Research Interests: | Grassland ecology, temperate grasslands, semi-arid grasslands, ecological tipping points, forage provision analysis, plant life strategy, hyperspectral sensing, remote sensing, machine learning | |
Education: |
Since 2022: Since 2019: 2016 - 2018: 2014-2016: 2013-2016: | |
Positions held: |
Since 2019 2017 - 2019 | |
Selected publications:
- Muro J., ; Linstädter A., Magdon P., Wöllauer S., Männer F.A., Schwarz L.-M., Ghazaryan G., Schultz J., Malenovský Z., Dubovyk O. (2022). Predicting plant biomass and species richness in temperate grasslands across regions, time, and land management with remote sensing and deep learning. Remote Sensing of Environment, 282, 113262. doi: 10.1016/j.rse.2022.113262