Fraunhofer Institute for Computer Graphics Research IGDSenior Scientist Peatlands
Since December 2023, Daniel Pönisch has been working at Fraunhofer IGD in applied research on peatland rewetting, greenhouse gas emissions, and digitalization. His particular research interests include the use of sensor-based and automated methods for collecting and analyzing real-time biogeochemical data and remote sensing data. His work focuses on the development of an AI-based monitoring system for effective environmental and peatland management. He is jointly responsible for establishing the “Peatlands” research focus at Fraunhofer IGD and serves as scientific lead for the projects “VALPEATS – Valuation of Ecosystem Services from Peatlands” and “KIMoPa – AI-supported Monitoring of Paludiculture.” The aim is to develop innovative solutions for environmental and peatland monitoring through collaboration and knowledge transfer.
PhD (Leibniz Institute for Baltic Sea Research Warnemünde)
He accomplished the PhD at the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) in the field of marine chemistry. The focus was on the marine carbon cycle in the shallow waters of the Baltic Sea and the release of climate-relevant trace gases from peat sediments and coastal peatlands. High-resolution multi-sensor measurement platforms were developed and used for the biogeochemical observation of the temporally and spatially highly dynamic processes. The work was carried out as part of the DFG Research Training Group Baltic TRANSCOAST, which enables interdisciplinary research at the interface between the marine and terrestrial ecosystems.
Daniel Pönisch studied chemistry (B.Sc.) at the Technische Universität Dresden and chemistry (M.Sc.) at the University of Rostock.
Research interests
- Greenhouse gas dynamics and release in marine environments and peatlands
- Environmental monitoring and biogeochemical in situ sensors (e.g. CO₂, CH4, CTD, O₂, chlorophyll a, PO43-, water level)
- Development and use of multi-sensor measurement platforms for autonomous recording of biogeochemical variables
- Use of AI-based monitoring systems and digital solutions for environmental and peatland management