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Dr. Michael Förster
- © M. Förster
Senior Scientist
Phone: +49 (0)30 / 314 - 72 79 8
Email: michael.foerster(at)tu-berlin.de
Room: EB 236b
Consultation hour: by arrangement
| Date and place of birth: 1975 (Burgstädt, Saxony, Germany) |
| 2018 | Visiting Scientist at the Joint Research Center (JRC) in Ispra, Italy (Bioeconomy Unit) |
| 2012 | Visiting Scientist at the University Utrecht, Netherlands (Department of Physical Geography) |
| 2010 | Visiting Scientist at the European Academy Bolzano (EURAC), Italy (Institute for Applied Remote Sensing) |
| since 2009 | Post-doctoral Research Fellow Technische Universität Berlin, Institute of Landscape Architecture and Environmental Planning, Department of Geoinformation Processing for Landscape and Environmental Planning |
| 2003-2008 | Research Scientist Technische Universität Berlin, Institute of Landscape Architecture and Environmental Planning, Department of Geoinformation Processing for Landscape and Environmental Planning |
| 2001-2003 | Consultant and GIS-Coordinator Environmental Consulting and Planning Agency - Froelich & Sporbeck, Potsdam, Germany |
| 1999-2001 | Research Associate Geo-Forschungs-Zentrum (GFZ) Potsdam, Section 1.4 (Remote Sensing) |
| 1998-1999 | Exchange Student (ERASMUS) University of Southampton, UK |
| 1996-2003 | Studies of Geoecology Universität Potsdam, Germany |
| 2003 | Diploma, University of Potsdam Grade: 1,1 (on a scale from 1 to 6, where 1 is highest) |
| 2009 | Doctorate, Technische Universität Berlin, summa cum laude |
Research Topics
- Development of methods to analyse the dynamics of ecosystems from time-series (optical and SAR), especially for degradation processes or abrupt damages (e.g. caused by fire or storms)
- Relation of temporal and spectral signals to plant traits and biophysical variables (xantophyll, nitrogen, chlorophyll and fluorescence)
- Derivation of operationalizable and comprehensive environmental indicators that are needed for the effective implementation of management measures (e.g. within the framework of the European NATURA 2000 requirements) or for a better understanding of ecosystems
- Interaction of vegetation structure, which can be measured with LiDAR or SAR, with spectral information for the evaluation of forest properties
- Combining spatially very high resolution data (drones) with satellite data to understand ecohydrological processes and especially to derive hydrological variables such as soil moisture content or interception
Articles
| Citation key | Clasen2015 |
|---|---|
| Author | Clasen, A. and Somers, B. and Pipkins, K. and Tits, L. and Segl, K. and Brell, M. and Kleinschmit, B. and Spengler, D. and Lausch, A. and Förster, M. |
| Pages | 26 |
| Year | 2015 |
| ISSN | 2072-4292 |
| DOI | 10.3390/rs71115361 |
| Journal | remote sensing |
| Volume | 2015 |
| Number | 7 |
| Month | November |
| Abstract | Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference fractional abundances, as well as spectral measurements of the canopy components, could be precisely determined from a crane measurement platform situated in a deciduous forest in North-East Germany. In contrast to most other studies, which only use leaf and soil endmembers, this experimental setup allowed for the inclusion of a bark endmember for the unmixing of components within the canopy. This study demonstrates that the inclusion of additional endmembers markedly improves the accuracy. A mean absolute error of 7.9% could be achieved for the fractional occurrence of the leaf endmember and 5.9% for the bark endmember. In order to evaluate the results of this field-based study for airborne and satellite-based remote sensing applications, a transfer to Airborne Imaging Spectrometer for Applications (AISA) and simulated Environmental Mapping and Analysis Program (EnMAP) and Sentinel-2 imagery was carried out. All sensors were capable of unmixing crown components with a mean absolute error ranging between 3% and 21%. |
| Bibtex Type of Publication | Article |