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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 | Doepper2020 |
|---|---|
| Author | Döpper, V. and Gränzig, T. and Kleinschmit, B. and Förster, M. |
| Pages | 1-22 |
| Year | 2020 |
| DOI | https://doi.org/10.3390/rs12101552 |
| Journal | remote sensing |
| Volume | 12 |
| Number | 1552 |
| Abstract | Thermal infrared measurements acquired with unmanned aerial systems (UAS) allow for high spatial resolution and flexibility in the time of image acquisition to assess ground surface temperature. Nevertheless, thermal infrared cameras mounted on UAS suffer from low radiometric accuracy as well as low image resolution and contrast hampering image alignment. Our analysis aims to determine the impact of the sun elevation angle (SEA), weather conditions, land cover, image contrast enhancement, geometric camera calibration, and inclusion of yaw angle information and generic and reference pre-selection methods on the point cloud and number of aligned images generated by Agisoft Metashape. We, therefore, use a total amount of 56 single data sets acquired on different days, times of day, weather conditions, and land cover types. Furthermore, we assess camera noise and the effect of temperature correction based on air temperature using features extracted by structure from motion. The study shows for the first time generalizable implications on thermal infrared image acquisitions and presents an approach to perform the analysis with a quality measure of inter-image sensor noise. Better image alignment is reached for conditions of high contrast such as clear weather conditions and high SEA. Alignment can be improved by applying a contrast enhancement and choosing both, reference and generic pre-selection. Grassland areas are best alignable, followed by cropland and forests. Geometric camera calibration hampers feature detection and matching. Temperature correction shows no effect on radiometric camera uncertainty. |
| Bibtex Type of Publication | Kleinschmit |
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Fachgebiet Geoinformation in der Umweltplanung
Sekretariat EB5
Room EB 236a
Straße des 17. Juni 145
D - 10623 Berlin
Tel.: +49 (0)30 314 - 73 29 0
Fax: +49 (0)30 314 - 23 50 7
e-mail query
Sekretariat EB5
Room EB 236a
Straße des 17. Juni 145
D - 10623 Berlin
Tel.: +49 (0)30 314 - 73 29 0
Fax: +49 (0)30 314 - 23 50 7
e-mail query