TU Berlin

Geoinformation in Environmental PlanningFörster, Michael

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Dr. Michael Förster

Lupe

Senior Scientist

Phone: +49 (0)30 / 314 - 72 79 8

Email:

Room: EB 236b
Consultation hour: by arrangement

Personal Data
Date and place of birth: 1975 (Burgstädt, Saxony, Germany)
Employment and academic vita
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
Degrees
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

Hierarchical classification with subsequent aggregation of heathland habitats using an intra-annual RapidEye time-series
Citation key Fenske2020
Author Fenske, K. and Feilhauer, H. and Förster, M. and Stellmes, M. and Waske, B.
Pages 1-13
Year 2020
ISSN 0303-2434
DOI https://doi.org/10.1016/j.jag.2019.102036
Journal International Journal of Applied Earth Observation and Geoinformation
Volume 87
Abstract Mapping heathland habitats is generally challenging due to fine-scale habitats as well as spectral ambiguities between different classes. A multi-seasonal time-series of multispectral RapidEye data from several phenological stages was analysed towards the classification of different vegetation communities. A 3-level hierarchical dependent classification using Import Vector Machines was tested, based on the assumption that a probabilistic output per class would help the mapping. The first level of the hierarchical classification was related to the moisture gradient, which was derived from Ellenberg’s moisture indicative value. The second level aimed to separate plant alliances; the third level differentiated individual plant associations. For the final integration of the three classification levels, two approaches were implemented: (i) the F1-score and (ii) the maximum classification probability. The overall classification accuracies of both methods were found to be similar, around 0.7. Nevertheless, based on our expert knowledge we found the probabilistic approach to provide a more realistic picture and to be more practical compared to the result using the F1-score from the management point of view. In addition, the overall performance of the maximum probabilistic approach is better in the sense that the same accuracy of 0.7 was achieved with a differentiation of 33 classes instead of only 13 classes for the F1-score, meaning that the method is able to separate more spectral classes at a more detailed level providing the same accuracy.
Link to publication Download Bibtex entry

Other Publications

2020

Fenske, K., Feilhauer, H., Förster, M., Stellmes, M. and Waske, B. (2020): Hierarchical classification with subsequent aggregation of heathland habitats using an intra-annual RapidEye time-series. International Journal of Applied Earth Observation and Geoinformation, 87, pp. 1-13. doi: https://doi.org/10.1016/j.jag.2019.102036


Döpper, V., Gränzig, T., Kleinschmit, B. and Förster, M. (2020): Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification.. remote sensing, 12(10). doi: https://doi.org/10.3390/rs12101552


2019

Kattenborn, T., Lopatina, J., Förster, M., Braun, A. C. and Fassnacht, F. E. (2019): UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment, 227(2019), pp. 61-73.


2018

Holtgrave, A.-K., Förster, M., Greifeneder, F., Notarnicola, C. and Kleinschmit, B. (2018): Estimation of Soil Moisture in Vegetation-Covered Floodplains with Sentinel-1 SAR Data Using Support Vector Regression. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2018, pp. 85–101.


Klinke, R., Kuechly, H., Frick, A., Förster, M., Schmidt, T., Holtgrave, A.-K. a. K. B., Spengler, D. and Neumann, C. (2018): Indicator-Based Soil Moisture Monitoring ofWetlands by Utilizing Sentinel and Landsat Remote Sensing Data. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2018, pp. 71–84.


2017


Schmidt, J., Fassnacht, F. E., Neff, C., Lausch, A., Kleinschmit, B., Förster, M. and Schmidtlein, S. (2017): Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status. International Journal of Applied Earth Observation and Geoinformation, 60, pp. 61-71. doi: http://dx.doi.org/10.1016/j.jag.2017.04.005


2016

Gärtner, P., Förster, M. and Kleinschmit, B. (2016): The benefit of synthetically generated RapidEye and Landsat 8 data fusion time series for riparian forest disturbance monitoring. Remote Sensing of Environment, 2016(177), pp. 237-247. doi: doi:10.1016/j.rse.2016.01.028


2015

Baur, A. H., Lauf, S., Förster, M. and Kleinschmit, B. (2015): Estimating greenhouse gas emissions of European cities — Modeling emissions with only one spatial and one socioeconomic variable. Science of the Total Environment, 2015(520), pp. 49-58. doi: 10.1016/j.scitotenv.2015.03.030


Rocchini, D., Andreo, V., Förster, M., Gutierrez, A., Gillespie, W., Hauffe, H., He, K., Kleinschmit, B., Mairota, P., Marcantonio, M., Metz, M., Nagendra, H., Pareeth, S., Ponti, L., Ricotta, C., Rizzoli, A., Schaab, G., Zebisch, M., Zorer, R. and Neteler, M. (2015): Potential of remote sensing to predict species invasions: A modelling perspective. Progress in Physical Geography, 39(3), pp. 283-309. doi: 10.1177/0309133315574659


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