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Dr. Michael Förster

Lupe [1]
© M. Förster

Senior Scientist

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

Email: michael.foerster(at)tu-berlin.de [2]

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

Publications

  • Articles
  • Other Publications

Articles

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2022

Duarte Rocha, A., Vulova, S., van der Tol, C., Förster, M. and Kleinschmit, B. (2022): Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data [8]. Hydrology and Earth System Sciences, 26(4). doi: doi.org/10.5194/hess-26-1111-2022 [9]

2021

Rocchini, D., Salvatori, N., Beierkuhnlein, C., Chiarucci, A., de Boissieu, F., Förster, M., Garzon-Lopez, C., Gillespie, T. W., Hauffe, H., He, K., Kleinschmit, B., Lenoir, J., Malavasi, M., Moudrý, V., Nagendra, H., Payne, D., Šímová, P., Torresani, M., Wegmann, M. and Féret, J.-B. (2021): From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing. [10]. Ecological Informatics, 61, pp. 1-10. doi: https://doi.org/10.1016/j.ecoinf.2020.101195 [11]

Gränzig, T., Fassnacht, F. E., Kleinschmit, B. and Förster, M. (2021): Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach. [12]. International Journal of Applied Earth Observation and Geoinformation, 96(102281).

2020

Fersch, B., Francke, T., Heistermann, M., Schrön, M., Döpper, V., Jakobi, J., Baroni, G., Blume, T., Bogena, H., Budach, C., Gränzig, T., Förster, M., Güntner, A., Hendricks Franssen, H., Kasner, M., Köhli, M., Kleinschmit, B., Kunstmann, H., Patil, A., Rasche, D., Scheiffele, L., Schmidt, U., Szulc-Seyfried, S., Weimar, J., Zacharias, S., Zreda, M., Heber, B., Kiese, R., Mares, V., Mollenhauer, H., Völksch, I. and Oswald, S. (2020): A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany [13]. Earth System Science Data, 12, pp. 2289-2309. doi: https://doi.org/10.5194/essd-12-2289-2020 [14]

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 [15]. International Journal of Applied Earth Observation and Geoinformation, 87, pp. 1-13. doi: https://doi.org/10.1016/j.jag.2019.102036 [16]

Döpper, V., Gränzig, T., Kleinschmit, B. and Förster, M. (2020): Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification. [17]. remote sensing, 12(1552), pp. 1-22. doi: https://doi.org/10.3390/rs12101552 [18]

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 [19]. 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 [20]. 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 [21]. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2018, pp. 71–84.

2017

Möller, M., Gerstmann, H., Gao, F., Dahms, T. C. and Förster, M. (2017): Coupling of phenological information and simulated vegetation index time series: Limitations and potentials for the assessment and monitoring of soil erosion risk [22]. Catena, 170, pp. 192-205. doi: 10.1016/j.catena.2016.11.016 [23]

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Other Publications

UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data
Citation key Kattenborn20190
Author Kattenborn, T. and Lopatina, J. and Förster, M. and Braun, A. C. and Fassnacht, F. E.
Pages 61-73
Year 2019
Journal Remote Sensing of Environment
Volume 227
Number 2019
Abstract Invasive plant species can pose major threats to biodiversity, ecosystem functioning and services. Satellite based remote sensing has evolved as an important technology to spatially map the occurrence of invasive species in space and time. With the new era of the Sentinel missions, Synthetic Aperture Radar (SAR) and multispectral data are now freely available and repeatedly acquired on a high spatial and temporal resolution for the entire globe. However, the high potential of such sensors for automatic mapping procedures cannot be fully harnessed without sufficient and appropriate reference data for model calibration. Reference data are commonly acquired in field surveys, which however, are often relatively expensive and affected by sampling and observer bias. Moreover, a direct transferability to the remote sensing perspective and scale is difficult. Accordingly, we firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover of three woody invasive species Pinus radiata, Ulex europaeus and Acacia dealbata occurring in Chile. Secondly, we test the upscaling of the estimated species cover to the spatial scale of Sentinel-1 and Sentinel-2. The proposed workflow includes the visual sampling of respective canopies in UAV orthomosaics and the subsequent spatial extrapolations using MaxEnt with spectral (RGB, Hyperspectral), textural (2D) and canopy structural (3D) predictors derived from UAV-based photogrammetry. These UAV-based maps are then used to train random forest models with multitemporal Sentinel-1 and Sentinel-2 data to map the invasive species cover on large spatial scales. Our results show that the semi-automatic UAV-based mapping of the three invasive species results in accurate predictions. Depending on the predictor combination, the correlation was 0.70, 0.77 and 0.90 for Pinus radiatia, Ulex europaeus, Acacia dealbata, respectively. Among the three species, we observed clear differences in the model performance between the tested photogrammetric predictors and their combinations (spectral, 2D texture or 3D structure). For scaling up the UAV-based estimates to the satellite-scale, the Sentinel-2 data (multispectral) were more important than Sentinel-1 data (SAR). An independent validation revealed that the R2 of the upscaling accounted for 0.78 or higher for all species and RMSE lower than 12%. Our results hence demonstrate that UAV-based reference data acquisitions are a promising alternative to traditional field surveys if the target species are directly identifiable in the UAV data.
Link to original publication [29] Download Bibtex entry [30]

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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 [32]
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