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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

A dense network of cosmic-ray neutron sensors for soil moisture observation in a highly instrumented pre-Alpine headwater catchment in Germany
Citation key Fersch2020
Author Fersch, B. and Francke, T. and Heistermann, M. and Schrön, M. and Döpper, V. and Jakobi, J. and Baroni, G. and Blume, T. and Bogena, H. and Budach, C. and Gränzig, T. and Förster, M. and Güntner, A. and Hendricks Franssen, H.J. and Kasner, M. and Köhli, M. and Kleinschmit, B. and Kunstmann, H. and Patil, A. and Rasche, D. and Scheiffele, L. and Schmidt, U. and Szulc-Seyfried, S. and Weimar, J. and Zacharias, S. and Zreda, M. and Heber, B. and Kiese, R. and Mares, V. and Mollenhauer, H. and Völksch, I. and Oswald, S.
Pages 2289-2309
Year 2020
ISSN 1866-3508
DOI https://doi.org/10.5194/essd-12-2289-2020
Journal Earth System Science Data
Volume 12
Abstract Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 [Titel anhand dieser DOI in Citavi-Projekt übernehmen] (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b [Titel anhand dieser DOI in Citavi-Projekt übernehmen] (Fersch et al., 2020b).
Bibtex Type of Publication Kleinschmit
Download Bibtex entry

Other Publications

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.. Ecological Informatics, 61, pp. 1-10. doi: https://doi.org/10.1016/j.ecoinf.2020.101195


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. Earth System Science Data, 12, pp. 2289-2309. doi: https://doi.org/10.5194/essd-12-2289-2020


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(1552), pp. 1-22. 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


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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