Status	Guest researcher from Thünen-Institut
E-Mail	marcel.schwieder@geo.hu-berlin.de
Office location	Rudower Chaussee 16, Room 2'210
Phone	+49 (0)30 2093-9429
Fax	+49 (0)30 2093-6848
Postal address	Unter den Linden 6, 10099 Berlin, Germany

About me

I studied Geography at Humboldt-Universität zu Berlin because of my interest in ongoing global changes and underlying processes. I really enjoy being outdoors and experiencing nature. Thus, I am happy that I have the opportunity to contribute to a growing body of research, which aims to better understand our complex Earth system in order to sustain our environment for future generations.

My reserach focus is on the use of optical remote sensing data to map and monitor vegetation in (semi-) natural ecosystems and to analyze its behaviour over time.

 

Curriculum Vitae

since 03/2021	Researcher. Thünen-Institut, Institut für Betriebswirtschaft, Braunschweig, Germany. MonViA project "Monitoring der biologischen Vielfalt in Agrarlandschaften mit Fernerkundung" Guest researcher, Earth Observation Lab, HU-Berlin, Germany.
2018-2021	Postdoctoral researcher. Geomatics Lab, HU-Berlin, Germany. SattGrün project - integration of Sentinel-1 and Sentinel-2 time series for grassland monitoring
2014-2018	Doctoral researcher. Geomatics Lab, HU-Berlin, Germany. Landsat time series for phenological analysis of Cerrado vegetation
2013-2017	Research assistant. Geomatics Lab, HU-Berlin, Germany. Environmental Mapping and Analysis Program (EnMAP) Teaching (GIS, Q-Team)
2012	Field work (Internship) in Alaska, USA. Alfred-Wegener-Institute. Helmholtz-Zentrum für Polar- und Meeresforschung, Potsdam, Germany.
2010-2013	Student collaborator. Geomatics Lab, HU-Berlin, Germany.
2011-2013	Master of Science in Physical Geography of Human-Environment Systems. HU-Berlin, Germany. Land system science Advanced statistics and methods of remote sensing and geoinformation systems
2008-2011	Bachelor of Science Geography. HU-Berlin, Germany. Focus on remote sensing, climatology and geoinformation systems Subsidary subject technical environmental protection (TU Berlin)

Publications

Lobert, F., Holtgrave, A.-K., Schwieder, M., Pause, M., Gocht, A., Vogt, J., & Erasmi, S. (2021). Detection of mowing events from combined Sentinel-1, Sentinel-2, and Landsat 8 time series with machine learning In T. Astor, & I. Dzene (Eds.), 21st Symposium of the European Grassland Federation. Universität Kassel, Germany (online).

Tetteh, G.O., Gocht, A., Schwieder, M., Erasmi, S., & Conrad, C. (2020). Unsupervised Parameterization for Optimal Segmentation of Agricultural Parcels From Satellite Images in Different Agricultural Landscapes. Remote Sensing, 12, 3096.

Schwieder, M., Buddeberg, M., Kowalski, K., Pfoch, K., Bartsch, J., Bach, H., Pickert, J., & Hostert, P. (2020). Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

Schwieder, M., Leitão, P.J., Pinto, J.R.R., Teixeira, A.M.C., Pedroni, F., Sanchez, M., Bustamante, M.M., & Hostert, P. (2018). Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna. Carbon Balance and Management, 13, 7.

Schwieder, M., Leitão, P.J., da Cunha Bustamante, M.M., Ferreira, L.G., Rabe, A., Hostert, P., 2016. Mapping Brazilian savanna vegetation gradients with Landsat time series. International Journal of Applied Earth Observation and Geoinformation 52, 361-370.

Schwieder, M., Leitão, P.J., Suess, S., Senf, C., Hostert, P., 2014. Estimating Fractional Shrub Cover Using Simulated EnMAP Data: A Comparison of Three Machine Learning Regression Techniques. Remote Sensing 6, 3427-3445.

A full list of publications is available at ORCID.