| Status | Doctoral researcher |
| franz.schug@geo.hu-berlin.de | |
| Office location | Rudower Chaussee 16, Room 2'212, 12489 Berlin, Germany |
| Phone | +49 (0)30 2093-6883 |
| Postal address | Unter den Linden 6, 10099 Berlin, Germany |
About me
My current research contributes to the MAT_STOCKS project. We use different remote sensing and geoinformation methods to map and quantify anthropogenic material stock patterns in the broad context of social sustainability and transformation research.
Generally, my research interest is related to methods and technologies in remote sensing, geodata processing, modeling and visualization and their application in urban areas, e.g. for urban expansion and land cover change monitoring.
Curriculum Vitae
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since 03/2018 |
Doctoral Researcher, Geomatics Lab Humboldt-Universität zu Berlin, Germany. |
| 2014 - 2017 |
M.Sc. Physical Geography Humboldt-Universität zu Berlin, Germany
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| 2015 - 2016 |
Visiting Student Simon Fraser University, Canada
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2011 - 2014 |
B.Sc. Geography (major) / Computer Sciences (minor) Humboldt-Universität zu Berlin, Germany.
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Please find a complete résumé for download here (pdf)
Publications
Wellmann, T.; Lausch, A.; Andersson, E.; Knapp, S.; Cortinovis, C.; Jache, J.; Scheuer, S.; Kremer, P.; Mascarenhas, A.; Kraemer, R.; Haase, A.; Schug, F.; Haase, D. (2020): Remote Sensing in urban planning: Contributions towards ecologically sound policies? Landscape and Urban Planning, vol. 204. doi: https://doi.org/10.1016/j.landurbplan.2020.103921
Schug, F.; Frantz, D.; Okujeni, A.; van der Linden, S.; Hostert, P. (2020): Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data. Remote Sensing of Environment, vol. 246. doi: 10.1016/j.rse.2020.111810
Wellmann, T.; Schug, F.; Haase, D.; Pflugmacher, D.; van der Linden, S. (2020): Green growth? On the relation between popoulation density, land use and vegetation cover fractions in a city using a 30-years Landsat time series. Landscape and Urban Planning, vol. 202. doi: 10.1016/j.landurbplan.2020.103857
Schug, F.; Okujeni, A.; Hauer, J.; Hostert, P.; Nielsen, J. Ø.; van der Linden, S. (2018): Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Lnadsat time series. Remote Sensing of Environment, vol. 210, 218-227. doi: 10.1016/j.rse.2018.03.022