Earth Observation and Geoinformation Science Lab

Prof. Dr. Sebastian van der Linden

Head of Lab

Room 207

talk time: Mo. 14-16 h

phone: +49 3834 420 4500


My research aims at a better understanding of land surface processes that can be mapped and monitored with optical remote sensing data. Triggered by global (environmental) change the management, composition and state of land cover changes at different intensities through space and time. This includes, for example, shrub encroachment following land abandonment, the gradual expansion of urban areas or their densification in the context of rural-urban migration, or the distribution of vegetation types under climate change. A better understanding of these processes and their underlying drivers and relations requires accurate and repeatable quantitative measures. For more than ten years and through several projects, my colleagues and I have worked on approaches for quantitative mapping of land cover from multi- and hyperspectral data. We use machine learning algorithms and sophisticated learning strategies to achieve more accurate and temporally robust mapping models. Recently, we have started a systematic exploration of spatial-temporal transfer of empirical models. Developments are included into the EnMAP-Box, a python plug-in for QGIS that is developed in our group.

My teaching reaches from basic modules on geoinformationscience and remote sensing to advanced remote sensing modules. The latter usually include field work and deal with applied questions of environmental science, e.g. land use monitoring or forest and vegetation remote sensing.



Curriculum Vitae

Curriculum Vitae

since 10/2019 University of Greifswald - Professor for Geoinformation Science and Remote Sensing at the Department of Geography and Geology
01/2007– 09/2019 Humboldt-Universität zu Berlin – Senior scientist and lecturer at the Earth Observation Lab of the Geography Department
10/2012 – 03/2018 Managing director (part-time) of the IRI THESys of Humboldt-Universität zu Berlin
10/2006– 12/2006 Columbia University, New York – Guest scientist at Lamont-Doherty Earth Observatory
01/2004– 09/2006 Humboldt-Universität zu Berlin & Center for Remote Sensing of Land Surfaces at Bonn University – PhD student in scholarship program of German Environmental Foundation (DBU)
Dr. rer. nat., Mathematisch-Naturwissenschaftliche Fakultät (01/2008), Dissertation title: xxx (link)
10/1996– 10/2002 Trier University – Student of Applied Environmental Sciences, Majors Remote Sensing, Climatologie, Minors Geomathematics, Hydrology
  09/1999 – 06/2000 University of Edinburgh – Exchange student
  Diplom-Umweltwissenschaftler (MSc equivalent 10/2002)
Selected Publications

Selected Publications

  • van der Linden S, Okujeni A, Canters F, Degerickx J, Heiden U, Hostert P, Priem F, Somers B, Thiel F (2018). Imaging Spectroscopy of Urban Environments. Surveys in Geophysics, pages pending. 10.1007/s10712-018-9486-y
  • Jakimow B, Griffiths P, van der Linden S, Hostert P (2018). Mapping pasture management in the Brazilian Amazon from dense Landsat time series. Remote Sensing of Environment, 205(Supplement C), 453-468. 10.1016/j.rse.2017.10.009
  • Schug F, Okujeni A, Hauer J, Hostert P, Nielsen JØ, van der Linden S (2018): Mapping patterns of urban development in Ouagadougou, Burkina Faso, using machine learning regression modeling with bi-seasonal Landsat time series. Remote Sensing of Environment, 210, 218-227. 10.1016/j.rse.2018.03.022
  • Suess S, van der Linden S, Okujeni A, Griffiths P, Leitão P, Schwieder M, Hostert P (2018). Characterizing 32 years of shrub cover dynamics in Southern Portugal using annual Landsat composites and machine learning regression modeling. Remote Sensing of Environment, 219, 353-364. 10.1016/j.rse.2018.10.004
  • van der Linden S, Rabe A, Held M, Jakimow B, Leitão P, Okujeni A, Schwieder M, Suess S, Hostert P (2015). The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing. Remote Sensing, 7(9), 11249. 10.3390/rs70911249
  • Okujeni A, van der Linden S, Hostert P (2015). Extending the Vegetation-Impervious-Soil model using simulated EnMAP data and machine learning. Remote Sensing of Environment, 158, 69-80. 10.1016/j.rse.2014.11.009