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We contribute to:

The Land System Science Cluster at the Geography Department of the Humboldt-Universität zu Berlin


IRI THESys: Integrative Research Institute on Transformations of Human-Environment Systems at Humboldt-Universität zu Berlin

Geomatics Lab

Colloquium SS 2014



Lab address book


Postal address

Humboldt-Universität zu Berlin
Geography Department
Geomatics Lab

Unter den Linden 6
10099 Berlin



Rudower Chaussee 16
12489 Berlin



Room 2'227
Tel.:  +49 (030) 2093-6905
Fax:  +49 (030) 2093-6848

Faculty of Mathematics and Natural Sciences - Geomatics

Welcome to the Geomatics Lab!

Lab focus

The Geomatics Lab focuses on a better understanding of coupled human-environment systems based on remote sensing data and geoinformation. Specific interest lies on land use / land cover change and related ecosystem services.

Deforestation Brasilian Amazon Rainforest

Patterns and timing of deforestation (from 1986:light purple - 2011:brown) of the Brasilian Amazon rainforest, based on the analysis of deep time series of Landsat data.

Our methodological foundation is based on advanced remote sensing data analyses. Themes of interest include landscape to sub-continental mapping and monitoring, deep time series analysis and imaging spectroscopy. Our research contributes to the Global Land Project, the EnMAP Core Science Team and the Landsat Science Team.

Upcoming events

published 15th April 2014

28.04., 1pm to 3pm, Room 2'108; LSSC Colloquium: Three 15 min master thesis talks:

Oreas Kotschi - The changing global soybean network: relations between soybean consumption and its land use footprint.

Banjanin Bleyhl - Mapping European Bison Habitat in the Caucasus Mountains.

Philippe Rufin - Remote Sensing of Pasture degradation in Southern Parà: Woody Encroachment Trajectories from landsat TM and ETM+ Data (1984-2012)


published 15th April 2014

New paper in Remote Sensing of Environment

Landsat-8: Science and product vision for terrestrial global change research 

This paper provides background on Landsat 8 capabilities and the Landsat Science Team research priorities. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, large area, long-term terrestrial data records at "Landsat-scale". There is a great demand for such products for resource management, for climate change and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, e.g. Sentinel-2, and consideration of successor Landsat mission requirements.

published 15th April 2014

New paper published in Remote Sensing on "Estimating Fractional Shrub Cover Using Simulated EnMAP Data: a Comparison of Three Machine Learning Regression Techniques" by Marcel Schwieder et. al.

In this research we used simulated hyperspectral EnMAP data for sub-pixel mapping of shrub cover in southern Portugal. Therefore, we compared the prediction performance of Support Vector Regression, Random Forest Regression and Partial Least Squares Regression. In our tests SVR showed the best results, followed by RF and PLSR. We used the SVR model to produce a fractional shrub cover map for our study area, which revealed comprehensible patterns of shrubs. These patterns were particularly pronounced between regions with special land management incentives and those regions that were mainly left unmanaged. We showed that EnMAP data are valuable for detailed mapping of gradients in natural to semi-natural ecosystems and that machine learning algorithms enable us to do that in an efficient way.

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