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Faculty of Mathematics and Natural Sciences - Earth Observation Lab


student group on a boat in BrazilYou plan to focus on Earth Observation?

Here you find all information needed to structure your studies with a focus on Earth Observation: course descriptions, and good practice examples of theses or MAPs from previous courses.



Berlin strret imprerssion, buildings and infrastructureBuilding height map of Germany

In our study we have produced the first ever high-resolution building height map of entire Germany. For this, we have used a full year of all available Sentinel-1A/B and Sentinel-2A/B data along with official 3D building models and machine learning regression. Read the open access paper, look at the map viewer, or download the data.

Two maps of Germany side by side showing cloud-free observations and start of season estimates. Annual spring phenology from combined Landsat and Sentinel-2 time series

In this study, we combined Landsat and Sentinel-2 time series to estimate annual spring phenology of broadleaf forests across Germany. The choice of vegetation index affected our estimates more than the choice of model. We found that the combination of Landsat 7/8 and Sentinel-2 improved start of season estimates considerably compared to single-sensor time series. Read the full article here.

Cropland and forest change mapsTopographic correction matters: Land-cover mapping in the Caucasus

In our study we found that topographic correction matters for land-cover mapping, especially for discriminating forest types in steep terrain, and we examined three decades of Landsat imagery revealing that cropland loss was the most prevalent land-cover change in the Caucasus. Find out more here.

simulated EnMap mosaics "Spring Summer Fall"Three-season simulated EnMAP mosaics for the San Francisco Bay Area, USA

Interested in using hyperspectral EnMAP imagery? Check out our simulated EnMAP mosaics derived from airborne AVIRIS imagery acquired over three dates in 2013. AVIRIS images were simulated to match expected EnMAP characteristics, and secondary geometric and brightness gradient corrections make this dataset analysis ready. Data can be downloaded from GFZ Dataservices.

Vegetation cover trends in Berlin, Germany30-years Landsat time series for relating population, land use and vegetation in a city

Green growth in a city? Our new publication develops a methodology for contrasting vegetation- and population density developments over long time periods using a Landsat time series and spectral unmixing in a densifying city and different land use classes. Read the article here.