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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.



Multi-year national crop type mapping for Germany

We used all available Sentinel-2 and Landsat-8 data along with information from Sentinel-1 and various environmental data sets to map national agricultural land cover. Together with partners from the Thünen Institute and the Leibniz Centre for Agricultural Landscape Research, we differentiated 20 agricultural classes for 2017, 2018 and 2019. Explore the preliminary maps here.


Building Types and Gridded Population, classified thumbnail-mapGridded Population from Earth Observation Data

We mapped gridded population in Germany based on building density, height and types from Earth Observation data. Including the vertical structure of buildings considerably increases accuracy by eliminating systematic underestimation in agglomerations and overestimation in rural areas. All layers are also suitable to produce large-area bottom-up population estimates. Read the article and explore results here.

Total material stocks in Berlin, GermanyWall-to-Wall Material Stocks at 10m resolution from Earth Observation Data

We mapped material stock patterns in Austria and Germany from Sentinel-1/2-derived maps of building area, height and types. Using material intensity factors, the mass of eight types of materials in buildings and infrastructure could be distinguished. Total mass amounted to ∼540 t/cap in Austria and ∼450 t/cap in Germany. Read the article and explore results here.

Remote Sensing Classification Forest GBMapping Forest Biomass with Multispectral and Hyperspectral Imagery

We investigate synergies between Landsat time series and simulated hyperspectral EnMAP data for mapping forest aboveground biomass. We found that combining the dense spectral EnMAP data with dense temporal Landsat information yielded better results than either sensor separately. Read the full open-access article here.

Physical World Map image with place markersWeb-mapping for Q-Team course project presentations

Students of the Q-Team Remote Sensing for Settlements used methods of web-mapping to present results of their project work. Explore seven projects that mapped urban development and expansion or urban vegetation around the world using a variety of remote sensing and other geodata. Discover the projects here.

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.