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Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Education

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.

 

News

simulated EnMAP data for vegetation class fraction mapping across diverse ecoregions in CaliforniaMulti-season unmixing of vegetation class fractions

We investigated the potential of multi-season unmixing of simulated EnMAP data for vegetation class fraction mapping across diverse ecoregions in California. Relative to single-season unmixing and unmixing based on Landsat time series, we found our multi-season approach to support a more accurate and generalized mapping. Read the article here.

 

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.