Education
You 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
Wall-to-Wall Material Stocks at 10m resolution from Earth Observation Data
Wall-to-Wall Material Stocks at 10m resolution from Earth Observation DataWe 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.
Mapping Forest Biomass with Multispectral and Hyperspectral Imagery
Mapping Forest Biomass with Multispectral and Hyperspectral ImageryWe 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.
Web-mapping for Q-Team course project presentations
Web-mapping for Q-Team course project presentationsStudents 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.
Building height map of Germany
Building height map of GermanyIn 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.
Annual spring phenology from combined Landsat and Sentinel-2 time series
Annual spring phenology from combined Landsat and Sentinel-2 time seriesIn 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.
Topographic correction matters: Land-cover mapping in the Caucasus
Topographic correction matters: Land-cover mapping in the CaucasusIn 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.