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
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.
Three-season simulated EnMAP mosaics for the San Francisco Bay Area, USA
Three-season simulated EnMAP mosaics for the San Francisco Bay Area, USAInterested 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.
30-years Landsat time series for relating population, land use and vegetation in a city
30-years Landsat time series for relating population, land use and vegetation in a cityGreen 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.
Unmixing vegetation fractions with simulated EnMAP imagery
Unmixing vegetation fractions with simulated EnMAP imageryWe compared hyperspectral and multispectral image analysis for mapping hierarchical vegetation classes in the San Francisco Bay Area, USA using regression based unmxing. We found that EnMAP-based analysis consistently performed better than Landsat across ecoregions and classes, and particularly for more complex vegetation classes. Find out more here.
Land Cover Fraction Mapping with Synthetically Mixed Training Data from Sentinel-2 STMs
This study maps sub-pixel fractions of built-up surfaces and two vegetation types in Germany and Austria at 10m spatial resolution. We used all available Sentinel-2 imagery from 2017 and 2018 and find that spectral-temporal metrics (STM) can be used to create synthetic training data as an input for regression-based unmixing across large areas. Read and share the article and explore our results here.