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
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.
Mapping forest cover fractions throughout three decades of Landsat data
Mapping forest cover fractions throughout three decades of Landsat dataWe implemented a generalized regression-based unmixing approach for mapping coniferous and broadleaved forest cover fractions throughout three decades of Landsat data. This innovative strategy presents a solution for moving from categorical towards continuous change products for forest cover types. Find out more here.
Landsat uncovers a 73 % drop in grazing pressure in Kazakhstan
Landsat uncovers a 73 % drop in grazing pressure in KazakhstanWe developed a new methodology to quantify grazing footprint using all available Landsat imagery and a random forest classifier. Our novel index of grazing pressure correlated well with field-based grazing indicators such as number of dung piles and biomass. We produced annual grazing pressure maps for northern Kazakhstan from 1985 to 2017. The spatial footprint of heavy grazing decreased by >70% after the collapse of the Soviet Union. Read the full paper here.
Visualizing and labeling dense multi-sensor Earth observation time series
Visualizing and labeling dense multi-sensor Earth observation time seriesWe developed the EO Time Series Viewer, a QGIS plugin for user-friendly visualization, interpretation and labeling of multi-sensor time series data. The EO Time Series Viewer is open source and can be installed via the official QGIS Plugin repository. Read more about its capabilities, visualization- and labeling concepts in this article.
How to correct for brightness gradients in hyperspectral image mosaics
How to correct for brightness gradients in hyperspectral image mosaicsWe produced an analysis ready EnMAP image for regional-scale mapping in the SF Bay Area. Secondary geometric alignment and brightness gradient correction were applied to multiple AVIRIS flightlines simulated to resemble spaceborne acquisitions, yielding high quality hyperspectral mosaics. Find out more here.