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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.



satellite image classified - figure 11 of publicationLand 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.

map and graphs of the researchMapping forest cover fractions throughout three decades of Landsat data

We 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.

slice of grazing pressure maps northern Kazakhstan  - landsat satellite images and maps from research resultsLandsat uncovers a 73 % drop in grazing pressure in Kazakhstan

We 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.

blue mosaic with a white circle and clock hands positioned at 10 Minutes to nineVisualizing and labeling dense multi-sensor Earth observation time series

We 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.

Uncorrected and across-track brightness corrected image mosaic of the San Francisco Bay AreaHow to correct for brightness gradients in hyperspectral image mosaics

We 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.