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

Title and Logo of the EnMap ProjectUpdated Science Plan for EnMap online

The Science Plan describes the scientific background, applications, and activities of the Environmental Mapping and Analysis Program (EnMAP) imaging spectroscopy mission. The update reflects the excellent science happened since the publication of the last Science Plan in 2016. Discover the content of the Science Plan 2022 online here.

News paper FS 2022-10.pngHigh-resolution mapping of material stocks

A new Earth Observation Lab study maps the accumulation of human-made material stock in buildings and infrastructure over the last 33 years in Germany at high spatial resolution with present-day stock maps and remote sensing data from the Landsat Archive. We find an overall growth of stock of 13% and monitor stock growth with population decline in former East-Germany after 1990. Read the article here.

 

treespecies_thumbnail.pngTree species mapped with Sentinel-2 time series

We used Sentinel-2 time series and forest inventory data to map 17 tree species in Brandenburg state, Germany. You can find out more about our approach and how environmental factors and texture metrics influence map accuracy in our paper here.

 

 

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.

 

CTM-2019-previewMulti-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.