Faculty of Mathematics and Natural Sciences - Earth Observation Lab


Current projects

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conifer forest

With the more frequent occurrence of heat waves and droughts, the need for research into prevention and monitoring of forest fires is increasing. The aim of the project is the development of remote sensing methods and products for the spatially explicit representation of forest fire risk and the monitoring of forest fires in Germany. For this purpose, remote sensing based information will be collected and statistically evaluated together with meteorological data. More...

Logo of the Climate and Water under Change project - CliWaC

Altered water availability and related extreme events such as droughts strongly affect the state of ecosystems as well as their ecosystem functions and services. As member of the Einstein Research Unit Climate and Water under Change (CliWaC), the EOLab will develop and apply remote sensing methods to quantify spatial and temporal feedbacks between water availability and ecosystem properties. More...

burned forest trees with green regrowing vegetation underneath

The consequences of global climate change are more evident than ever before. Extreme heatwaves and prolonged periods of drought cause devastating fires in many regions of the world. The EnFireMap projects aims at developing remote sensing approaches for the analysis of ecosystems wherein fires are an integral part of the natural dynamics, however, experience a sharp increase in risks and intensities. More...

letters EnMap Box with spectral color radiance from the A downwards
EnMAP-Box Project

The EnMAP Box is a freely available, platform-independent software designed to visualize and process hyperspectral remote sensing data, and particularly developed to handle data from the EnMAP sensor. It is programmed in Python and provided as a plug-in for QGIS as free and open source software (FOSS). The EnMap-Box on GitHub.

More... Direct link to documentation and download

person contours and gear wheels associating group work
EnMAP Science Advisory Group

The Environmental Mapping and Analysis Program (EnMAP) is the German hyperspectral satellite mission. EOLab contributes to EnMAP science through our participation in the EnMAP Science Advisory Group (EnSAG). The EnSAG supports EnMAP science at large and communication with the wider science community of EnMAP users. We ensure scientific state of the art data exploitation across the two focal areas “validation” and “applications” – with EOLab focusing on the latter. The EnSAG is dedicated to the strategic planning and management of scientific algorithm and application development beyond the ground-segment. Patrick Hostert’s major interest is in making EnMAP data fit towards dense time series analyses with a focus on vegetation phenology. More...

Species-rich meadow in Bavaria

Extensively managed grasslands support high biodiversity and provide numerous ecosystem services. However, their occurrence has been declining in the agricultural landscapes of Germany over the past decades. ExGrünBy focuses on mapping mowing events on grasslands all over Bavaria in a joint effort with the Bavarian Environment Agency. Our maps support the coordination of surveying campaigns aimed at identifying extensively managed grasslands that show potential for conservation efforts. More...

Logo of the framework Foundations of workflows for large-scale scientific Data Analysis (FONDA)

Earth observing satellites play an important role in the study of climate and human impacts on the land system, but the ever increasing amount of data poses new challenges to scientists. Within the framework of FOuNdations of workflows for large-scale scientific Data Analysis (FONDA) we join efforts with computer scientists to improve portability, adaptability and dependability of our remote-sensing specific big data workflows for analysing the effects of extreme weather events on forest and agricultural land in Europe. More...

landscape with sky, foreground meadow with hey bales

Pastures support a large variety of ecosystem services, yet they only account for a small share of grasslands in Germany. Within GreenGrass, we analyze high-resolution Sentinel-1 and Sentinel-2 time series to enable the use of innovative management technologies such as virtual herding and fencing. Thereby, GreenGrass aims to develop a management system that balances economic requirements, the provision of ecosystem services and consumer needs. More...