Suggested by: Martin Sudmanns, Dirk Tiede
Examples of long Earth observation time series. Visualization capabilities are usually on a lower level than the analytical capabilities.
Short description
Long-term Earth observation data provide valuable insights into
environmental changes, particularly in sensitive regions like the Alps.
Visualizing and analysing these data dynamically can help better understand
temporal trends and support environmental monitoring. For example, Essential
Climate Variables (ECVs), such as vegetation indices or snow cover, are key
indicators for assessing the impacts of climate change in mountainous regions
and require data for multiple decades.
- Developing dynamic visualizations of long EO time series to highlight temporal trends in selected ECVs for the Alpine region.
- Using Sen2Cube.at queries to efficiently extract and analyze semantic data related to ECVs, such as vegetation health or snow cover dynamics.
- Demonstrating the applicability of the approach through case studies of key climate variables in specific Alpine areas.
This master thesis workflow will provide innovative tools for environmental monitoring, offering dynamic and accessible visualizations of climate-related changes that can be used to support decision-making in the Alpine region.
Suggested reading
Sudmanns, M., Augustin, H., van der Meer, L., Baraldi, A., & Tiede, D. (2021). The Austrian semantic EO data cube infrastructure. Remote Sensing, 13(23), 4807. https://www.mdpi.com/2072-4292/13/23/4807
Start
As soon as possible
Prerequisites/qualification
+ programming skills
+ Earth observation
+ Experience or willingness to get familiar with (3D) visualisation engines (Blender, unity, …)
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