High-volume EO data processing to reveal causalities between land surface dynamics and animal movement pathways

• Applicant: Professorin Dr. Claudia Künzer
• Subject Area: Methods in Artificial Intelligence and Machine Learning ; Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions ; Physical Geography
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 522760169

The impact of climate and environmental change on the habitats and movement of animals is threatening species all around the world, leading to modified movement and migration patterns, a decrease or disappearance of populations, an altered food chain, and sometimes severe impact son the whole ecosystem. The causes for these developments are usually a mix of many different parameters, including changes in temperature, precipitation, wind, vegetation composition and health, snow cover duration, start/end of snow cover season, frost, but also human-induces factors such as the presence of infrastructure and human settlements. Today, studies about the impact of climate change on animal movements comprise the combination of one or two of the aforementioned factors and GPS location data originating from the studied animals. The reasons why ecological studies very often do not include a larger number and longer time series of environmental parameters have their origin in the need to process very huge amounts of Earth Observation (EO) data and being able to program/script their own code. It is therefore the goal of this project to develop flexible and reusable components for the analysis and processing of large amounts of EO data in combination with animal movement data. The developed components will be compatible for integration in available EO exploitation platforms, will be able to incorporate any desired amount and diversity of EO and environmental data, and will allow the user to ingest their own data and code – if desired. Relying on the potentials of facilitating Artificial Intelligence (AI) methods for the analysis of EO data, this project will significantly contribute to the understanding and quantification of climate change impacts on animal species. In addition, the developments will enable ecological researchers from other institutions to easily adapt or use the methods for their own research.

• DFG Programme: Research Units
• Subproject of: FOR 5696: SOS: Serverless Scientific Computing and Engineering for Earth Observation and Sustainability Research
• Co-Investigator: Dr. Andreas Dietz