UndercoverEisAgenten - Monitoring Permafrost Thaw in the Arctic using Local Knowledge and UAVs

• Main Authors: Mueller, Marlin M., Thiel, Christian, Kaiser, Soraya, Lenz, Josefine, Langer, Moritz, Lantuit, Hugues, Marx, Sabrina, Fritz, Oliver, Zipf, Alexader
• Format: Conference Object
• Language: unknown
• Published: 2023
• Subjects: Institut für Datenwissenschaften; Datenanalyse und -intelligenz; Arctic; Canada; Global warming; permafrost
• Online Access: https://elib.dlr.de/194963/

The Arctic is experiencing severe changes to its landscapes due to the thawing of permafrost influenced by the twofold increase of temperature across the Arctic due to global warming compared to the global average. This process, which affects the livelihoods of indigenous people, is also associated with the further release of greenhouse gases and also connected to ecological impacts on the arctic flora and fauna. These small-scale changes and disturbances to the land surface caused by permafrost thaw have been inadequately documented. To better understand and monitor land surface changes, the project "UndercoverEisAgenten" is using a combination of local knowledge, satellite remote sensing, and data from unmanned aerial vehicles (UAVs) to study permafrost thaw impacts in Northwest Canada. The high-resolution UAV data will serve as a baseline for further analysis of optical and radar remote sensing time series data. The project aims to achieve two main goals: 1) to demonstrate the value of using unmanned aerial vehicle (UAV) data in remote regions of the global north, and 2) to involve young citizen scientists from schools in Canada and Germany in the process. By involving students in the project, the project aims to not only expand the use of remote sensing in these regions, but also provides educational opportunities for the participating students. By using UAVs and satellite imagery, the project aims to develop a comprehensive archive of observable surface features that indicate the degree of permafrost degradation. This will be accomplished through the use of automatic image enhancement techniques, as well as classical image processing approaches and machine learning-based classification methods. The data is being prepared to be shared and analyzed through a web-based crowd mapping application. The project aims to involve the students in independently acquiring data and developing their own scientific questions through the use of this application. In September 2022, a first expedition was conducted in the ...