UndercoverEisAgenten - The Arctic Permafrost Project

Kurzfassung

The current warming rate of arctic permafrost landscapes exceeds the global warming rate by two- to threefold. This leads to rapidly changing landscape changes like thawing of permafrost, erosion or thermokarst affecting the livelihood of indigenous people in the far north. Besides this strong socio-economic impact on arctic communities as well as flora and fauna, the thawing of permafrost leads to a vast release of stored greenhouse gases into the atmosphere. Permafrost landscapes are defined as continuously frozen soil for at least two consecutive years and can reach depths of hundreds of meters. As roughly 25% of the landmass in the northern hemisphere is covered by permafrost a vast land area is threatened by thawing and the subsequent landscape changes. In contrast to the visible melting of glaciers and sea ice, the thawing of permafrost in the subsurface cannot be directly observed. On the one hand, this complicates the scientific assessment of the climate impact on the entire Arctic. On the other hand, the largely invisible thawing of permafrost has consequences for social perception of the problem. The goal of this project is to improve the data basis on thawing permafrost with the help of high-resolution UAV (unmanned aerial vehicle) and satellite images together with citizen scientists, especially school students (Fig. 1). To this end, school classes in Germany and the Canadian Arctic will collaborate on the analysis of high-resolution remote sensing data. The students will use a mobile application to map striking structures and changes in the land surface on satellites and drone images. Utilizing feedback from co-creative workshops with German teachers, concepts are being developed to introduce the different topics of this projects into school curricula of German high schools. This could be implemented in the form of project weeks, special topic classes or excursions of classes to research institutes for the practical application of the learned topics. An important component of the project is also the collection of high-resolution remote sensing data by community members and students from Aklavik (Canada) using low-cost consumer-grade drones. By repeatedly recording the land surface with low-cost and easy-to-use drones, citizens in the Arctic can make a significant contribution to the research of climate impacts in the Arctic. These multitemporal remote sensing datasets can in turn be used by German school students to get a direct connection to the partner community in Aklavik. With the UAV datasets collected by Canadian citizens as well as additional satellite remote sensing datasets a unique reference dataset documenting thawing permafrost in the Arctic can be created. As the visible thawing effects are mostly on a small scale ranging from disturbances of a few centimeters to a few dozen meters, very high-resolution datasets are necessary to adequately detect these changes. Structure from Motion (SfM) is used to create very high-resolution 3D models of the landscape to incorporate height information into our datasets in addition the spectral information from the RGB images of the drones. The polygonal structures of thawing or degrading permafrost can be identified by spectral differences due to the surrounding water boundaries as well as by geometric differences due to lifting and lowering features of the polygons (Fig. 2). By utilizing local knowledge together with very high-resolution UAV datasets acquired over multiple years we can better understand and monitor the landcover changes attributable to permafrost thaw. This can be applied by integrating our future datasets in the current permafrost models to improve predictability and accuracy. One challenge of the SfM technique using low-cost consumer-grade drones is the susceptibility to height errors in the 3D models leading to so called “doming” or “bowling” effects, where the point clouds’ vertical information is distorted due to inadequate knowledge of the camera parameters and/or low GNSS accuracy of the UAV hardware. While literature mostly presents the use of correction approaches such as real-time kinematic (RTK) or ground control points (GCPs) this is not feasible in a citizen science context due to its high complexity. In order to allow for multitemporal analyses nonetheless, different mitigation and optimization approaches need to be applied. One suggested strategy to minimize vertical errors in the SfM models is the use of oblique images. As the datasets need to be easily reproducible as well as fulfill scientific standards, a standardized easy-to-use workflow needs to be established for the citizen scientists. For this, we utilize DJI’s Mini 2 drones in combination with the “Litchi for DJI” mobile application as the controller software. This combination allows for the easy creation of flight mission with standardized parameters to enable reproducible results. Before the implementation in the field, the optimal parameters for the highest accuracy and lowest model errors are determined. This project aims to enable the creation of better climate adaptation planning tools for the local population as well as engage Canadian and German students and citizen scientists to highlight the necessity for permafrost protection and research. The exchange between Germany and Canada citizen scientists highlights the global impact of the issue of permafrost thawing in a more direct way compared to traditional research projects. The scientific data generated by the project will provide entirely new insights into biophysical processes in Arctic regions and help to understand the state and changes of permafrost in the Arctic on a large scale.