Tabea Rettelbach

Tabea Rettelbach

Facilitating Machine Learning on Super-High Resolution Earth Observation Data for Detecting and Quantifying Arctic Permafrost Thaw Dynamics (2019 - )

Temperatures in the Arctic are warming. They’re warming even more rapidly than in other regions of the World. Unfortunately, due to their remoteness, these highly sensitive environments are not fully researched and explored yet, and thus represent a significant unknown in our Earth system models and our general understanding of their importance.

One of the major components of the Arctic is permafrost, which describes any ground that stays at, or below 0 °C for at least two consecutive years. Permafrost covers around one fourth of the landmass in the northern hemisphere and can reach depths of up to 1000 meters. With the warming atmosphere however, these soils are recently also experiencing higher temperatures, and large regions are starting to thaw. In the thawing ground, bacteria are now able to decompose century-old organic carbon, effectively releasing large amounts of carbon dioxide into the atmosphere, thus amplifying the atmospheric greenhouse effect even further. Researchers estimate that this permafrost holds up to 1400 Gt of carbon dioxide, about twice the amount that is currently in our planet’s atmosphere. It is therefore crucial for us to understand the rate of permafrost degradation in order to make the most accurate predictions for our climate and estimate its significance to life on Earth.

One of multiple possibilities for monitoring permafrost regions, is via remote sensing, where we can make use of aerial imagery (i.e., from satellites, airplanes, drones) to observe and analyze features at the ground’s surface that are very specific for permafrost soils. One example of such characteristic landscapes is polygonal thermokarst. These landscapes are characterized by large polygons (approximately 10-50 m across) that, in the undegraded state, show elevated rims at their borders. With an ongoing warming, these rims will however degrade and erode to become lower troughs between these polygons. With ongoing degradation, single troughs will gradually connect and the network of channels between the polygons steadily grows. A higher connectivity of troughs thus advances the drainage of surface water for entire landscapes. By evaluating digital elevation models in combination with multispectral imagery (in the RGB and near-infrared wavelengths), we can observe this process of degradation and permafrost thaw as well as the hydrological state of the landscape from above.

So far, studies on quantifying this trough connectivity are limited to local field studies and regional hydrological models only. My research therefore focuses on the extraction of trough characteristics from high spatial resolution aerial imagery and digital elevation data, to model this network of channels as a graph (a concept for network analysis from discrete mathematics and computer sciences). Graph theory provides a multitude of metrics that allow the analysis of underlying network characteristics such as the connectivity of single channels, the intensity of the connections, but also width and depth of single troughs (which can be stored as edge weights).  The figure below shows some preliminary results of the terrain analysis of a study area in northern Alaska, USA. Further, by evaluating the graphs of channels from multiple steps in time, it is possible to analyze the rate of degradation. This can be inferred when the merging of multiple smaller graphs into fewer larger graphs occurs, when certain connections arise, but also from the increasing values of depth and width of the channels as time advances. With sufficiently available data and dense time series, even the prediction of future network development is possible.

Being able to monitor the hydrological regime of a landscape and quantifying the local thaw rate of permafrost is crucial in order to gain insight into its contribution to greenhouse gases in the atmosphere and therefore its role in climate change. On a methodological level, processing this type of information as graphs, opens up possibilities to consider even small-scale changes on a trough-by-trough level and offers significantly shorter computing times as compared to calculations based on spatial raster-imagery. This allows analysis coverage of much larger regions with available processing capacities, which is an important step towards a pan-Arctic and holistic understanding of the Earth’s permafrost.

Peer-reviewed Publications (journal or conference)

  1. T. Rettelbach, M. Langer, I. Nitze, B. Jones. V. Helm, J-Ch. Freytag, and G. Grosse (2021). A quantitative graph-based approach to monitoring ice-wedge trough dynamics in polygonal permafrost landscapes. Remote Sens. 13, 3098.
  2. W. J. Foster, G. Ayzel, J. Münchmeyer, T. Rettelbach, N. Kitzmann, T. T. Isson, M. Mutti, and M. Aberhan (2021). Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology, 1-15.
  3. T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-C. Freytag, and G. Grosse (2022). From images to hydrologic networks - Understanding the Arctic landscape with graphs. In ACM Proceedings of the 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022). 

Other (presentations at conferences or preprints)

  1. T. Rettelbach, M. Langer, I. Nitze, B. Jones. J. Boike, J-Ch. Freytag, and G. Grosse. Potential von Graphen für die quantitative Analyse von tauenden Eiskeilpolygonnetzwerken. (Oral presentation), 11. Treffen des AK Permafrost der DGP, Online, 11 December 2020.
  2. T. Rettelbach, G. Grosse, I. Nitze, J. Brauchle, T. Bucher, M. Gessner, B.M. Jones, J. Boike, M. Langer, and J-Ch. Freytag. A quantitative graph-based assessment of ice-wedge trough dynamics in polygonal thermokarst landscapes of the Anaktuvuk river fire scar. (Oral presentation), AGU Fall Meeting, Online, 1-17 December 2020.
  3. T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-Ch. Freytag, and G. Grosse. Quantifying erosional dynamics in ice-wedge networks with computer vision and graph theory. (Oral presentation), Regional Conference on Permafrost, Online, 24-29 October 2021.
  4. T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-Ch. Freytag, and G. Grosse. Evaluating the effects of tundra fires on soil microtopography and hydrologic surface networks in polygonal permafrost landscapes. (Oral presentation), AGU Fall Meeting, News Orleans / Online, USA, 13-17 December 2021.
  5. T. Rettelbach, I. Nitze, and G. Grosse. Polar-6 airborne expedition Perma-X West Alaska 2021. (Oral presentation), 12. Treffen des AK Permafrost der DGP, Online, 06 May 2022.
  6. T. Rettelbach, I. Nitze, S. Schäffler, S. Barth, I. Grünberg, J. Hammar, M. Gessner, T. Bucher, J. Brauchle, T. Sachs, J. Boike, and G. Grosse. Super-high-resolution Earth observation datasets of North American permafrost landscapes. (Oral and poster presentation), 8th NASA ABoVE Science Team Meeting, Fairbanks, Alaska, USA, 9-12 May 2022.
  7. T. Rettelbach, C. Witharana, A. Liljedahl, M. Langer, I. Nitze, J-C. Freytag, and G. Grosse. The evolution of ice-wedge polygon networks in tundra fire scars. (Poster presentation), 16th International Circumpolar Remote Sensing Symposium, Fairbanks, Alaska, USA, 16-20 May 2022.
  8. T. Rettelbach, M. Langer, I. Nitze, V. Helm, J-C. Freytag, and G. Grosse. Quantifying rapid permafrost thaw with computer vision and graph theory. (Poster presentation), ESA Living Planet Symposium, Bonn, 23-27 May 2022. 
  9. T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-C. Freytag, and G. Grosse. From images to hydrologic networks - Understanding the Arctic landscape with graphs. (Oral presentation), 34th International Conference on Scientific and Statistical Database Management, Copenhagen, Denmark, 6-8 July 2022.