Machine-Learning-Based Measurement of Ice Wedge Polygon Properties
A rapid assessment of wet-tundra Arctic landscape conditions.
Ice wedge polygons are ubiquitous features in wet-tundra Arctic landscapes. Their topographic properties control the distribution of water, vegetation, and biogeochemical processes. Measuring and counting these small-scale landscape features across the Arctic is an extremely difficult proposition, but necessary to assess the state and dynamics of the landscape. A new machine-learning approach can now quickly quantify these small-scale features at regional scales, enabling improved estimates of ecosystem processes across large swaths of the Arctic landscape.
This new capability now enables scientists to quickly assess the number, configuration, and state of ice wedge polygons across large swaths of the Arctic. With this technology, scientists will be able to quickly measure how these land forms are responding to rapid Arctic warming and concurrent permafrost degradation that is reshaping local to regional topography. Products from this technology are informing models to project how changes in the structure of Arctic landscapes will influence feedbacks to the climate system.
Ice wedge polygons are the surface expression of ice wedges, or vertical veins of ground ice that divide tundra landscapes into a network of polygonal units, 10 to 30 m across. These polygons pervade the Arctic tundra and are categorized as low-centered polygons, which are surrounded by rims of soil several tens of centimeters high, or high-centered polygons, surrounded by troughs on the order of a meter deep. The spatial distribution of these two types of polygon controls important landscape processes, including redistribution of windblown snow, thermal regulation of the underlying permafrost, runoff and evaporation, and surface emissions of two important but very different greenhouse gasses, carbon dioxide, and methane. Therefore, mapping polygon types across the Arctic is vital for understanding the hydrologic function of landscapes, as well as potential fluxes of carbon into the atmosphere. However, directly delineating each polygon across the Arctic is impractical. Scientists at the University of Texas in collaboration with Los Alamos National Laboratory have developed a new approach that utilizes machine-learning algorithms to analyze high-resolution digital elevation maps from airborne remote sensing. This approach has been shown to be fast and accurate at two test sites with complex polygonal terrain, near Prudhoe Bay and Utqiagvik (formerly Barrow), Alaska. The algorithm allows scientists to quickly and accurately inventory polygonal forms across broad tundra landscapes, ultimately informing projections of the fate of the large stock of organic matter stored in Arctic soils.
The University of Texas at Austin
Los Alamos National Laboratory
Funding is provided by the Terrestrial Ecosystem Science program and Next-Generation Ecosystem Experiments (NGEE)–Arctic project of the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science, and the Earth and Space Science Fellowship program of the National Aeronautics and Space Administration (NASA).
Abolt, C.J., M.H. Young, A.L. Atchley, and C.J. Wilson. “Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models.” The Cryosphere 13(1), 234–245 (2019). [DOI:10.5194/tc-13-237-2019].