02923nas a2200181 4500008004100000245015400041210006900195260004800264300000700312490000900319520225400328653002202582653001502604653002902619100002002648700002502668856004802693 2023 eng d00aCharacterizing velocity gradients in the McMurdo Dry Valleys with high-resolution optical imagery, feature-tracking methods, and in situ observations0 aCharacterizing velocity gradients in the McMurdo Dry Valleys wit aBoulder, CObUniversity of Colorado Boulder a430 vM.S.3 a

In the water-limited McMurdo Dry Valleys – the largest ice-free region of Antarctica – glaciers are the only significant source of meltwater to ephemeral streams and ecosystems. Understanding changes to the flow of these glaciers, along with characterizing how they are growing or shrinking, can elucidate how these physical controls on the region’s hydrology and ecology are responding to changing environmental conditions. Because in situ observations are difficult to collect and discrete by nature, various computer vision techniques are increasingly used to track glacier surface velocities from satellite optical and radar datasets. The most common of these techniques is image cross-correlation in the form of template matching or particle image velocimetry (PIV). Although exact methods vary, generally, cross-correlation works by co-registering temporally offset image pairs using similarity in pixel values resulting from distinctive surface features, such as crevasses, large sastrugi and snow dunes. When surface features are well-resolved with respect to an image pair’s spatial resolution, and well-preserved relative to its temporal resolution, velocity estimates can have sub-pixel precision. However, when glaciers lack meaningful surface texture – a common occurrence for the low-gradient, frozen-bed glaciers of the McMurdo Dry Valleys – cross correlation performs poorly. Using high-resolution (sub-meter per pixel) imagery can help derive texture from minor cracks and small erratics on otherwise smooth, uncrevassed surfaces, but additional adaptations are needed to improve cross correlation performance. These include additional filtering and histogram stretching steps, along with informed search area windows. Velocity estimates derived from this workflow are shown to be comparable to recorded ranges, in situ measurements, and manual feature tracking, but provide a much higher spatial and temporal resolution dataset. Alongside contemporaneous analyses of glacier mass balance, terminal thickness and surface roughness, these glacier velocity data help characterize how glaciers are dynamically evolving in response to environmental conditions in the Dry Valleys.

10across correlation10acryosphere10aglacier surface velocity1 aVanderwilt, Mia1 aGooseff, Michael, N. uhttps://www.proquest.com/docview/2916377519