IN029-07
ICESat-2, SkySat, WorldView and Sentinel: Automated Extraction of High-Resolution Spatial Information for Investigation of Surging and Fast-Moving Glaciers

Friday, 11 December 2020: 20:54
Virtual
Ute C Herzfeld, Univ Colorado Boulder, Electrical, Computer and Energy Engineering and CIRES, Boulder, CO, United States, Matthew William Lawson, University of Colorado Boulder, Computer Science, Boulder, CO, United States, Adam Hayes, University of Colorado Boulder, Applied Mathematics, Boulder, CO, United States, Lawrence J Hessburg IV, University of Colorado Boulder, Boulder, CO, United States and Thomas Trantow, University of Colorado Boulder, ECEE, Boulder, CO, United States
Recorded Presentation
Abstract:
Glacial acceleration is the largest source of uncertainty in sea-level-rise assessment, according to the Intergovernmental Panel on Climate Change. Of the different types of glacial acceleration, surging is the least understood. In this paper, we demonstrate how a combination of automated algorithms dedicated to analysis of two entirely different observation types - satellite altimetry from NASA's ICESat-2 and satellite imagery from Planet SkySat - can aid in advancing glaciology, utilizing state-of-the art remote sensing /Earth observation technology.

NASA's Ice, Cloud and land Elevation Satellite ICESat-2, launched 15~September~2018, carries the first space-borne multi-beam micro-pulse photon counting laser altimeter system, the Advanced Topographic Laser Altimeter System (ATLAS). ATLAS observations are collected in three pairs of weak and strong beams with 0.7m nominal along-track spacing (under clear-sky conditions). The recording of the observations as a photon-point cloud requires a dedicated algorithm for identification of signal photons and determination of surface heights. As a solution, we developed the density-dimension algorithm for ice surfaces, the DDA-ice. ATLAS data analyzed with the DDA-ice allow determination of heights over heavily crevassed ice surfaces, which are characteristics of accelerating glaciers.

The study presented here builds on a special multi-component data set, obtained through synoptic observations of an Arctic glacier system during surge (Negribreen, Svalbard): Airborne altimeter and image data collected during our ICESat-2 validation campaign, and SkySat image data from a special acquisition collected as part of NASA's Commercial Smallsat Data Acquisitions Pilot program. These are complemented by WorldView (Maxar) and ESA Sentinel-1 data. With a spatial resolution of 0.7-0.86m, SkySat data and WorldView lend themselves to automated classification of crevasse types. Altogether, we obtain a characterization in 3 dimensions that allows discrimination of ice-surface types from surging glaciers (Negribreen) and continuously fast-moving and accelerating glaciers (Jakobshavn Isbrae) based on morphological characteristics.

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