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