Airborne Validation of ICESat-2 ATLAS Data Over Crevassed Surfaces and Other Complex Glacial Environments: Results From Experiments of Laser Altimeter and Kinematic GPS Data Collection From a Helicopter Over a Surging Arctic Glacier (Negribreen, Svalbard)

Abstract

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7~m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905~nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1)~Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2)~Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3)~Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25~m, while distance between predicted and actual ICESat-2 ground track was on the order of 9~m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4)~The Lasertech Universal Laser System (ULS), operated at up to 300~m above ground level, yields full return frequency (400~Hz) and 0.06-0.08~m on-ice along-track spacing of height measurements. (5)~Cross-over differences of airborne laser altimeter data are 0.1918 $\pm$ 2.385~m along straight paths over generally crevassed terrain, which implies a precision of approximately 2.4~m for ICESat-2 validation experiments. (6)~In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)-(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge.

Combining “Deep Learning” and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-Image System to Create VarioCNN for Glacier Surges

“…Airborne geophysical field observations including imagery of the Negribreen Glacier System during surge in 2017 and 2018, collected by the Geomathematics, Remote Sensing and Cryospheric Sciences Laboratory at the University of Colorado Boulder, Boulder, CO, USA. (Ute Herzfeld, Thomas Trantow and field assistants) are available through the Arctic Data Center of the U.S. National Science Foundation [175].…”

Combining "Deep Learning" and Physically Constrained Neural Networks to Derive Complex Glaciological Change Processes from Modern High-Resolution Satellite Imagery: Application of the GEOCLASS-image System to Create VarioCNN for Glacier Surges

“…We use the terminology of intrinsic criteria for quantitative, computational criteria (cross-entropy measure of training loss, confidence of classification result, co-occurence matrix) and extrinsic criteria for glaciological criteria which are typically based on airborne field observations of the glacier system during surge and on additional expert knowledge on the evolution of crevasse types during a surge [16,18,20,127,128,158]. The application of extrinsic criteria is best explained in an applied example of image labeling and in the geophysical interpretation (see sections (7.6) and ( 11)).…”

“…The strong velocity gradient leads to so-called shear holes near the folded moraines (Figure 3c,e). However, the region identified as class (4) includes a wider part, which actually coincides with the region of the "retreating bay" [158], where the ice has retreated along the previous Negribreen-Ordonnansbreen medial moraine, leaving an area where open water is covered with ice chunks of various sizes, rendering "chaos class" (class 4) surface types. The second area, where class (4) is identified, covers much of the region of multi-directional crevasses (class (3)) in 2016, and field observations and imagery that this is a region of "chaos", as described above (Figure 3d,f).…”