Assessing UAV-based laser scanning for monitoring glacial processes and interactions at high spatial and temporal resolutions

• Published in: Frontiers in Remote Sensing
• Main Authors: Nathaniel R. Baurley, Christopher Tomsett, Jane K. Hart
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2022
• Subjects: uncrewed aerial vehicles; laser scanning; structure from motion (SFM); surface elevation changes; glacier calving; glacier dynamics; Geophysics. Cosmic physics; QC801-809; Meteorology. Climatology; QC851-999; glacier; Iceland
• Online Access: https://doi.org/10.3389/frsen.2022.1027065; https://doaj.org/article/ebb4a4d520f64806818c332fe0b9561b

Uncrewed Aerial Vehicles (UAVs), in combination with Structure from Motion (SfM) photogrammetry, have become an established tool for reconstructing glacial and ice-marginal topography, yet the method is highly dependent on several factors, all of which can be highly variable in glacial environments. However, recent technological advancements, related primarily to the miniaturisation of new payloads such as compact Laser Scanners (LS), has provided potential new opportunities for cryospheric investigation. Indeed, UAV-LS systems have shown promise in forestry, river, and snow depth research, but to date the method has yet to be deployed in glacial settings. As such, in this study we assessed the suitability of UAV-LS for glacial research by investigating short-term changes in ice surface elevation, calving front geometry and crevasse morphology over the near-terminus region of an actively calving glacier in southeast Iceland. We undertook repeat surveys over a 0.1 km2 region of the glacier at sub-daily, daily, and weekly temporal intervals, producing directly georeferenced point clouds at very high spatial resolutions (average of >300 points per m−2 at 40 m flying height). Our data has enabled us to: 1) Accurately map surface elevation changes (Median errors under 0.1 m), 2) Reconstruct the geometry and evolution of an active calving front, 3) Produce more accurate estimates of the volume of ice lost through calving, and 4) Better detect surface crevasse morphology, providing future scope to extract size, depth and improve the monitoring of their evolution through time. We also compared our results to data obtained in parallel using UAV-SfM, which further emphasised the relative advantages of our method and suitability in glaciology. Consequently, our study highlights the potential of UAV-LS in glacial research, particularly for investigating glacier mass balance, changing ice dynamics, and calving glacier behaviour, and thus we suggest it has a significant role in advancing our knowledge of, and ability to ...