UAV-Based Photogrammetry and LiDAR for the Characterization of Ice Morphology Evolution

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Teng Li, Baogang Zhang, Wen Xiao, Xiao Cheng, Zhenhong Li, Jian Zhao
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2020
• Subjects: Antarctica; change detection; digital elevation model (DEM); ice doline; light detection and ranging (LiDAR); structure-from-motion (SfM); Ocean engineering; TC1501-1800; Geophysics. Cosmic physics; QC801-809; Antarctic; Antarc*
• Online Access: https://doi.org/10.1109/JSTARS.2020.3010069; https://doaj.org/article/b5a3751123ab4e4ea0d3cd4918857181

Ice doline is a particular kind of ice morphology, usually scattered on ice streams which are mostly far from the existing research bases. For this reason, glaciologists rarely have opportunities to document its developments in detail. Satellite observations are too coarse to capture such fine features, whereas unmanned aerial vehicle (UAV)-based structure-from-motion (SfM) and light detection and ranging (LiDAR) technologies have revolutionized geosciences research, especially in less accessed polar regions. We developed two bespoke UAV systems for glaciological investigation and carried out four campaigns during two consecutive Chinese Antarctic expeditions in 2017 and 2018. Founded on manual coregistration and accuracy assessment, a successful application to characterize a doline's spatio-temporal evolution is presented in this article. The overlying weight of surface melting directly triggered the collapse event on Jan 30, 2017 near the Dalk Glacier, and then the newborn doline grew for another 8135.6 m 2 in area and 280 303.38 m 3 in volume by early 2018. The UAV-based results revealed the doline's changes during a year, showing a maximum horizontal extension of 50 m and vertical subsidence of more than 10 m. Furthermore, we evaluate the photogrammetry and LiDAR systems and find the former is cost-effective and time-efficient on a large-scale survey, while the latter enjoys a better capability to characterize ice morphological details. Based on systematic comparisons, other pros and cons of the two techniques are discussed. To achieve the best performance for relevant applications in similar scenarios, we recommend adopting an integrated approach, in which the LiDAR restores the fine features on the basis of extensive SfM coverage.