Arctic coastal erosion: UAV-SfM data collection strategies for planimetric and volumetric measurements

• Published in: Arctic Science
• Main Authors: Andrew Clark, Brian Moorman, Dustin Whalen, Paul Fraser
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: Canadian Science Publishing 2021
• Subjects: uav-sfm; arctic coastal erosion; oblique imagery; coastal retrogressive thaw slump; volumetric coastal erosion; Environmental sciences; GE1-350; Environmental engineering; TA170-171; Arctic; Northwest Territories; Canada; Ice; permafrost; Sea ice
• Online Access: https://doi.org/10.1139/as-2020-0021; https://doaj.org/article/3466c778aed84e2ca69f9abe77489a0f

Above average warming in the Arctic is leading to increasing permafrost temperatures and a reduction in sea ice cover, which are expected to contribute to increasing rates of Arctic coastal erosion and sediment release. We studied a 1.5 km stretch of coastline off Richard’s Island, Northwest Territories, Canada, consisting of multiple retrogressive thaw slumps (RTSs) with varying degrees of activity over a one-year period. Multi-temporal 2D and 3D geomorphic analysis was based on unmanned aerial vehicle-Structure-from-Motion (UAV-SfM) data sets collected in 2018 and 2019. Over the observation period, −3.9 m and −1.1 m of planimetric cliff edge and toe retreat occurred, respectively, and corresponded to an average volumetric change of 8.1 m3 m−1. The accuracy of UAV-SfM-derived digital elevation models was tested using 12 data collection and processing scenarios, testing the influence of off-nadir camera angle, flight pattern, and georeferencing strategy. We found that oblique imaging and georeferencing strategy had a large influence on vertical accuracy and variability across the study site and has implications for studying volumetric changes in RTSs. This study furthers the geomorphological understanding of RTS processes by highlighting the complex relationship between planimetric and volumetric change along rapidly retreating Arctic coasts, and demonstrates advancements in measurement practices for UAV-SfM data sets.