UAV-SfM and Geographic Object-Based Image Analysis for Measuring Multi-Temporal Planimetric and Volumetric Erosion of Arctic Coasts

• Published in: Canadian Journal of Remote Sensing
• Main Authors: Andrew Clark, Brian J. Moorman, Dustin Whalen
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: Taylor & Francis Group 2023
• Subjects: Environmental sciences; GE1-350; Technology; T; Arctic; Canada; Kugmallit Bay; Northwest Territories; Tuktoyaktuk
• Online Access: https://doi.org/10.1080/07038992.2023.2211679; https://doaj.org/article/e5efb705e924410f839f9a31864e9e60

Monitoring and quantifying the rapid changes along Arctic coasts is becoming increasingly important as above average warming in the Arctic is contributing to increasing rates of erosion leading to dramatic impacts on coastal ecosystems and communities. Understanding the impacts of Arctic coastal erosion on the climate system across large coastal scales requires improvements in measurement techniques. We analyzed two coastal sites in Kugmallit Bay (near Tuktoyaktuk, Northwest Territories, Canada), over a one-week and one-year time interval. Using high-resolution imagery from Unoccupied Aerial Vehicles with Structure from Motion (UAV-SfM), we investigated the influence of unique coastal indicator features on reported planimetric and volumetric measurements and explored the use of Geographic Object Based Image Analysis (GEOBIA) to semi-automate the process of coastal feature extraction. We observed temporally dependent differences between coastal feature movements, planimetrically and volumetrically, and object-based feature extraction accuracy was found to be feature dependent. Our research has made methodological improvements to Arctic coastal measurements, particularly at high spatiotemporal scales, which highlights considerations relevant to broad scale Arctic coastal monitoring and quantification.