Operational Framework for Rapid, Very-high Resolution Mapping of Glacial Geomorphology Using Low-cost Unmanned Aerial Vehicles and Structure-from-Motion Approach

Ewertowski, Marek; Tomczyk, Aleksandra; Evans, David JA; Roberts, David H; Ewertowski, Wojciech

Abstract

This study presents the operational framework for rapid, very-high resolution mapping of glacial geomorphology, with the use of budget Unmanned Aerial Vehicles and a structure-from-motion approach. The proposed workflow comprises seven stages: (1) Preparation and selection of the appropriate platform; (2) transport; (3) preliminary on-site activities (including optional ground-control-point collection); (4) pre-flight setup and checks; (5) conducting the mission; (6) data processing; and (7) mapping and change detection. The application of the proposed framework has been illustrated by a mapping case study on the glacial foreland of Hørbyebreen, Svalbard, Norway. A consumer-grade quadcopter (DJI Phantom) was used to collect the data, while images were processed using the structure-from-motion approach. The resultant orthomosaic (1.9 cm ground sampling distance—GSD) and digital elevation model (7.9 cm GSD) were used to map the glacial-related landforms in detail. It demonstrated the applicability of the proposed framework to map and potentially monitor detailed changes in a rapidly evolving proglacial environment, using a low-cost approach. Its coverage of multiple aspects ensures that the proposed framework is universal and can be applied in a broader range of settings.

Citation

Ewertowski, M., Tomczyk, A., Evans, D. J., Roberts, D. H., & Ewertowski, W. (2019). Operational Framework for Rapid, Very-high Resolution Mapping of Glacial Geomorphology Using Low-cost Unmanned Aerial Vehicles and Structure-from-Motion Approach. Remote Sensing, 11(1), Article 65. https://doi.org/10.3390/rs11010065

• Journal Article Type: Article
• Acceptance Date: Dec 24, 2018
• Online Publication Date: Jan 1, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Jan 15, 2019
• Publicly Available Date: Jan 15, 2019
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 1
• Article Number: 65
• DOI: https://doi.org/10.3390/rs11010065
• Public URL: https://durham-repository.worktribe.com/output/1305361