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Surveying drifting icebergs and ice islands : deterioration detection and mass estimation with aerial photogrammetry and laser scanning

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Crawford_2018_RS_Surveyingdriftingicebergs_CC.pdf (29.78Mb)
Date
08/04/2018
Author
Crawford, Anna J.
Mueller , Derek
Joyal, Gabriel
Keywords
Icebergs
Ice islands
Terrestrial laser scanning
Structure-from-motion photogrammetry
Change detection
Ice hazards
GE Environmental Sciences
T Technology (General)
DAS
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Abstract
Icebergs and ice islands (large, tabular icebergs) are challenging targets to survey due to their size, mobility, remote locations, and potentially difficult environmental conditions. Here, we assess the precision and utility of aerial photography surveying with structure-from-motion multi-view stereo photogrammetry processing (SfM) and vessel-based terrestrial laser scanning (TLS) for iceberg deterioration detection and mass estimation. For both techniques, we determine the minimum amount of change required to reliably resolve iceberg deterioration, the deterioration detection threshold (DDT), using triplicate surveys of two iceberg survey targets. We also calculate their relative uncertainties for iceberg mass estimation. The quality of deployed Global Positioning System (GPS) units that were used for drift correction and scale assignment was a major determinant of point cloud precision. When dual-frequency GPS receivers were deployed, DDT values of 2.5 and 0.40 m were calculated for the TLS and SfM point clouds, respectively. In contrast, values of 6.6 and 3.4 m were calculated when tracking beacons with lower-quality GPS were used. The SfM dataset was also more precise when used for iceberg mass estimation, and we recommend further development of this technique for iceberg-related end-uses.
Citation
Crawford , A J , Mueller , D & Joyal , G 2018 , ' Surveying drifting icebergs and ice islands : deterioration detection and mass estimation with aerial photogrammetry and laser scanning ' , Remote Sensing , vol. 10 , no. 4 , 575 . https://doi.org/10.3390/rs10040575
Publication
Remote Sensing
Status
Peer reviewed
DOI
https://doi.org/10.3390/rs10040575
ISSN
2072-4292
Type
Journal article
Rights
Copyright © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Collections
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/16996

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