Offshore Unexploded Ordnance Detection and Data Quality Control-A Guideline
The detection of old submerged ammunition that is located on or below the seabed is an important preparatory work for offshore projects. Multibeam echosounder, side-scan sonar, sub-bottom profiler, and magnetometers are the sensor types that are most commonly used for the task. Survey design decisio...
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Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing 2022, Vol.15, p.7483-7498 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The detection of old submerged ammunition that is located on or below the seabed is an important preparatory work for offshore projects. Multibeam echosounder, side-scan sonar, sub-bottom profiler, and magnetometers are the sensor types that are most commonly used for the task. Survey design decisions center around both detection campaign efficiency and sufficient data quality to find specific reference objects. This article presents a comprehensive workflow for unexploded ordnance surveys in the sea and focuses on aspects of quantitative data quality control. For this purpose, data quality factors and corresponding threshold values were developed for each of the main sensor types. The authors designed and moderated a workshop-based stakeholder engagement process to establish expert consensus on suitable data quality factors and appropriate calculation of thresholds that define which data are sufficient and which are not. This approach was accompanied by a literature review of existing guidelines, standards, and survey recommendations. A quantitative description of data quality eases analyzing and comparing newly acquired and existing data. This article presents the results of this process, thereby providing guidance for the planning, execution, and quality control of a technical investigation with the aim of finding unexploded ordnance in the sea. The defined data quality factors derived from the literature review and collective expert knowledge can be used in the suggested workflow. |
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ISSN: | 1939-1404 2151-1535 |
DOI: | 10.1109/JSTARS.2022.3200144 |