Intercomparisons of sea ice thickness and concentration from visual observation, EM-31 measurements, and video imagery

dc.contributor.advisorXie, Hongjie
dc.contributor.authorWagner, Penelope
dc.contributor.committeeMemberAckley, Stephen F.
dc.contributor.committeeMemberBirnbaum, Stuart
dc.date.accessioned2024-03-08T17:35:24Z
dc.date.available2024-03-08T17:35:24Z
dc.date.issued2009
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractAntarctic sea ice dynamics are largely affected by ocean and wind forcing because it is surrounded by the open ocean, whereas Arctic sea ice is surrounded by a land mass. Opportunities to study the variations in sea ice conditions are infrequent due to the remote location and relative expense. For that reason, it is necessary to develop methods that will allow efficient and effective collection of sea ice measurements for integration with large-scale models and validation schemes for satellite products. The use of automated devices will improve estimates on sea ice trends for the Antarctic region. Collecting ice thickness distribution trends from drilling transects can be a cumbersome ordeal and provides very little data over a large area. Therefore, it is necessary to consider using automated devices to assist in further data collection for future cruises. The first part of this study focused on compiling various datasets from the SIMBA cruise (Sea Ice Mass Balance in the Antarctic) which included ship-based sea ice observations, an electromagnetic induction device (EM-31), and video imagery (Evaluative Imagery Support Camera (EIS Cam 1)) to evaluate which automated device provided the best method to measure the sea ice thickness distribution. Remote sensing applications were used for image analysis with data from EIS Cam 1 to measure thickness of overturned ice that was being broken by the ship's hull. The thickness distribution of EIS Cam 1 and the EM-31 were then compared with the ASPeCt (Antarctic Sea Ice Processes and Climate) ship-based observations to evaluate how well each device performs. Since the footprints of three datasets were different from each other, only the frequency of the ice thickness distribution was gauged and compared. The EM-31 data overall performed better than the video imagery, for the reason that it was measuring ice conditions far enough from the ship's base, where it was capable of measuring ridged and deformation features not present in the video footprint. The study also shows potential good results for level ice up to 2.50m, although the ship's track will be biased toward thinner ice and may cause the EM-31 to oversample thin ice compared to the thicker ice surrounding the narrow track. However, under those conditions the EM-31 will act as an appropriate supplement for ASPeCt visual observations taken hourly from the ship's bridge. The second part of this study evaluated sea ice concentration data recorded with the use of video imagery (EIS Cam 2) compared with ship-based ice observations. Images from the inbound and outbound transects were classified using techniques provided by Weissling et al. (2009) to ascertain the amount of error between camera measurements and ship-based observations. Analysis of these comparisons found poor correlations during evening conditions due to highlights and shadows generated by ridging, deformation features on the sea ice, and darker lighting conditions, in which EIS Cam 2 either underestimated concentration values up to 30% when the ASPeCt ice concentration was over 80% or overestimated ice concentration up to 60% when ASPeCt ice concentration was less than 80%. Large over- or under-estimation from ASPeCt observers was also possible due to the night condition, which was seasonally dependant. However, there was an overall good agreement between both datasets during the day time where EIS Cam 2 and ASPeCt differed approximately ∼5% (inbound track) or 10% (outbound track). The errors with the datasets were related to the coarse resolution of ASPeCt parameters and the inability for the EIS Cam 2 to distinguish shadows (from ridges or the ship) and/or very thin ice types from open water when the unsupervised classification method was applied. However overall, EIS Cam 2 is advantageous in providing a constant record of sea ice concentration for a large field of view that can be used to support quality assurance purposes for ASPeCt records or supplement future cruises without an observer.
dc.description.departmentEarth and Environmental Science
dc.format.extent125 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781109298079
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6111
dc.languageen
dc.subjectAntarctica
dc.subjectEIS Cam
dc.subjectEM-31
dc.subjectPenelope Wagner
dc.subjectSIMBA
dc.subjectVideo Imagery
dc.subject.classificationEnvironmental science
dc.subject.classificationGeophysics
dc.subject.classificationPhysical oceanography
dc.subject.lcshSea ice -- Antarctica -- Bellingshausen Sea -- Remote sensing
dc.subject.lcshSea ice -- Antarctica -- Bellingshausen Sea -- Measurement
dc.titleIntercomparisons of sea ice thickness and concentration from visual observation, EM-31 measurements, and video imagery
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentEarth and Environmental Science
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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