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

机译:通过目测,EM-31测量和视频图像进行海冰厚度和浓度的比对。

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摘要

Antarctic 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.
机译:南极海冰动力学在很大程度上受海洋和风强迫的影响,因为它被开阔的海洋所包围,而北极海冰则被陆地包围。由于地理位置偏远且费用昂贵,因此很少有研究海冰状况变化的机会。出于这个原因,有必要开发一种方法,该方法将允许有效,有效地收集海冰测量数据,以与卫星产品的大规模模型和验证方案集成。使用自动化设备将改善对南极地区海冰趋势的估计。;从钻探断面收集冰厚度分布趋势可能很麻烦,并且在大范围内提供的数据很少。因此,有必要考虑使用自动化设备来协助进一步收集数据,以供将来的航行使用。本研究的第一部分着重于从SIMBA巡航(南极海冰质量平衡)中收集各种数据集,包括基于船的海冰观测,电磁感应设备(EM-31)和视频图像(评估图像支持)。相机(EIS Cam 1)),以评估哪种自动化设备提供了测量海冰厚度分布的最佳方法。遥感应用程序通过EIS Cam 1的数据进行图像分析,以测量被船体打破的倾覆冰的厚度。然后,将EIS Cam 1和EM-31的厚度分布与基于ASPeCt(南极海冰过程和气候)的舰船观测结果进行比较,以评估每个设备的性能。由于三个数据集的足迹彼此不同,因此仅测量并比较了冰厚度分布的频率。总体而言,EM-31数据的性能要优于视频图像,这是因为它测量的冰况离船底足够远,该位置能够测量视频足迹中不存在的山脊和变形特征。这项研究还表明,对于2.50m以下的水平冰来说,潜在的良好结果是可能的,尽管与狭窄航道周围的较厚冰相比,该船的航迹将偏向更薄的冰,并且可能导致EM-31对稀薄的冰进行过采样。但是,在这种情况下,EM-31将作为每小时从船桥上进行的ASPeCt视觉观察的适当补充。本研究的第二部分评估了使用视频图像记录的海冰浓度数据(EIS Cam 2)与基于舰船的冰观测相比。使用Weissling等人提供的技术对来自入站和出站样板的图像进行分类。 (2009年)确定照相机测量值与舰载观测值之间的误差量。对这些比较的分析发现,由于起伏,海冰变形特征和较暗的光照条件而产生的高光和阴影,在夜间条件下相关性较差,在这种情况下,当ASPeCt冰浓度为50%时,EIS Cam 2要么低估了高达30%的浓度值。当ASPeCt冰浓度小于80%时,冰浓度超过80%或被高估了60%。由于夜间条件的不同,夜间的情况也可能导致ASPeCt观察者的估计过高或过低。但是,白天的两个数据集之间总体上有很好的一致性,其中EIS Cam 2和ASPeCt相差约5%(入站跟踪)或10%(出站跟踪)。数据集的错误与ASPeCt参数的粗分辨率以及当采用无监督分类方法时,EIS Cam 2无法从开放水域中区分阴影(来自山脊或船舶)和/或非常稀薄的冰类型有关。但是,总的来说,EIS凸轮2可以在大视野范围内提供恒定的海冰浓度记录,这可用于支持ASPeCt记录的质量保证目的或在没有观察员的情况下补充将来的航行。

著录项

  • 作者

    Wagner, Penelope.;

  • 作者单位

    The University of Texas at San Antonio.;

  • 授予单位 The University of Texas at San Antonio.;
  • 学科 Geophysics.;Physical Oceanography.;Environmental Sciences.
  • 学位 M.S.
  • 年度 2009
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地球物理学;海洋物理学;环境科学基础理论;
  • 关键词

  • 入库时间 2022-08-17 11:37:38

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