An object-based SAR image iceberg detection algorithm applied to the Amundsen Sea

Mazur, Aleksandra; Wåhlin, Anna; Krężel, Adam

doi:10.1016/j.rse.2016.11.013

Cited by 55 publications

(83 citation statements)

References 45 publications

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“…Although surface freshwater fluxes from ERA‐Interim have significant uncertainties at high latitudes (Bromwich et al, ), we conclude that oceanic freshwater transport must play a significant role in this summertime freshening in the Ross Sea. Previous studies have identified that the westward flow of the Antarctic Coastal Current (Jacobs & Giulivi, ; Smith, Sedwick, et al, ) brings freshwater from the Amundsen Sea into the eastern Ross Sea, as a combination of liquid freshwater, sea ice, and small icebergs (Mazur et al, ). Moffat et al () discussed the dynamics of buoyancy‐driven coastal currents and their dependence on the seasonal modulation of melt rate.…”

Section: Discussionmentioning

confidence: 99%

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Evolution of the Seasonal Surface Mixed Layer of the Ross Sea, Antarctica, Observed With Autonomous Profiling Floats

et al. 2019

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Oceanographic conditions on the continental shelf of the Ross Sea, Antarctica, affect sea ice production, Antarctic Bottom Water formation, mass loss from the Ross Ice Shelf, and ecosystems. Since ship access to the Ross Sea is restricted by sea ice in winter, most upper ocean measurements have been acquired in summer. We report the first multiyear time series of temperature and salinity throughout the water column, obtained with autonomous profiling floats. Seven Apex floats were deployed in 2013 on the midcontinental shelf, and six Air‐Launched Autonomous Micro Observer floats were deployed in late 2016, mostly near the ice shelf front. Between profiles, most floats were parked on the seabed to minimize lateral motion. Surface mixed layer temperatures, salinities, and depths, in winter were −1.8 °C, 34.34, and 250–500 m, respectively. Freshwater from sea ice melt in early December formed a shallow (20 m) surface mixed layer, which deepened to 50–80 m and usually warmed to above −0.5 °C by late January. Upper‐ocean freshening continued throughout the summer, especially in the eastern Ross Sea and along the ice shelf front. This freshening requires substantial lateral advection that is dominated by inflow from melting of sea ice and ice shelves in the Amundsen Sea and by inputs from the Ross Ice Shelf. Changes in upper‐ocean freshwater and heat content along the ice shelf front in summer affect cross‐ice front advection, ice shelf melting, and calving processes that determine the rate of mass loss from the grounded Antarctic Ice Sheet in this sector.

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Section: Discussionmentioning

confidence: 99%

“…icebergs (Mazur et al, 2017). Moffat et al (2008) discussed the dynamics of buoyancy-driven coastal currents and their dependence on the seasonal modulation of melt rate.…”

Section: Journal Of Geophysical Research: Oceansmentioning

confidence: 99%

Evolution of the Seasonal Surface Mixed Layer of the Ross Sea, Antarctica, Observed With Autonomous Profiling Floats

et al. 2019

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“…We also compared the results with the segmentation-based method proposed by Williams et al (1999), which overestimates or underestimates iceberg area by ±20%. The method by Wesche and Dierking (2012) tends to overestimate iceberg area by approximately 10 ± 21%, while Mazur et al (2017) presented average errors of ±25%. Based on the performance analysis, the method achieves a robust detection accuracy of 97.76%.…”

Section: 1029/2019jc015205mentioning

confidence: 93%

“…To estimate the error of iceberg area, we compared the automatic sightings with manually selected icebergs by counting the number of pixels using an image editor software. Then, we applied the equation described in section 2.3, similar to the one used by Williams et al (1999), Silva and Bigg (2005), Wesche and Dierking (2015), and Mazur et al (2017). Positive and negative pixel count deviations were noted, on average 10 ± 4%, associated to variations of the iceberg contour definition resulting from the automatic method.…”

Section: 1029/2019jc015205mentioning

confidence: 99%

Three Years of Near‐Coastal Antarctic Iceberg Distribution From a Machine Learning Approach Applied to SAR Imagery

et al. 2019

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Mass loss around the Antarctic Ice Sheet is driven by basal melting and iceberg calving, which constitute the two dominant paths of freshwater flux into the Southern Ocean. Although of similar magnitude, icebergs play an important and still not fully understood role in the balance of heat and freshwater around Antarctica. This lack of understanding is partly due to operational difficulties in large-scale monitoring in polar regions, despite observational and remote sensing efforts. In this study, a novel machine learning approach, augmented by visual inspection, was applied to three Synthetic Aperture Radar (SAR) mosaics of the whole Antarctic continent and its adjacent coastal zone. Although originally intended for a mapping of the Antarctic continent, the SAR mosaics allow us to document the evolution and distribution of the size (and mass) of icebergs in the pan-Antarctic near-coastal zone for the years 1997, 2000, and 2008. Our novel algorithm identified 7,649 icebergs in 1997, 13,712 icebergs in 2000, and 7,246 icebergs in 2008 with surface areas between 0.1 and 4,567.82 km 2 and total masses of 4,641.53, 6,862.81, and 5,263.69 Gt, respectively. Large regional variability was observed, although a zonal pattern distribution is present. This has implications for future climate modeling studies that try to estimate the freshwater flux from melting icebergs, which demands a realistic representation of the interannually varying near-coastal iceberg pattern to initialize the simulations. Plain Language SummaryWhen icebergs melt in the Southern Ocean, they cool the surrounding ocean. They also distribute freshwater, which potentially impacts the circulation, biological activity, sea-ice cover, and the formation of the densest waters of the world's oceans. However, all these influences are not fully understood because we are lacking reliable methods to detect icebergs from space via satellites. This study has the main objective to determine how iceberg mass is distributed in the coastal zone around the Antarctic continent and how this distribution changes between individual years. We show that a novel machine learning approach can be applied to this problem, which is capable to identify icebergs in satellite images of the near-coastal ocean region almost automatically. The method also works in severe conditions, e.g. when the icebergs are affected by ocean waves or when they are surrounded by sea ice. Our results complement the ongoing discussion about the distribution of Antarctic icebergs in open-ocean regions that are not affected by sea ice. The resulting data can also be used in computer models that simulate the input of iceberg freshwater into the ocean.

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“…OBIA can also introduce a variety of contextual (e.g., land use type, ice type), texture (e.g., homogeneity, entropy, and dissimilarity) and shape (e.g., area) variables compared to pixel-based analysis, which only provides spectral information [18]. OBIA has been shown to improve iceberg detection using wide-swath SAR images in the Amundsen Sea [20].…”

Section: Introductionmentioning

confidence: 99%

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

et al. 2017

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Abstract:The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (f p ) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two~5.2 km long profiles over FYI-and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring f p using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring f p . A stronger negative correlation was observed between FYI thickness and f p (Spearman r s = −0.85) compared to FYI roughness and f p (r s = −0.52). The association between MYI thickness and f p was also negative (r s = −0.56), whereas there was no association between MYI roughness and f p . 47% of spring f p variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high f p ) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low f p ). This work provides an important contribution towards the parameterizations of f p in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring f p .

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An object-based SAR image iceberg detection algorithm applied to the Amundsen Sea

Cited by 55 publications

References 45 publications

Evolution of the Seasonal Surface Mixed Layer of the Ross Sea, Antarctica, Observed With Autonomous Profiling Floats

Evolution of the Seasonal Surface Mixed Layer of the Ross Sea, Antarctica, Observed With Autonomous Profiling Floats

Three Years of Near‐Coastal Antarctic Iceberg Distribution From a Machine Learning Approach Applied to SAR Imagery

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

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