Remote Sensing of Antarctic Glacier and Ice-Shelf Front Dynamics—A Review

Baumhoer, Celia; Dietz, A.J.; Dech, Stefan; Kuenzer, Claudia

doi:10.3390/rs10091445

Cited by 54 publications

(53 citation statements)

References 141 publications

(262 reference statements)

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“…Lastly, a part of Marie Byrd Land is selected because of the multi-year ice areas. Within this area, the Getz Ice Shelf was also chosen to test our algorithm on time series generation as little is known about the current frontal fluctuations [2]. The Getz Ice Shelf is the largest ice shelf along West Antarctica with an area of 33,395 km 2 and pinned with islands along the front [32].…”

Section: Study Areasmentioning

confidence: 99%

“…Only in this way, recent changes in glacier and ice shelf front movement can be detected. Additionally, our understanding of forces controlling calving front location (CFL) change is still limited as continuous Antarctic coastal-change time series are scarce [2]. This is attributed to the lack of suitable amounts of remote sensing imagery and the time-consuming manual delineation of calving fronts.…”

Section: Introductionmentioning

confidence: 99%

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Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning

et al. 2019

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Sea level rise contribution from the Antarctic ice sheet is influenced by changes in glacier and ice shelf front position. Still, little is known about seasonal glacier and ice shelf front fluctuations as the manual delineation of calving fronts from remote sensing imagery is very time-consuming. The major challenge of automatic calving front extraction is the low contrast between floating glacier and ice shelf fronts and the surrounding sea ice. Additionally, in previous decades, remote sensing imagery over the often cloud-covered Antarctic coastline was limited. Nowadays, an abundance of Sentinel-1 imagery over the Antarctic coastline exists and could be used for tracking glacier and ice shelf front movement. To exploit the available Sentinel-1 data, we developed a processing chain allowing automatic extraction of the Antarctic coastline from Seninel-1 imagery and the creation of dense time series to assess calving front change. The core of the proposed workflow is a modified version of the deep learning architecture U-Net. This convolutional neural network (CNN) performs a semantic segmentation on dual-pol Sentinel-1 data and the Antarctic TanDEM-X digital elevation model (DEM). The proposed method is tested for four training and test areas along the Antarctic coastline. The automatically extracted fronts deviate on average 78 m in training and 108 m test areas. Spatial and temporal transferability is demonstrated on an automatically extracted 15-month time series along the Getz Ice Shelf. Between May 2017 and July 2018, the fronts along the Getz Ice Shelf show mostly an advancing tendency with the fastest moving front of DeVicq Glacier with 726 ± 20 m/yr.

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Section: Study Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning

et al. 2019

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“…However, recent studies [16,20,21] have reported substantial changes in the remnant LBIS, namely the Scar Inlet Ice Shelf (SIIS), and its tributary glaciers during the same time, suggesting that the ice front retreat, ice flow acceleration, and enhanced surface features are key indicators producing its instability. The dynamic behavior of glaciers and ice shelves affects the contribution of the Antarctic ice sheet to global sea level rise, and one important parameter of ice sheet dynamics is the location of glacier and ice shelf fronts [22]. A change in the position of an ice shelf front reflects a change in the stability of that ice shelf to some extent.…”

Section: Introductionmentioning

confidence: 99%

Evolving Instability of the Scar Inlet Ice Shelf based on Sequential Landsat Images Spanning 2005–2018

Qiao

Guo

et al. 2019

Remote Sensing

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Following the large-scale disintegration of the Larsen B Ice Shelf (LBIS) in 2002, ice flow velocities for its remnants and tributary glaciers began to increase. In this study, we used sequential Landsat images spanning 2005-2018 to produce detailed maps of the ice flow velocities and surface features for the Scar Inlet Ice Shelf (SIIS). Our results indicate that the ice flow velocities for the SIIS and its tributary glaciers (Flask and Leppard Glaciers) have substantially increased since 2005. Surface features, such as rifts and crevasses, have also substantially increased in both scope and scale and are particularly evident in the region between the Leppard Glacier and the Jason Peninsula. Several indicators-including the acceleration of ice flows, the rapid growth of major surface rifts, the heavily enhanced surface crevasses, and the dynamic position of the ice front-point to the evolving instability of the SIIS. These same indicators describe the conditions for the LBIS leading up to its 2002 collapse. To date, however, the SIIS remains intact. The formation of fast ice supporting the ice shelf front, combined with moderate mean summer temperatures, may be preventing or delaying its collapse.

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“…Baumhoer et al [14] provides an overview of satellite sensors and existing methods used to detect Antarctic glacier and ice shelf front dynamics. Calving fronts of glaciers and ice shelves are in general mapped using Synthetic Aperture Radar (SAR) and optical satellite sensors [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…Other studies have provided coastlines for selected smaller study regions (for example, [22][23][24][25]). Baumhoer et al [14] consolidated these existing CFL products to map and assess patterns of glacier and ice shelf front changes around Antarctica between 1972/1975 and 2009/2015. They reported difficulties caused by data gaps in time and space, differences in temporal sampling and the variety of different methods necessitating undesirable assumptions.…”

Section: Introductionmentioning

confidence: 99%

Sub-Annual Calving Front Migration, Area Change and Calving Rates from Swath Mode CryoSat-2 Altimetry, on Filchner-Ronne Ice Shelf, Antarctica

et al. 2019

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Mapping the time-variable calving front location (CFL) of Antarctic ice shelves is important for estimating the freshwater budget, as an indicator of changing ocean and structural conditions or as a precursor of dynamic instability. Here, we present a novel approach for deriving regular and consistent CFLs based on CryoSat-2 swath altimetry. The CFL detection is based on the premise that the shelf edge is usually characterized by a steep ice cliff, which is clearly resolved in the surface elevation data. Our method applies edge detection and vectorization of the sharp ice edge in gridded elevation data to generate vector shapefiles of the calving front. To show the feasibility of our approach, we derived a unique data set of ice-front positions for the Filchner-Ronne Ice Shelf (FRIS) between 2011 and 2018 at a 200 m spatial resolution and biannual temporal frequency. The observed CFLs compare well with independently derived ice front positions from Sentinel-1 Synthetic Aperture Radar imagery and are used to calculate area change, advance rates, and iceberg calving rates. We measure an area increase of 810 ± 40 km2 a−1 for FRIS and calving rates of 9 ± 1 Gt a−1 and 7 ± 1 Gt a−1 for the Filchner and Ronne Ice Shelves, respectively, which is an order of magnitude smaller than their steady-state calving flux. Our findings demonstrate that the “elevation-edge” method is complementary to standard CFL detection techniques. Although at a reduced spatial resolution and less suitable for smaller glaciers in steep terrain, it enables to provide CFLs at regular intervals and to fill existing gaps in time and space. Moreover, the method simultaneously provides ice thickness, required for mass budget calculation, and has a degree of automation which removes the need for heavy manual intervention. In the future, altimetry data has the potential to deliver a systematic and continuous record of change in ice shelf calving front positions around Antarctica. This will greatly benefit the investigation of environmental forcing on ice flow and terminus dynamics by providing a valuable climate data record and improving our knowledge of the constraints for calving models and ice shelf freshwater budget.

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Remote Sensing of Antarctic Glacier and Ice-Shelf Front Dynamics—A Review

Cited by 54 publications

References 141 publications

Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning

Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning

Evolving Instability of the Scar Inlet Ice Shelf based on Sequential Landsat Images Spanning 2005–2018

Sub-Annual Calving Front Migration, Area Change and Calving Rates from Swath Mode CryoSat-2 Altimetry, on Filchner-Ronne Ice Shelf, Antarctica

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