Remote sensing of the Bering Glacier Region

Shuchman, Robert A.; Josberger, Edward G.; Jenkins, Liza K.; Payne, John; Hatt, Charles R.; Spaete, L.

doi:10.1130/2010.2462(03)

Cited by 5 publications

(6 citation statements)

References 2 publications

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“…Early observations of a surge in 1938–1940 go back to R. M. Krimmel (USGS). Bering Glacier surged again in 1957–1967 (Molnia & Post, ; Post, ) and in 1993–1997 (Fatland & Lingle, , ; Fleischer et al, ; Herzfeld, ; Herzfeld & Mayer, ; Josberger et al, ; Lingle et al, ; Mayer & Herzfeld, ; Molnia, ; Molnia & Post, , ; Molnia & Williams, ; Roush et al, ; Shuchman & Josberger, ; Shuchman et al, ).…”

Section: The Bering‐bagley Glacier System: Geography and Surge Historymentioning

confidence: 99%

Crevasses as Indicators of Surge Dynamics in the Bering Bagley Glacier System, Alaska: Numerical Experiments and Comparison to Image Data Analysis

Trantow

Herzfeld

2018

JGR Earth Surface

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One of the largest sources of uncertainty in sea level rise prediction is glacial acceleration, of which the surge phenomenon is the least understood type. The surge of the Bering Bagley Glacier System (BBGS), Alaska, in 2011–2013 has provided a rare opportunity to study the surge phenomenon in a large and complex glacier system. A surge results in widespread crevassing throughout the glacier system complicating many traditional techniques used to study glacier dynamics. In this paper, we utilize crevassing as a means to investigate the recent BBGS surge through numerical modeling and geostatistical data analysis. Following the principles of structural glaciology, image‐based crevasse characterizations are obtained through geostatistical methods applied to Landsat‐7 data, supplemented by airborne field observations. On the modeling side, a 3‐D full‐Stokes finite element model of the BBGS is developed and applied to investigate ice dynamics and surface structures during the recent surge. A von Mises criterion is adopted to simulate crevassing at the glacier surface, oriented along the axes of maximum principal tensile stress. To facilitate evaluation of model‐ and data‐derived crevasse characteristics, three different comparison methods are introduced. General agreement in the model‐data comparisons indicates that the model has the ability to represent the BBGS system during peak acceleration. The crevasse‐based approach is also employed to optimize the basal sliding parameter and the von Mises stress threshold in the model. Results further indicate that bed topography is an important constraint in modeling the surge process.

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Section: The Bering‐bagley Glacier System: Geography and Surge Historymentioning

confidence: 99%

Crevasses as Indicators of Surge Dynamics in the Bering Bagley Glacier System, Alaska: Numerical Experiments and Comparison to Image Data Analysis

Trantow

Herzfeld

2018

JGR Earth Surface

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“…Bering Glacier last surged in 1993-95 (Post, 1972; Lingle and others, 1993; Molnia and Post, 1995, 2010b; Herzfeld and Mayer, 1997; Fatland and Lingle, 1998; Herzfeld, 1998; Mayer and Herzfeld, 2000; Molnia, 2001, 2008; Lingle and Fatland, 2003; Roush and others, 2003; Fleisher and others, 2010; Josberger and others, 2010; Shuchman and Josberger, 2010; Shuchman and others, 2010). The geography of Bering Glacier-Bagley Ice Valley, with place names, is described in Molnia and Post (2010a).…”

Section: The Surge Of the Bering-bagley Glacier System In 2011mentioning

confidence: 99%

“…Remote-sensing data analysis has been applied in research on the last surge of Bering Glacier (1993–95), resulting in valuable velocity measurements, documentation of front advance and qualitative descriptions of components of the surge process (Fatland and others, 2003; Roush and others, 2003; Molnia and Post, 2010b; Shuchman and others, 2010). For example, Lingle and Fatland (2003) suggest a possible progression qualitatively (in summary, water needs to be stored in a distributed system in the glacier but then the actual surge may be triggered by meteorologic events).…”

Section: Introduction: the Problem Of Understanding Surge Progressionmentioning

confidence: 99%

Bering Glacier and Bagley Ice Valley surge 2011: crevasse classification as an approach to map deformation stages and surge progression

2013

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The dynamics of a surge is manifested in the crevasse patterns: literally, deformation state frozen in ice. This basic observation is utilized as the concept of an automated approach to map and analyze deformation stages and progression of surge kinematics. The classification method allows imagery to be used as geophysical data and is applied to aerial observations (photographic and video imagery, GPS data) collected in September 2011 during the surge of the Bering Glacier–Bagley Ice Valley system, Alaska, USA. As the third dimension that complements two-dimensional imagery, ice-surface elevation is observed using aerial laser altimetry. The classification method builds on concepts from signal processing, geostatistical data analysis and neural networks. Steps include calculation of generalized directional vario functions from image data and composition into feature vectors. The vario function operates as an information filter that retains spatial characteristics at an intermediate scale that captures crevasse spacing, anisotropy and other generalized roughness properties. Association of feature vectors to crevasse classes and hence deformation types employs a connectionist algorithm. In general, the connectionist–geostatistical classification allows the mapping of kinematic changes in crevassed glaciers.Dedicated to the memory of Austin Post

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“…In spite of this acceleration, the terminus did not advance, likely due to increased calving into Vitus Lake. In fact, the terminus of Bering Glacier has retreated annually since the end of the previous surge in 1995 (Shuchmann and others, 2010). It is only since the surge front reached the terminus in 2011 that the terminus has advanced, roughly 2-4 km (see colored crosses in Fig.…”

Section: Discussionmentioning

confidence: 99%