Modeling Iceberg Drift in the Barents Sea from Field Data

Diansky, N. A.; Marchenko, Aleksey; Panasenkova, Irina; Фомин, В. В.

doi:10.3103/s1068373918050059

Cited by 2 publications

(1 citation statement)

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“…In [12], a drift model makes use of wind predictions for estimating positions and trajectories of icebergs observed in ENVISAT/ASAR images. More complete delineations, such as the statistical, kinematic and dynamic models, require hydro-meteorological data and both atmosphere and ocean circulation models [13]. In general, modeling the interacting forces is a very complex task [14,15].…”

Section: Introductionmentioning

confidence: 99%

On the Detection and Long-Term Path Visualisation of A-68 Iceberg

Lopez-Lopez

Parmiggiani²,

Moctezuma-Flores

et al. 2021

Remote Sensing

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The article presents a methodology for examining a temporal sequence of synthetic aperture radar (SAR) images, as applied to the detection of the A-68 iceberg and its drifting trajectory. Using an improved image processing scheme, the analysis covers a period of eighteen months and makes use of a set of Sentinel-1 images. A-68 iceberg calved from the Larsen C ice shelf in July 2017 and is one of the largest icebergs observed by remote sensing on record. After the calving, there was only a modest decrease in the area (about 1%) in the first six months. It has been drifting along the east coast of the Antarctic Peninsula, and is expected to continue its path for more than a decade. It is important to track the huge A-68 iceberg to retrieve information on the physics of iceberg dynamics and for maritime security reasons. Two relevant problems are addressed by the image processing scheme presented here: (a) How to achieve quasi-automatic analysis using a fuzzy logic approach to image contrast enhancement, and (b) The use of ferromagnetic concepts to define a stochastic segmentation. The Ising equation is used to model the energy function of the process, and the segmentation is the result of a stochastic minimization.

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