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

Lopez-Lopez, Ludwin; Parmiggiani, F.; Moctezuma-Flores, Miguel; Guerrieri, Lorenzo

doi:10.3390/rs13030460

Cited by 6 publications

(3 citation statements)

References 28 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth mentioning that remote sensing technology has been widely used to assess different aspects of iceberg properties comprising iceberg detection and iceberg trajectory (Lopez-Lopez et al, 2021;Braakmann-Folgmann et al, 2022), iceberg drift (Monteban et al, 2020), and iceberg draft (Liu et al, 2021). The geometrical properties of the exposed part of the icebergs are obtained from remote sensing studies.…”

Section: Comparison With the Previous Modelsmentioning

confidence: 99%

Determination of Parameters Affecting the Estimation of Iceberg Draft

2023

View full text Add to dashboard Cite

Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a profound understanding of the problem’s dominant parameters. As such, the dimensionless groups of the parameters affecting the iceberg draft estimation were determined for the first time in the present study. Using the dimensionless groups recognized and the linear regression (LR) analysis, nine LR models (i.e., LR 1 to LR 9) were developed and then validated using a comprehensive dataset, which has been constructed in this study. A sensitivity analysis distinguished the premium LR models and important dimensionless groups. The best LR model, as a function of all dimensionless parameters, was able to estimate the iceberg draft with the highest level of precision and correlation along with the lowest degree of complexity. The ratio of iceberg length to iceberg height as the “iceberg length ratio” and the ratio of iceberg width to iceberg height as the “iceberg width ratio” was detected as the important dimensionless groups in the estimation of the iceberg draft. An uncertainty analysis demonstrated that the best LR model was biased towards underestimating the iceberg drafts. The premium LR model outperformed the previous empirical models. Ultimately, a set of LR-based relationships were derived for estimating the iceberg drafts for practical engineering applications, e.g., the early stages of the iceberg management projects.

show abstract

Section: Comparison With the Previous Modelsmentioning

confidence: 99%

Determination of Parameters Affecting the Estimation of Iceberg Draft

2023

View full text Add to dashboard Cite

show abstract

“…However, since clouds make infrared observations impossible, the continuous monitoring of Antarctica can only be carried out by SAR imaging systems. Lopez-Lopez et al [31] gives an example of Antarctic iceberg monitoring by analyzing some elements of the drift trajectory of the A-68A iceberg using a set of Sentinel-1 SAR data that cover a period of eighteen months. The applied image processing scheme addresses two relevant problems: how to conduct quasi-automatic analysis using a fuzzy logic approach to image contrast enhancement and the use of ferromagnetic concepts to define a stochastic segmentation.…”

Section: Icebergs and Glaciersmentioning

confidence: 99%

Editorial for the Special Issue “Remote Sensing of the Polar Oceans”

Aulicino

Wadhams

2022

Remote Sensing

View full text Add to dashboard Cite

This Special Issue gathers papers reporting research on various aspects of the use of satellites for monitoring polar oceans. It includes contributions presenting improvements in the retrieval of sea ice concentration, extent and area, and concerning error information; the interannual and decadal variability of sea surface temperature and sea ice concentration in the Barents Sea; validation and comparison of Arctic salinity products; melt pond retrieval applying a Linear Polar algorithm to Landsat data; the characterization of surface layer freshening from sea surface salinity and coloured detrital matter in the Kara and Laptev Seas; multi-sensor estimations of chlorophyll-a concentrations in the Western Antarctic Peninsula; and enhanced techniques for detection and monitoring of glacier dynamics and iceberg paths.

show abstract

“…where ρ w and ρ i are the density of water and ice, respectively, and δ is the thickness correction of the upper firn layer. Here we assume 1025 and 915 kg m −3 of seawater and ice density, respectively (Griggs and Bamber, 2011;Chuter and Bamber, 2015;Zhang et al, 2020), and 15 m of typical firn layer correction (Griggs and Bamber, 2011;Lythe and Vaughan, 2001).…”

Section: Calculation Of Iceberg Thicknessmentioning

confidence: 99%

Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine

et al. 2021

View full text Add to dashboard Cite

Abstract. Sentinel-1 C-band synthetic aperture radar (SAR) images can be used to observe the drift of icebergs over the Southern Ocean with around 1–3 d of temporal resolution and 10–40 m of spatial resolution. The Google Earth Engine (GEE) cloud-based platform allows processing of a large quantity of Sentinel-1 images, saving time and computational resources. In this study, we process Sentinel-1 data via GEE to detect and track the drift of iceberg B43 during its lifespan of 3 years (2017–2020) in the Southern Ocean. First, to detect all candidate icebergs in Sentinel-1 images, we employ an object-based image segmentation (simple non-iterative clustering – SNIC) and a traditional backscatter threshold method. Next, we automatically choose and trace the location of the target iceberg by comparing the centroid distance histograms (CDHs) of all detected icebergs in subsequent days with the CDH of the reference target iceberg. Using this approach, we successfully track iceberg B43 from the Amundsen Sea to the Ross Sea and examine its changes in area, speed, and direction. Three periods with sudden losses of area (i.e., split-offs) coincide with periods of low sea ice concentration, warm air temperature, and high waves. This implies that these variables may be related to mechanisms causing the split-off of the iceberg. Since the iceberg is generally surrounded by compacted sea ice, its drift correlates in part with sea ice motion and wind velocity. Given that the bulk of the iceberg is under water (∼30–60 m freeboard and ∼150–400 m thickness), its motion is predominantly driven by the westward-flowing Antarctic Coastal Current, which dominates the circulation of the region. Considering the complexity of modeling icebergs, there is a demand for a large iceberg database to better understand the behavior of icebergs and their interactions with surrounding environments. The semi-automated iceberg tracking based on the storage capacity and computing power of GEE can be used for this purpose.

show abstract

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

Cited by 6 publications

References 28 publications

Determination of Parameters Affecting the Estimation of Iceberg Draft

Determination of Parameters Affecting the Estimation of Iceberg Draft

Editorial for the Special Issue “Remote Sensing of the Polar Oceans”

Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine

Contact Info

Product

Resources

About