Characterization of ice shelf fracture features using ICESat-2 – A case study over the Amery Ice Shelf

Shujie Wang, Patrick Alexander, Qiusheng Wu, Marco Tedesco, Song Shu

Research output: Contribution to journalArticlepeer-review

Abstract

Fractures are important structural features that affect the stress condition and stability of ice shelves. Previous studies have mainly focused on the measurement of fractures in the horizontal dimension. However, the vertical morphology of fractures could also be potentially important in determining their evolution and role in ice shelf stability. In this regard, the dense and high-resolution surface elevation measurements collected by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provide an excellent opportunity for studying fractures in the vertical dimension over a regional scale. Here we developed an object-oriented algorithm to automatically detect and characterize fracture features (including rifts, surface fractures, and the surface expressions of basal fractures) from ICESat-2 data. We successfully applied the algorithm to ICESat-2 L3A Land Ice Height (ATL06) data over the Amery Ice Shelf in East Antarctica, and retrieved the vertical structural information of two major fracture fields. The detected fracture features match well with the visible fracture lines in Landsat-8 satellite imagery, and the edges of fracture features are well-captured from the ATL06 data. We analyzed the fracture patterns in terms of surface depth, width, vertical shape, length, and orientation. We found that the fracture feature depth is a key factor determining the formation of new fractures and the spatial pattern of depth is closely related to rift formation. We anticipate that further application of the object-oriented method over other ice shelves will produce important insights regarding fracture formation and ice shelf vulnerability by combining ICESat-2-derived vertical information with the horizontal structure retrieved from satellite imagery.

Original languageEnglish (US)
Article number112266
JournalRemote Sensing of Environment
Volume255
DOIs
StatePublished - Mar 15 2021

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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