Damage detection on antarctic ice shelves using the normalised radon transform

Areas of structural damage mechanically weaken Antarctic ice shelves. This potentially preconditions ice shelves for disintegration and enhanced grounding line retreat. The development of damage and its feedback on marine ice sheet dynamics has been identified as key to future ice shelf stability and sea level contributions from Antarctica. However, it is one of the least understood processes that impact ice shelf instability since quantifying damage efficiently and accurately is a challenging task. Challenges relate to the complex surface of Antarctica, variations in viewing-illumination geometry, snow or cloud cover and variable signal-to-noise levels in satellite imagery. Therefore, automated damage assessment approaches require careful pre- and post-processing, lacking the option to be applied to wider spatiotemporal domains. Simultaneously, studies that use manual mapping are usually limited due to the effort required for extensive mapping, which either results in a limited spatial domain or the use of low resolution data.

This study proposes the NormalisEd Radon transform Damage detection (NeRD) method to detect damage features and their orientations from multi-source satellite imagery. NeRD performs robust, high resolution, large-scale damage assessments. NeRD is applied to the ice shelves in the Amundsen Sea Embayment (ASE) and validated with both manually labelled and existing fracture maps. Validation shows that NeRD detects damage with high recall and provides an accurate physical representation of multi-scale damage features and their orientation. Sensitivity analyses indicate NeRD is robust to different resolution parameter settings. NeRD consistently detects damage for different data sources ranging from optical Landsat 7/8 and Sentinel-2 optical to Synthetic Aperture Radar Sentinel-1 data. Therefore, NeRD paves the way for synergistic multi-source damage detection that overcomes remaining limitations from individual sources. Results show varying damage patterns on the ice shelves in the ASE area in austral summer 2020–2021, with most damage located on the Pine Island, Crosson and Thwaites ice shelves. We show a damage increase on the Pine Island ice shelf between 2013–2019, and display advection and rotation of crevasses. The detected damage orientation can provide insight in the type of crevasse opening mode and the development of damage over time. The damage maps produced with NeRD can help evaluate ice sheet models or machine learning approaches, improving our understanding of damage evolution.