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The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector. [ABSTRACT FROM AUTHOR]

Exact information on the calving front positions of marine- or lake-terminating glaciers is a fundamental glacier variable for analyzing ongoing glacier change processes and assessing other variables like frontal ablation rates. In recent years, researchers started implementing algorithms that can automatically detect the calving fronts on satellite imagery. Most studies use optical images, as calving fronts are often easy to distinguish in these images due to the sufficient spatial resolution and the presence of different spectral bands, allowing the separation of ice features. However, detecting calving fronts on synthetic aperture radar (SAR) images is highly desirable, as SAR images can also be acquired during the polar night and are independent of weather conditions (e.g., cloud cover), facilitating year-round monitoring worldwide. In this paper, we present a benchmark dataset of SAR images from multiple regions of the globe with corresponding manually defined labels providing information on the position of the calving front (https://doi.org/10.1594/PANGAEA.940950). With this dataset, different approaches for the detection of glacier calving fronts can be implemented, tested, and their performance fairly compared so that the most effective approach can be determined. The dataset consists of 681 samples, making it large enough to train deep learning segmentation models. It is the first dataset to provide long-term glacier calving front information from multi-mission data. As the dataset includes glaciers from Antarctica, Greenland, and Alaska, the wide applicability of models trained and tested on this dataset is ensured. The test set is independent of the training set so that the generalization capabilities of the models can be evaluated. We provide two sets of labels: one binary segmentation label to discern the calving front from the background, and one label for multi-class segmentation of different landscape classes. Unlike other calving front datasets, the presented dataset contains not only the labels but also the corresponding preprocessed and geo-referenced SAR images as PNG files. The ease of access to the dataset will allow scientists from other fields, such as data science, to contribute their expertise. With this benchmark dataset, we enable comparability between different front detection algorithms and improve the reproducibility of front detection studies. Moreover, we present one baseline model for each kind of label type. Both models are based on the U-Net, one of the most popular deep learning segmentation architectures. In the following two post-processing procedures, the segmentation results are converted into 1-pixel-wide front delineations. By providing both types of labels, both approaches can be used to address the problem. To assess the performance of different models, we suggest first reviewing the segmentation results using the recall, precision, F1 score, and the Jaccard index. Second, the front delineation can be evaluated by calculating the mean distance error to the labeled front. The presented vanilla models provide a baseline of 150 m ± 24 m mean distance error for the Mapple Glacier in Antarctica and 840 m ± 84 m for the Columbia Glacier in Alaska, which has a more complex calving front, consisting of multiple sections, compared with a laterally well constrained, single calving front of Mapple Glacier. [ABSTRACT FROM AUTHOR]

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. • We present The NormalisEd Radon transform Damage detection (NeRD) method. • The NeRD method consistently detects damage across spatiotemporal domains. • The NeRD method detects multi-scale damage features and their orientations. • The NeRD method is directly applicable on multi-source satellite data. [Display omitted] [ABSTRACT FROM AUTHOR]