Pixel-wise Distance Regression for Glacier Calving Front Detection and Segmentation

Glacier calving front position (CFP) is an important glaciological variable. Traditionally, delineating the CFPs has been carried out manually, which was subjective, tedious and expensive. Automating this process is crucial for continuously monitoring the evolution and status of glaciers. Recently, deep learning approaches have been investigated for this application. However, the current methods get challenged by a severe class-imbalance problem. In this work, we propose to mitigate the class-imbalance between the calving front class and the non-calving front class by reformulating the segmentation problem into a pixel-wise regression task. A Convolutional Neural Network gets optimized to predict the distance values to the glacier front for each pixel in the image. The resulting distance map localizes the CFP and is further post-processed to extract the calving front line. We propose three post-processing methods, one method based on statistical thresholding, a second method based on conditional random fields (CRF), and finally the use of a second U-Net. The experimental results confirm that our approach significantly outperforms the state-of-the-art methods and produces accurate delineation. The Second U-Net obtains the best performance results, resulting in an average improvement of about 21% dice coefficient enhancement

Image Classification of Marine-Terminating Outlet Glaciers using Deep Learning Methods

A wealth of research has focused on elucidating the key controls on mass loss from the Greenland and Antarctic ice sheets in response to climate forcing, specifically in relation to the drivers of marine-terminating outlet glacier change. Despite the burgeoning availability of medium resolution satellite data, the manual methods traditionally used to monitor change of marine-terminating outlet glaciers from satellite imagery are time-consuming and can be subjective, especially where a mélange of icebergs and sea-ice exists at the terminus. To address this, recent advances in deep learning applied to image processing have created a new frontier in the field of automated delineation of glacier termini. However, at this stage, there remains a paucity of research on the use of deep learning for pixel-level semantic image classification of outlet glacier environments. This project develops and tests a two-phase deep learning approach based on a well-established convolutional neural network (CNN) called VGG16 for automated classification of Sentinel-2 satellite images. The novel workflow, termed CNN-Supervised Classification (CSC), was originally developed for fluvial settings but is adapted here to produce multi-class outputs for test imagery of glacial environments containing marine-terminating outlet glaciers in eastern Greenland. Results show mean F1 scores up to 95% for in-sample test imagery and 93% for out-of-sample test imagery, with significant improvements over traditional pixel-based methods such as band ratio techniques. This demonstrates the robustness of the deep learning workflow for automated classification despite the complex characteristics of the imagery. Future research could focus on the integration of deep learning classification workflows with platforms such as Google Earth Engine (GEE), to classify imagery more efficiently and produce datasets for a range of glacial applications without the need for substantial prior experience in coding or deep learning

UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet

This study presents the application of a cost-effective, unmanned aerial vehicle (UAV) to investigate calving dynamics at a major marine-terminating outlet glacier draining the western sector of the Greenland ice sheet. The UAV was flown over Store Glacier on three sorties during summer 2013 and acquired over 2000 overlapping, geotagged images of the calving front at an ~40 cm ground sampling distance. Stereo-photogrammetry applied to these images enabled the extraction of high-resolution digital elevation models (DEMs) with vertical accuracies of ± 1.9 m which were used to quantify glaciological processes from early July to late August 2013. The central zone of the calving front advanced by ~500 m, whilst the lateral margins remained stable. The orientation of crevasses and the surface velocity field derived from feature tracking indicates that lateral drag is the primary resistive force and that ice flow varies across the calving front from 2.5 m d−1 at the margins to in excess of 16 m d−1 at the centreline. Ice flux through the calving front is 3.8 × 107 m3 d−1, equivalent to 13.9 Gt a−1 and comparable to flux-gate estimates of Store Glacier's annual discharge. Water-filled crevasses were present throughout the observation period but covered a limited area of between 0.025 and 0.24% of the terminus and did not appear to exert any significant control over fracture or calving. We conclude that the use of repeat UAV surveys coupled with the processing techniques outlined in this paper have great potential for elucidating the complex frontal dynamics that characterise large calving outlet glaciers

Uncertainty Assessment of Ice Discharge Using GPR-Derived Ice Thickness from Gourdon Glacier, Antarctic Peninsula

Ice cliffs within a glacier represent a challenge for the continuity equations used in many glacier models by interrupting the validity of input parameters. In the case of Gourdon Glacier on James Ross Island, Antarctica, a ∼300–500 m high, almost vertical cliff, separates the outlet glacier from its main accumulation area on the plateau of the island. In 2017 and 2018 we conducted ice thickness measurements during two airborne ground penetrating radar campaigns in order to evaluate differences to older measurements from the 1990s. The observed differences are mostly smaller than the estimated error bars. In comparison to the in situ data, the published “consensus ice thickness estimate” strongly overestimates the ice thickness at the outlet. We analyse three different interpolation and ice thickness reconstruction methods. One approach additionally includes the mass input from the plateau. Differences between the interpolation methods have a minor impact on the ice discharge estimation if the used flux gates are in areas with a good coverage of in situ measurements. A much stronger influence was observed by uncertainties in the glacier velocities derived from remote sensing, especially in the direction of the velocity vector in proximity to the ice cliff. We conclude that the amount of in situ measurements should be increased for specific glacier types in order to detect biases in modeled ice thickness and ice discharge estimations

Mapping Greenland ice sheet velocities at high temporal resolution using satellite based imagery

In this thesis, I develop and demonstrate a system for monitoring fluctuations in the speed of Greenland ice sheet outlet glaciers with high temporal frequency from imagery acquired by a range of satellite missions. This work is motivated by an ambition to utilise a new era of operational satellites to better understand how environmental changes are affecting the flow and mass of Greenland’s outlet glaciers. First, I exploited the systematic and frequent acquisition schedule of the Sentinel-1 satellite constellation to track weekly variations in the speed of four fast-flowing, marine-terminating glaciers - Jakobshavn Isbræ, Petermann Glacier, Zachariæ Isstrøm and Nioghalvfjerdsfjorden - between 2015–2017. By combining the Sentinel-1 data with an eight-year time-series derived from TerraSAR-X, I produced a decadal record of variations in glacier flow. On a technical level, I was able to demonstrate the value of Sentinel-1’s 6-day revisit time for glaciology, because it leads to an increase in the degree of correlation between consecutive images and also to improved tracking of movement near to the glacier calving fronts. On a scientific level, I was able to demonstrate that a strong correlation exists between iceberg calving events and glacier speedup, and to show for the first time that Jakobshavn Isbræ has begun to slow down. Next, I assessed the capability of the Sentinel-1 constellation to detect and chart seasonal changes in the speed of five slow-flowing glaciers situated in a 14,000 km2 land-terminating sector of central-west Greenland. These new measurements offer significantly improved spatial and temporal resolution when compared to previous missions, in all seasons. I was able to show that there are marked differences in the degree of seasonal speedup of the five glaciers – with summertime increases in ice flow ranging from 21 to 49 % - reinforcing the need for comprehensive monitoring and the challenges of making regional extrapolations. Thanks to the high temporal frequency afforded by Sentinel-1, I was also able to document for the first time the detailed spatial pattern of speedup persistence, and to show that short- lived peaks of melting match transient spikes in glacier velocity. Finally, I explored the added value and complementarity of the Sentinel-2 multi- spectral instrument (MSI) for tracking ice motion. I was able to combine measurements acquired by Sentinel-1 and Sentinel-2 to detect short-term changes in iceberg drift, iceberg calving, ice motion, and supraglacial lake area at Jakobshavn Isbræ. I also showed that measurements of glacier flow determined from both satellites are in good agreement, and that the spatial coverage they afford is greatest in opposing seasons, illustrating the promise of Sentinel-2 for glaciology

The past and future impact of ice tongue loss on outlet glaciers in northern Greenland

Ph. D. Thesis.Ice discharge from fast-flowing outlet glaciers across the Greenland Ice Sheet (GrIS) has increased in response to 21st century climate warming. These outlets are sensitive to changes at their terminus, particularly iceberg calving from floating tongues. Many glaciers have accelerated and thinned in response to recent retreat, but the impact of major calving events and ice tongue loss on ice discharge and sea level rise remains poorly constrained. Northern Greenland is the last region with floating ice tongues, but remains understudied compared to other regions of the ice sheet. The aim of this thesis is to quantify outlet glacier change across northern Greenland and assess the role of ice tongues in modulating past and future glacier dynamics. To address this aim I use: i) remote sensing to assess past glacier change across northern Greenland, and ii) two sets of numerical modelling experiments to simulate future ice tongue loss at Petermann Glacier. The key findings are that outlet glacier retreat rates have increased in the last two decades, but the dynamic response to retreat was dependent on terminus type (grounded vs floating) and glacier geometry. Grounded outlet glaciers retreated, accelerated and thinned, while the response of glaciers with ice tongues was more varied, and dependent on tongue confinement and bed topography inland of the grounding line. Modelling experiments on Petermann Glacier further corroborate these findings, demonstrating that unconfined portions of the tongue had little impact on dynamics, but removing confined sections closer to the grounding line accelerated ice flow. However, the long-term response (up to 100-years) to ice tongue loss was muted by the absence of a retrograde bed-slope in the grounding zone, which limited grounding line retreat. Overall, this thesis highlights the complexity of outlet glacier behaviour in northern Greenland. It also notes the variability between individual glacier responses to ice tongue loss. These factors require careful consideration when assessing future glacier sensitivity to ice tongue/shelf loss in both Greenland and Antarctica in order to accurately project accelerated ice discharge and ultimately global sea level rise.IAPETUS Doctoral Training Partnership. British Antarctic Surve