Abstract
Abstract. 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. The manual methods traditionally used to monitor change in satellite imagery of marine-terminating outlet glaciers are time-consuming and can be subjective, especially where mélange exists at the terminus. Recent advances in deep learning applied to image processing have created a new frontier in the field of automated delineation of glacier calving fronts. However, there remains a paucity of research on the use of deep learning for pixel-level semantic image classification of outlet glacier environments. Here, we apply and test a two-phase deep learning approach based on a well-established convolutional neural network (CNN) for automated classification of Sentinel-2 satellite imagery. The novel workflow, termed CNN-Supervised Classification (CSC) is adapted to produce multi-class outputs for unseen test imagery of glacial environments containing marine-terminating outlet glaciers in Greenland. Different CNN input parameters and training techniques are tested, with overall F1 scores for resulting classifications reaching up to 94 % for in-sample test data (Helheim Glacier) and 96 % for out-of-sample test data (Jakobshavn Isbrae and Store Glacier), establishing a state of the art in classification of marine-terminating glaciers in Greenland. Predicted calving fronts derived using optimal CSC input parameters have a mean deviation of 56.17 m (5.6 px) and median deviation of 24.7 m (2.5 px) from manually digitised fronts. This demonstrates the transferability and robustness of the deep learning workflow despite complex and seasonally variable imagery. Future research could focus on the integration of deep learning classification workflows with free cloud-based platforms, to efficiently classify imagery and produce datasets for a range of glacial applications without the need for substantial prior experience in coding or deep learning.
Highlights
Quantifying glacier change from remote sensing data is essential to improve our understanding of the impacts that climate change has on glaciers (Vaughan et al, 2013; Hill et al, 2017)
CSC is a two-phase workflow based on convolutional architectures which concatenates a convolutional neural network (CNN) to a multilayer perceptron (MLP) or compact CNN
After testing the performance of different band combinations, tile sizes, and patch sizes on seasonally variable test imagery, we find that classifications reach F 1 scores of up to 93.3 % for in-sample test imagery and 91 % for out-of-sample test imagery when using a phase one CNN trained only with data from Helheim Glacier and the overall optimal classification parameters
Summary
Quantifying glacier change from remote sensing data is essential to improve our understanding of the impacts that climate change has on glaciers (Vaughan et al, 2013; Hill et al, 2017). The labour-intense nature of manual digitisation can result in datasets with spatial or temporal limitations (Seale et al, 2011) With this in mind, the importance of processes occurring at marine-terminating outlet glaciers on a range of spatio-temporal scales
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