Change pattern identification of outlet glaciers in Greenland
Outlet glaciers in Greenland experience a combination of seasonal and climate-driven change. Nearby glaciers exhibit very different retreat and advance behavior despite being situated in similar climatic conditions. This highlights the demand to essentially improve our understanding of the driving mechanisms and to provide a basis for parameterizations of oceanic forcing that are fed into mass-loss projections. Temporal changes of glacial flow velocities are presumably linked to the evolution of the subglacial hydrological system. Depending on the type of subglacial system, the temporal acceleration of the glacier is represented by different characteristics. While this is typically investigated only along a central flow line, the spatial distribution contains more information on the cause of the acceleration. In a similar way, the spatial pattern of acceleration due to changes at the calving front is likely driven by upstream propagation of changes in stresses. Hence, understanding the mechanisms in detail requires an analysis of different physical variables in high temporal and spatial resolution and combination with ice modelling. With the new generation of satellites the era of big data has started in glaciology, and new efficient methods to analyze change patterns are required.
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Data product of Greenland glacier calving front locations delineated by deep learning, 2013 to 2021 (Technische Universität Dresden, 2023)Glacier calving front positions are derived by applying a deep learning method to multispectral Landsat-8 imagery. The product contains 9243 calving front positions across 23 Greenland outlet glaciers from March 2013 to ...
Manually delineated glacier calving fronts of 23 Greenland and 2 Antarctic outlet glaciers from 2013 to 2021 and source code for automated extraction by deep learning (Technische Universität Dresden, 2022)Supplementary material to the manuscript: Loebel, E., Scheinert, M., Horwath, M., Heidler, K., Christmann, J., Phan, L., Humbert, A., Zhu, X. (2022): Extracting glacier calving fronts by deep learning: the benefit of ...