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Data product of Greenland glacier calving front locations delineated by deep learning, 2013 to 2021
Metadaten
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Loebel, Erik - Technische Universität Dresden (ORCID: 0000-0001-9874-9295) | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Scheinert, Mirko - Technische Universität Dresden (ORCID: 0000-0002-0892-8941) | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Horwath, Martin - Technische Universität Dresden (ORCID: 0000-0001-5797-244X) | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Humbert, Angelika - Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung (ORCID: 0000-0002-0244-8760) | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Sohn, Julia - Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung (ORCID: 0000-0002-5044-1192) | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Heidler, Konrad - Technische Universität München (ORCID: 0000-0001-8226-0727) | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Liebezeit, Charlotte - Technische Universität Dresden | |
| Für den Inhalt der Forschungsdaten verantwortliche Person(en) | Zhu, Xiao Xiang - Technische Universität München (ORCID: 0000-0001-5530-3613) | |
| Kurzbeschreibung | 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 December 2021. Calving front positions are stored in ESRI shapefile format. Due to the high temporal resolution of this data product, mostly achieving sub-weekly sampling outside polar night, it provides unique opportunities to study seasonal and sub-seasonal terminus changes. The quality of these automatically extracted calving front locations has been validated against three independent validation datasets and is comparable to that of manually digitised fronts. The following glaciers are included in this data product: Kangiata Nunaata Sermia, Helheim Glacier, Kangerdlussuaq Glacier, Jakobshavn Isbræ, Eqip Sermia, Store Glacier, Rink Isbræ, Daugaard Jensen Glacier, Ingia Isbræ, Upernavik Isstrøm, Waltershausen Glacier, Hayes Glacier, Sverdrup Glacier, Kong Oscar Glacier, Døcker Smith Glacier, Harald Molke Bræ, Tracy Glacier, Humboldt Glacier, Zachariae Isstrøm, Nioghalvfjerdsbræ, Hagen Bræ, Academy Glacier and Ryder Glacier. | |
| Region(en) auf die sich die Daten beziehen | Greenland | |
| Weitere Schlagwörter | remote sensing | |
| Weitere Schlagwörter | satellite imagery | |
| Weitere Schlagwörter | machine learning | |
| Entstehungsjahr oder Entstehungszeitraum | 2023 | |
| Veröffentlichungsjahr | 2023 | |
| Herausgeber | Technische Universität Dresden | |
| Referenzen auf ergänzende Materialien | IsPartOf: 123456789/5680 (Handle) | |
| Inhalt der Forschungsdaten | Dataset: Automatically delineated calving front positions (format: ESRI shapefile) for 23 Greenland outlet glaciers from 2013 to 2021. | |
| Inhaber der Nutzungsrechte | Technische Universität Dresden | |
| Nutzungsrechte des Datensatzes | CC-BY-SA-4.0 | |
| Angabe der Fachgebiete | Geography | de |
| Angabe der Fachgebiete | Geological Science | de |
| Titel des Datensatzes | Data product of Greenland glacier calving front locations delineated by deep learning, 2013 to 2021 |
Dateien zu dieser Ressource
Die Datenpakete erscheinen in:
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Change pattern identification of marine-terminating outlet glaciers [5]
Marine-terminating outlet glaciers 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.