DOI: 10.5194/tc-15-1663-2021
论文题名: Calving front machine (CALFIN): Glacial termini dataset and automated deep learning extraction method for Greenland, 1972-2019
作者: Cheng D. ; Hayes W. ; Larour E. ; Mohajerani Y. ; Wood M. ; Velicogna I. ; Rignot E.
刊名: Cryosphere
ISSN: 19940416
出版年: 2021
卷: 15, 期: 3 起始页码: 1663
结束页码: 1675
语种: 英语
英文关键词: automation
; data set
; extraction method
; machinery
; mass balance
; model validation
; satellite imagery
; sea ice
; sea level
; Arctic
; Greenland
; Greenland Ice Sheet
英文摘要: Sea level contributions from the Greenland Ice Sheet are influenced by the rapid changes in glacial terminus positions. The documentation of these evolving calving front positions, for which satellite imagery forms the basis, is therefore important. However, the manual delineation of these calving fronts is time consuming, which limits the availability of these data across a wide spatial and temporal range. Automated methods face challenges that include the handling of clouds, illumination differences, sea ice mélange, and Landsat 7 scan line corrector errors. To address these needs, we develop the Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers, using neural networks. The results are often indistinguishable from manually curated fronts, deviating by on average 86.76 ± 1.43 m from the measured front. Landsat imagery from 1972 to 2019 is used to generate 22 678 calving front lines across 66 Greenlandic glaciers. This improves on the state of the art in terms of the spatiotemporal coverage and accuracy of its outputs and is validated through a comprehensive intercomparison with existing studies. The current implementation offers a new opportunity to explore subseasonal and regional trends on the extent of Greenland's margins and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise. © 2019 EDP Sciences. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164667
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: University of California at Irvine, Irvine, CA, United States; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; eScience Institute, Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States
Recommended Citation:
Cheng D.,Hayes W.,Larour E.,et al. Calving front machine (CALFIN): Glacial termini dataset and automated deep learning extraction method for Greenland, 1972-2019[J]. Cryosphere,2021-01-01,15(3)