Automated detection of ice cliffs within supraglacial debris cover

Ice cliffs within a supraglacial debris cover have been identified as a source for high ablation relative to the surrounding debris-covered area. Due to their small relative size and steep orientation, ice cliffs are difficult to detect using nadir-looking space borne sensors. The method presente...

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Main Authors: S. Herreid, F. Pellicciotti
Format: Article
Language:English
Published: Copernicus Publications 2018-05-01
Series:The Cryosphere
Online Access:https://www.the-cryosphere.net/12/1811/2018/tc-12-1811-2018.pdf
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spelling doaj-7583b6f1e9cc4c2a80bd16fac22101512020-11-25T01:53:38ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242018-05-01121811182910.5194/tc-12-1811-2018Automated detection of ice cliffs within supraglacial debris coverS. Herreid0F. Pellicciotti1Department of Geography, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, UKDepartment of Geography, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne, UKIce cliffs within a supraglacial debris cover have been identified as a source for high ablation relative to the surrounding debris-covered area. Due to their small relative size and steep orientation, ice cliffs are difficult to detect using nadir-looking space borne sensors. The method presented here uses surface slopes calculated from digital elevation model (DEM) data to map ice cliff geometry and produce an ice cliff probability map. Surface slope thresholds, which can be sensitive to geographic location and/or data quality, are selected automatically. The method also attempts to include area at the (often narrowing) ends of ice cliffs which could otherwise be neglected due to signal saturation in surface slope data. The method was calibrated in the eastern Alaska Range, Alaska, USA, against a control ice cliff dataset derived from high-resolution visible and thermal data. Using the same input parameter set that performed best in Alaska, the method was tested against ice cliffs manually mapped in the Khumbu Himal, Nepal. Our results suggest the method can accommodate different glaciological settings and different DEM data sources without a data intensive (high-resolution, multi-data source) recalibration.https://www.the-cryosphere.net/12/1811/2018/tc-12-1811-2018.pdf
collection DOAJ
language English
format Article
sources DOAJ
author S. Herreid
F. Pellicciotti
spellingShingle S. Herreid
F. Pellicciotti
Automated detection of ice cliffs within supraglacial debris cover
The Cryosphere
author_facet S. Herreid
F. Pellicciotti
author_sort S. Herreid
title Automated detection of ice cliffs within supraglacial debris cover
title_short Automated detection of ice cliffs within supraglacial debris cover
title_full Automated detection of ice cliffs within supraglacial debris cover
title_fullStr Automated detection of ice cliffs within supraglacial debris cover
title_full_unstemmed Automated detection of ice cliffs within supraglacial debris cover
title_sort automated detection of ice cliffs within supraglacial debris cover
publisher Copernicus Publications
series The Cryosphere
issn 1994-0416
1994-0424
publishDate 2018-05-01
description Ice cliffs within a supraglacial debris cover have been identified as a source for high ablation relative to the surrounding debris-covered area. Due to their small relative size and steep orientation, ice cliffs are difficult to detect using nadir-looking space borne sensors. The method presented here uses surface slopes calculated from digital elevation model (DEM) data to map ice cliff geometry and produce an ice cliff probability map. Surface slope thresholds, which can be sensitive to geographic location and/or data quality, are selected automatically. The method also attempts to include area at the (often narrowing) ends of ice cliffs which could otherwise be neglected due to signal saturation in surface slope data. The method was calibrated in the eastern Alaska Range, Alaska, USA, against a control ice cliff dataset derived from high-resolution visible and thermal data. Using the same input parameter set that performed best in Alaska, the method was tested against ice cliffs manually mapped in the Khumbu Himal, Nepal. Our results suggest the method can accommodate different glaciological settings and different DEM data sources without a data intensive (high-resolution, multi-data source) recalibration.
url https://www.the-cryosphere.net/12/1811/2018/tc-12-1811-2018.pdf
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