Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia
Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. A...
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doaj-4688337e08d64dffb2796e5b2b4c02bb2020-11-24T22:27:52ZengCopernicus PublicationsThe Cryosphere1994-04161994-04242015-09-01951747175910.5194/tc-9-1747-2015Improving semi-automated glacier mapping with a multi-method approach: applications in central AsiaT. Smith0B. Bookhagen1F. Cannon2Institute for Earth and Environmental Sciences, Universität Potsdam, Potsdam, GermanyInstitute for Earth and Environmental Sciences, Universität Potsdam, Potsdam, GermanyGeography Department, University of California, Santa Barbara, USAStudies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity. <br><br> In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10 % of glacier areas, as compared to a ~ 750 glacier control data set, and can reliably classify a given Landsat scene in 3–5 min. <br><br> The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data.http://www.the-cryosphere.net/9/1747/2015/tc-9-1747-2015.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
T. Smith B. Bookhagen F. Cannon |
spellingShingle |
T. Smith B. Bookhagen F. Cannon Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia The Cryosphere |
author_facet |
T. Smith B. Bookhagen F. Cannon |
author_sort |
T. Smith |
title |
Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia |
title_short |
Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia |
title_full |
Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia |
title_fullStr |
Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia |
title_full_unstemmed |
Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia |
title_sort |
improving semi-automated glacier mapping with a multi-method approach: applications in central asia |
publisher |
Copernicus Publications |
series |
The Cryosphere |
issn |
1994-0416 1994-0424 |
publishDate |
2015-09-01 |
description |
Studies of glaciers generally require precise glacier outlines. Where these
are not available, extensive manual digitization in a geographic information
system (GIS) must be performed, as current algorithms struggle to delineate
glacier areas with debris cover or other irregular spectral profiles.
Although several approaches have improved upon spectral band ratio
delineation of glacier areas, none have entered wide use due to complexity or
computational intensity.
<br><br>
In this study, we present and apply a glacier mapping algorithm in Central
Asia which delineates both clean glacier ice and debris-covered glacier
tongues. The algorithm is built around the unique velocity and topographic
characteristics of glaciers and further leverages spectral and spatial
relationship data. We found that the algorithm misclassifies between 2 and
10 % of glacier areas, as compared to a ~ 750 glacier control
data set, and can reliably classify a given Landsat scene in 3–5 min.
<br><br>
The algorithm does not completely solve the difficulties inherent in
classifying glacier areas from remotely sensed imagery but does represent a
significant improvement over purely spectral-based classification schemes,
such as the band ratio of Landsat 7 bands three and five or the normalized
difference snow index. The main caveats of the algorithm are (1)
classification errors at an individual glacier level, (2) reliance on manual
intervention to separate connected glacier areas, and (3) dependence on
fidelity of the input Landsat data. |
url |
http://www.the-cryosphere.net/9/1747/2015/tc-9-1747-2015.pdf |
work_keys_str_mv |
AT tsmith improvingsemiautomatedglaciermappingwithamultimethodapproachapplicationsincentralasia AT bbookhagen improvingsemiautomatedglaciermappingwithamultimethodapproachapplicationsincentralasia AT fcannon improvingsemiautomatedglaciermappingwithamultimethodapproachapplicationsincentralasia |
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