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Glacier snowline determination from Terrestrial Laser Scanning intensity data


Prantl, Hannah; Nicholson, Lindsey; Sailer, Rudolf; Hanzer, Florian; Juen, Irmgard; Rastner, Philipp (2017). Glacier snowline determination from Terrestrial Laser Scanning intensity data. Geosciences, 7(3):60.

Abstract

Accurately identifying the extent of surface snow cover on glaciers is important for extrapolating end of year mass balance measurements, constraining the glacier surface radiative energy balance and evaluating model simulations of snow cover. Here, we use auxiliary information from Riegl VZ-6000 Terrestrial Laser Scanner (TLS) return signals to accurately map the snow cover over a glacier throughout an ablation season. Three classification systems were compared, and we find that supervised classification based on TLS signal intensity alone is outperformed by a
rule-based classification employing intensity, surface roughness and an associated optical image, which achieves classification accuracy of 68–100%. The TLS intensity signal shows no meaningful relationship with surface or bulk snow density. Finally, we have also compared our Snow Line Altitude (SLA) derived from TLS with SLA derived from the model output, as well as one Landsat image. The results of the model output track the SLA from TLS well, however with a positive bias. In contrast, automatic Landsat-derived SLA slightly underestimates the SLA from TLS. To conclude, we demonstrate that the snow cover extent can be mapped successfully using TLS, although the snow
mass remains elusive.

Abstract

Accurately identifying the extent of surface snow cover on glaciers is important for extrapolating end of year mass balance measurements, constraining the glacier surface radiative energy balance and evaluating model simulations of snow cover. Here, we use auxiliary information from Riegl VZ-6000 Terrestrial Laser Scanner (TLS) return signals to accurately map the snow cover over a glacier throughout an ablation season. Three classification systems were compared, and we find that supervised classification based on TLS signal intensity alone is outperformed by a
rule-based classification employing intensity, surface roughness and an associated optical image, which achieves classification accuracy of 68–100%. The TLS intensity signal shows no meaningful relationship with surface or bulk snow density. Finally, we have also compared our Snow Line Altitude (SLA) derived from TLS with SLA derived from the model output, as well as one Landsat image. The results of the model output track the SLA from TLS well, however with a positive bias. In contrast, automatic Landsat-derived SLA slightly underestimates the SLA from TLS. To conclude, we demonstrate that the snow cover extent can be mapped successfully using TLS, although the snow
mass remains elusive.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2017
Deposited On:07 Aug 2017 14:01
Last Modified:17 Aug 2017 09:46
Publisher:MDPI Publishing
ISSN:2076-3263
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3390/geosciences7030060

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Language: English
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Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)