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| Titel |
Snow depth mapping in high-alpine catchments using digital photogrammetry |
| VerfasserIn |
Y. Bühler, M. Marty, L. Egli, J. Veitinger, T. Jonas, P. Thee, C. Ginzler |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1994-0416
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| Digitales Dokument |
URL |
| Erschienen |
In: The Cryosphere ; 9, no. 1 ; Nr. 9, no. 1 (2015-02-06), S.229-243 |
| Datensatznummer |
250116747
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| Publikation (Nr.) |
 copernicus.org/tc-9-229-2015.pdf |
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| Zusammenfassung |
| Information on snow depth and its spatial distribution is crucial for
numerous applications in snow and avalanche research as well as in hydrology
and ecology. Today, snow depth distributions are usually estimated using
point measurements performed by automated weather stations and observers in
the field combined with interpolation algorithms. However, these
methodologies are not able to capture the high spatial variability of the
snow depth distribution present in alpine terrain. Continuous and accurate
snow depth mapping has been successfully performed using laser scanning but
this method can only cover limited areas and is expensive. We use the
airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m
spatial resolution to derive digital surface models (DSMs) of winter and
summer terrains in the neighborhood of Davos, Switzerland. The DSMs are
generated using photogrammetric image correlation techniques based on the
multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the
photogrammetric products, we compare these
products with the following independent data sets acquired simultaneously: (a)
manually measured snow depth plots; (b) differential Global Navigation
Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d)
ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be
used to map snow depth at 2 m resolution with a vertical depth
accuracy of ±30 cm (root mean square error) in the complex topography
of the Alps. The snow depth maps presented have an average accuracy that is
better than 15 % compared to the average snow depth of 2.2 m over the
entire test site. |
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