Snow depth mapping in high-alpine catchments using digital photogrammetry
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...
| Published in: | The Cryosphere |
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| Main Authors: | , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2015
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-9-229-2015 https://noa.gwlb.de/receive/cop_mods_00017650 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00017605/tc-9-229-2015.pdf https://tc.copernicus.org/articles/9/229/2015/tc-9-229-2015.pdf |
| Summary: | 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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