Riche, Fabienne; Schneebeli, Martin; Tschanz, Stefan A. (2012). Design-based stereology to quantify structural properties of artificial and natural snow using thin sections. Cold regions science and technology, 2012(79-80), pp. 67-74. Amsterdam: Elsevier 10.1016/j.coldregions.2012.03.008
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The quantification of the structural properties of snow is traditionally based on model-based stereology. Model-based stereology requires assumptions about the shape of the investigated structure. Here, we show how the density, specific surface area, and grain boundary area can be measured using a design-based method, where no assumptions about structural properties are necessary. The stereological results were also compared to X-ray tomography to control the accuracy of the method. The specific surface area calculated with the stereological method was 19.8 ± 12.3% smaller than with X-ray tomography. For the density, the stereological method gave results that were 11.7 ± 12.1% larger than X-ray tomography. The statistical analysis of the estimates confirmed that the stereological method and the sampling used are accurate. This stereological method was successfully tested on artificially produced ice beads but also on several snow types. Combining stereology and polarisation microscopy provides a good estimate of grain boundary areas in ice beads and in natural snow, with some limitatio
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy 09 Interdisciplinary Units > Microscopy Imaging Center (MIC) |
UniBE Contributor: |
Tschanz, Stefan A. |
Subjects: |
500 Science > 550 Earth sciences & geology |
ISSN: |
0165-232X |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Factscience Import |
Date Deposited: |
04 Oct 2013 14:39 |
Last Modified: |
05 Dec 2022 14:12 |
Publisher DOI: |
10.1016/j.coldregions.2012.03.008 |
Web of Science ID: |
000305201300007 |
BORIS DOI: |
10.7892/boris.15849 |
URI: |
https://boris.unibe.ch/id/eprint/15849 (FactScience: 223333) |