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Particle Size Sampling and Object-Oriented Image Analysis for Field Investigations of Snow Particle Size, Shape, and Distribution
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
Stockholm University, Faculty of Science, Department of Physical Geography and Quaternary Geology.
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2013 (English)In: Arctic, Antarctic and Alpine research, ISSN 1523-0430, E-ISSN 1938-4246, Vol. 45, no 3, 330-341 p.Article in journal (Refereed) Published
Abstract [en]

Snow particle size is an important parameter strongly affecting snow cover broadband albedo from seasonally snow covered areas and ice sheets. It is also important in remote sensing analyses because it influences the reflectance and scattering properties of the snow. We have developed a digital image processing method for the capture and analysis of data of snow particle size and shape. The method is suitable for quick and reliable data capture in the field. Traditional methods based on visual inspection of samples have been used but do not yield quantitative data. Our method provides an alternative to both simpler and more complex methods by providing a tool that limits the subjective effect of the visual analysis and provides a quantitative particle size distribution. The method involves image analysis software and field efficient instrumentation in order to develop a complete process-chain easily implemented under field conditions. The output from the analysis is a two-dimensional analysis of particle size, shape, and distributions for each sample. The results of the segmentation process were validated against manual delineation of snow particles. The developed method improves snow particle analysis because it is quantitative, reproducible, and applicable for different types of field sites.

Place, publisher, year, edition, pages
2013. Vol. 45, no 3, 330-341 p.
National Category
Environmental Sciences Physical Geography Ecology
Identifiers
URN: urn:nbn:se:su:diva-100676DOI: 10.1657/1938-4246-45.3.330ISI: 000329533200003OAI: oai:DiVA.org:su-100676DiVA: diva2:695461
Funder
Swedish Research Council
Note

AuthorCount:6;

Available from: 2014-02-11 Created: 2014-02-10 Last updated: 2017-12-06Bibliographically approved

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Ingvander, SusanneBrown, Ian A.Jansson, PeterHolmlund, PerRosqvist, Gunhild
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