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Assessment of techniques for analyzing snow crystals in two dimensions

  • Stuart John Bartlett (a1) (a2), Jean-Daniel Rüedi (a1), Alasdair Craig (a1) (a2) and Charles Fierz (a1)

Abstract

Three-dimensional (3-D) snow analysis techniques provide comprehensive and accurate snow microstructure data. Nevertheless, there remains a requirement for less elaborate methods for snow characterization, as numerical snow models such as SNOWPACK are presently based on two-dimensional (2-D) grain analysis. We present a detailed assessment of various methods and shape descriptors used for snow characterization from digitized images. Dendricity, the ratio of the square of grain perimeter to its area, allows distinction between new and old snow while sphericity distinguishes between faceted and rounded grains. The concept of sphericity is based on curvature, yet another powerful shape descriptor. However, curvatures obtained from images of disaggregated snow grains depend on both resolution and methods chosen. We compared the standard parabola method with a cubic smoothing spline approach for curvature measurement. Applying both methods to parametrically generated shapes, descriptor values were compared with their analytical counterparts. The spline method was found to be able to measure a wider range of curvatures accurately, but both methods suffered from a filtering effect. Although some descriptor errors were as high as 50%, a method for effectively outlining snow grains was found. As well as assessing the classification potential of 2-D analysis on full samples, new descriptors were also investigated.

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References

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Bartelt, P. and Lehning, M.. 2002. A physical SNOWPACK model for the Swiss avalanche warning. Part I. Numerical model. Cold Reg. Sci. Technol., 35(3), 123–145.
Baunach, T. 1999. Snow metamorphism under temperature gradients in the snow cover. (Universität GH Essen.)
Cesar, R.M. Jr, and da Fontoura Costa, L.. 1997. Application and assessment of multiscale bending energy for morphometric characterization of neural cells. Rev. Sci. Instr., 68(5), 2177–2186.
Colbeck, S.C. and 7 others. 1990. The international classification for seasonal snow on the ground. Wallingford, Oxon., International Association of Scientific Hydrology. International Commission on Snow and Ice.
da Fontoura Costa, L. and Cesar, R.M. Jr., 2001. Shape analysis and classification: theory and practice. Boca Raton, FL, CRC Press.
de Boor, C. 1978. A practical guide to splines. New York, Springer-Verlag.
Dierckx, P. 1993. Curve and surface fitting with splines. Oxford, etc., Oxford University Press.
Fierz, C. and Baunach, T.. 2000. Quantifying grain-shape changes in snow subjected to large temperature gradients. Ann. Glaciol., 31, 439–444.
Guyomarc’h, G. and Mérindol, L.. 1995. Protéon: vers une prévision locale du transport de neige par le vent. In Sivardière, F., ed. Les apports de la recherche scientifique `a la sécurité neige, glace et avalanche. Actes de Colloque, Chamonix 30 mai–3 juin 1995. Grenoble, Association Nationale pour l’Etude de la Neige et des Avalanches, 97–102.
Jähne, B. 2002. Digital image processing. Fifth edition. Berlin, Springer.
Lehning, M., Bartelt, P., Brown, B. and Fierz, C.. 2002a. A physical SNOWPACK model for the Swiss avalanche warning service. Part III. Meteorological forcing, thin layer formation and evaluation. Cold Reg. Sci. Technol., 35(3), 169–184.
Lehning, M., Bartelt, P., Brown, B., Fierz, C and Satyawali, P.. 2002b. A physical SNOWPACK model for the Swiss avalanche warning. Part II. Snow microstructure. Cold Reg. Sci. Technol., 35(3), 147–167.
Lesaffre, B., Pougatch, E. and Martin, E.. 1998. Objective determination of snow-grain characteristics from images. Ann. Glaciol., 26, 112–118.
Pratt, W.K. 1991. Digital image processing. Second edition. New York, John Wiley and Sons.
Santaló, L.A. 2004. Integral geometry and geometric probability. Second edition. Cambridge, etc., Cambridge University Press.
Seul, M., O’Gorman, L. and Sammon, M.J.. 2000. Practical algorithms for image analysis: descriptions, examples, and code. Cambridge, etc., Cambridge University Press.
Sladoje, N., Nyström, I. and Saha, P.K.. 2003. Perimeter and area estimations of digitized objects with fuzzy borders. In Nystrom, I., Sanniti di Baja, G. and Svensson, S., eds. Discrete Geometry for Computer Imagery 11th International Conference, 19–21 November 2003, Naples, Italy. Proceedings. Berlin, etc., Springer, 368–377.
Worring, M. and Smeulders, A.W.M.. 1993. Digital curvature estimation. CVGIP: Image Understanding, 58(3), 366–382.

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