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High temporal resolution monitoring of snow cover using oblique view ground-based pictures

Published online by Cambridge University Press:  29 November 2011

D. Laffly ,
É. Bernard ,
M. Griselin ,
F. Tolle ,
J-M. Friedt ,
G. Martin  and
C. Marlin
D. Laffly
Affiliation:
University of Toulouse, GEODE, UMR 5602 CNRS, Toulouse, France (dominique.laffly@gmail.com)
É. Bernard
Affiliation:
University of Franche-Comté, THÉMA, UMR 6049 CNRS, Besançon, France
M. Griselin
Affiliation:
University of Franche-Comté, THÉMA, UMR 6049 CNRS, Besançon, France
F. Tolle
Affiliation:
University of Franche-Comté, THÉMA, UMR 6049 CNRS, Besançon, France
J-M. Friedt
Affiliation:
University of Franche-Comté, FEMTO-ST, UMR 6174 CNRS, Besançon, France
G. Martin
Affiliation:
University of Franche-Comté, FEMTO-ST, UMR 6174 CNRS, Besançon, France
C. Marlin
Affiliation:
University of Paris-Sud, IDES, UMR 8148 CNRS, Orsay, France
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Abstract

Due to poor weather conditions including common heavy cloud cover at polar latitudes, daily satellite imaging is not always accessible. Nevertheless, fast events including heavy rainfall inducing floods appear as significant in the ice and snow budget while being ignored by satellite based studies since the slower sampling rate is unable to observe such short phenomena. We complement satellite imagery with a set of ground based autonomous automated high resolution digital cameras. The recorded oblique views, acquired at a rate of 3 images per day, are processed for comparison with the spaceborne imagery. Delaunay triangulation based mapping using a dense set of reference points provides the means for an accurate projection by applying a rubber sheeting algorithm. The measurement strategy of identifying binary information of ice and snow cover is illustrated through the example of a particular flood event. We observe a snow cover evolution from 100% to 44.5% and back to 100% over a period of 2 weeks.

Type
Research Article
Information
Polar Record , Volume 48 , Special Issue 1: Papers from the 11th Circumpolar Remote Sensing Symposium , January 2012 , pp. 11 - 16
Copyright
Copyright © Cambridge University Press 2011

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