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Estimating Fractional Snow Cover in Mountain Environments with Fuzzy Classification
Abstract
The disproportionate amount of water runoff from mountains to surrounding arid and semiarid lands has generated much research in snow water equivalent (SWE) modeling. A primary input in SWE models is snow covered area (SCA) which is generally obtained via satellite imagery. Mixed pixels in alpine snow studies complicate SCA measurements and can reduce accuracy. A simple method was developed to estimate fractional snow cover using freely available Landsat and data derived from DEMs, commercial and free software, as well as fuzzy classification and recursive partitioning. The authors attempted to develop a cost effective technique for estimating fractional snow cover for resource and recreation managers confined by limited budgets and resources. Results indicated that the method was non-sensitive (P = 0.426) to differences in leaf area index and solar radiation between 4 March 2000 and 13 March 2003. Fractional snow cover was predicted consistently despite variation in model parameters between years, indicating that the developed method may be a viable way for monitoring fractional snow cover in mountainous areas where capital and resources are limited.
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