In Alpine regions, snow is a predominant environmental factor. High accurate snow monitoring in the Alpine
Region is of great importance as temporal and spatial variations in snow coverage. It is required for various purposes
such as meteorological modelling, climate studies, snow mapping estimation of stored water equivalent or snowmelt
runoff prediction. In contrast to conventional in situ snow observations, remote sensing data regularly provide spatial
snow cover information which can be used for climate induced studies on snow cover variability. The main objectives of
this study are to assess the accuracy of chronological sequences derived from fractional snow cover maps as well as to
detect and analyze temporal and spatial variability patterns within the Alpine Region based on different statistical
applications. Time series of more than 20 years (1985 - 2007) are used to derive spatial and temporal snow cover
dynamics.
Water resources in Northern Italy has dramatically shortened in the past 10 to 20 years, and recent phenomena connected to
the climate change have further sharpened the trend. To match the observable and collected information with this
experience and find methodologies to improve the water management cycle in the Lombardy Region, University of Milan
Bicocca, Fondazione Lombardia per l'Ambiente and ARPA Lombardia are currently funding a project, named "Regional
Impact of Climatic Change in Lombardy Water Resources: Modelling and Applications" (RICLIC-WARM). In the
framework of this project, the analysis of the fraction of water available and provided to the whole regional network by the
snow cover of the Alps will be investigated by means of remotely sensed data. While there are already a number of
algorithms devoted to this task for data coming from various and different sensors in the visible and infrared regions, no
operative comparison and analytical analysis of the advantages and drawbacks of using different data has been attempted.
This idea will pave the way for a fusion of the available information as well as a multi-source mapping procedure which
will be able to exploit successfully the huge quantity of data available for the past and the even larger amount that may be
accessed in the future. To this aim, a comparison on selected dates for the whole 2000/2006 period was performed.
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