Mathematics – Probability
Scientific paper
May 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004esasp.549e..22t&link_type=abstract
Proceedings of MERIS User Workshop (ESA SP-549). 10-13 November 2003, ESA-ESRIN, Frascati, Italy. Editor: H. Lacoste. Published
Mathematics
Probability
Scientific paper
The ENVISAT mission with a suite of high performance sensors offers some opportunities for mapping snow cover at regional and catchment scale. The geometric resolution of MERIS data and the spectral resolution of AATSR data are suitable for these purposes. A new approach, developed in the framework of the GLASNOWMAP project (ESA-DUP2) for monitoring snow cover in Alpine regions, based on the combined use of MERIS and AATSR observations, and topographic information, is proposed. As MERIS spectral bands are not completely proper for the discrimination of snow from clouds - due to the lack of short wave infrared channels - a multisource classification scheme has been developed to combine the results obtained by the classification of MERIS data with the information on cloud distribution as derived from AATSR data; the integration is performed with the aid of snow elevation distribution as derived from the Digital Elevation Model. A supervised fuzzy statistical classifier (Wang 1990) has been chosen to perform classification of MERIS images, being particularly suited for the representation of land cover class mixture. The classifier bases estimates of the distribution of pixels in multispectral space on the concept of the probability measure of fuzzy events to produce an output of the proportions of individual components. A cloud normalized index has been defined to extract clouds from AATSR images previously registered and resampled on MERIS images. The results of MERIS and AATSR processing are integrated to produce a snow cover map masked over the cloud covered areas, taking into account also the elevation. The Alpine region is selected as test area to demonstrate the potential and limitations of the novel approach. In particular, the attention is focused on three regions of Northern Italy (Valle d'Aosta, Piemonte, Lombardia). The first results obtained by the application of this new method to Earth Observation data will be presented and analysed.
Brivio P. A.
Carrara P.
Fantoni Daniel
Gnocchi S.
Ober Giovanna
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