An algorithm has been developed for snow recognition (SR) over mountainous areas of Europe from satellite imagery. The algorithm uses Meteosat Second Generations (MSG) instrument Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) data that are acquired in every 15 minutes through whole day. Although SEVIRI has low spatial resolution, its high temporal resolution provides a better discrimination capacity between ice clouds and snow. Discrimination of snow and clouds is the most challenging part of snow recognition algorithm development. The proposed algorithm relies on Satellite Application Facility to support Nowcasting and Very Short Range Forecastings (SAFNWC) cloud products. A final thematic map has been produced which is consisting of 3 different classes: snow, cloud and land. Validation of the SEVIRI SR product was held in three stages.The obtained high performance of the SR product is presented with the analysis results.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12609911/index.pdf |
| Date | 01 September 2008 |
| Creators | Surer, Serdar |
| Contributors | Sorman, Ali Unal |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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