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|Title:||Delineating sea surface water quality regions from remotely sensed data using textural information||Authors:||Kitsiou, Dimitra
Vasios, George K.
|Keywords:||geostatistics;K-medoids clustering;marine eutrophication;multivariable variogram;SeaWiFS||Category:||Civil Engineering||Field:||Engineering and Technology||Issue Date:||16-Mar-2015||Source:||3rd International Conference on Remote Sensing and Geoinformation of the Environment, RSCy 2015, Paphos, Cyprus, 16 March 2015 through 19 March 2015||Conference:||International Conference on Remote Sensing and Geoinformation of the Environment||Abstract:||© 2015 Copyright SPIE. The delineation of ocean regions with similar water quality characteristics is an all important component of the study of marine environment with direct implications for management actions. Marine eutrophication constitutes an important facet of ocean water quality, and pertains to the natural process representing excessive algal growth due to nutrient supply of marine systems. Remote sensing technology provides the de-facto means for marine eutrophication assessment over large regions of the ocean, with increasingly high spatial and temporal resolutions. In this work, monthly measurements of sea water quality variables - chlorophyll, nitrates, phosphates, dissolved oxygen - obtained from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) with spatial resolution 0.125 degrees for the East Mediterranean region over the period January 1999 to December 2010, are used to define regions or zones of similar eutrophication levels. A novel variant of the K-medoids clustering algorithm is proposed, whereby the spatial association of the different variables (multivariate textural information) is explicitly accounted for in terms of the multivariate variogram; i.e., a measure of joint dissimilarity between different variables as a function of geographical distance. Similar water quality regions are obtained for various months and years, focusing on the spring season and on the qualitative comparison of the traditional and proposed classification methods. The results indicate that the proposed clustering method yields more physically meaningful clusters due to the incorporation of the multivariate textural information.||Description:||Proceedings of SPIE - The International Society for Optical Engineering Volume 9535, 2015, Article number 95351W||URI:||https://ktisis.cut.ac.cy/handle/10488/14393||ISBN:||978-162841700-5||DOI:||10.1117/12.2192565||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια /Conference papers - poster -presentation|
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