Delineating sea surface water quality regions from remotely sensed data using textural information

Phaedon C. Kyriakidis; George K. Vasios; Dimitra Kitsiou

doi:10.1117/12.2192565

19 June 2015 Delineating sea surface water quality regions from remotely sensed data using textural information

Phaedon C. Kyriakidis, George K. Vasios, Dimitra Kitsiou

Proceedings Volume 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015); 95351W (2015) https://doi.org/10.1117/12.2192565
Event: Third International Conference on Remote Sensing and Geoinformation of the Environment, 2015, Paphos, Cyprus

Abstract

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.

Citation Download Citation

Phaedon C. Kyriakidis, George K. Vasios, and Dimitra Kitsiou "Delineating sea surface water quality regions from remotely sensed data using textural information", Proc. SPIE 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), 95351W (19 June 2015); https://doi.org/10.1117/12.2192565

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