DELINEATING SEA SURFACE WATER QUALITY REGIONS FROM REMOTELY SENSED DATA USING TEXTURAL INFORMATION

被引:2
作者
Kyriakidis, Phaedon C. [1 ,2 ]
Vasios, George K. [1 ]
Kitsiou, Dimitra [3 ]
机构
[1] Univ Aegean, Dept Geog, Mitilini 81100, Lesvos, Greece
[2] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA
[3] Univ Aegean, Dept Marine Sci, Mitilini 81100, Lesvos, Greece
来源
THIRD INTERNATIONAL CONFERENCE ON REMOTE SENSING AND GEOINFORMATION OF THE ENVIRONMENT (RSCY2015) | 2015年 / 9535卷
关键词
marine eutrophication; SeaWiFS; K-medoids clustering; geostatistics; multivariable variogram; COASTAL MARINE EUTROPHICATION; CHLOROPHYLL; CLASSIFICATION; VARIOGRAM;
D O I
10.1117/12.2192565
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
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页数:7
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