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

机译:利用纹理信息从遥感数据中划定海表水质区域

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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.
机译:划定具有相似水质特征的海洋区域是海洋环境研究的所有重要组成部分,直接影响管理行动。海洋富营养化是海洋水质的一个重要方面,属于自然过程,代表由于海洋系统养分的供应而导致藻类过度生长的过程。遥感技术以越来越高的空间和时间分辨率为事实上的方法提供了在大面积海洋上进行海洋富营养化评估的手段。在这项工作中,在此期间从东地中海地区的空间分辨率为0.125度的海景宽视野传感器(SeaWiFS)获得的海水质量变量(叶绿素,硝酸盐,磷酸盐,溶解氧)的月度测量值从1999年1月到2010年12月,用于定义类似富营养化水平的区域或区域。提出了一种新颖的K-medoids聚类算法变体,其中,根据多元变异函数明确地说明了不同变量(多元纹理信息)的空间关联;即衡量不同变量之间作为地理距离的函数的联合相异性的方法。在不同的月份和年份,都获得了相似的水质区域,重点是春季以及传统和建议分类方法的定性比较。结果表明,由于引入了多元纹理信息,因此提出的聚类方法产生了更具物理意义的聚类。

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