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CLASSIFICATION OF SATELLITE DERIVED CHLOROPHYLL A SPACE-TIME SERIES BY MEANS OF QUANTILE REGRESSION: AN APPLICATION TO THE ADRIATIC SEA

机译:卫星衍生叶绿素的分类通过量子回归衍生空间序列:亚得里亚海的应用

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In this paper, we present the results of a classification of Adriatic waters, based on spatial time series of remotely sensed Chlorophyll type-a. The study was carried out using a clustering procedure combining quantile smoothing and an agglomerative clustering algorithms. The smoothing function includes a seasonal term, thus allowing one to classify areas according to “similar” seasonal evolution, as well as according to “similar” trends. This methodology, which is here applied for the first time to Ocean Colour data, is more robust with respect to other classical methods, as it does not require any assumption on the probability distribution of the data. This approach was applied to the classification of an eleven year long time series, from January 2002 to December 2012, of monthly values of Chlorophyll type-a concentrations covering the whole Adriatic Sea. The data set was made available by ACRI (http://hermes.acri.fr) in the framework of the Glob- Colour Project (http://www.globcolour.info). Data were obtained by calibrating Ocean Colour data provided by different satellite missions, such as MERIS, SeaWiFS and MODIS. The results clearly show the presence of North- South and West-East gradient in the level of Chlorophyll, which is consistent with literature findings. This analysis could provide a sound basis for the identification of “water bodies” and of Chlorophyll type-a thresholds which define their Good Ecological Status, in terms of trophic level, as required by the implementation of the Marine Strategy Framework Directive. The forthcoming availability of Sentinel-3 OLCI data, in continuity of the previous missions, and with perspective of more than a 15-year monitoring system, offers a real opportunity of expansion of our study as a strong support to the implementation of both the EU Marine Strategy Framework Directive and the UNEP-MAP Ecosystem Approach in the Mediterranean.
机译:在本文中,我们提出亚德里亚水域的分类的结果,基于空间时间序列遥感叶绿素A型的。该研究进行了使用聚类程序组合位数平滑和凝聚聚类算法。平滑函数包括季节性术语,因此允许一个进行分类的区域根据“相似的”季节变化,以及根据“相似”的趋势。这种方法,这是用在这里首次以海洋颜色数据,相对于其他传统方法更稳健,因为它不需要对数据的概率分布做任何假设。这种方法适用于一个十一长时间序列的分类,从2002年1月至2012年12月,叶绿素A型浓度覆盖整个亚得里亚海的月度值。该数据集是在Glob-颜色项目(http://www.globcolour.info)的框架内提供由ACRI(http://hermes.acri.fr)。数据通过校准由不同卫星任务,如MERIS,SeaWiFS的和MODIS提供海洋颜色数据而获得的。结果清楚地表明南北和西东梯度的叶绿素的水平,这与文献研究结果是一致的存在。这种分析可以为“水体”的识别和叶绿素类 - 它定义了良好的生态状态,在营养水平方面,所要求的海洋战略框架指令的实施阈值提供了坚​​实的基础。哨兵3 OLCI数据,在以前的任务的连续性,并有超过15年的监视系统的角度出发,为我们的研究扩展为两个欧盟实施了强有力的支持一个真正的机会即将可用性海洋战略框架指令,并在地中海UNEP-MAP生态系统方法。

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