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June 2016 Clustering Chlorophyll-a satellite data using quantiles
Carlo Gaetan, Paolo Girardi, Roberto Pastres, Antoine Mangin
Ann. Appl. Stat. 10(2): 964-988 (June 2016). DOI: 10.1214/16-AOAS923

Abstract

The use of water quality indicators is of crucial importance to identify risks to the environment, society and human health. In particular, the Chlorophyll type A (Chl-a) is a shared indicator of trophic status and for monitoring activities it may be useful to discover local dangerous behaviours (for example, the anoxic events). In this paper we consider a comprehensive data set, covering the whole Adriatic Sea, derived from Ocean Colour satellite data, during the period 2002–2012, with the aim of identifying homogeneous areas. Such zonation is becoming extremely relevant for the implementation of European policies, such the Marine Strategy Framework Directive. As an alternative to clustering based on an “average” value over the whole period, we propose a new clustering procedure for the time series. The procedure shares some similarities with the functional data clustering and combines nonparametric quantile regression with an agglomerative clustering algorithm. This approach permits to take into account some features of the time series as nonstationarity in the marginal distribution and the presence of missing data. A small simulation study is also presented for illustrating the relative merits of the procedure.

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Carlo Gaetan. Paolo Girardi. Roberto Pastres. Antoine Mangin. "Clustering Chlorophyll-a satellite data using quantiles." Ann. Appl. Stat. 10 (2) 964 - 988, June 2016. https://doi.org/10.1214/16-AOAS923

Information

Received: 1 February 2015; Revised: 1 March 2016; Published: June 2016
First available in Project Euclid: 22 July 2016

zbMATH: 06625677
MathSciNet: MR3528368
Digital Object Identifier: 10.1214/16-AOAS923

Rights: Copyright © 2016 Institute of Mathematical Statistics

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Vol.10 • No. 2 • June 2016
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