The Annals of Applied Statistics

Clustering Chlorophyll-a satellite data using quantiles

Carlo Gaetan, Paolo Girardi, Roberto Pastres, and Antoine Mangin

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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.

Article information

Source
Ann. Appl. Stat. Volume 10, Number 2 (2016), 964-988.

Dates
Received: February 2015
Revised: March 2016
First available in Project Euclid: 22 July 2016

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1469199901

Digital Object Identifier
doi:10.1214/16-AOAS923

Mathematical Reviews number (MathSciNet)
MR3528368

Zentralblatt MATH identifier
06625677

Keywords
Functional data clustering quantile sheet nonparametric regression clustering methods surface water classification satellite data

Citation

Gaetan, Carlo; Girardi, Paolo; Pastres, Roberto; Mangin, Antoine. Clustering Chlorophyll-a satellite data using quantiles. Ann. Appl. Stat. 10 (2016), no. 2, 964--988. doi:10.1214/16-AOAS923. http://projecteuclid.org/euclid.aoas/1469199901.


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