Functional PCA for Remotely Sensed Lake Surface Water Temperature Data

Gong, M., Miller, C. and Scott, E. (2015) Functional PCA for Remotely Sensed Lake Surface Water Temperature Data. In: Spatial Statistics 2015: Emerging Patterns, Avignon, France, 9-12 Jun 2015, pp. 127-130. (doi: 10.1016/j.proenv.2015.05.015)

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Functional principal component analysis is used to investigate a high-dimensional surface water temperature data set of Lake Victoria, which has been produced in the ARC-Lake project. Two different perspectives are adopted in the analysis: modelling temperature curves (univariate functions) and temperature surfaces (bivariate functions). The latter proves to be a better approach in the sense of both dimension reduction and pattern detection. Computational details and some results from an application to Lake Victoria data are presented.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Scott, Professor Marian and Miller, Professor Claire
Authors: Gong, M., Miller, C., and Scott, E.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Copyright Holders:Copyright © 2015 The Authors
First Published:First published in Procedia Environmental Sciences 26:127-130
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
595861Global Observatory of Lake Responses to Environmental Change (GloboLakes).Claire MillerNatural Environment Research Council (NERC)NE/J022810/1M&S - STATISTICS