Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30513
Appears in Collections:Biological and Environmental Sciences Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Wilkie, Craig J
Scott, E Marian
Miller, Claire
Tyler, Andrew N
Hunter, Peter D
Spyrakos, Evangelos
Title: Data Fusion of Remote-sensing and In-lake chlorophyll a Data Using Statistical Downscaling
Citation: Wilkie CJ, Scott EM, Miller C, Tyler AN, Hunter PD & Spyrakos E (2015) Data Fusion of Remote-sensing and In-lake chlorophyll a Data Using Statistical Downscaling. Stein A (Editor) & Allard D (Editor) Spatial Statistics conference 2015, Avignon, France, 09.06.2015-12.06.2015. Procedia Environmental Sciences, 26, pp. 123-126. https://doi.org/10.1016/j.proenv.2015.05.014
Issue Date: 2015
Conference Name: Spatial Statistics conference 2015
Conference Dates: 2015-06-09 - 2015-06-12
Conference Location: Avignon, France
Abstract: Chlorophyll a is a green pigment, used as an indirect measure of lake water quality. Its strong absorption of blue and red light allows for quantification through satellite images, providing better spatial coverage than traditional in-lake samples. However, grid-cell scale imagery must be calibrated spatially using in-lake point samples, presenting a change-of-support problem. This paper presents a method of statistical downscaling, namely a Bayesian spatially-varying coefficient regression, which assimilates remotely-sensed and in-lake data, resulting in a fully calibrated spatial map of chlorophyll a with associated uncertainty measures. The model is applied to a case study dataset from Lake Balaton, Hungary.
Status: VoR - Version of Record
Rights: © 2015 The Authors. Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

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