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ISSN : 2332-2594

Journal of Climatology & Weather Forecasting
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Research Article

Analyzing Periodicity in Remote Sensing Images for Lake Malawi

Alinune Musopole*
University of Malawi, The Polytechnic Blantyre, Malawi
*Corresponding Author : Alinune Musopole
University of Malawi, The Polytechnic Blantyre, Malawi
Tel: +265996265883
E-mail: [email protected]
Received: December 12, 2015; Accepted: January 27, 2016; Published: February 03, 2016
Citation: Musopole A (2016) Analyzing Periodicity in Remote Sensing Images for Lake Malawi. J Climatol Weather Forecasting 4:154. doi:10.4172/2332-2594.1000154
Copyright: © 2016 Musopole A. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Climate change is one of the biggest challenges that we are fighting in the 21st century. One of the indicators of climate change is lake surface water temperature (LSWT)-LSWT is expected to be periodic and a move away from periodicity verifies change in climate. With surface temperature of water on a lake obtained at high frequency both spatially and temporally, the volume of data is high. One of the ways used in reducing dimensionality of data is by approaching the data as functional data- functional principal components (fPCs) reduce dimensionality by giving modes of variation that are dominant in the data. In this paper we apply a method called principal periodic components (PPCs) that is capable of separating variability in the data into that which is nearly-periodic and that which is non-periodic, on LSWT data for Lake Malawi. We also carry out a test to check whether there is any exact annual variation in the data or not. The data are remote sensing images. The analysis has shown that there is no any exact annual variation in LSWT data for Lake Malawi- LSWT for Lake Malawi, though with strong periodicity, is not strictly periodic.

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