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International Journal of Remote Sensing Volume 30, 2009 - Issue 10
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Remote Sensing Letters

Seasonal trend analysis of image time series

J. Ronald Eastman Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA; Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
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Florencia Sangermano Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA; Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USACorrespondence[email protected]
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Bardan Ghimire Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA; Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
,
Honglei Zhu Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
,
Hao Chen Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
,
Neeti Neeti Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA; Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
,
Yongming Cai Department of Geography, University of Cincinnati, 2600 Clifton Ave., Cincinnati, OH 45221, USA
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Elia A. Machado Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA; Graduate School of Geography, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
&
Stefano C. Crema Clark Labs, Clark University, 950 Main Street, Worcester, MA 01610‐1477, USA
 show all
Pages 2721-2726 | Received 27 Aug 2008, Accepted 08 Dec 2008, Published online: 11 Jun 2009
 
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Abstract

A procedure is introduced for the analysis of seasonal trends in time series of Earth observation imagery. Called Seasonal Trend Analysis (STA), the procedure is based on an initial stage of harmonic analysis of each year in the series to extract the annual and semi‐annual harmonics. Trends in the parameters of these harmonics over years are then analysed using a robust median‐slope procedure. Finally, images of these trends are used to create colour composites highlighting the amplitudes and phases of seasonality trends. The technique specifically rejects high‐frequency sub‐annual noise and is robust to short‐term interannual variability up to a period of 29% of the length of the series. It is, thus, a very effective procedure for focusing on the general nature of longer‐term trends in seasonality.

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