Seasonal trend analysis of image time series

  • Authors:

  • J. Ronald Eastman;Florencia Sangermano;Bardan Ghimire;Honglei Zhu;Hao Chen;Neeti Neeti;Yongming Cai;Elia A. Machado;Stefano C. Crema

  • Affiliations:

  • Clark Labs, Clark University, Worcester, MA 01610-1477, USA,Graduate School of Geography, Clark University, Worcester, MA 01610-1477, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA,Graduate School of Geography, Clark University, Worcester, MA 01610-1477, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA,Graduate School of Geography, Clark University, Worcester, MA 01610-1477, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA,Graduate School of Geography, Clark University, Worcester, MA 01610-1477, USA;Department of Geography, University of Cincinnati, Cincinnati, OH 45221, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA,Graduate School of Geography, Clark University, Worcester, MA 01610-1477, USA;Clark Labs, Clark University, Worcester, MA 01610-1477, USA

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 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.