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Extracting information about vegetation seasons in Africa from pathfinder AVHRR NDVI imagery using temporal filtering and least-squares fits to asymmetric Gaussian functions

Author:
  • Lars Eklundh
  • Per Jönsson
Publishing year: 2003
Language: English
Pages: 215-225
Publication/Series: Proceedings of SPIE - The International Society for Optical Engineering
Volume: 4885
Document type: Conference paper
Publisher: The International Society for Optical Engineering

Abstract english

Time-series of NASA/NOAA Pathfinder AVHRR Land (PAL) data have been analysed to extract parameters describing the seasonality of vegetation in Africa. Two methods have been developed to fit smooth curves to the time-series. The first method is based on an adaptive Savitzky-Golay filtering technique, and the second on non-linear least-squares fits of asymmetric Gaussian model functions. Both processing methods involve a preliminary definition of the number and timing of growing seasons using a least-squares fit of sinusoidal functions and a second order polynomial. The fit to the sinusoidal functions is used to determine the type of seasonal pattern (uni-modal or bi-modal) and to obtain starting values for the non-linear Gaussian function fits to the data. The processing incorporates qualitative information on cloudiness from the CLAVR dataset. The resulting smooth curves are used for defining parameters describing the growing seasons. The method has been applied to PAL NDVI data, and resulting imagery have been generated that show parameters such as beginnings and ends of seasons, seasonal integrated NDVI, seasonal amplitudes etc. The results indicate that the two methods complement each other and that they may be suitable in different areas depending on the behaviour of the NDVI signal.

Keywords

  • Physical Geography
  • Temporal filtering
  • Seasonality
  • Gaussian functions

Other

Image and Signal Processing for Remote Sensing VII
2002-09-24 - 2002-09-27
Agia Pelagia, Greece
Published
  • ISSN: 0277-786X
  • ISSN: 1996-756X
  • CODEN: PSISDG
E-mail: lars [dot] eklundh [at] nateko [dot] lu [dot] se

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Dept of Physical Geography and Ecosystem Science

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Dept of Physical Geography and Ecosystem Science

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Department of Physical Geography and Ecosystem Science
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