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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

Eklundh, Lars LU and Jönsson, Per (2003) Image and Signal Processing for Remote Sensing VII In Proceedings of SPIE - The International Society for Optical Engineering 4885. p.215-225
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Temporal filtering, Seasonality, Gaussian functions
in
Proceedings of SPIE - The International Society for Optical Engineering
volume
4885
pages
215 - 225
publisher
The International Society for Optical Engineering
conference name
Image and Signal Processing for Remote Sensing VII
external identifiers
  • wos:000182227400023
  • other:CODEN: PSISDG
  • scopus:0037951423
ISSN
0277-786X
1996-756X
DOI
language
English
LU publication?
yes
id
5fc37e38-daaa-457d-b95b-465f8f359e42 (old id 610877)
date added to LUP
2007-11-27 11:55:56
date last changed
2018-05-29 11:56:53
@inproceedings{5fc37e38-daaa-457d-b95b-465f8f359e42,
  abstract     = {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.},
  author       = {Eklundh, Lars and Jönsson, Per},
  booktitle    = {Proceedings of SPIE - The International Society for Optical Engineering},
  issn         = {0277-786X},
  keyword      = {Temporal filtering,Seasonality,Gaussian functions},
  language     = {eng},
  pages        = {215--225},
  publisher    = {The International Society for Optical Engineering},
  title        = {Extracting information about vegetation seasons in Africa from pathfinder AVHRR NDVI imagery using temporal filtering and least-squares fits to asymmetric Gaussian functions},
  url          = {http://dx.doi.org/},
  volume       = {4885},
  year         = {2003},
}