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
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/610877
- author
- Eklundh, Lars
LU
and Jönsson, Per
- organization
- publishing date
- 2003
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Temporal filtering, Seasonality, Gaussian functions
- host publication
- Proceedings of SPIE - The International Society for Optical Engineering
- volume
- 4885
- pages
- 215 - 225
- publisher
- SPIE
- conference name
- Image and Signal Processing for Remote Sensing VII
- conference location
- Agia Pelagia, Greece
- conference dates
- 2002-09-24 - 2002-09-27
- external identifiers
-
- wos:000182227400023
- other:CODEN: PSISDG
- scopus:0037951423
- ISSN
- 1996-756X
- 0277-786X
- DOI
- 10.1117/12.463117
- language
- English
- LU publication?
- yes
- id
- 5fc37e38-daaa-457d-b95b-465f8f359e42 (old id 610877)
- date added to LUP
- 2016-04-01 11:48:22
- date last changed
- 2024-01-07 21:12:56
@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 = {{1996-756X}},
keywords = {{Temporal filtering; Seasonality; Gaussian functions}},
language = {{eng}},
pages = {{215--225}},
publisher = {{SPIE}},
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/10.1117/12.463117}},
doi = {{10.1117/12.463117}},
volume = {{4885}},
year = {{2003}},
}