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Exploring and improving NOAA AVHRR NDVI image quality for African Drylands

Seaquist, Jonathan LU ; Chappell, A. and Eklundh, Lars LU (2002) 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002) In International Geoscience and Remote Sensing Symposium (IGARSS) 4. p.2006-2008
Abstract
The accuracy of NOAA AVHRR NDVI maximum value composites can be poor due to interference from several sources, including cloud cover. The objectives of this paper are; 1. to accurately quantify noise in this imagery over Africa using geostatistics, and 2. to test four compositing techniques that may be able to reduce this noise. The nugget of the variogram model is used to compute standardized noise for five sites across Africa over 4 seasons. After removing trend and anisotropy in the NDVI sub-scenes, standardized noise estimates range from 18.5% in West Zambia to 68.2% in northern Congo. Four automated compositing methods are also tested over the West African Sahel for 13-day periods in order to improve image quality: the MVC, Maximum... (More)
The accuracy of NOAA AVHRR NDVI maximum value composites can be poor due to interference from several sources, including cloud cover. The objectives of this paper are; 1. to accurately quantify noise in this imagery over Africa using geostatistics, and 2. to test four compositing techniques that may be able to reduce this noise. The nugget of the variogram model is used to compute standardized noise for five sites across Africa over 4 seasons. After removing trend and anisotropy in the NDVI sub-scenes, standardized noise estimates range from 18.5% in West Zambia to 68.2% in northern Congo. Four automated compositing methods are also tested over the West African Sahel for 13-day periods in order to improve image quality: the MVC, Maximum Value Temperature (MVT), a two-criteria algorithm that compares the two highest NDVI values for a period thereafter retaining the value with the smallest scan angle (MVCMISC), and a temperature-based algorithm similar to MVCMISC (MVTMISC). Results show that the MVT performs best for minimising cloud contamination, while the MVC is better for removing extreme scan angles. For the dual criteria algorithms, the MVTMISC performs best. The MVCMISC is better able to reduce scan angle bias for all land cover classes during the dry season, with the MVTMISC giving superior performance over the vegetative season. This work has implications for interpreting NDVI data in the context of famine early warning and developing biophysical descriptors of the African land surface at broad scales. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
African drylands, Normalized difference vegetation index, Maximum value temperature
in
International Geoscience and Remote Sensing Symposium (IGARSS)
volume
4
pages
2006 - 2008
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002)
external identifiers
  • wos:000179116800655
  • other:CODEN: IGRSE3
  • scopus:0036399034
DOI
10.1109/IGARSS.2002.1026428
language
English
LU publication?
yes
id
af54e877-af9c-4931-9acc-308e17bd8319 (old id 611095)
date added to LUP
2007-11-28 15:49:02
date last changed
2017-01-01 07:59:28
@inproceedings{af54e877-af9c-4931-9acc-308e17bd8319,
  abstract     = {The accuracy of NOAA AVHRR NDVI maximum value composites can be poor due to interference from several sources, including cloud cover. The objectives of this paper are; 1. to accurately quantify noise in this imagery over Africa using geostatistics, and 2. to test four compositing techniques that may be able to reduce this noise. The nugget of the variogram model is used to compute standardized noise for five sites across Africa over 4 seasons. After removing trend and anisotropy in the NDVI sub-scenes, standardized noise estimates range from 18.5% in West Zambia to 68.2% in northern Congo. Four automated compositing methods are also tested over the West African Sahel for 13-day periods in order to improve image quality: the MVC, Maximum Value Temperature (MVT), a two-criteria algorithm that compares the two highest NDVI values for a period thereafter retaining the value with the smallest scan angle (MVCMISC), and a temperature-based algorithm similar to MVCMISC (MVTMISC). Results show that the MVT performs best for minimising cloud contamination, while the MVC is better for removing extreme scan angles. For the dual criteria algorithms, the MVTMISC performs best. The MVCMISC is better able to reduce scan angle bias for all land cover classes during the dry season, with the MVTMISC giving superior performance over the vegetative season. This work has implications for interpreting NDVI data in the context of famine early warning and developing biophysical descriptors of the African land surface at broad scales.},
  author       = {Seaquist, Jonathan and Chappell, A. and Eklundh, Lars},
  booktitle    = {International Geoscience and Remote Sensing Symposium (IGARSS)},
  keyword      = {African drylands,Normalized difference vegetation index,Maximum value temperature},
  language     = {eng},
  pages        = {2006--2008},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Exploring and improving NOAA AVHRR NDVI image quality for African Drylands},
  url          = {http://dx.doi.org/10.1109/IGARSS.2002.1026428},
  volume       = {4},
  year         = {2002},
}