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AVHRR NDVI for monitoring and mapping of vegetation and drought in East African environments

Eklundh, Lars LU (1996)
Abstract (Swedish)
Popular Abstract in Swedish

Data från AVHRR-sensorn ombord på NOAA satelliten används operationellt för att övervaka vegetation och torka i global och regional skala. Ett vegetationsindex (NDVI) används som en indikator på grön växtbiomassa.



Analys av AVHRR NDVI-data över Östafrika pekar på att det finns ett starkt samband mellan den ytmässiga fördelingen av vegetation och NDVI i arida och semi-arida områden. Emellertid så visar analys av tidsserier av NDVI-data att endast ett svagt samband kan etableras med nederbördsdata. Slutsatsen av detta är att AVHRR NDVI-data kan användas för spatiell kartering av vegetation och torka, men är starkt begränsade för övervakning i tiden. Emedan användbarheten av dessa... (More)
Popular Abstract in Swedish

Data från AVHRR-sensorn ombord på NOAA satelliten används operationellt för att övervaka vegetation och torka i global och regional skala. Ett vegetationsindex (NDVI) används som en indikator på grön växtbiomassa.



Analys av AVHRR NDVI-data över Östafrika pekar på att det finns ett starkt samband mellan den ytmässiga fördelingen av vegetation och NDVI i arida och semi-arida områden. Emellertid så visar analys av tidsserier av NDVI-data att endast ett svagt samband kan etableras med nederbördsdata. Slutsatsen av detta är att AVHRR NDVI-data kan användas för spatiell kartering av vegetation och torka, men är starkt begränsade för övervakning i tiden. Emedan användbarheten av dessa data för övervakning är låg, så kan fenologisk vegetationsinformation, användbar för ytmässig kartering och som indata i ekosystem-modeller, extraheras ur datatypen. (Less)
Abstract
In this thesis, an assessment is made of the performance of the Normalized Difference Vegetation Index (NDVI) derived from coarse resolution data from the NOAA AVHRR sensor, with particular reference to mapping and monitoring of drought and vegetation in East African vegetation and climatic conditions. Methods for analysing time series of satellite data are investigated.



By studying the empirical relationship between long term averages of rainfall and NDVI, a strong spatial relationship is established. The influence of additional environmental factors on the relationship is studied, and prediction models are established. The temporal relationships between NDVI and rainfall are investigated for individual rainfall... (More)
In this thesis, an assessment is made of the performance of the Normalized Difference Vegetation Index (NDVI) derived from coarse resolution data from the NOAA AVHRR sensor, with particular reference to mapping and monitoring of drought and vegetation in East African vegetation and climatic conditions. Methods for analysing time series of satellite data are investigated.



By studying the empirical relationship between long term averages of rainfall and NDVI, a strong spatial relationship is established. The influence of additional environmental factors on the relationship is studied, and prediction models are established. The temporal relationships between NDVI and rainfall are investigated for individual rainfall stations, by formulating distributed lag models. These relationships are found to be very weak, on 10-day, monthly and annual basis. Noise, or random variation in the NDVI data, is estimated using a geostatistical method. Noise levels are found to be very high for individual scenes, but low for a long term average NDVI image. The studies suggest that the capability of AVHRR NDVI for vegetation monitoring is very limited, although the data can be used for mapping of spatial vegetation distribution.



Fourier Series are used to extract the seasonality of vegetation, timing and length of the growing seasons, minima, maxima and rates of increase and decrease in the NDVI data. Applying this method, very important parameters for vegetation mapping and for global and regional ecosystem modelling, can be estimated. It is concluded that, although NDVI data from NOAA AVHRR have serious limitations for monitoring, they are an important source of information on the spatial distribution of global and regional vegetation. (Less)
Please use this url to cite or link to this publication:
author
opponent
  • Dr. Compton Tucker, Jim
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Geologi, fysisk geografi, physical geography, Geology, phenology, seasonality, Fourier series, geostatistics, signal-to-noise ratio
pages
187 pages
publisher
Lund University Press
defense location
Sölvegatan 13, 3rd floor, room 308
defense date
1996-03-22 10:15
external identifiers
  • other:ISRN: LUNBDS/NBNG--96/1126--SE
  • scopus:0030482060
ISSN
0346-6787
ISBN
91-7966-359-1
language
English
LU publication?
yes
id
ec6f3b55-cc88-46a6-93c5-83b07cd35228 (old id 17584)
date added to LUP
2007-05-24 09:46:08
date last changed
2017-08-27 05:23:04
@phdthesis{ec6f3b55-cc88-46a6-93c5-83b07cd35228,
  abstract     = {In this thesis, an assessment is made of the performance of the Normalized Difference Vegetation Index (NDVI) derived from coarse resolution data from the NOAA AVHRR sensor, with particular reference to mapping and monitoring of drought and vegetation in East African vegetation and climatic conditions. Methods for analysing time series of satellite data are investigated.<br/><br>
<br/><br>
By studying the empirical relationship between long term averages of rainfall and NDVI, a strong spatial relationship is established. The influence of additional environmental factors on the relationship is studied, and prediction models are established. The temporal relationships between NDVI and rainfall are investigated for individual rainfall stations, by formulating distributed lag models. These relationships are found to be very weak, on 10-day, monthly and annual basis. Noise, or random variation in the NDVI data, is estimated using a geostatistical method. Noise levels are found to be very high for individual scenes, but low for a long term average NDVI image. The studies suggest that the capability of AVHRR NDVI for vegetation monitoring is very limited, although the data can be used for mapping of spatial vegetation distribution.<br/><br>
<br/><br>
Fourier Series are used to extract the seasonality of vegetation, timing and length of the growing seasons, minima, maxima and rates of increase and decrease in the NDVI data. Applying this method, very important parameters for vegetation mapping and for global and regional ecosystem modelling, can be estimated. It is concluded that, although NDVI data from NOAA AVHRR have serious limitations for monitoring, they are an important source of information on the spatial distribution of global and regional vegetation.},
  author       = {Eklundh, Lars},
  isbn         = {91-7966-359-1},
  issn         = {0346-6787},
  keyword      = {Geologi,fysisk geografi,physical geography,Geology,phenology,seasonality,Fourier series,geostatistics,signal-to-noise ratio},
  language     = {eng},
  pages        = {187},
  publisher    = {Lund University Press},
  school       = {Lund University},
  title        = {AVHRR NDVI for monitoring and mapping of vegetation and drought in East African environments},
  year         = {1996},
}