AVHRR NDVI for monitoring and mapping of vegetation and drought in East African environments
Eklundh, Lars LU
(1996)
- 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) - 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/17584
- author
- Eklundh, Lars
LU
- supervisor
- opponent
-
- Dr. Compton Tucker, Jim
- organization
- publishing date
- 1996
- 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 10:15:00
- external identifiers
-
- other:ISRN: LUNBDS/NBNG--96/1126--SE
- scopus:0030482060
- ISBN
- 91-7966-359-1
- language
- English
- LU publication?
- yes
- id
- ec6f3b55-cc88-46a6-93c5-83b07cd35228 (old id 17584)
- date added to LUP
- 2016-04-01 16:13:05
- date last changed
- 2022-01-28 18:06:50
@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}},
keywords = {{Geologi; fysisk geografi; physical geography; Geology; phenology; seasonality; Fourier series; geostatistics; signal-to-noise ratio}},
language = {{eng}},
publisher = {{Lund University Press}},
school = {{Lund University}},
title = {{AVHRR NDVI for monitoring and mapping of vegetation and drought in East African environments}},
year = {{1996}},
}