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Climate variability and satellite : observed vegetation responses in Tanzania

Timiza, Wilbert (2011) In Lunds universitets Naturgeografiska institution - Seminarieuppsatser
Dept of Physical Geography and Ecosystem Science
Abstract
Climate and vegetation growth of an area are interrelated processes; both take place on small to global scales. However, climate change and variability impacts on vegetation in most places in the world including Tanzania is of great concern since vegetation supports many socio-economic sectors, and plays a crucial role in atmospheric greenhouse gas moderation. This study aims at investigating the changes in satellite observed vegetation greenness through the use of Normalized Difference Vegetation Index (NDVI) and its relationship with the climatic factors, mainly rainfall and sea surface temperatures (SST) in Tanzania. Advanced Very High Resolution Radiometer (AVHRR)-NDVI data from 1982 to 2008 (27 years) were used in this study. These... (More)
Climate and vegetation growth of an area are interrelated processes; both take place on small to global scales. However, climate change and variability impacts on vegetation in most places in the world including Tanzania is of great concern since vegetation supports many socio-economic sectors, and plays a crucial role in atmospheric greenhouse gas moderation. This study aims at investigating the changes in satellite observed vegetation greenness through the use of Normalized Difference Vegetation Index (NDVI) and its relationship with the climatic factors, mainly rainfall and sea surface temperatures (SST) in Tanzania. Advanced Very High Resolution Radiometer (AVHRR)-NDVI data from 1982 to 2008 (27 years) were used in this study. These data have been proved to be useful in studying vegetation-climate relationships in various places including East Africa and Tanzania. Correlation and simple linear regression analyses were employed to reveal the nature and magnitude of the relationship as well as trends in both rainfall and NDVI data. This study found on average decreasing trends for both NDVI and rainfall for most part of the country during the study period. Despite these trends being statistically significant, they are weak ones. The coefficients of explanation obtained from the relationship between NDVI, rainfall and SST were improved (between 50% and 80%) with the use of noise filtering technique and time lagging of the datasets. This improvement is in comparison with the weak and less than 50% before filtering and lagging of the data. These results suggest that variability in rainfall or SST, especially the Niño 3.4 SST can explain only half of the variability in vegetation amidst other environmental and human factors. Rainfall variability was found to explain more of the vegetation variability in the unimodal than in the bimodal areas while Niño 3.4 SST explains more of the vegetation variability in the bimodal than in the unimodal areas. This study has improved the current understanding of the vegetation and rainfall trends together with their relationships. The study also forms a basis for future studies in climate-vegetation relationship studies. (Less)
Please use this url to cite or link to this publication:
author
Timiza, Wilbert
supervisor
organization
year
type
H1 - Master's Degree (One Year)
subject
keywords
NOAA-AVHRR-NDVI, rainfall, sea surface temperature, Tanzania, trend, correlation, climate variability
publication/series
Lunds universitets Naturgeografiska institution - Seminarieuppsatser
report number
205
funder
European Commission, Erasmus/Socrates Program
language
English
id
2153998
date added to LUP
2011-12-21 12:46:26
date last changed
2011-12-21 12:46:26
@misc{2153998,
  abstract     = {{Climate and vegetation growth of an area are interrelated processes; both take place on small to global scales. However, climate change and variability impacts on vegetation in most places in the world including Tanzania is of great concern since vegetation supports many socio-economic sectors, and plays a crucial role in atmospheric greenhouse gas moderation. This study aims at investigating the changes in satellite observed vegetation greenness through the use of Normalized Difference Vegetation Index (NDVI) and its relationship with the climatic factors, mainly rainfall and sea surface temperatures (SST) in Tanzania. Advanced Very High Resolution Radiometer (AVHRR)-NDVI data from 1982 to 2008 (27 years) were used in this study. These data have been proved to be useful in studying vegetation-climate relationships in various places including East Africa and Tanzania. Correlation and simple linear regression analyses were employed to reveal the nature and magnitude of the relationship as well as trends in both rainfall and NDVI data. This study found on average decreasing trends for both NDVI and rainfall for most part of the country during the study period. Despite these trends being statistically significant, they are weak ones. The coefficients of explanation obtained from the relationship between NDVI, rainfall and SST were improved (between 50% and 80%) with the use of noise filtering technique and time lagging of the datasets. This improvement is in comparison with the weak and less than 50% before filtering and lagging of the data. These results suggest that variability in rainfall or SST, especially the Niño 3.4 SST can explain only half of the variability in vegetation amidst other environmental and human factors. Rainfall variability was found to explain more of the vegetation variability in the unimodal than in the bimodal areas while Niño 3.4 SST explains more of the vegetation variability in the bimodal than in the unimodal areas. This study has improved the current understanding of the vegetation and rainfall trends together with their relationships. The study also forms a basis for future studies in climate-vegetation relationship studies.}},
  author       = {{Timiza, Wilbert}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Lunds universitets Naturgeografiska institution - Seminarieuppsatser}},
  title        = {{Climate variability and satellite : observed vegetation responses in Tanzania}},
  year         = {{2011}},
}