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Using remotely sensed data to explore spatial and temporal relationships between photosynthetic productivity of vegetation and malaria transmission intensities in selected parts of Africa

Okumu, Fredros Oketch LU (2011) In LUMA-GIS Thesis GISM01 20091
Dept of Physical Geography and Ecosystem Science
Abstract
Spatial and temporal variations in malaria transmission are naturally associated with prevailing climatic and environmental factors, for example rainfall, humidity, temperature and human activities. These factors influence malaria transmission mainly in non-deterministic ways, making them less appropriate for accurate geographical mapping of malaria risk. One distinctive phenomenon, ‘photosynthetic productivity of vegetation’, is similarly affected by these factors, yet it can be easily estimated from remotely sensed data using standardized indices. In this study, multiple linear regression techniques are used to explore spatial and temporal associations between photosynthetic productivity of vegetation (measured as Normalized Difference... (More)
Spatial and temporal variations in malaria transmission are naturally associated with prevailing climatic and environmental factors, for example rainfall, humidity, temperature and human activities. These factors influence malaria transmission mainly in non-deterministic ways, making them less appropriate for accurate geographical mapping of malaria risk. One distinctive phenomenon, ‘photosynthetic productivity of vegetation’, is similarly affected by these factors, yet it can be easily estimated from remotely sensed data using standardized indices. In this study, multiple linear regression techniques are used to explore spatial and temporal associations between photosynthetic productivity of vegetation (measured as Normalized Difference Vegetation Index (NDVI)) and malaria transmission intensities (measured as Entomological Inoculation Rate (EIR)). The study shows significant relationships between NDVI and EIR both at continental level and at a number of the selected study sites. Moreover, in three of four sites where temporal analysis was conducted, a similarity of linear trends is observed between EIRs and means of current and previous month NDVIs. Both NDVI and EIR are significantly associated with altitude as well as to a rural/urban dummy variable. It is concluded that spatial and temporal variations in photosynthetic productivity of vegetation are strongly related to variations in malaria transmission at respective places and periods. Results of this basic exploration imply that vegetation production is a potential indicator of situations favourable for malaria transmission, and can therefore be used to improve mapping of geographical extents of risk of malaria, and perhaps several other vector borne diseases. (Less)
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author
Okumu, Fredros Oketch LU
supervisor
organization
course
GISM01 20091
year
type
H2 - Master's Degree (Two Years)
subject
keywords
vegetation production, vegetation index, geographical information systems, remote sensing, malaria transmission, NDVI, EIR
publication/series
LUMA-GIS Thesis
report number
10
language
English
id
3559180
date added to LUP
2013-02-28 14:15:04
date last changed
2013-02-28 14:15:04
@misc{3559180,
  abstract     = {Spatial and temporal variations in malaria transmission are naturally associated with prevailing climatic and environmental factors, for example rainfall, humidity, temperature and human activities. These factors influence malaria transmission mainly in non-deterministic ways, making them less appropriate for accurate geographical mapping of malaria risk. One distinctive phenomenon, ‘photosynthetic productivity of vegetation’, is similarly affected by these factors, yet it can be easily estimated from remotely sensed data using standardized indices. In this study, multiple linear regression techniques are used to explore spatial and temporal associations between photosynthetic productivity of vegetation (measured as Normalized Difference Vegetation Index (NDVI)) and malaria transmission intensities (measured as Entomological Inoculation Rate (EIR)). The study shows significant relationships between NDVI and EIR both at continental level and at a number of the selected study sites. Moreover, in three of four sites where temporal analysis was conducted, a similarity of linear trends is observed between EIRs and means of current and previous month NDVIs. Both NDVI and EIR are significantly associated with altitude as well as to a rural/urban dummy variable. It is concluded that spatial and temporal variations in photosynthetic productivity of vegetation are strongly related to variations in malaria transmission at respective places and periods. Results of this basic exploration imply that vegetation production is a potential indicator of situations favourable for malaria transmission, and can therefore be used to improve mapping of geographical extents of risk of malaria, and perhaps several other vector borne diseases.},
  author       = {Okumu, Fredros Oketch},
  keyword      = {vegetation production,vegetation index,geographical information systems,remote sensing,malaria transmission,NDVI,EIR},
  language     = {eng},
  note         = {Student Paper},
  series       = {LUMA-GIS Thesis},
  title        = {Using remotely sensed data to explore spatial and temporal relationships between photosynthetic productivity of vegetation and malaria transmission intensities in selected parts of Africa},
  year         = {2011},
}