Malaria Journal
BioMed Central
Open Access
Methodology
Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya Thomas P Eisele*1, Joseph Keating1, Chris Swalm2, Charles M Mbogo3, Andrew K Githeko4, James L Regens5, John I Githure6, Linda Andrews7 and John C Beier8 Address: 1Department of International Health and Development, Tulane University, School of Public Health and Tropical Medicine, New Orleans, LA, USA, 2Department of Pharmacology, Tulane University, School of Public Health and Tropical Medicine, New Orleans, LA, USA, 3Centre for Geographic Medicine Research, Kenya Medical Research Institute, Kilifi, Kenya, 4Centre for Vector Biology and Control Research, Kenya Medical Research Institute, Kisumu, Kenya, 5Institute for Science and Public Policy, Sarkeys Energy Center, University of Oklahoma, Norman, OK, USA, 6Human Health Division, International Centre of Insect Physiology and Ecology, Nairobi, Kenya, 7USRA, NASA Ames Research Center, Moffett Field, CA, USA and 8Global Public Health Program, University of Miami, Miami, FL, USA Email: Thomas P Eisele* -
[email protected]; Joseph Keating -
[email protected]; Chris Swalm -
[email protected]; Charles M Mbogo -
[email protected]; Andrew K Githeko -
[email protected]; James L Regens -
[email protected]; John I Githure -
[email protected]; Linda Andrews -
[email protected]; John C Beier -
[email protected] * Corresponding author
Published: 10 December 2003 Malaria Journal 2003, 2:44
Received: 11 August 2003 Accepted: 10 December 2003
This article is available from: http://www.malariajournal.com/content/2/1/44 © 2003 Eisele et al; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
Abstract Background: Remote sensing technology provides detailed spectral and thermal images of the earth's surface from which surrogate ecological indicators of complex processes can be measured. Methods: Remote sensing data were overlaid onto georeferenced entomological and human ecological data randomly sampled during April and May 2001 in the cities of Kisumu (population ≈ 320,000) and Malindi (population ≈ 81,000), Kenya. Grid cells of 270 meters × 270 meters were used to generate spatial sampling units for each city for the collection of entomological and human ecological field-based data. Multispectral Thermal Imager (MTI) satellite data in the visible spectrum at five meter resolution were acquired for Kisumu and Malindi during February and March 2001, respectively. The MTI data were fit and aggregated to the 270 meter × 270 meter grid cells used in field-based sampling using a geographic information system. The normalized difference vegetation index (NDVI) was calculated and scaled from MTI data for selected grid cells. Regression analysis was used to assess associations between NDVI values and entomological and human ecological variables at the grid cell level. Results: Multivariate linear regression showed that as household density increased, mean grid cell NDVI decreased (global F-test = 9.81, df 3,72, P-value =
