Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya
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 dat...
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ftdoajarticles:oai:doaj.org/article:82ae82d4a97c425896fb37d4a3fadc27 2023-05-15T15:16:11+02:00 Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya Regens James L Githeko Andrew K Mbogo Charles M Swalm Chris Keating Joseph Eisele Thomas P Githure John I Andrews Linda Beier John C 2003-12-01T00:00:00Z https://doi.org/10.1186/1475-2875-2-44 https://doaj.org/article/82ae82d4a97c425896fb37d4a3fadc27 EN eng BMC http://www.malariajournal.com/content/2/1/44 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-2-44 1475-2875 https://doaj.org/article/82ae82d4a97c425896fb37d4a3fadc27 Malaria Journal, Vol 2, Iss 1, p 44 (2003) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2003 ftdoajarticles https://doi.org/10.1186/1475-2875-2-44 2022-12-30T22:41:32Z 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 = <0.01; adjusted R 2 = 0.26). Given household density, the number of potential anopheline larval habitats per grid cell also increased with increasing values of mean grid cell NDVI (global F-test = 14.29, df 3,36, P-value = <0.01; adjusted R 2 = 0.51). Conclusions NDVI values obtained from MTI data were successfully overlaid onto georeferenced entomological and human ecological data spatially sampled at a scale of 270 meters × 270 meters. Results demonstrate that NDVI at such a scale was sufficient to describe variations in entomological and human ecological parameters across both cities. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 2 1 44 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
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English |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Regens James L Githeko Andrew K Mbogo Charles M Swalm Chris Keating Joseph Eisele Thomas P Githure John I Andrews Linda Beier John C Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
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 = <0.01; adjusted R 2 = 0.26). Given household density, the number of potential anopheline larval habitats per grid cell also increased with increasing values of mean grid cell NDVI (global F-test = 14.29, df 3,36, P-value = <0.01; adjusted R 2 = 0.51). Conclusions NDVI values obtained from MTI data were successfully overlaid onto georeferenced entomological and human ecological data spatially sampled at a scale of 270 meters × 270 meters. Results demonstrate that NDVI at such a scale was sufficient to describe variations in entomological and human ecological parameters across both cities. |
format |
Article in Journal/Newspaper |
author |
Regens James L Githeko Andrew K Mbogo Charles M Swalm Chris Keating Joseph Eisele Thomas P Githure John I Andrews Linda Beier John C |
author_facet |
Regens James L Githeko Andrew K Mbogo Charles M Swalm Chris Keating Joseph Eisele Thomas P Githure John I Andrews Linda Beier John C |
author_sort |
Regens James L |
title |
Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya |
title_short |
Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya |
title_full |
Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya |
title_fullStr |
Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya |
title_full_unstemmed |
Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya |
title_sort |
linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of kenya |
publisher |
BMC |
publishDate |
2003 |
url |
https://doi.org/10.1186/1475-2875-2-44 https://doaj.org/article/82ae82d4a97c425896fb37d4a3fadc27 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 2, Iss 1, p 44 (2003) |
op_relation |
http://www.malariajournal.com/content/2/1/44 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-2-44 1475-2875 https://doaj.org/article/82ae82d4a97c425896fb37d4a3fadc27 |
op_doi |
https://doi.org/10.1186/1475-2875-2-44 |
container_title |
Malaria Journal |
container_volume |
2 |
container_issue |
1 |
container_start_page |
44 |
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1766346474038231040 |