Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya.

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya. Research Abstract Details 

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Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya. Abstract Text:

Benjamin G Jacob,Ephantus J Muturi,Joseph M Mwangangi,Jose Funes,Erick X Caamano,Simon Muriu,Josephat Shililu,John Githure,Robert J Novak,

BACKGROUND: We examined algorithms for malaria mapping using the impact of reflectance calibration uncertainties on the accuracies of three vegetation indices (VI)'s derived from QuickBird data in three rice agro-village complexes Mwea, Kenya. We also generated inferential statistics from field sampled vegetation covariates for identifying riceland Anopheles arabiensis during the crop season. All aquatic habitats in the study sites were stratified based on levels of rice stages; flooded, land preparation, post-transplanting, tillering, flowering/maturation and post-harvest/fallow. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties using the red channel (band 3: 0.63 to 0.69 microm) and the near infra-red (NIR) channel (band 4: 0.76 to 0.90 microm) to generate the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The Atmospheric Resistant Vegetation Index (ARVI) was also evaluated incorporating the QuickBird blue band (Band 1: 0.45 to 0.52 microm) to normalize atmospheric effects. In order to determine local clustering of riceland habitats Gi*(d) statistics were generated from the ground-based and remotely-sensed ecological databases. Additionally, all riceland habitats were visually examined using the spectral reflectance of vegetation land cover for identification of highly productive riceland Anopheles oviposition sites. RESULTS: The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions. Logistic regression analyses of all field sampled covariates revealed emergent vegetation was negatively associated with mosquito larvae at the three study sites. In addition, floating vegetation (-ve) was significantly associated with immature mosquitoes in Rurumi and Kiuria (-ve); while, turbidity was also important in Kiuria. All spatial models exhibit positive autocorrelation; similar numbers of log-counts tend to cluster in geographic space. The spectral reflectance from riceland habitats, examined using the remote and field stratification, revealed post-transplanting and tillering rice stages were most frequently associated with high larval abundance and distribution. CONCLUSION: NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland An. arabiensis aquatic habitats. However, combining spectral reflectance of riceland habitats from QuickBird and field sampled data can develop and implement an Integrated Vector Management (IVM) program based on larval productivity.

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya. Publishing Authors By Initials

BG Jacob,EJ Muturi,JM Mwangangi,J Funes,EX Caamano,S Muriu,J Shililu,J Githure,RJ Novak,

For similar investigative techniques: epidemiologic methods: statistics as topic: probability: uncertainty research abstracts see: investigative techniques: epidemiologic methods: statistics as topic: probability: uncertainty research

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Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: International journal of health geographics

VOLUME: 6

Page Numbers: 21

Journal Abbreviation:

ISSN: 1476-072X

DAY: 5

MONTH: 06

YEAR: 2007

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya. Information

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LANGUAGE: eng

NlmUniqueID: 101152198

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya. Keywords Mesh Terms:

KEYWORDS: Uncertainty

MESH TERMS: statistics & numerical data

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Grant and Affiliation Information for Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya.

AFFILIATION: Illinois Natural History Survey, Center for Ecological Entomology, Champaign, Illinois 61820, USA. bjacob@uiuc.edu

Country: England

AGENCY: United States PHS

GRANT: U01A154889

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MEDLINETA: Int J Health Geogr

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