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

Jacob BG, Muturi EJ, Mwangangi JM, Funes J, Caamano EX, Muriu S, Shililu J, Githure J, Novak RJ - Int J Health Geogr (2007)

Bottom Line: The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions.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.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.

View Article: PubMed Central - HTML - PubMed

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

ABSTRACT

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.

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Map of the three study sites: Kangichiri, Kiura and Rurumi in the Central Kenyan Rice Scheme.
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Figure 1: Map of the three study sites: Kangichiri, Kiura and Rurumi in the Central Kenyan Rice Scheme.

Mentions: The sampling strategy used for the collection of larval site data was developed for an earlier research project and has been described in detail elsewhere [19]. Briefly, base maps including major roads and hydrography for the three study sites were generated using differentially corrected global positioning systems (DGPS) data in ArcInfo 9.1® (Earth Systems Research Institute Redlands, CA, USA) (Figure 1). Each riceland An. arabiensis larval habitat with its associated land cover attributes from the three study sites were entered into a Vector Control Management System® (VCMS) (Advanced Computer Resources Corp (ACR), 100 Perimeter Road Nashua, NH, USA) database). A digitized custom grid, tracing each riceland habitat was generated in Arc Info 9.1® [21]. A unique identifier was placed in each grid cell (i.e. polygon). The digitized grid extended out to a 1 km distance from the external boundary of the three study sites providing a 1 km radial area. Grid cells were stratified based on LULC transition throughout the crop season and defined as: 1) flooding; 2) land preparation; 3) post-transplanting; 4) tillering; 5) flowering; and 6) maturation and 7) fallow/post-harvest. Probability proportional to size sampling, based on the proportion of grid cells within each stratum was used to randomly select 50 grid cells in each study site for entomological sampling.


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

Jacob BG, Muturi EJ, Mwangangi JM, Funes J, Caamano EX, Muriu S, Shililu J, Githure J, Novak RJ - Int J Health Geogr (2007)

Map of the three study sites: Kangichiri, Kiura and Rurumi in the Central Kenyan Rice Scheme.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1904442&req=5

Figure 1: Map of the three study sites: Kangichiri, Kiura and Rurumi in the Central Kenyan Rice Scheme.
Mentions: The sampling strategy used for the collection of larval site data was developed for an earlier research project and has been described in detail elsewhere [19]. Briefly, base maps including major roads and hydrography for the three study sites were generated using differentially corrected global positioning systems (DGPS) data in ArcInfo 9.1® (Earth Systems Research Institute Redlands, CA, USA) (Figure 1). Each riceland An. arabiensis larval habitat with its associated land cover attributes from the three study sites were entered into a Vector Control Management System® (VCMS) (Advanced Computer Resources Corp (ACR), 100 Perimeter Road Nashua, NH, USA) database). A digitized custom grid, tracing each riceland habitat was generated in Arc Info 9.1® [21]. A unique identifier was placed in each grid cell (i.e. polygon). The digitized grid extended out to a 1 km distance from the external boundary of the three study sites providing a 1 km radial area. Grid cells were stratified based on LULC transition throughout the crop season and defined as: 1) flooding; 2) land preparation; 3) post-transplanting; 4) tillering; 5) flowering; and 6) maturation and 7) fallow/post-harvest. Probability proportional to size sampling, based on the proportion of grid cells within each stratum was used to randomly select 50 grid cells in each study site for entomological sampling.

Bottom Line: The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions.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.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.

View Article: PubMed Central - HTML - PubMed

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

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus