2004
DOI: 10.1080/01431160310001598944
|View full text |Cite
|
Sign up to set email alerts
|

Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

Abstract: The aim of this study was to determine whether remotely sensed data could be used to identify rice-related malaria vector breeding habitats in an irrigated rice growing area near Niono, Mali. Early stages of rice growth show peak larval production, but Landsat sensor data are often obstructed by clouds during the early part of the cropping cycle (rainy season). In this study, we examined whether a classification based on two Landsat Enhanced Thematic Mapper (ETM)+ scenes acquired in the middle of the season an… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
28
0

Year Published

2006
2006
2013
2013

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 39 publications
(28 citation statements)
references
References 45 publications
0
28
0
Order By: Relevance
“…Over the study period, the mean density per house was 69. 5 Spatial analysis of malaria transmission parameters. Bivariate and multiple non-spatial and spatial Poisson models were fitted to assess the association between mosquito density and rice growth-related environmental features ( Table 1).…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Over the study period, the mean density per house was 69. 5 Spatial analysis of malaria transmission parameters. Bivariate and multiple non-spatial and spatial Poisson models were fitted to assess the association between mosquito density and rice growth-related environmental features ( Table 1).…”
Section: Resultsmentioning
confidence: 99%
“…Our previous study used remote sensing data to map anopheline breeding sites and described the relation between mosquito densities, survival rates, zoophilic rates, and vectorial capacity to explain the low prevalence of malaria. 5,6 In the current study, we reanalyze the same data to assess the effects of rice growth environmental features on malaria transmission parameters to get an insight into the spatial variation of malaria risk within a large-scale irrigated ricecultivation area. This work was complemented by repeated cross-sectional anopheline larval collections in selected rice plots, which will be published elsewhere.…”
Section: Introductionmentioning
confidence: 99%
“…They found that the higher spectral sensitivity of Hyperion and the higher spatial resolution of ASTER yielded better results than the Landsat TM. Diuk-Wasser et al found that Landsat 7 ETM+ was effective at identifying mosquito habitats in non-urban environments, but attempted no comparison with higher resolution image data [12]. Research employing data at resolutions that are equal to, or higher than, the 30 m available from the Landsat series has been extensive.…”
Section: Introductionmentioning
confidence: 99%
“…An alternative approach is to use landscape variables derived from remote sensing satellites as predictors, with or without incorporating the effects of spatial dependence. Pertinent examples include vectors of Eastern equine encephalomyelitis (Moncayo et al 2000), tick vectors of Lyme disease (Brownstein et al 2003, Guerra et al 2001, Kitron et al 1996, sand fly vectors of leishmaniasis (Cross et al 1996, Elnaiem et al 2003, Miranda et al 1998, Thomson et al 1999, tse-tse fly vectors of African trypanosomiasis (Kitron et al 1996, Rogers 2000, and mosquito vectors of malaria (Beck et al 1994, Diuk-Wasser et al 2004, Thomson et al 1996, Wood et al 1991a,b, 1992. Of these models, however, only a few have been validated with an independent dataset (Beck et al 1997, Brownstein et al 2004).…”
Section: Introduction Wmentioning
confidence: 99%