Distinguishing high and low anopheline-producing rice fields using remote sensing and GIS technologies

Wood, B. L.; Washino, Robert K.; Beck, Louisa R.; Hibbard, Kathy; Pitcairn, Michael J.; Roberts, Donald R.; Rejmánková, Eliška; Paris, J. F.; Hacker, Carl D.; Salute, J. S.; Sebesta, P. D.; Legters, Llewellyn J.

doi:10.1016/s0167-5877(05)80014-5

Cited by 52 publications

(45 citation statements)

References 4 publications

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“…splendens was positively associated with the number of tillers and hence more common (Sunish and Reuben 2001) and are to be expected because of the complex nature of the interactions between some of the factors in the ecosystem. In addition, factors out side the rice Þelds such as vector resting sites and bloodmeal sources also have been reported to affect larval production (Wood et al 1991(Wood et al , 1992Minakawa et al 2002). The six predictor variables can be categorized in two interdependent groups.…”

Section: Discussionmentioning

confidence: 99%

Mosquito Species Succession and Physicochemical Factors Affecting Their Abundance in Rice Fields in Mwea, Kenya

Muturi¹,

Mwangangi

Shililu

et al. 2007

J Med Entomol

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The succession of mosquito species and abiotic factors affecting their distribution and abundance in rice (Oryza spp.) Þelds was investigated over a 16-wk rice growing cycle covering the period between January and May 2006. Fifteen experimental rice plots were sampled for mosquito larvae and characterized based on rice height, number of tillers, ßoating vegetation cover, water depth, water temperature, turbidity, salinity, pH, dissolved oxygen, total dissolved solids, and conductivity. Microscopic identiÞcation of 3,025 larvae yielded nine mosquito species predominated by Anopheles arabiensis Patton (45.0%), Culex quinquefasciatus Say (35.8%), Anopheles pharoensis Theobald (9.0%) and Ficalbia splendens Theobald (7.1%). Other species, including Anopheles rufipes Gough, Anopheles coustani Laveran, Anonopheles maculipalpis Giles, Culex annulioris Theobald, and Culex poicilipes Theobald made up 3.1% of the total collection. Anopheles gambiae s.l., Cx. quinquefasciatus, and An. pharoensis occurred throughout the cycle, but they were more abundant up to 4 wk posttransplanting with peaks after fertilizer application. As rice plants became established, three groups of mosquitoes were recognized: the Þrst groups included An. rufipes, Fl. splendens, and Cx. annulioris, which occurred throughout much of the second half of the rice cycle, whereas the second group included Cx. poicilipes, which was found in the middle of the rice cycle. An. coustani and An. maculipalpis formed the third group occurring toward the end of the cycle. Dissolved oxygen, number of tillers, and rice height were negatively associated with the abundance of An. arabiensis and Cx. quinquefasciatus larvae. In addition, Cx. quinquefasciatus also was associated with water depth (Ϫve) and turbidity (ϩve). Abundance of An. pharoensis larvae was signiÞcantly associated with water temperature (ϩve), the number of tillers (Ϫve), and rice height (Ϫve), whereas Fl. splendens was signiÞcantly associated with the number of tillers (ϩve). The results demonstrate a complex nature of the interactions between some of the factors in the ecosystem and mosquito species abundance and calls for time-dependent and speciesspeciÞc mosquito control operations.

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Section: Discussionmentioning

confidence: 99%

Mosquito Species Succession and Physicochemical Factors Affecting Their Abundance in Rice Fields in Mwea, Kenya

Muturi¹,

Mwangangi

Shililu

et al. 2007

J Med Entomol

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“…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%

Modeling the Spatial Distribution of Mosquito Vectors for West Nile Virus in Connecticut, USA

Diuk‐Wasser

Brown

Andreadis

et al. 2006

Vector-Borne and Zoonotic Diseases

155

142

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The risk of transmission of West Nile virus (WNV) to humans is associated with the density of infected vector mosquitoes in a given area. Current technology for estimating vector distribution and abundance is primarily based on Centers for Disease Control and Prevention (CDC) light trap collections, which provide only point data. In order to estimate mosquito abundance in areas not sampled by traps, we developed logistic regression models for five mosquito species implicated as the most likely vectors of WNV in Connecticut. Using data from 32 traps in Fairfield County from 2001 to 2003, the models were developed to predict high and low abundance for every 30 ؋ 30 m pixel in the County. They were then tested with an independent dataset from 16 traps in adjacent New Haven County. Environmental predictors of abundance were extracted from remotely sensed data. The best predictive models included non-forested areas for Culex pipiens, surface water and distance to estuaries for Cx. salinarius, surface water and grasslands/agriculture for Aedes vexans and seasonal difference in the normalized difference vegetation index and distance to palustrine habitats for Culiseta melanura. No significant predictors were found for Cx. restuans. The sensitivity of the models ranged from 75% to 87.5% and the specificity from 75% to 93.8%. In New Haven County, the models correctly classified 81.3% of the traps for Cx. pipiens, 75.0% for Cx. salinarius, 62.5% for Ae. vexans, and 75.0% for Cs. melanura. Continuous surface maps of habitat suitability were generated for each species for both counties, which could contribute to future surveillance and intervention activities.

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“…Remote sensing has been used to predict which California rice fields will have the highest production of A. freeborni larvae nearly 2 months before the peak larval density occurs Wood et al, 1991). Beck et al (1994) used images from Landsat Thematic Mapper (TM) to estimate the risk of malaria in 40 villages in Chiapas, Mexico, based on two environmental factors: transitional swamp and unimproved pasture.…”

Section: Introductionmentioning

confidence: 99%

Use of IKONOS and Landsat for malaria control in the Republic of Korea

Masuoka¹,

Claborn²,

Andre³

et al. 2003

Remote Sensing of Environment

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Malaria reemerged in the Republic of Korea (ROK) in 1993. While limited numbers of U.S. soldiers in high-risk areas use chloroquine/ primaquine chemoprophylaxis to prevent malaria, control of mosquito larvae through larviciding also can be used to reduce the risk of malaria transmission. In order to estimate the cost of larviciding, accurate estimates of the spatial extent of mosquito larval habitats are necessary. The purpose of this study was to determine whether an accurate estimate of the area covered by mosquito larval habitats can be obtained using Landsat 7 Enhanced Thematic Mapper+ (ETM+) and/or IKONOS data for the Korean test site.To estimate the area covered by larval habitats near Camp Greaves [Paekyeon-Ri, near Tongil-Chon (village)] in the ROK, an IKONOS and a Landsat 7 ETM+ image were classified using a parallelepiped classification. In a comparison with rice paddy field sites, 24 (92%) of the sites were classified correctly on the IKONOS image and 17 (65%) were classified correctly on the Landsat image. Comparing the classifications on a pixel-by-pixel basis, the agreement between the two classifications was 79%. Part of the disagreement was due to the difference in resolution of the two images. In spite of local differences, the two classifications produced similar area estimates.Although either Landsat or IKONOS could be used in Korea for a reasonable estimate of habitat area, only IKONOS can resolve small irrigation ponds. While ponds represent a small portion of the total larval habitat, they are an important source for mosquito breeding during the late rice-growing season in the ROK since they contain higher larval densities. High-resolution imagery, such as IKONOS, would be necessary for planning and implementing treatment of these smaller habitats. D

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Distinguishing high and low anopheline-producing rice fields using remote sensing and GIS technologies

Cited by 52 publications

References 4 publications

Mosquito Species Succession and Physicochemical Factors Affecting Their Abundance in Rice Fields in Mwea, Kenya

Mosquito Species Succession and Physicochemical Factors Affecting Their Abundance in Rice Fields in Mwea, Kenya

Modeling the Spatial Distribution of Mosquito Vectors for West Nile Virus in Connecticut, USA

Use of IKONOS and Landsat for malaria control in the Republic of Korea

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