Research

Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands

Emmanuel Mushinzimana¹ , Stephen Munga¹ , Noboru Minakawa² , Li Li³ , Chen-chieh Feng³ , Ling Bian³ , Uriel Kitron⁴ , Cindy Schmidt⁵ , Louisa Beck⁵ , Guofa Zhou² , Andrew K Githeko¹ and Guiyun Yan²

¹  Climate and Human Health Research Unit, Centre for Vector Biology and Control Research, Kenya Medical Research Institute, Kenya

²  Program in Public Health, University of California at Irvine, Irvine, CA 92697, USA

³  National Center for Geographic Information and Analysis and Department of Geography, New York State University at Buffalo, Buffalo, NY 14260, USA

⁴  Department of Pathobiology, College of Veterinary Medicine, University of Illinois, Urbana, IL 61802, USA

⁵  Center for Health Applications of Aerospace Related Technologies, Ecosystem Science and Technology Branch, NASA Ames Research Center, Moffett Field, CA 94035, USA

author email corresponding author email

Malaria Journal 2006, 5:13doi:10.1186/1475-2875-5-13

Published:	16 February 2006

Abstract

Background

In the past two decades the east African highlands have experienced several major malaria epidemics. Currently there is a renewed interest in exploring the possibility of anopheline larval control through environmental management or larvicide as an additional means of reducing malaria transmission in Africa. This study examined the landscape determinants of anopheline mosquito larval habitats and usefulness of remote sensing in identifying these habitats in western Kenya highlands.

Methods

Panchromatic aerial photos, Ikonos and Landsat Thematic Mapper 7 satellite images were acquired for a study area in Kakamega, western Kenya. Supervised classification of land-use and land-cover and visual identification of aquatic habitats were conducted. Ground survey of all aquatic habitats was conducted in the dry and rainy seasons in 2003. All habitats positive for anopheline larvae were identified. The retrieved data from the remote sensors were compared to the ground results on aquatic habitats and land-use. The probability of finding aquatic habitats and habitats with Anopheles larvae were modelled based on the digital elevation model and land-use types.

Results

The misclassification rate of land-cover types was 10.8% based on Ikonos imagery, 22.6% for panchromatic aerial photos and 39.2% for Landsat TM 7 imagery. The Ikonos image identified 40.6% of aquatic habitats, aerial photos identified 10.6%, and Landsate TM 7 image identified 0%. Computer models based on topographic features and land-cover information obtained from the Ikonos image yielded a misclassification rate of 20.3–22.7% for aquatic habitats, and 18.1–25.1% for anopheline-positive larval habitats.

Conclusion

One-metre spatial resolution Ikonos images combined with computer modelling based on topographic land-cover features are useful tools for identification of anopheline larval habitats, and they can be used to assist to malaria vector control in western Kenya highlands.