English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background  A supervised land cover classification was developed from very high resolution IKONOS satellite data and extensive ground truth sampling of a ca. 10 sq km malaria-endemic lowland in western Kenya. The classification was then applied to an investigation of distribution of larval  Anopheles  habitats. The hypothesis was that the distribution and abundance of aquatic habitats of larvae of various species of mosquitoes in the genus  Anopheles  is associated with identifiable landscape features.    Results and discussion  The classification resulted in 7 distinguishable land cover types, each with a distinguishable vegetation pattern, was highly accurate (89%, Kappa statistic = 0.86), and had a low rate of omission and commission errors. A total of 1,198 habitats and 19,776  Anopheles  larvae of 9 species were quantified in samples from a rainy season, and 184 habitats and 582 larvae from a dry season.  Anopheles gambiae  s.l. was the dominant species complex (51% of total) and  A. arabiensis  the dominant species. Agricultural land covers (mature maize fields, newly cultivated fields, and pastured grasslands) were positively associated with presence of larval habitats, and were located relatively close to stream channels; whilst nonagricultural land covers (short shrubs, medium shrubs, tall shrubs, and bare soil around residences) were negatively associated with presence of larval habitats and were more distant from stream channels. Number of larval habitats declined exponentially with distance from streams. IKONOS imagery was not useful in direct detection of larval habitats because they were small and turbid (resembling bare soil), but was useful in localization of them through statistical associations with specific land covers.    Conclusion  A supervised classification of land cover types in rural, lowland, western Kenya revealed a largely human-modified and fragmented landscape consisting of agricultural and domestic land uses. Within it, larval habitats of  Anopheles  vectors of human malaria were associated with certain land cover types, of largely agricultural origin, and close to streams. Knowledge of these associations can inform malaria control to gather information on potential larval habitats more efficiently than by field survey and can do so over large areas.

A supervised land cover classification of a western Kenya lowland endemic for human malaria: associations of land cover with larval Anopheles habitats