Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS

被引:67
作者
Kitron, U
Otieno, LH
Hungerford, LL
Odulaja, A
Brigham, WU
Okello, OO
Joselyn, M
MohamedAhmed, MM
Cook, E
机构
[1] Dept. of Veterinary Pathobiology, College of Veterinary Medicine, University of Illinois, Urbana
[2] Livestock Pests Research Programme, ICIPE, Nairobi
[3] Biomathematics Research Unit, ICIPE, Nairobi
[4] Illinois Natural History Survey, Champaign
关键词
geographic information system; remote sensing; spatial statistics;
D O I
10.2307/5883
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
1. Satellite imagery, geographic information systems (GIS) and spatial statistics provide tools for studies of population dynamics of disease vectors in association with habitat features on multiple spatial scales. 2. Tsetse flies were collected during 1988-90 in biconical traps located along transects in Ruma National Park in the Lambwe Valley, western Kenya. Fine spatial resolution data collected by Landsat Thematic Mapper (TM) satellite and reference ground environmental data were integrated in a GIS to identify factors associated with local variations of fly density. 3. Statistical methods of spatial autocorrelation and spatial filtering were applied to determine spatial components of these associations. Strong positive spatial associations among traps occurred within transects and within the two ends of the park. 4. From satellite data, TM band 7, which is associated with moisture content of soil and vegetation, emerged as being consistently highly correlated with fly density. Using several spectral bands in a multiple regression, as much as 87% of the variance in fly catch values could be explained. 5. When spatial filtering was applied, a large component of the association between fly density and spectral data was shown to be the result of other determinants underlying the spatial distributions of both fly density and spectral values. Further field studies are needed to identify these determinants. 6. The incorporation of remotely sensed data imagery into a GIS with ground data on fly density and environnmental conditions can be used to predict favourable fly habitats in inaccessible sites, and to determine number and location of fly suppression traps in a local control programme.
引用
收藏
页码:371 / 380
页数:10
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