Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS
Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS
复制标题
DOI：
10.2307/5883
复制
发表时间：
1996-05-01
影响因子：
4.8
通讯作者：
Cook, E
中科院分区：
文献类型：
--
作者：
Kitron, U;Otieno, LH;Cook, E
1. 2.卫星图像、地理信息系统（GIS）和空间统计为在多个空间尺度上研究与生境特征相关的病媒种群动态提供了工具。1988- 1990年期间，在肯尼亚西部兰姆威河谷鲁玛国家公园沿样带设置的双锥形捕蝇器中采集了采采蝇。将Landsat Thematic Mapper （TM）卫星收集的精细空间分辨率数据和参考地面环境数据整合到GIS中，以确定与苍蝇密度局部变化相关的因素。应用空间自相关和空间滤波的统计方法来确定这些关联的空间成分。在样带内和公园两端，捕集器之间存在强烈的空间正相关。从卫星数据中可以看出，与土壤和植被含水量相关的TM波段7与蝇密度呈高度相关。在多元回归中使用多个光谱波段，可以解释多达87%的苍蝇捕获值方差。当应用空间滤波时，苍蝇密度和光谱数据之间的关联的很大一部分被证明是苍蝇密度和光谱值的空间分布背后的其他决定因素的结果。需要进一步的实地研究来确定这些决定因素。将遥感数据图像与苍蝇密度和环境条件的地面数据结合到地理信息系统中，可用于预测难以到达地点的有利苍蝇栖息地，并确定在当地控制方案中捕蝇陷阱的数量和位置。
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.