Beck L R, Rodriguez M H, Dister S W, Rodriguez A D, Rejmankova E, Ulloa A, Meza R A, Roberts D R, Paris J F, Spanner M A
Johnson Controls World Services, NASA Ames Research Center, Moffett Field, California.
Am J Trop Med Hyg. 1994 Sep;51(3):271-80. doi: 10.4269/ajtmh.1994.51.271.
A landscape approach using remote sensing and geographic information system (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus abundance data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high and low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where 1) the landscape elements critical to vector survival are known and 2) these elements can be detected at remote sensing scales.
利用遥感和地理信息系统（GIS）技术开发了一种景观方法，以区分按成年白纹伊蚊丰度定义的疟疾传播高风险和低风险村庄。对墨西哥恰帕斯州南部一个地区的卫星数据进行数字处理，以生成景观要素地图。利用GIS程序确定已收集白纹伊蚊丰度数据的40个村庄周围地图景观要素的比例。使用逐步判别分析和逐步线性回归研究病媒丰度与景观要素比例之间的关系。两项分析均表明，就解释病媒丰度而言，最重要的景观要素是过渡性沼泽和未管理的牧场。针对这两个要素生成的判别函数能够正确区分病媒丰度高和低的村庄，总体准确率为90%。回归结果发现，过渡性沼泽和未管理牧场的比例在雨季中后期均可预测病媒丰度。这种将遥感数据和GIS功能相结合以识别病媒与人类高接触风险村庄的方法，为依赖劳动密集型实地技术的疟疾监测项目提供了一个有前景的工具。这在缺乏准确监测能力可能导致在实际上需要直接行动时却不采取疟疾控制行动的地区尤为重要。一般来说，这种景观方法可应用于满足以下两个条件的地区的其他媒介传播疾病：1）已知对病媒生存至关重要的景观要素；2）这些要素可在遥感尺度上检测到。
