Hay S I, Snow R W, Rogers D J
Trypanosomiasis and Land-use in Africa (TALA) Research Group, Department of Zoology, University of Oxford, UK.
Trans R Soc Trop Med Hyg. 1998 Jan-Feb;92(1):12-20. doi: 10.1016/s0035-9203(98)90936-1.
This article describes research that predicts the seasonality of malaria in Kenya using remotely sensed images from satellite sensors. The predictions were made using relationships established between long-term data on paediatric severe malaria admissions and simultaneously collected data from the Advanced Very High Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric Administrations (NOAA) polar-orbiting meteorological satellites and the High Resolution Radiometer (HRR) on the European Organization for the Exploitation of Meteorological Satellites' (EUMETSAT) geostationary Meteosat satellites. The remotely sensed data were processed to provide surrogate information on land surface temperature, reflectance in the middle infra-red, rainfall, and the normalized difference vegetation index (NDVI). These variables were then subjected to temporal Fourier processing and the fitted Fourier data were compared with the mean percentage of total annual malaria admissions recorded in each month. The NDVI in the preceding month correlated most significantly and consistently with malaria presentations across the 3 sites (mean adjusted r2 = 0.71, range 0.61-0.79). Regression analyses showed that an NDVI threshold of 0.35-0.40 was required for more than 5% of the annual malaria cases to be presented in a given month. These thresholds were then extrapolated spatially with the temporal Fourier-processed NDVI data to define the number of months, in which malaria admissions could be expected across Kenya in an average year, at an 8 x 8 km resolution. The resulting maps were compared with the only existing map (Butler's) of malaria transmission periods for Kenya, compiled from expert opinion. Conclusions are drawn on the appropriateness of remote sensing techniques for compiling national strategies for malaria intervention.
本文描述了一项利用卫星传感器的遥感图像预测肯尼亚疟疾季节性的研究。预测是通过建立小儿重症疟疾入院长期数据与同时收集的美国国家海洋和大气管理局（NOAA）极地轨道气象卫星上的高级甚高分辨率辐射计（AVHRR）以及欧洲气象卫星应用组织（EUMETSAT）地球静止气象卫星Meteosat上的高分辨率辐射计（HRR）数据之间的关系来进行的。对遥感数据进行处理，以提供有关地表温度、中红外反射率、降雨量和归一化植被指数（NDVI）的替代信息。然后对这些变量进行时间傅里叶处理，并将拟合的傅里叶数据与每个月记录的全年疟疾入院总数的平均百分比进行比较。前一个月的NDVI与三个地点的疟疾发病情况相关性最强且最一致（平均调整r2 = 0.71，范围0.61 - 0.79）。回归分析表明，要使给定月份出现超过5%的年度疟疾病例，NDVI阈值需要达到0.35 - 0.40。然后利用时间傅里叶处理后的NDVI数据在空间上外推这些阈值，以确定在平均年份里肯尼亚各地预计出现疟疾入院情况的月数，分辨率为8×8公里。将生成的地图与根据专家意见编制的肯尼亚疟疾传播期唯一现有地图（巴特勒地图）进行比较。得出了关于遥感技术在编制疟疾干预国家战略方面适用性的结论。
