Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data
Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data
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DOI：
10.1016/s0035-9203(98)90936-1
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发表时间：
1998-01-01
影响因子：
2.2
通讯作者：
Rogers, DJ
中科院分区：
文献类型：
--
作者：
Hay, SI;Snow, RW;Rogers, DJ
本文介绍了研究预测的季节性疟疾在肯尼亚使用卫星传感器的遥感图像。这些预测是利用关于儿科重症疟疾入院的长期数据与同时从美国国家海洋和大气管理局（诺阿）极轨道气象卫星上的高级甚高分辨率辐射计和欧洲气象卫星开发组织上的高分辨率辐射计收集的数据之间建立的关系进行的。（EUMETSAT）地球静止气象卫星。对遥感数据进行处理，以提供关于地表温度、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立叶处理，并将拟合的傅立叶数据与每个月记录的年度疟疾总入院人数的平均百分比进行比较。前一个月的NDVI与3个地点的疟疾表现的相关性最显著和一致（平均调整r（2）= 0.71，范围0.61-0.79）。回归分析表明，0.35-0.40的NDVI阈值需要超过5%的年度疟疾病例在一个给定的月份。然后，用傅立叶处理的归一化差异植被指数时间数据对这些阈值进行空间外推，以8x8公里分辨率确定肯尼亚平均每年疟疾发病的月数。由此产生的地图进行了比较，唯一的现有地图（巴特勒）的疟疾传播期为肯尼亚，从专家意见汇编。得出的结论是，遥感技术是否适合于编制国家疟疾干预战略。
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 r(2) = 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.