Omumbo J A, Hay S I, Goetz S J, Snow R W, Rogers D J
Trypanosomiasis and Land-Use in Africa (TALA) Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, United Kingdom and with the Kenya Medical Research Institute/Wellcome Trust collaborative programme, P.O. Box 43640, Nairobi, Kenya.
Photogramm Eng Remote Sensing. 2002 Feb;68(2):161-166.
Remotely sensed imagery has been used to update and improve the spatial resolution of malaria transmission intensity maps in Tanzania, Uganda, and Kenya. Discriminant analysis achieved statistically robust agreements between historical maps of the intensity of malaria transmission and predictions based on multitemporal meteorological satellite sensor data processed using temporal Fourier analysis. The study identified land surface temperature as the best predictor of transmission intensity. Rainfall and moisture availability as inferred by cold cloud duration (ccd) and the normalized difference vegetation index (ndvi), respectively, were identified as secondary predictors of transmission intensity. Information on altitude derived from a digital elevation model significantly improved the predictions. "Malaria-free" areas were predicted with an accuracy of 96 percent while areas where transmission occurs only near water, moderate malaria areas, and intense malaria transmission areas were predicted with accuracies of 90 percent, 72 percent, and 87 percent, respectively. The importance of such maps for rationalizing malaria control is discussed, as is the potential contribution of the next generation of satellite sensors to these mapping efforts.
遥感影像已被用于更新和提高坦桑尼亚、乌干达和肯尼亚疟疾传播强度地图的空间分辨率。判别分析在疟疾传播强度的历史地图与基于使用时间傅里叶分析处理的多时相气象卫星传感器数据的预测之间达成了具有统计学稳健性的一致性。该研究确定地表温度是传播强度的最佳预测指标。分别由冷云持续时间（ccd）和归一化植被指数（ndvi）推断出的降雨量和水分可用性被确定为传播强度的次要预测指标。从数字高程模型得出的海拔信息显著改善了预测。“无疟疾”地区的预测准确率为96%，而仅在靠近水源处发生传播的地区、中度疟疾地区和高强度疟疾传播地区的预测准确率分别为90%、72%和87%。文中讨论了此类地图对于合理开展疟疾防治工作的重要性，以及下一代卫星传感器对这些制图工作的潜在贡献。
