Beck L R, Rodriguez M H, Dister S W, Rodriguez A D, Washino R K, Roberts D R, Spanner M A
Johnson Controls World Services, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, California, USA.
Am J Trop Med Hyg. 1997 Jan;56(1):99-106. doi: 10.4269/ajtmh.1997.56.99.
A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.
在墨西哥恰帕斯州南部，对两种基于遥感的模型进行了盲测，这两种模型用于预测村庄中白纹伊蚊的成虫数量，而成虫数量是疟疾传播风险的一个指标。一种模型是使用判别分析方法开发的，另一种则基于回归分析。这些模型于1992年利用陆地卫星专题绘图仪（TM）卫星数据和地理信息系统功能，为恰帕斯州塔帕丘拉周边地区开发。利用两种遥感景观要素，判别模型能够成功区分白纹伊蚊数量多和少的村庄，总体准确率为90%。为了测试这些模型的预测能力，利用多时相TM数据生成了塔帕丘拉西北部惠斯特拉地区的景观图，在该地区使用这些模型对40个村庄的风险进行预测。在测试结束前，所得预测结果并未公布。独立地，在随机选择的40个已进行预测的村庄中收集了白纹伊蚊数量数据。这些数据随后被用于评估模型的准确性。判别模型准确预测了79%的高数量村庄和50%的低数量村庄，总体准确率为70%。回归模型正确识别出了蚊虫数量最多的10个村庄中的7个。该测试表明，为一个地区生成的基于遥感的模型可以成功应用于另一个类似地区。
