Diuk-Wasser M A, Bagayoko M, Sogoba N, Dolo G, Touré M B, Traoré S F, Taylor C E
Department of Organismic Biology, Ecology and Evolution, University of California, Los Angeles, 621 Charles Young Drive, Los Angeles, CA 90095-1606, USA.
Int J Remote Sens. 2004 Jan;25(2):359-376. doi: 10.1080/01431160310001598944.
The aim of this study was to determine whether remotely sensed data could be used to identify rice-related malaria vector breeding habitats in an irrigated rice growing area near Niono, Mali. Early stages of rice growth show peak larval production, but Landsat sensor data are often obstructed by clouds during the early part of the cropping cycle (rainy season). In this study, we examined whether a classification based on two Landsat Enhanced Thematic Mapper (ETM)+ scenes acquired in the middle of the season and at harvesting times could be used to map different land uses and rice planted at different times (cohorts), and to infer which rice growth stages were present earlier in the season. We performed a maximum likelihood supervised classification and evaluated the robustness of the classifications with the transformed divergence separability index, the kappa coefficient and confusion matrices. Rice was distinguished from other land uses with 98% accuracy and rice cohorts were discriminated with 84% accuracy (three classes) or 94% (two classes). Our study showed that optical remote sensing can reliably identify potential malaria mosquito breeding habitats from space. In the future, these 'crop landscape maps' could be used to investigate the relationship between cultivation practices and malaria transmission.
本研究的目的是确定遥感数据是否可用于识别马里尼奥诺附近灌溉水稻种植区与水稻相关的疟疾媒介滋生栖息地。水稻生长早期幼虫产量最高，但在作物生长周期早期（雨季），陆地卫星传感器数据常被云层遮挡。在本研究中，我们研究了基于在季节中期和收获时获取的两幅陆地卫星增强型专题绘图仪（ETM）+影像进行的分类，是否可用于绘制不同土地利用类型以及不同时间（种植期）种植的水稻分布图，并推断季节早期存在哪些水稻生长阶段。我们进行了最大似然监督分类，并用变换散度可分离性指数、kappa系数和混淆矩阵评估分类的稳健性。水稻与其他土地利用类型的区分准确率为98%，水稻种植期的区分准确率为84%（三类）或94%（两类）。我们的研究表明，光学遥感能够从太空可靠地识别潜在的疟疾蚊子滋生栖息地。未来，这些“作物景观图”可用于研究种植方式与疟疾传播之间的关系。
