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Mapping rice field anopheline breeding habitats in Mali West Africa using Landsat ETM+ sensor data

机译：使用Landsat ETM +传感器数据绘制西非马里水稻田间按蚊繁殖生境的图

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摘要

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.
机译：这项研究的目的是确定是否可以使用遥感数据确定马里尼奥诺附近一个灌溉水稻种植区的水稻相关疟疾媒介育种栖息地。水稻生长的早期阶段显示出幼虫产量最高，但是在播种周期的早期（雨季），Landsat传感器的数据经常被云遮挡。在这项研究中，我们研究了基于季节中期和收获时获得的两个Landsat增强主题地图绘制器（ETM）+场景的分类是否可用于绘制不同土地用途和在不同时间（群组）种植的水稻的地图，并推断出该季节较早的水稻生长阶段。我们执行了最大似然监督分类，并使用转换后的散度可分离性指数，kappa系数和混淆矩阵评估了分类的鲁棒性。稻米与其他土地用途的区分精度为98％，而稻米群组的区分为84％（三个等级）或94％（两个等级）。我们的研究表明，光学遥感技术可以从太空中可靠地识别出潜在的疟疾蚊子繁殖栖息地。将来，这些“作物景观图”可用于调查耕作方法与疟疾传播之间的关系。

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