Randriamihaja Mauricianot, Randrianjatovo Tokiniaina M, Evans Michelle V, Ihantamalala Felana A, Herbreteau Vincent, Révillion Christophe, Delaitre Eric, Catry Thibault, Garchitorena Andres
ONG PIVOT, Ranomafana, Madagascar.
Institut de Recherche Pour Le Développement, UMR 224 MIVEGEC (IRD, UM, CNRS), Montpellier, France.
Malar J. 2025 Apr 1;24(1):107. doi: 10.1186/s12936-025-05344-3.
Progress in malaria elimination has been hindered by recent changes in mosquito behaviour and increased insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticidal nets. There is, therefore, increasing interest in the use of larval source management (LSM) to supplement current insecticide-based interventions. However, LSM implementation requires the characterization of larval habitats at fine spatial and temporal scales to ensure interventions are well-placed and well-timed. Remotely sensed optical imagery captured via drones or satellites offers one way to monitor larval habitats remotely, but its use at large spatio-temporal scales has important limitations.
A method using radar imagery is proposed to monitor flooding dynamics in individual rice fields, a primary larval habitat, over very large geographic areas relevant to national malaria control programmes aiming to implement LSM at scale. This is demonstrated for a 3971 km malaria-endemic district in Madagascar with over 17,000 rice fields. Rice field mapping on OpenStreetMap was combined with Sentinel-1 satellite imagery (radar, 10 m) from 2016 to 2022 to train a classification model of radar backscatter to identify rice fields with vegetated and open water, resulting in a time-series of weekly flooding dynamics for thousands of rice fields.
From these time-series, over a dozen indicators useful for LSM implementation, such as the timing and frequency of flooding seasons, were obtained for each rice field. These monitoring tools were integrated into an interactive GIS dashboard for operational use by vector control programmes, with results available at multiple scales (district, sub-district, rice field) relevant for different phases of LSM intervention (e.g. prioritization of sites, implementation, follow-up).
Scale-up of these methods could enable wider implementation of evidence-based LSM interventions and reduce malaria burdens in contexts where irrigated agriculture is a major transmission driver.
疟疾消除工作的进展受到蚊子行为近期变化以及对传统病媒控制措施（如室内滞留喷洒和长效杀虫蚊帐）产生的杀虫剂抗性增加的阻碍。因此，人们越来越关注使用幼虫源管理（LSM）来补充当前基于杀虫剂的干预措施。然而，实施幼虫源管理需要在精细的空间和时间尺度上对幼虫栖息地进行特征描述，以确保干预措施的位置恰当且时机适宜。通过无人机或卫星获取的遥感光学图像提供了一种远程监测幼虫栖息地的方法，但其在大时空尺度上的应用存在重要局限性。
提出了一种利用雷达图像的方法，以监测各个稻田（主要幼虫栖息地）在与旨在大规模实施幼虫源管理的国家疟疾控制计划相关的非常大的地理区域内的洪水动态。这在马达加斯加一个3971平方公里的疟疾流行区得到了证明，该地区有超过17000块稻田。将OpenStreetMap上的稻田地图与2016年至2022年的哨兵-1卫星图像（雷达，10米）相结合，训练一个雷达后向散射分类模型，以识别有植被和开阔水域的稻田，从而得出数千块稻田每周洪水动态的时间序列。
从这些时间序列中，为每块稻田获得了十几个对实施幼虫源管理有用的指标，如洪水季节的时间和频率。这些监测工具被整合到一个交互式地理信息系统仪表板中，供病媒控制计划实际使用，结果可在与幼虫源管理干预的不同阶段（如地点优先级确定、实施、跟进）相关的多个尺度（地区、分区、稻田）上获取。
扩大这些方法的应用可以使基于证据的幼虫源管理干预措施得到更广泛的实施，并在灌溉农业是主要传播驱动因素的情况下减轻疟疾负担。
