The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?
The application of drones for mosquito larval habitat identification in rural environments: a practical approach for malaria control?
复制标题
无人机在农村环境中识别蚊子幼虫栖息地的应用：控制疟疾的实用方法？
DOI：
10.1101/2020.08.05.237933
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Stanton M
中科院分区：
文献类型：
--
作者：
Stanton M
蚊媒疾病的时空趋势受幼虫栖息地的位置和季节性的驱动。疾病控制的一种方法是通过改变幼虫栖息地来减少蚊子数量，称为幼虫源管理（LSM）。在疟疾控制方面，由于人们认为难以确定目标地区，目前认为最低限度标准在农村地区不切实际。高分辨率无人机测绘被认为是解决这一障碍的实际解决方案。在本文中，作者的经验，无人机领导的幼虫栖息地识别在马拉维被用来评估这种approach.MethodsDrone映射和幼虫调查的可行性进行了2018年和2020年之间在卡松古区，马拉维。水体和水生植被的图像中使用手动方法和基于地理对象的图像分析（GeoOBIA）和分类的性能进行了比较。此外，还记录了关于捕捉无人机图像为疟疾控制提供信息的实际方面的观察结果，包括成本、时间、计算和技能要求。幼虫采样点的特点是在无人机图像和广义线性混合模型中可见的生物因素，以确定其与幼虫present.ResultsImagery覆盖面积为8.9 km2，在8个站点被捕获。幼虫栖息地的特点，成功地确定使用GeoOBIA的图像捕获的标准相机（中位精度= 98%），并没有显着的改善后，观察到的数据从近红外传感器。然而，与人工识别相比，这种方法需要更多的处理时间和技术技能。从326个站点捕获的幼虫样本证实，无人机捕获的特征，包括水生植被的存在和类型，显着相关的幼虫presentation.ConclusionsThis研究表明，潜在的无人机获取的图像，以支持蚊子幼虫的栖息地识别在农村，疟疾流行地区，虽然技术挑战被确定，这可能会阻碍这种方法的规模。然而，已经确定了潜在的解决方案，包括加强与马拉维等国家蓬勃发展的无人机行业的联系。因此，无人机、图像分析和病媒控制领域的专家需要进一步磋商，以制定更详细的指导意见，说明如何在疟疾控制中最有效地利用这一技术。
BackgroundSpatio-temporal trends in mosquito-borne diseases are driven by the locations and seasonality of larval habitat. One method of disease control is to decrease the mosquito population by modifying larval habitat, known as larval source management (LSM). In malaria control, LSM is currently considered impractical in rural areas due to perceived difficulties in identifying target areas. High resolution drone mapping is being considered as a practical solution to address this barrier. In this paper, the authors’ experiences of drone-led larval habitat identification in Malawi were used to assess the feasibility of this approach.MethodsDrone mapping and larval surveys were conducted in Kasungu district, Malawi between 2018 and 2020. Water bodies and aquatic vegetation were identified in the imagery using manual methods and geographical object-based image analysis (GeoOBIA) and the performances of the classifications were compared. Further, observations were documented on the practical aspects of capturing drone imagery for informing malaria control including cost, time, computing, and skills requirements. Larval sampling sites were characterized by biotic factors visible in drone imagery and generalized linear mixed models were used to determine their association with larval presence.ResultsImagery covering an area of 8.9 km2across eight sites was captured. Larval habitat characteristics were successfully identified using GeoOBIA on images captured by a standard camera (median accuracy = 98%) with no notable improvement observed after incorporating data from a near-infrared sensor. This approach however required greater processing time and technical skills compared to manual identification. Larval samples captured from 326 sites confirmed that drone-captured characteristics, including aquatic vegetation presence and type, were significantly associated with larval presence.ConclusionsThis study demonstrates the potential for drone-acquired imagery to support mosquito larval habitat identification in rural, malaria-endemic areas, although technical challenges were identified which may hinder the scale up of this approach. Potential solutions have however been identified, including strengthening linkages with the flourishing drone industry in countries such as Malawi. Further consultations are therefore needed between experts in the fields of drones, image analysis and vector control are needed to develop more detailed guidance on how this technology can be most effectively exploited in malaria control.
登录
查看更多内容
影响因子：
3.7
作者：
Beck-Johnson LM;Nelson WA;Paaijmans KP;Read AF;Thomas MB;Bjørnstad ON
通讯作者：
Bjørnstad ON
影响因子：
3.2
作者：
Vantaux A;Ouattarra I;Lefèvre T;Dabiré KR
通讯作者：
Dabiré KR
影响因子：
3
作者：
Tokarz R;Novak RJ
通讯作者：
Novak RJ
附加文件 7：使用低成本无人机绘制疟疾病媒栖息地地图
DOI：
10.6084/m9.figshare.c.3666382_d2
发表时间：
2017
期刊：
Journal of Hydrology: Regional Studies
影响因子：
--
作者：
A. Hardy;M. Makame;DĂłnall Cross;S. Majambere;M. Msellem
通讯作者：
M. Msellem
适合资源匮乏环境下生产运营的低成本多功能无人机设计与开发
DOI：
--
发表时间：
2018
期刊：
影响因子：
--
作者：
Zachary D. Standridge
通讯作者：
Zachary D. Standridge