• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用遥感数据预测蚊虫栖息地和疟疾季节:实践、问题与展望

From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives.

作者信息

Hay S I, Snow R W, Rogers D J

机构信息

Trypanosomiasis and Land-use in Africa (TALA) Research Group, Department of Zoology, University of Oxford, South Parks Road, Oxford, UK OX1 3PS.

出版信息

Parasitol Today. 1998 Aug;14(8):306-13. doi: 10.1016/s0169-4758(98)01285-x.

DOI:10.1016/s0169-4758(98)01285-x
PMID:17040796
Abstract

Remote sensing techniques are becoming increasingly important for identifying mosquito habitats, investigating malaria epidemiology and assisting malaria control. Here, Simon Hay, Bob Snow and David Rogers review the development of these techniques, from aerial photographic identification of mosquito larval habitats on the local scale through to the space-based survey of malaria risk over continental areas using increasingly sophisticated airborne and satellite-sensor technology. They indicate that previous constraints to uptake are becoming less relevant and suggest how future delays in the use of remotely sensed data in malaria control might be avoided.

摘要

遥感技术在识别蚊虫栖息地、调查疟疾流行病学以及协助疟疾防控方面正变得越来越重要。在此,西蒙·海伊、鲍勃·斯诺和大卫·罗杰斯回顾了这些技术的发展历程,从在地方尺度上通过航空摄影识别蚊虫幼虫栖息地,到利用日益复杂的机载和卫星传感器技术对大陆地区的疟疾风险进行天基调查。他们指出,以往阻碍采用这些技术的因素正变得越来越无关紧要,并提出了如何避免未来在疟疾防控中使用遥感数据时出现延迟的建议。

相似文献

1
From predicting mosquito habitat to malaria seasons using remotely sensed data: practice, problems and perspectives.利用遥感数据预测蚊虫栖息地和疟疾季节:实践、问题与展望
Parasitol Today. 1998 Aug;14(8):306-13. doi: 10.1016/s0169-4758(98)01285-x.
2
Identification and characterization of larval and adult anopheline mosquito habitats in the Republic of Korea: potential use of remotely sensed data to estimate mosquito distributions.韩国按蚊幼虫和成虫栖息地的识别与特征分析:利用遥感数据估计蚊虫分布的潜力
Int J Health Geogr. 2005 Jul 13;4:17. doi: 10.1186/1476-072X-4-17.
3
Spatial-temporal distribution of Anopheles larval habitats in Uganda using GIS/remote sensing technologies.利用 GIS/遥感技术研究乌干达按蚊幼虫栖息地的时空分布。
Malar J. 2018 Nov 12;17(1):420. doi: 10.1186/s12936-018-2567-z.
4
Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data.利用多尺度遥感数据评估斯威士兰环境因素与疟疾媒介滋生地之间的关系。
Geospat Health. 2015 Jun 3;10(1):302. doi: 10.4081/gh.2015.302.
5
Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.美国东部地区遥感气溶胶光学厚度与PM2.5之间关系的评估及统计建模
Res Rep Health Eff Inst. 2012 May(167):5-83; discussion 85-91.
6
Spatial distribution and habitat characterization of mosquito species during the dry season along the Mara River and its tributaries, in Kenya and Tanzania.肯尼亚和坦桑尼亚沿马拉河及其支流在旱季的蚊子种类的空间分布和生境特征。
Infect Dis Poverty. 2018 Jan 18;7(1):2. doi: 10.1186/s40249-017-0385-0.
7
Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya.肯尼亚中部三个水稻农业村庄综合体中用于疟疾绘图的植被协变量的远程和实地量化。
Int J Health Geogr. 2007 Jun 5;6:21. doi: 10.1186/1476-072X-6-21.
8
Spatial relationship between adult malaria vector abundance and environmental factors in western Kenya highlands.肯尼亚西部高地成年疟疾媒介丰度与环境因素之间的空间关系。
Am J Trop Med Hyg. 2007 Jul;77(1):29-35.
9
Remote sensing as a tool for mapping mosquito breeding habitats and associated health risk to assist control efforts and development plans: a case study in Wadi El Natroun, Egypt.遥感作为绘制蚊虫滋生地及相关健康风险地图以辅助控制工作和发展规划的工具:埃及纳特龙湖谷的案例研究
J Egypt Soc Parasitol. 2004 Aug;34(2):367-82.
10
Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data.利用多时相气象卫星传感器数据预测肯尼亚的疟疾季节。
Trans R Soc Trop Med Hyg. 1998 Jan-Feb;92(1):12-20. doi: 10.1016/s0035-9203(98)90936-1.

引用本文的文献

1
Identifying mosquito plant hosts from ingested nectar secondary metabolites.从摄入的花蜜次生代谢产物中识别蚊子的植物宿主。
Sci Rep. 2025 Feb 22;15(1):6488. doi: 10.1038/s41598-025-88933-1.
2
Spotting from satellite: modeling habitat suitability in central Italy using Sentinel-2 and deep learning techniques.卫星观测:利用哨兵2号和深度学习技术对意大利中部的栖息地适宜性进行建模
Front Vet Sci. 2024 Jul 4;11:1383320. doi: 10.3389/fvets.2024.1383320. eCollection 2024.
3
Unmanned aerial vehicles for surveillance and control of vectors of malaria and other vector-borne diseases.
用于监测和控制疟疾等媒介传播疾病的媒介的无人机。
Malar J. 2023 Jan 20;22(1):23. doi: 10.1186/s12936-022-04414-0.
4
The use of drones for mosquito surveillance and control.利用无人机进行蚊虫监测与控制。
Parasit Vectors. 2022 Dec 16;15(1):473. doi: 10.1186/s13071-022-05580-5.
5
Satellite Observations and Malaria: New Opportunities for Research and Applications.卫星观测与疟疾:研究与应用的新机遇。
Trends Parasitol. 2021 Jun;37(6):525-537. doi: 10.1016/j.pt.2021.03.003. Epub 2021 Mar 25.
6
Dengue risk assessment using multicriteria decision analysis: A case study of Bhutan.利用多准则决策分析进行登革热风险评估:以不丹为例。
PLoS Negl Trop Dis. 2021 Feb 10;15(2):e0009021. doi: 10.1371/journal.pntd.0009021. eCollection 2021 Feb.
7
Predicting Aedes aegypti infestation using landscape and thermal features.利用景观和热特征预测埃及伊蚊滋生。
Sci Rep. 2020 Dec 10;10(1):21688. doi: 10.1038/s41598-020-78755-8.
8
Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation.南非林波波省疟疾发病率建模:经典与贝叶斯估计方法比较。
Int J Environ Res Public Health. 2020 Jul 13;17(14):5016. doi: 10.3390/ijerph17145016.
9
Spatial Associations Between Land Use and Infectious Disease: Zika Virus in Colombia.土地利用与传染病的空间关联:哥伦比亚的寨卡病毒。
Int J Environ Res Public Health. 2020 Feb 11;17(4):1127. doi: 10.3390/ijerph17041127.
10
Wetlands and Malaria in the Amazon: Guidelines for the Use of Synthetic Aperture Radar Remote-Sensing.亚马逊湿地与疟疾:合成孔径雷达遥感应用指南
Int J Environ Res Public Health. 2018 Mar 7;15(3):468. doi: 10.3390/ijerph15030468.