Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

被引:21
|
作者
Machault, Vanessa [1 ]
Yebakima, Andre [2 ]
Etienne, Manuel [2 ]
Vignolles, Cecile [3 ]
Palany, Philippe [4 ]
Tourre, Yves M. [5 ]
Guerecheau, Marine [1 ]
Lacaux, Jean-Pierre [1 ]
机构
[1] Univ Toulouse 3, Lab Aerol, Observ Midi Pyrenees, F-31400 Toulouse, France
[2] Agence Reg Sante SD LAV, Conseil Gen Martinique, Serv Demousticat & Lutte Antivectorielle, F-97262 Fort De France, Martinique, France
[3] Ctr Natl Etud Spatiales, Direct Strategie & Programmes Terre Environm Clim, F-31400 Toulouse, France
[4] Meteo France Direct Inter Reg Antilles Guyane, F-97888 Fort De France, Martinique, France
[5] Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA
来源
关键词
dengue; remote-sensing; risk mapping; Aedes aegypti; medical entomology; AEDES-AEGYPTI DIPTERA; RIFT-VALLEY FEVER; PREMISE CONDITION INDEX; CULICIDAE SURVEILLANCE; VECTOR CONTROL; DYNAMICS; ABUNDANCE; DISEASES; SITES; TRANSMISSION;
D O I
10.3390/ijgi3041352
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, French Antilles) using high spatial resolution remote-sensing environmental data and field entomological and meteorological information are presented. This tele-epidemiology methodology allows monitoring the dynamics of diseases closely related to weather/climate and environment variability. A Geoeye-1 image was processed to extract landscape elements that could surrogate societal or biological information related to the life cycle of Aedes vectors. These elements were subsequently included into statistical models with random effect. Various environmental and meteorological conditions have indeed been identified as risk/protective factors for the presence of Aedes aegypti immature stages in dwellings at a given date. These conditions were used to produce dynamic high spatio-temporal resolution maps from the presence of most containers harboring larvae. The produced risk maps are examples of modeled entomological maps at the housing level with daily temporal resolution. This finding is an important contribution to the development of targeted operational control systems for dengue and other vector-borne diseases, such as chikungunya, which is also present in Martinique.
引用
收藏
页码:1352 / 1371
页数:20
相关论文
共 50 条
  • [21] ET Mapping with High-Resolution Airborne Remote Sensing Data in an Advective Semiarid Environment
    Chavez, J. L.
    Gowda, P. H.
    Howell, T. A.
    Garcia, L. A.
    Copeland, K. S.
    Neale, C. M. U.
    JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2012, 138 (05) : 416 - 423
  • [22] A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image
    Wan, Luoma
    Zhang, Hongsheng
    Lin, Guanghui
    Lin, Hui
    ANNALS OF GIS, 2019, 25 (01) : 45 - 55
  • [23] Comparative Analysis of Machine-Learning Models for Soil Moisture Estimation Using High-Resolution Remote-Sensing Data
    Li, Ming
    Yan, Yueguan
    LAND, 2024, 13 (08)
  • [24] High-resolution remote sensing mapping of global land water
    Liao AnPing
    Chen LiJun
    Chen Jun
    He ChaoYing
    Cao Xin
    Chen Jin
    Peng Shu
    Sun FangDi
    Gong Peng
    SCIENCE CHINA-EARTH SCIENCES, 2014, 57 (10) : 2305 - 2316
  • [25] High-resolution remote sensing mapping of global land water
    AnPing Liao
    LiJun Chen
    Jun Chen
    ChaoYing He
    Xin Cao
    Jin Chen
    Shu Peng
    FangDi Sun
    Peng Gong
    Science China Earth Sciences, 2014, 57 : 2305 - 2316
  • [26] Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review
    Neyns, Robbe
    Canters, Frank
    REMOTE SENSING, 2022, 14 (04)
  • [27] High-resolution remote sensing mapping of global land water
    LIAO AnPing
    CHEN LiJun
    CHEN Jun
    HE ChaoYing
    CAO Xin
    CHEN Jin
    PENG Shu
    SUN FangDi
    GONG Peng
    ScienceChina(EarthSciences), 2014, 57 (10) : 2305 - 2316
  • [28] High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder
    Zhou, Weixun
    Shao, Zhenfeng
    Diao, Chunyuan
    Cheng, Qimin
    REMOTE SENSING LETTERS, 2015, 6 (10) : 775 - 783
  • [29] Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
    Fayaz, Muhammad
    Nam, Junyoung
    Dang, L. Minh
    Song, Hyoung-Kyu
    Moon, Hyeonjoon
    APPLIED SCIENCES-BASEL, 2024, 14 (05):
  • [30] Urban cadastral mapping using very high resolution remote sensing data
    K. N. Prudhvi Raju
    Sarvajeet Kumar
    Kshitij Mohan
    Manish Kumar Pandey
    Journal of the Indian Society of Remote Sensing, 2008, 36 : 283 - 288