Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa

International Journal of Health Geographics

Rainer Sauerborn⁵  Ali Sié¹  Cécile Vignolles³  Jean-Pierre Lacaux²  Vanessa Machault²  Peter Dambach⁴ 

[1] Centre de Recherche en Santé de Nouna, Nouna, Burkina Faso;Observatoire Midi Pyrénées/Laboratoire d'Aérologie, Toulouse, France;Centre National d'Etudes Spatiales (CNES), Toulouse, France;Institute of Public Health, University of Heidelberg, Heidelberg, Germany;Centre for Global Health Research, Umeå University, Umeå, Sweden

关键词: TRMM;    MODIS;    Digital elevation model;    Geographic information system;    Burkina Faso;    Rural West Africa;    Malaria;    SPOT 5 satellite;    High spatial resolution;    Remote sensing;   

Others  :  811928
DOI  :  10.1186/1476-072X-11-8

 received in 2012-01-03, accepted in 2012-03-23,  发布年份 2012

【 摘 要 】

Introduction

The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping.

Methods

A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST).

Results

The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points.

Conclusions

Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming.

【 授权许可】

   
2012 Dambach et al; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】

• [1]Rogers DJ, Randolph SE, Snow RW, Hay SI: Satellite imagery in the study and forecast of malaria. Nature 2002, 415:710-715.
• [2]Machault V, Vignolles C, Borchi F, Vounatsou P, Pages F, Briolant S, et al.: The use of remotely sensed environmental data in the study of malaria. Geospat Health 2011, 5:151-168.
• [3]Beck LR, Lobitz BM, Wood BL: Remote sensing and human health: new sensors and new opportunities. Emerg Infect Dis 2000, 6:217-227.
• [4]Craig MH, Snow RW, le Sueur D: A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today 1999, 15:105-111.
• [5]Snow RW, Craig MH, Deichmann U, le Sueur D: A preliminary continental risk map for malaria mortality among African children. Parasitol Today 1999, 15:99-104.
• [6]Ceccato P, Connor SJ, Jeanne I, Thomson MC: Application of geographical information systems and remote sensing technologies for assessing and monitoring malaria risk. Parassitologia 2005, 47:81-96.
• [7]Clarke KC, McLafferty SL, Tempalski BJ: On epidemiology and geographic information systems: a review and discussion of future directions. Emerg Infect Dis 1996, 2:85-92.
• [8]Diuk-Wasser MA, Bagayoko M, Sogoba N, Dolo G, Toure MB, Traore SF, et al.: Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM + sensor data. Int J Remote Sens 2004, 25:359-376.
• [9]Hay SI, Snow RW, Rogers DJ: Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg 1998, 92:12-20.
• [10]Thomas CJ, Lindsay SW: Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing. Trans R Soc Trop Med Hyg 2000, 94:159-163.
• [11]Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Rejmankova E, Ulloa A, et al.: Remote-sensing as a landscape epidemiologic tool to identify villages at high-risk for malaria transmission. AmJTrop Med Hyg 1994, 51:271-280.
• [12]Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Washino RK, Roberts DR, et al.: Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico. AmJTrop Med Hyg 1997, 56:99-106.
• [13]Rejmankova E, Roberts DR, Pawley A, Manguin S, Polanco J: Predictions of adult anopheles-albimanus densities in villages based on distances to remotely-sensed larval habitats. AmJTrop Med Hyg 1995, 53:482-488.
• [14]Roberts DR, Paris JF, Manguin S, Harbach RE, Woodruff R, Rejmankova E, et al.: Predictions of malaria vector distribution in Belize based on multispectral satellite data. AmJTrop Med Hyg 1996, 54:304-308.
• [15]Adimi F, Soebiyanto RP, Safi N, Kiang R: Towards malaria risk prediction in Afghanistan using remote sensing. Malaria Journal 2010, 9:125-136. BioMed Central Full Text
• [16]Charoenpanyanet A, Chen X: Satellite-based modeling of Anopheles mosquito densities on heterogeneous land cover in Western Thailand. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2008, 37:159-164.
• [17]Briet OJT, Dossou-Yovo J, Akodo E, van de Giesen N, Teuscher TM: The relationship between Anopheles gambiae density and rice cultivation in the savannah zone and forest zone of Cote d'Ivoire. Trop Med Int Health 2003, 8:439-448.
• [18]Diuk-Wasser MA, Toure MB, Dolo G, Bagayoko M, Sogoba N, Sissoko I, et al.: Effect of rice cultivation patterns on malaria vector abundance in rice-growing villages in Mali. AmJTrop Med Hyg 2007, 76:869-874.
• [19]Robert V, Gazin P, Carnevale P: Malaria transmission in three sites surrounding the area of Bobo Dioulasso (Burkina Faso): The savanna, a rice field and the city. Bull Soc Vector Ecol 1987, 12:41-43.
• [20]Shuttle Radar Topography Mission [http://www2.jpl.nasa.gov/srtm/] webcite 2011.
• [21]Jarvis A, Reuter HI, Nelson A, Guevara E: Hole-Filled Seamless SRTM data V4. International Centre for Tropical Agriculture (CIAT); 2008.
• [22]Land Processes DAAC: MODIS Reprojection Tool User's Manual 2011.
• [23]Marechal F, Ribeiro N, Lafaye M, Guell A: Satellite imaging and vector-borne diseases: the approach of the French National Space Agency. Geospatial Health 2008, 3:1-5.
• [24]Lacaux JP, Tourre YM, Vignolles C, Ndione JA, Lafaye M: Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal. Remote Sens Environ 2006, 106:66-74.
• [25]Vignolles C, Lacaux JP, Tourre YM, Bigeard G, Ndione JA, Lafaye M: Rift Valley fever in a zone potentially occupied by Aedes vexans in Senegal: dynamics and risk mapping. Geospat Health 2009, 3:211-220.
• [26]Machault V, Gadiaga L, Vignolles C, Jarjaval F, Bouzid S, Sokhna C, et al.: Highly focused anopheline breeding sites and malaria transmission in Dakar. Malaria Journal 2009, 8:138-159. BioMed Central Full Text
• [27]Machault V: Utilisation de données d'observation de la terre par satellite pour l'evaluation des densités vectorielles et de la transmission du paludisme. Marseille: Université de la méditerranée, Thèse de doctorat, Faculté de médecine de Marseille; 2010.
• [28]Dambach P, Sie A, Lacaux JP, Vignolles C, Machault V, Sauerborn R: Using high spatial resolution remote sensing for risk mapping of malaria occurrence in the Nouna district, Burkina Faso. Glob Health Action 2009., 2doi:10.3402/gha.v2i0.2094
• [29]Rouse JW, Hass RH, Schell JA, Deering DW: Monitoring vegetation systems in the great plains with ERTS. 1973, 309-317.
• [30]Huete AR: A soil-adjusted vegetation index (SAVI). Remote Sens Environ 1988, 25:295-309.
• [31]Gao B: NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 1996, 58:257-266.
• [32]McFeeters SK: The use of the normalised difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 1996, 17:1425-1432.
• [33]Xu H: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 2006, 27:3015-3033.
• [34]Cho S-H, Lee H-W, Shin E-H, Lee H-I, Lee W-G, Kim C-H, et al.: A mark-release-recapture experiment with Anopheles sinensis in the northern part of Gyeonggi-do, Korea. Kor J Parasitol 2002, 40:139-148.
• [35]Ejercito A, Urbino M: Flight range of gravid and newly emerged Anopheles. Bulletin of the World Health Organisation 1951, 4:663-671.
• [36]Quraishi MS, Esghi N, Faghih MA: Flight range, lengths of gonotrophic cycles, and longevity of P-32-labeled Anopheles stephensi mysorensis. J Econ Entomol 1966, 59:50-55.
• [37]Balls MJ, Bodker R, Thomas CJ, Kisinza W, Msangeni HA, Lindsay SW: Effect of topography on the risk of malaria infection in the Usambara Mountains, Tanzania. Trans R Soc Trop Med Hyg 2004, 98:400-408.
• [38]Rogers DJ: Satellite imagery, tsetse and trypanosomiasis in Africa. Prev Vet Med 1991, 11:201-220.
• [39]Thomson MC, Connor SJ: Environmental information systems for the control of arthropod vectors of disease. Med Vet Entomol 2000, 14:227-244.
• [40]Tourre YM, Jarlan L, Lacaux JP, Rotela CH, Lafaye M: Spatio-temporal variability of NDVI-precipitation over southernmost South America: possible linkages between climate signals and epidemics. Environmental Research Letters 2008, 3:1-9.
• [41]Gillies MT, Coetzee M: A supplement to the anophelinae of Africa south of the Sahara (Afrotropical region). Johannesburg: The South African Institute for Medical Research; 1987:55.
• [42]Gimnig JE, Ombok M, Kamau L, Hawley WA: Characteristics of larval anopheline (Diptera: Culicidae) habitats in western Kenya. J Med Entomol 2001, 38:282-288.
• [43]Matthys B, N'Goran EK, Kone M, Koudou BG, Vounatsou P, Cisse G, et al.: Urban agricultural land use and characterization of mosquito larval habitats in a medium-sized town of Cote d'Ivoire. J Vector Ecol 2006, 31:319-333.
• [44]Bayoh MN, Lindsay SW: Effect of temperature on the development of the aquatic stages of Anopheles gambiae sensu stricto (Diptera: Culicidae). Bull Entomol Res 2003, 93:375-381.
• [45]Pampana E: A Textbook of Malaria Eradication. London: Oxford Publishing; 1969.
• [46]Takken W, Dekker T, Wijnholds YG: Odor-mediated flight behavior of Anopheles gambiaeGiles Sensu Stricto and A-stephensiListon in response to CO2, acetone, and 1-octen-3-ol (Diptera: Culicidae). J Insect Behav 1997, 10:395-407.
• [47]Mbogo CN, Glass GE, Forster D, Kabiru EW, Githure JI, Ouma JH, et al.: Evaluation of light traps for sampling anopheline mosquitoes in Kilifi, Kenya. J Am Mosq Control Assoc 1993, 9:260-263.