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

Abstract

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.

Figure 1

Digital Elevation Model of the survey region [20]. Classes were built using natural breaks (Jenks-Caspall-algorithm). The villages and the study villages are presented respectively in blue and dark red.

Figure 2

Technical steps within the approach of Tele-epidemiology.

Figure 3

Installation of a mosquito light trap and giving instructions to an operator in charge for trap surveillance.

Figure 4

Captured (blue) and predicted (red) Anopheles numbers for 10 study villages with continuous larvae sampling and position of buffer zone within the satellite scene for the duration of mosquito captures (2nd September - 23 rd October 2009).

Figure 5

Adult Anopheles predictions for 37 villages within the satellite scene of SPOT 5 for the 1st October 2009. Data used for the predictions in all 40 villages have been derived from villages within the "base zone for calculations", the zone in which data was taken during fieldwork.