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.

Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring

“…By exploiting its multispectral nature [31,45], the algebraic rational of different reflective bands has prevailed over any other single-band indicator. Several indices have been proposed in the literature, including the Normalized Difference Water Index (NDWI) [46][47][48][49], Normalized Difference Water Index 2 (NDWI2) [20,21,[45][46][47], Modified Normalized Difference Water Index (MNDWI) [17][18][19][20][21][50][51][52], and the Normalized Difference Pond Index (NDPI) [52][53][54][55][56]. MNDWI [19,45,57] and NDWI2 perform equally well and generally better than the others.…”

New approaches for integrated and cost-effective malaria vector control

“…Following this line of argumentation, possible cost savings through the selective treatment of larval breeding sites were investigated. Remote sensing derived risk maps were used to identify breeding sites with the highest larval productivity [24][25][26] . Cost savings turned out to be disproportionally higher than the increase in mosquito numbers due to the omission of treating breeding sites.…”

Geographical information systems-based spatial analysis and implications for syphilis interventions in Jiangsu province, People’s Republic of China

“…As climate and environmental factors play an important role in influencing public health and thus have wide applications for epidemic studies, the authors selected six risk factors for the spatial correlation analysis: the annual mean temperature, the normalised difference vegetation index (NDVI) for spring, summer, autumn and winter and the distribution of lakes, rivers, railways and highways Jagai et al, 2009;Shakoor et al, 2010;Dambach et al, 2012).…”