Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study

Abstract

Abstract. The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m 2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.

Modelling the spatial distribution of Fasciola hepatica in dairy cattle in Europe

“…McCann et al (2010), for example, described the prevalence of fasciolosis in England and Wales at the postcode level with each polygon having a mean surface of 20,000 km 2 . On the other end of the spectrum, some examples can be found describing the potential habitat sites on a single farm with a very high spatial resolution De Roeck et al, 2014).…”

Sheep and Fasciola hepatica in Europe: the GLOWORM experience

“…Some papers highlight climate factors as important predictors (Caminade et al, 2015), while others emphasize environmental features, such as vegetation indices (Valencia-López et al, 2012), and/or soil type (Selemetas et al, 2014), the presence of small water bodies (De Roeck et al, 2014), a combination of environmental factors (Musella et al, , 2014, herd/flock density and other management factors (Bennema et al, 2011). The present study highlights the importance of year-to-year variation of mean temperature, rainfall and seasonality in the explanation of within-country frequency of infection, while the long-term trend of mean temperature and rainfall dominated country-to-country prevalence rates.…”

Longitudinal study on the temporal and micro-spatial distribution of Galba truncatula in four farms in Belgium as a base for small-scale risk mapping of Fasciola hepatica

“…In terms of spectral resolution, standard Red Green Blue (RGB) camera's provide sufficient spectral information to create a baseline map of the presence of the SWBs at farm level, however, investigations should determine whether for example cameras with near-infrared channels can provide added value that can be used to delineate the SWBs. The raw images could then be translated into SWBs, using either pixel-based classification methods or segmentation algorithms [38].…”