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Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission depends upon the presence of snails, which serve as intermediate hosts for the parasite. Some efforts have been made to classify snail habitats with remotely sensed data, but not to estimate snail abundance that is an important parameter in schistosomiasis transmission modeling. In this research, snail density was predicted by integrating the field survey and satellite images of different spatial resolution. A mountainous environment near Xichang city, in southwest Sichuan province, China, was chosen as the test site. Land-cover and land-use information extracted from 4 m resolution Ikonos data and elevation data derived from ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) data were used as reference for scaling up to greater spatial extents. Therefore, we estimated land-cover and land-use fraction data at the 30 m resolution level based on classification results from the Ikonos data. Snail abundance for each 30 m resolution grid was then predicted by regressing field survey data with land-cover and land-use fractions. Subsequently, a snail density map was generated using the territory of each of the over 200 residential groups as a mapping unit. An R 2 of 0.87 was obtained between the average snail density predicted and that surveyed for 19 groups. With such a model, we were able to extrapolate scattered snail abundance surveyed at a limited number of sites to the entire area. Spatial autocorrelation of snail distribution was considered as one of the possible factors in predicting snail density and tested for further model calibration.

Snail Density Prediction for Schistosomiasis Control Using Ikonos and ASTER Images