Application Of GIS And RS To The Prediction Of Oncomelenia Snails In Marshlands Of Jiangning County

Schistosomiasis due to schistosoma japonicum (S. Japonicum) is a severe health problem along and down to the basin of the Yangtze River in China. Its distribution corresponds with that of the intermediate host Oncomcelania snails, which distribute in endemic. For the survival of snails relate closely to the environmental factors of habitats, the study on the relationships between the environmental factors and the distribution of snails are important for the prevention of schistosomiasis and control of snails. Imagery from satellite remote sensing is a digital database of environmental factors with excellent temporal and spatial references, so it becomes a useful tool for the prediction of alive-snails and surveillance of snail habitats. Based on the epidemiological investigation in 2000, this study was to explore the application of remote sensing (RS) to the prediction of the snails in marshland in Jiangning County using the geographic information system (GIS) and spatial analysis.The investigation showed that snails breed both in marshland and mountainous regions in Jiangning County in 2000. The areas of snail habitats in marshlands are about 10.7928 million square meters, which accounted about 98.27% of that of the total snail habitats in Jiangning county. And the average density of alive-snails in habitats of marshlands is about 1.68 per pixels, which are higher than that of mountainous regions. So we can draw our conclusion that theemphasis for the snail control and the prevention of schistosomiasis in Jiangning County should be put on the marshlands.The GIS established from the digitized topo-sheet of 1:50 000 and the co-ordinates of the geographical centroid for each snail-breeding site in 2000 were subjected to analyze the distribution characters of snail habitats in spatial in Jiangning County using ARC VIEW 8.1. It demonstrated that the snail habitats do not distribute randomly and there are some regions that had more snail habitats than others. The spatial scan statistics in further detected 2 spatial aggregations for alive-snails in marshlands and 4 in mountainous regions (P<0.001) in which the density of alive-snail were higher significantly than that outside these areas with P<0.001. This indicated that there are some factors in these aggregation areas favorable for the snail survival. Modeling the semi-variogram of the alive-snails in marshlands depicted the spatial autocorrelation of the alive-snails with rang about 0.0301, which means that the samples separated by distances closer than the range are spatially auto-correlated and the variogram of the alive-snails bydistance could be estimated by the formula ( h ) described asWhereas the samples separated by a distance greater than the range are spatially uncorrelated.The multi-variation regression analysis demonstrated that the density of alive-snails in marshland in Jiangning County related to the environmental factors of snail habitats derived from the imagery of Landsat ETM+. The regression formula showed as Y1=2.481+3.219MSAVI-9.143Wetness-0.261T, where Y1represented the square-root of alive-snails in habitats of marshland and MASVI, Wetness and T represented the environmental proxies derived from Landsat ETM+ imagery for vegetation index, land surface moisture and land surface temperature respectively. And the determination coefficient of the regression function is about 0.282 with P <0.0001. This indicated that there were great residuals using the regression function to estimate the density of alive-snails in marshland and some important determinants had not been included in the model. So we analyzed the spatial characters of the regression residuals in semi-variogram and established prediction model (Y2) to estimate the residuals regression function using the ordinary kriging. Then we used both the regression model for the density of alive-snails and the prediction model for the regression residuals to estimate the distribution of alive-snails. The last model for theprediction of alive-snails in marshlan...