Present monitoring and prediction of schistosomiasis’s intermediate parasite, snail, are based on re-mote sensing image’s spectral signatures, and the calculation result is in fact an incom-plete-constraints solutio...Present monitoring and prediction of schistosomiasis’s intermediate parasite, snail, are based on re-mote sensing image’s spectral signatures, and the calculation result is in fact an incom-plete-constraints solution. TM image of the Poyang Lake region on October 31, 2005 was combined with GIS thematic data (DEM, boundary of the Poyang Lake, vegetation, soil and land use) to make a prediction on snail spatial distribution in the region by remote sensing, geo-informatics and knowl-edge-driven modeling according to mechanism of snail occurrence. Result shows that with change of overall fuzzy membership of snail occurrence from high to low, snail occurrence of the snail samples of validation group goes up to 81% within 10% high fuzzy membership range, denoting high efficiency of the model in predicting snail occurrence.展开更多
基金the research project of Jiangxi Provincial Educational Bureau in 2007 (No. 137[2007])the National Natural Science Foundation of China (Grant No. 30590370)
文摘Present monitoring and prediction of schistosomiasis’s intermediate parasite, snail, are based on re-mote sensing image’s spectral signatures, and the calculation result is in fact an incom-plete-constraints solution. TM image of the Poyang Lake region on October 31, 2005 was combined with GIS thematic data (DEM, boundary of the Poyang Lake, vegetation, soil and land use) to make a prediction on snail spatial distribution in the region by remote sensing, geo-informatics and knowl-edge-driven modeling according to mechanism of snail occurrence. Result shows that with change of overall fuzzy membership of snail occurrence from high to low, snail occurrence of the snail samples of validation group goes up to 81% within 10% high fuzzy membership range, denoting high efficiency of the model in predicting snail occurrence.
