Precision mapping of snail habitat in lake and marshland areas: integrating environmental and textural indicators using Random Forest modeling

Abstract

Abstract Background Schistosomiasis japonica poses a significant health issue in China, largely due to the spatial distribution of Oncomelania hupensis, the only intermediate host of Schistosoma, which directly affects schistosomiasis incidence. This study therefore aimed to address the limitations in existing remote sensing studies, particularly the oversight of spatial scale and seasonal variations in snail habitats by introducing a multi-source data-driven Random Forest approach. Methods This method effectively integrates bottomland and ground-surface texture data with traditional environmental variables for a more comprehensive and accurate snail habitat analysis. Four distinct models focusing on lakes and marshlands in Guichi, China, were developed: the baseline model, including ground-surface texture, bottomland variables, and environmental variables; Model 1, including only environmental variables; Model 2, including ground-surface texture and environmental variables; and Model 3, including bottomland and environmental variables. Results The baseline model outperformed the others, achieving a true skill statistic of 0.93, accuracy of 0.97, kappa statistic of 0.94, and area under the curve of 0.98. The findings identified key high-risk snail habitats, particularly along major rivers and lakes in a belt-like distribution, particularly near the Yangtze River, Qiu Pu River, and surrounding areas of Shengjin Lake, Jiuhua River, and Qingtong River. Conclusions This study providing vital data for effective snail monitoring, control strategies, and schistosomiasis prevention. This approach may also be applicable in locating other epidemic hosts with similar survival and ecological characteristics.