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Table 3 Using RS-extracted environmental indices as covariates in the process of spatial data modeling

From: Remote sensing and disease control in China: past, present and future

Disease Study area Study aim RS Spatial analysis Reference
schistosomiasis Jiangning county To predict snail density. Landsat ETM+, 30 m Linear regression analysis and Kriging interpolation [59]
schistosomiasis Xichang city, Sichuan province To predict snail density. Ikonos, 4 m; ASTER, 30 m Linear regression and semi-variogram analysis [60]
schistosomiasis Jiangsu province To study the spatio-temporal variation of schistosomiasis infection risk. NOAA-AVHRR, 1 km Bayesian spatial modeling [61]
malaria Southeastern Yunnan Province To study the relationship of RS-extracted NDVI to Anopheles density and malaria incidence rate. NOAA-AVHRR, 1 km principal component analysis, factor analysis and grey correlation analysis [62]
schistosomiasis Jiahu village of Yugan county (Poyang Lake) To study quantitative relationships between snail density and various environmental indices from RS images. Landsat TM, 30 m Linear regression analysis [63]
schistosomiasis Eryuan county, Yunnan Province To understand ecological variability of snail distribution. SPOT5, 5 m Bayesian spatial modeling [64]
schistosomiasis Guichi region, Anhui province To identify the risk regions of schistosomiasis. NOAA-AVHRR, 1 km; CBERS, 20 M Generalized additive models [65]