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] |