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Table 2 Characteristics of studying classification techniques of RS images for disease control during 2003-present

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

Disease Study area Study aim RS Spatial analysis Reference
schistosomiasis Dongzhi county, Anhui province To explore appropriate index for monitoring snail habitats. Landsat TM, 30 m Unsupervised classification [50]
schistosomiasis Jiangning county To analyze the vegetation characteristics of snail habitats. Landsat ETM+, 30 m Unsupervised classification [51]
schistosomiasis Poyang Lake To identify snail habitats. Landsat TM, 30 m Unsupervised classification and tasseled-cap transformation [52]
schistosomiasis Zhongxiang city,Hubei province To identify snail habitats. Landsat TM, 30 m Neural network analysis [53]
schistosomiasis Poyang lake To identify snail habitats. Landsat TM, 30 m Knowledge-based Decision trees [54]
schistosomiasis Guichi region, Anhui province To identify snail habitats. CBERS, 20 m Index-based quantitative classification [55]
schistosomiasis Poyang lake To predict the distribution of snail habitats. Landsat TM, 30 m Fuzzy classification [56]
schistosomiasis Dali city, Yunnan province To predict the suitability of snail habitats. Landsat TM, 30 m Suitability modeling technique [57]
plague Tongyu county, Jilin province To identify appropriate regions for the living of Spermophilus dauricus. Landsat TM, 30 m Unsupervised classification [58]