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

Parasites & Vectors

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

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]

ISSN: 1756-3305