title: [Application of satellite image for surveillance of vegetation landscapes of Oncomelenia-snail habitats in marshland using unsupervised classification].
abstract: OBJECTIVE
  To explore the vegetation landscapes in marshland snail habitats using satellite image.
  
  
  METHODS
  The false-color composition image from band 2, 3 and 4 of LANDSAT ETM + images was classified in the ERDAS IMAGINE 8.5 to analyze the vegetation types in the marshland of Jiangning county, using the unsupervised classification. The efficiency of classification was evaluated by the transformed divergence. The overlaid layers of the classified vegetation image and the vector layer of snail habitats distribution were used to analyze the relationship between the snail distribution and the landscape types.
  
  
  RESULTS
  The land-cover of marshland in LANDSAT ETM234 image in Jiangning county could be classified into 10 types, including water, bare soil, sandy and other landscapes while the transformed divergence analysis showed that there were misclassified pixes between some types especially for the non-continuous types. The study indicated that through adding the NDVI image in the process of classification efficiency of classification and eliminate misclassification in the non-continuous type could be improved. Analysis on the overlaid layer of the vector of snail distribution and the classified image proved that the vegetation covers in marshland snail habitats in Jiangning mainly belonged to type 3, 5 and 6, that responded to the beach with sparse vegetation, exuberant weed and bulrush respectively. The density of snails in the bulrush was higher than that in other 2 landscapes.
  
  
  CONCLUSIONS
  The vegetation type in the marshland snail habitats could be distinguished from the satellite image, which was helpful for the surveillance of snail habitat in marshland and for the prevention of schistosomiasis.
authors.affiliation: Department of Epidemiology, Faculty of Prevention Medicine, Fourth Military Medical University, Xi'an 710032, China.
authors.forename: Zhi-ying
authors.initials: ZY
authors.lastname: Zhang
authors.forename: De-zhong
authors.initials: DZ
authors.lastname: Xu
authors.forename: Xiao-nong
authors.initials: XN
authors.lastname: Zhou
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.forename: Zhi-dong
authors.initials: ZD
authors.lastname: Sun
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.forename: Zi-li
authors.initials: ZL
authors.lastname: Gong
authors.forename: Shi-jun
authors.initials: SJ
authors.lastname: Liu
mesh_terms: D000818:Animals; D001947:Breeding; D002681:China / Q000453:epidemiology; D004199:Disease Vectors; D004463:Ecology; D004784:Environmental Monitoring; D062665:Epidemiological Monitoring; D006801:Humans; D010944:Plants; D017215:Satellite Communications / Q000295:instrumentation; D012549:Schistosoma japonicum / Q000145:classification / Q000254:growth & development; D012554:Schistosomiasis japonica / Q000453:epidemiology / Q000635:transmission*; D012908:Snails / Q000254:growth & development / Q000469:parasitology / Q000502:physiology