[Application of satellite image for surveillance of vegetation landscapes of Oncomelenia-snail habitats in marshland using unsupervised classification].
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.
Zhang
Zhi-ying
ZY
Department of Epidemiology, Faculty of Prevention Medicine, Fourth Military Medical University, Xi'an 710032, China.
Xu
De-zhong
DZ
Zhou
Xiao-nong
XN
Zhou
Yun
Y
Sun
Zhi-dong
ZD
Zhang
Bo
B
Gong
Zi-li
ZL
Liu
Shi-jun
SJ
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