[Retrieving eco-environment factors relevant to Oncomelania snail distribution based on QuickBird image].
OBJECTIVE
To estimate snail distribution by using high spatial resolution QuickBird image on the basis of retrieving the eco-environment factors relevant to snail distribution.


METHODS
Combined with the well-positioned ground data of Oncomelania snails, the meter-level high spatial resolution QuickBird image was used to retrieve the eco-environment factors related to snail distribution in Jiangxin village of Dangtu county, Anhui Province. The factors included vegetation (vegetation index and vegetation cover ratio) and soil (soil texture, soil cover type and humidity). A qualitative analysis was made by using principle component analysis, K-T transformation and supervision classification methods to retrieve the eco-environment factors. The vegetation index NDVI (Normalized Difference Vegetation Index) and MSAVI (Modified Soil Adjustment Vegetation Index) were calculated, and LAI (Leaf area index) and F (vegetation cover ratio) were retrieved. Information from QuickBird data and corresponding ground data were then used to analyze the relationship between snail distribution and environmental factors by using ArcGIS and statistical software.


RESULTS
Snail data were received from 153 ground distribution spots and a GIS database on spacial distribution of snails was established. This database covered snail density, NDVI, MSAVI, LAI(NDVI), LAI(MSAVI), F(NDVI), F(MSAVI), PCA-1, PCA-2, PCA-3, KT-1, KT-2 and KT-3. Statistical analysis showed that the snail density could be estimated by LAINDVI and FMSAVI quantitatively based on the following regression model: Y = -3.919 + 1.22 LAI(MSVI) + 16.076 F(MSAVI). Decision index of the regression model was 0.2.


CONCLUSIONS
A quantitative regression model between Oncomelania snail distribution and environmental variables retrieved from QuickBird images has been established, which may have a wide application prospect. KT-1, KT-2 and KT-3. Statistical analysis showed that the snail density could be estimated by LAINDVI and FMSAVI quantitatively based on the following regression model: Y = -3.919 + 1.22 LAI(MSAVI) + 16.076 F(MSAVI). Decision index of the regression model was 0.2.


CONCLUSIONS
A quantitative regression model between Oncomelania snail distribution and environmental variables retrieved from QuickBird images has been established, which may have a wide application prospect.
China Remote Sensing Satellite Ground Station, Chinese Academy of Sciences, Beijing 100086, China.
Qing-ni
QN
Huang
Ling-li
LL
Tang
Xiao-guang
XG
Jiang
Zhao
Z
Chen
Xiao-nong
XN
Zhou
D000818:Animals; D002681:China; D017753:Ecosystem; D001330:Electronic Data Processing; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D007091:Image Processing, Computer-Assisted; D045483:Rivers; D012908:Snails / Q000254:growth & development*