Title of article :
Satellite remote sensing of space–time plankton variability in the Bay of Bengal: Connections to cholera outbreaks
Author/Authors :
Jutla، نويسنده , , Antarpreet S. and Akanda، نويسنده , , Ali S. and Islam، نويسنده , , Shafiqul، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Abstract :
Cholera bacteria exhibit strong association with coastal plankton. Characterization of space–time variability of chlorophyll, a surrogate for plankton abundance, in the northern Bay of Bengal is an essential first step to develop any methodology for predicting cholera outbreaks in the Bengal Delta region using remote sensing. This study quantifies the space–time distribution of chlorophyll, using the data from SeaWiFS, in the Bay of Bengal region using 10 years of satellite data. Variability of chlorophyll at daily scale, irrespective of spatial averaging, resembles white noise. At a monthly scale, chlorophyll shows distinct seasonality and chlorophyll values are significantly higher close to the coast than in the offshore regions. At pixel level (9 km) on monthly scale, on the other hand, chlorophyll does not exhibit much persistence in time. With increased spatial averaging, temporal persistence of chlorophyll increases and lag 1 autocorrelation stabilizes around 0.60 for 1296 km2 or larger areal averages. In contrast to the offshore regions, spatial analyses of chlorophyll suggest that only coastal region has a stable correlation length of 100 km. Presence (absence) of correlation length in the coastal (offshore) regions indicate that the two regions may have two separate processes controlling the production of phytoplankton. This study puts a lower limit on space–time averaging of satellite measured plankton at 1296 km2 monthly scale to establish relationships with cholera incidence in Bengal Delta.
Keywords :
Chlorophyll , phytoplankton , Remote sensing , SeaWiFS , cholera , Spatial and temporal , Variability , Variogram , Coastal hydrology
Journal title :
Remote Sensing of Environment
Journal title :
Remote Sensing of Environment