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Activity Number:
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215
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Type:
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Contributed
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Date/Time:
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Tuesday, August 13, 2002 : 12:00 PM to 1:50 PM
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Sponsor:
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Section on Statistics & the Environment*
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| Abstract - #301869 |
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Title:
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A Spatio-temporal Covariance Analysis to Relate River Discharges to Remotely Sensed Ocean Color
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Author(s):
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Ernst Linder*+ and Joseph Salisbury and Veronica Pocsik
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Affiliation(s):
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University of New Hampshire and University of New Hampshire and University of New Hampshire
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Address:
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M 307 Kingsbury Hall, UNH, Durham, New Hampshire, 03824, USA
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Keywords:
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Hierarchical Models ; Spatial Autoregression ; Remote Sensing ; Biological Oceanography
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Abstract:
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The importance of marine biological activity with respect to climate have recently been recognized. Furthermore, ocean biology is strongly impacted by freshwater discharges along the shores. Investigations about the effects of freshwater influx on ocean biological activity have centered on relating remotely sensed ocean color data to measured river discharge. We improve the typical pixel-by-pixel analysis of covariance between daily ocean color and river discharge by modeling individual pixel covariances hierarchically as a conditional Gaussian autoregressive spatial random field within the complete spatio-temporal ensemble of ocean color time series. Space time hierarchical modeling greatly improves the significance of covariances and provides a statistical framework for inverse prediction of freshwater influx based on ocean color remotely sensed data. Our modeling and analysis framework is illustrated with Gulf of Mexico daily ocean color (SEAWIFS) data for 2000 and 2001.
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