Conference on Applied Statistics in Agriculture

A COMPARISON OF SPATIAL PREDICTION METHODS USING INTENSE SPATIALLY-ACQUIRED WATER QUALITY DATA

Water quality information obtained through intensive spatial sampling using automated devices provides opportunities to monitor and forecast the spatial distribution of nutrients and phytoplankton concentrations, and help establish water circulation patterns in estuarine and coastal waters. To be cost effective, efficient sampling designs and estimation methodologies must first be developed. As a starting basis, we applied an original transect sampling design that was used to estimate the spatial distribution of chlorophyll a, salinity, and temperature in the Cienaga Grande de Santa Marta, a coastal lagoon in Colombia. We superimposed the transects over satellite images of the lagoon obtained in the period 1993-2001 to evaluate the efficiency and accuracy of using such transects to estimate the distribution of water quality variables. The satellite images were taken in 1993 (SPOT-3), 1995 (Landsat-6), and 1999 (Landsat-6), and water reflectance values were used as a “proxy” for the water quality variables. Spatial prediction using kriging and thin-plate smoothing splines were used to predict reflectance for a grid network of points taken from the images, and predictions were compared with observed values to compare methods and transect routes. Rapid changes in reflectance in short distances (for example , caused by phytoplankton blooms), complicated the analysis, and neither method proved superior over all transect routes and images, although the kriging predictor remained relatively consistent in performance over the various selected sampling routes.