Optimizing sampling strategies for estimating mean water quality in lakes using geostatistical techniques with remote sensing
In planning a sampling regime, it is desirable that the sampling procedure should involve minimum estimation error for a given sample size or minimum sampling effort for a given accuracy. Two approaches for matching sampling effort to accuracy may be used: a classical approach, which ignores spatial dependence between observations, and uses a random scheme; and a geostatistical approach, which exploits spatial dependence, and uses a systematic scheme. Four Airborne Thematic Mapper images of two British lakes were processed to provide a chlorophyll index, reflecting variations in chlorophyll-a concentration. Spatial structure was characterized using the variogram, and the modelled variogram was used in Kriging to plan sampling regimes for estimating the mean chlorophyll. For a given sample size, the systematic scheme incurred less error than the random scheme; and for a given error, the systematic scheme required smaller sample sizes than the random scheme. The relative advantage of the systematic approach over the random sampling approach increased with an increase in sample size and an increase in the proportion of variance in the data that was spatially dependent. This paper demonstrates that the sampling regime must be calibrated to the spatial dynamics of the lake under investigation, and suggests that remote sensing is the ideal means by which to determine such dynamics.