Optimising 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.<br/

Monitoring responses of vegetation to stress

International audienc

Modelling spatial distributions of Ceratium hirundnella and Mycrocystis in a small productive British lake

The short-term relationships between the spatial distributions of phytoplankton and the environmental conditions of Esthwaite Water, a small eutrophic lake in the English Lake District, UK, were examined using a hydrodynamic model. Spatial distributions of phytoplankton were simulated on two occasions the first, when the population was dominated by dinoflagellates; and the second, when the population was dominated by cyanobacteria.Vertical motility of the dinoflagellate Ceratium hirundinellaand buoyancy of the cyanobacteria Microcystis ssprm.were estimated as functions of irradiance. Water velocity fields were estimated through solving the 3-D Navier–Stokes equations on a finite-volume, unstructured non-orthogonal grid. Simulated circulation patterns of water and phytoplankton were similar to those obtained through field observations. Near-surface drift currents were initiated by wind stress, which then generated return currents along the seasonal thermocline. Aggregations of motile Ceratiumthat existed near the thermocline were pushed upwind by the deep return currents and accumulated at upwelling areas. In contrast, near-surface aggregations of Microcystiswere pushed downwind by the surface currents and accumulated at downwelling areas. Horizontal and vertical phytoplankton distributions resulted from the interaction between the vertical motility of the phytoplankton (dependent upon the light environment) and the velocity vectors at the depths at which the phytoplankton accumulated (dependent upon wind stress and morphometry). Modelling showed that phytoplankton motility and buoyancy greatly affect phytoplankton spatial distributions.<br/

Candidate high spectral resolution infrared indices for crop cover

International audienc

Dynamic modelling of the spatio-temporal distribution of phytoplankton in a small productive English lake

The relationships between the spatio-temporal distribution of phytoplankton concentration and the environmental conditions of Esthwaite Water (a small eutrophic lake in the English Lake District, U.K.) were examined using a 3-D computational fluid dynamics (CFD) model. The water velocity field was obtained through solving the 3-D Navier Stokes equation for turbulent flow on a finite-volume, unstructured non-orthogonal grid. The spatio-temporal distributions of two types of phytoplankton were modelled: the cyanobacterium Microcystis, and the dinoflagellate Ceratium. Cyanobacterial buoyancy were estimated according to the Kromkamp and Walsby model, and dinoflagellate motility was estimated according to a model that we devised using empirical data from Esthwaite Water and other similar lakes. Circulation patterns of water and phytoplankton, as simulated by the CFD model, were similar to those obtained through field observations. Downwind surface drift currents were initiated by wind stress, with sub-surface return gradient currents initiated near the thermocline. Near-surface accumulations of cyanobacteria were pushed downwind by the surface currents and accumulated at downwelling areas, and near-thermocline accumulations of dinoflagellates were pushed upwind by the sub-surface return currents, and accumulated at upwelling areas. In all cases, the Coriolis force greatly influenced patterns, causing a clockwise deflection of water flow and phytoplankton accumulation. Through the use of the CFD model, it was possible to conclude that the horizontal and vertical phytoplankton distributions resulted from the interaction between the vertical motility of the phytoplankton (dependent on the light environment) and the velocity vectors at the depths at which the phytoplankton accumulated (dependent upon wind stress and basin morphometry)