Once applied only to problems of mining-reserves assessment or petroleum-reservoir characterization, geostatistics is now being used in an increasingly large number of disciplines in environmental sciences. On the one hand, it enables the analysis and handling, in a rigorous probabilistic framework of the issues of spatial and temporal interpolation of continuous or categorical environmental variables. On the other hand, the methodology is also used to design and optimize sampling campaigns. "Geostatistics for Environmental Applications" contains forty selected contributions covering the latest progress in a broad spectrum of fields including air quality, climatology, ecology, groundwater hydrology, surface hydrology, oceanography, soil contamination, epidemiology and health, natural hazards, and remote sensing.

Change of support: an inter-disciplinary challenge

Combining categorical and continuous information using Bayesian Maximum Entropy
Geostatistical prediction of spatial extremes and their extent
Monitoring network optimisation using support vector machines
Bayesian Kriging with lognormal data and uncertain variogram parameters
Kriging of scale-invariant data: optimal parameterization of the autocovariance model
Scaling Effects on Finite-Domain Fractional Brownian Motion
The delineation of fishing times and locations for the Shark Bay scallop fishery
A spatial extension of CART: application to classification of ecological data
Using a Markov-type model to combine trawl and acoustic data in fish surveys
Mapping unobserved factors on vine plant mortality
Analysis and modelling of spatially and temporally varying phenological phases
Detection of spatial clusters and outliers in cancer rates using geostatistical filters and spatial neutral models
Geostatistical assessment of long term human exposure to air pollution
Air quality models resulting from multi-source emissions
Variogram estimation with noisy data in the space-time domain: application to air quality modelling
Multiple-point geostatistics: a powerful tool to improve groundwater flow and transport predictions in multi-modal formations
Simulation of radionuclide mass fluxes in a heterogeneous clay formation locally disturbed by excavation
Modeling density-dependent flow using hydraulic conductivity distributions obtained by means of non-stationary indicator simulation
Random field approach to seawater intrusion in heterogeneous coastal aquifers: unconditional simulations and statistical analysis
Uncertainty estimation of well catchments: semianalytical post-processing
Conditional moments of residence time of sorbent solutes under radial flow
Impact of the choice of the variogram model on flow and travel time predictors in radial flows
Strategies to determine dispersivities in heterogeneous aquifers
Solving the groundwater inverse problem by successive flux estimation
Inverse problem for highly heterogeneous porous media: the factorial geostatistical analysis in differential system method
Inverse stochastic estimation of well capture zones with application to the Lauswiesen site (Tübingen, Germany)
"Soft" geostatistical analysis of radioactive soil contamination
Modelling the spatial distribution of copper in the soils around a metal smelter in northwestern Switzerland
Towards a real-time multi-phase sampling strategy optimization
Spatio-temporal mapping of sea floor sediment pollution in the North Sea
Merging Landsat TM and SPOT-P images with geostatistical stochastic simulation
Characterising local spatial variation in land cover using geostatistical functions and the discrete wavelet transform
Distinguishing features from outliers in automatic Kriging-based filtering of MBES data: a comparative study
Forecasting volcanic eruptions using geostatistical methods
Delineation of estuarine management units: Evaluation of an automatic procedure
Estimating indicators of river quality by geostatistics
Stochastic simulation of rainfall using a spacetime geostatistical algorithm
Inferring the lateral subsurface correlation structure from georadar data: Methodological background and experimental evidence.