Geostatistical Analysis of Spatial Data

First, geostatistics provides descriptive tools such as semivariograms to characterize the spatial pattern of continuous and categorical soil attributes. Various interpolation (kriging) techniques capitalize on the spatial correlation between observations to predict attribute values at unsampled locations using information related to one or several attributes. An important contribution of geostatistics is the assessment of the uncertainty about unsampled values, which usually takes the form of a map of the probability of exceeding critical values, such as regulatory thresholds in pollution or criteria for soil quality. This uncertainty assessment can be combined with expert knowledge for decision making such as delineation of contaminated areas where remedial measures should be taken or fertile areas where specific management plans can be developed. Last, stochastic simulation allows one to generate several models (images) of the spatial distribution of attribute values, all of which are consistent with the information available. A given scenario (remediation process, land use policy) can be applied to the set of realizations, allowing the uncertainty of the response (remediation efficiency, soil productivity) to be assessed.