Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes - such as the distribution of pollution - vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner's repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.
Preface
1 Introduction 1
1.1 Why geostatistics? 1
1.2 A little history 6
1.3 Finding your way 8
2 Basic Statistics 11
2.1 Measurement and summary 11
2.2 The normal distribution 18
2.3 Covariance and correlation 19
2.4 Transformations 20
2.5 Exploratory data analysis and display 22
2.6 Sampling and estimation 26
3 Prediction and Interpolation 37
3.1 Spatial interpolation 37
3.2 Spatial classification and predicting from soil maps 42
4 Characterizing Spatial Processes: The Covariance and Variogram 47
4.1 Introduction 47
4.2 A stochastic approach to spatial variation: the theory of regionalized variables 48
4.3 Spatial covariance 50
4.4 The covariance function 53
4.5 Intrinsic variation and the variogram 54
4.6 Characteristics of the spatial correlation functions 55
4.7 Which variogram? 60
4.8 Support and Krige's relation 60
4.9 Estimating semivariances and covariances 65
5 Modelling the Variogram 77
5.1 Limitations on variogram functions 79
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