(Ebook) Geostatistics for Environmental Scientists, Second Edition by Richard Webster, Margaret A. Oliver(auth.) ISBN 9780470028582, 9780470517277, 0470028580, 0470517271

(Ebook) Geostatistics for Environmental Scientists, Second Edition by Richard Webster, Margaret A. Oliver(auth.) ISBN 9780470028582, 9780470517277, 0470028580, 0470517271

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.Content: Chapter 1 Introduction (pages 1–10): Chapter 2 Basic Statistics (pages 11–35): Chapter 3 Prediction and Interpolation (pages 37–46): Chapter 4 Characterizing Spatial Processes: The Covariance and Variogram (pages 47–76): Chapter 5 Modelling the Variogram (pages 77–107): Chapter 6 Reliability of the Experimental Variogram and Nested Sampling (pages 109–138): Chapter 7 Spectral Analysis (pages 139–152): Chapter 8 Local Estimation or Prediction: Kriging (pages 153–194): Chapter 9 Kriging in the Presence of Trend and Factorial Kriging (pages 195–218): Chapter 10 Cross?Correlation, Coregionalization and Cokriging (pages 219–242): Chapter 11 Disjunctive Kriging (pages 243–266): Chapter 12 Stochastic Simulation (pages 267–283):