Richard Webster, Margaret A. Oliver

Geostatistics for Environmental Scientists (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 330
• Erscheinungstermin: 22. Oktober 2007
• Englisch
• ISBN-13: 9780470517260
• Artikelnr.: 37294243

Autorenporträt

Richard Webster, Rothamsted Research, Harpenden Dr Webster is the Senior Research Fellow at Rothamsted Research. Margaret A. Oliver, Visiting Professor, Department of Soil Science, University of Reading Professor Oliver has taught geostatistics, applied statistics, multivariate analysis and pedology to undergraduates and postgraduates. She also established a short geostatistics course while at the University of Birmingham, which has now been taught in several countries (e.g. Sweden, USA and Mexico). She is the author of over 70 papers and two co-authored books.

Inhaltsangabe

Preface 1 Introduction 2 Basic Statistics 3 Prediction and Interpolation 4
Characterizing Spatial Processes: The Covariance and Variogram 5 Modelling
the Variogram 6 Reliability of the Experimental Variogram and Nested
Sampling 7 Spectral Analysis 8 Local Estimation or Prediction: Kriging 9
Kriging in the Presence of Trend and Factorial Kriging 10
Cross-Correlation, Coregionalization and Cokriging 11 Disjunctive Kriging
12 Stochastic Simulation (new file) Appendix A Appendix B References Index