Geostatistics for Environmental ScientistsGeostatistics 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. |
Contents
| xi | |
| 1 | |
| 11 | |
3 Prediction and Interpolation | 37 |
4 Characterizing Spatial Processes The Covariance and Variogram | 47 |
5 Modelling the Variogram | 77 |
6 Reliability of the Experimental Variogram and Nested Sampling | 109 |
7 Spectral Analysis | 139 |
9 Kriging in the Presence of Trend and Factorial Kriging | 195 |
10 CrossCorrelation Coregionalization and Cokriging | 219 |
11 Disjunctive Kriging | 243 |
12 Stochastic Simulation | 267 |
Appendix A Aidememoire for Spatial Analysis | 285 |
Appendix B GenStat Instructions for Analysis | 293 |
| 299 | |
| 309 | |
Other editions - View all
Geostatistics for Environmental Scientists Richard Webster,Margaret A. Oliver No preview available - 2007 |
Geostatistics for Environmental Scientists Richard Webster,Margaret A. Oliver No preview available - 2007 |
Common terms and phrases
analysis anisotropy autocorrelation autovariograms average block kriging Broom’s Barn Farm c0 ¼ Chapter coefficients cokriging common logarithms components coregionalization covariance function cross-variograms cumulative distribution data points describe deviation dimensions disjunctive kriging distance parameter effect equation error estimation variance example experimental variogram fields Gaussian gðhÞ ¼ GenStat geostatistics graph grid Hermite polynomials increases kriged estimates kriging system kriging variance lag distance Lagrange multiplier linear logarithms matrix maximum mean squared measured method mg lÀ1 nodes normal distribution nugget variance obtain Oliver ordinary kriging outliers Parzen window plotted potassium prediction punctual kriging random variables range region REML residuals sample variogram sampling interval sampling points scale second-order stationary semivariances shows simple kriging simulated simulated annealing skewness soil solid line spatial variation spherical function standard normal stationary processes statistical survey Table target point threshold transect transformation trend turning bands vector Webster weights window Wyre Forest Zðx ZðxÞ zero