Geostatistics for Environmental Scientists

by ;
Edition: 2nd
Format: Hardcover
Pub. Date: 2007-11-12
Publisher(s): WILEY
List Price: $193.96

Buy New

Usually Ships in 8 - 10 Business Days.
$184.72

Rent Textbook

Select for Price
There was a problem. Please try again later.

Rent Digital

Rent Digital Options
Online:1825 Days access
Downloadable:Lifetime Access
$163.20
$163.20

Used Textbook

We're Sorry
Sold Out

How Marketplace Works:

  • This item is offered by an independent seller and not shipped from our warehouse
  • Item details like edition and cover design may differ from our description; see seller's comments before ordering.
  • Sellers much confirm and ship within two business days; otherwise, the order will be cancelled and refunded.
  • Marketplace purchases cannot be returned to eCampus.com. Contact the seller directly for inquiries; if no response within two days, contact customer service.
  • Additional shipping costs apply to Marketplace purchases. Review shipping costs at checkout.

Summary

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.

Author Biography

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.

Table of Contents

Prefacep. xi
Introductionp. 1
Why geostatistics?p. 1
Generalizingp. 2
Descriptionp. 5
Interpretationp. 5
Controlp. 5
A little historyp. 6
Finding your wayp. 8
Basic Statisticsp. 11
Measurement and summaryp. 11
Notationp. 12
Representing variationp. 13
The centrep. 15
Dispersionp. 16
The normal distributionp. 18
Covariance and correlationp. 19
Transformationsp. 20
Logarithmic transformationp. 21
Square root transformationp. 21
Angular transformationp. 22
Logit transformationp. 22
Exploratory data analysis and displayp. 22
Spatial aspectsp. 25
Sampling and estimationp. 26
Target population and unitsp. 28
Simple random samplingp. 28
Confidence limitsp. 29
Student's tp. 30
The x[superscript 2] distributionp. 31
Central limit theoremp. 32
Increasing precision and efficiencyp. 32
Soil classificationp. 35
Prediction and Interpolationp. 37
Spatial interpolationp. 37
Thiessen polygons (Voronoi polygons, Dirichlet tessellation)p. 38
Triangulationp. 38
Natural neighbour interpolationp. 39
Inverse functions of distancep. 40
Trend surfacesp. 40
Splinesp. 42
Spatial classification and predicting from soil mapsp. 42
Theoryp. 43
Summaryp. 45
Characterizing Spatial Processes: The Covariance and Variogramp. 47
Introductionp. 47
A stochastic approach to spatial variation: the theory of regionalized variablesp. 48
Random variablesp. 48
Random functionsp. 49
Spatial covariancep. 50
Stationarityp. 52
Ergodicityp. 53
The covariance functionp. 53
Intrinsic variation and the variogramp. 54
Equivalence with covariancep. 54
Quasi-stationarityp. 55
Characteristics of the spatial correlation functionsp. 55
Which variogram?p. 60
Support and Krige's relationp. 60
Regularizationp. 63
Estimating semivariances and covariancesp. 65
The variogram cloudp. 65
h-Scattergramsp. 66
Average semivariancesp. 67
The experimental covariance functionp. 73
Modelling the Variogramp. 77
Limitations on variogram functionsp. 79
Mathematical constraintsp. 79
Behaviour near the originp. 80
Behaviour towards infinityp. 82
Authorized modelsp. 82
Unbounded random variationp. 83
Bounded modelsp. 84
Combining modelsp. 95
Periodicityp. 97
Anisotropyp. 99
Fitting modelsp. 101
What weights?p. 104
How complex?p. 105
Reliability of the Experimental Variogram and Nested Samplingp. 109
Reliability of the experimental variogramp. 109
Statistical distributionp. 109
Sample size and designp. 119
Sample spacingp. 126
Theory of nested sampling and analysisp. 127
Link with regionalized variable theoryp. 128
Case study: Youden and Mehlich's surveyp. 129
Unequal samplingp. 131
Case study: Wyre Forest surveyp. 134
Summaryp. 138
Spectral Analysisp. 139
Linear sequencesp. 139
Gilgai transectp. 140
Power spectrap. 142
Estimating the spectrump. 144
Smoothing characteristics of windowsp. 148
Confidencep. 149
Spectral analysis of the Caragabal transectp. 150
Bandwidths and confidence intervals for Caragabalp. 150
Further reading on spectral analysisp. 152
Local Estimation or Prediction: Krigingp. 153
General characteristics of krigingp. 154
Kinds of krigingp. 154
Theory of ordinary krigingp. 155
Weightsp. 159
Examplesp. 160
Kriging at the centre of the latticep. 161
Kriging off-centre in the lattice and at a sampling pointp. 169
Kriging from irregularly spaced datap. 172
Neighbourhoodp. 172
Ordinary kriging for mappingp. 174
Case studyp. 175
Kriging with known measurement errorp. 180
Summaryp. 180
Regional estimationp. 181
Simple krigingp. 183
Lognormal krigingp. 185
Optimal sampling for mappingp. 186
Isotropic variationp. 188
Anisotropic variationp. 190
Cross-validationp. 191
Scatter and regressionp. 193
Kriging in the Presence of Trend and Factorial Krigingp. 195
Non-stationarity in the meanp. 195
Some backgroundp. 196
Application of residual maximum likelihoodp. 200
Estimation of the variogram by REMLp. 200
Practicalitiesp. 203
Kriging with external driftp. 203
Case studyp. 205
Factorial kriging analysisp. 212
Nested variationp. 212
Theoryp. 212
Kriging analysisp. 213
Illustrationp. 218
Cross-Correlation, Coregionalization and Cokrigingp. 219
Introductionp. 219
Estimating and modelling the cross-correlationp. 222
Intrinsic coregionalizationp. 224
Example: CEDAR Farmp. 226
Cokrigingp. 228
Is cokriging worth the trouble?p. 231
Example of benefits of cokrigingp. 232
Principal components of coregionalization matricesp. 235
Pseudo-cross-variogramp. 241
Disjunctive Krigingp. 243
Introductionp. 243
The indicator approachp. 246
Indicator codingp. 246
Indicator variogramsp. 247
Indicator krigingp. 249
Disjunctive krigingp. 251
Assumptions of Gaussian disjunctive krigingp. 251
Hermite polynomialsp. 252
Disjunctive kriging for a Hermite polynomialp. 254
Estimation variancep. 256
Conditional probabilityp. 256
Change of supportp. 257
Case studyp. 257
Other case studiesp. 263
Summaryp. 266
Stochastic Simulationp. 267
Introductionp. 267
Simulation from a random processp. 268
Unconditional simulationp. 270
Conditional simulationp. 270
Technicalitiesp. 271
Lower-upper decompositionp. 272
Sequential Gaussian simulationp. 273
Simulated annealingp. 274
Simulation by turning bandsp. 276
Algorithmsp. 277
Uses of simulated fieldsp. 277
Illustrationp. 278
Aide-memoire for Spatial Analysisp. 285
Introductionp. 285
Notationp. 285
Screeningp. 285
Histogram and summaryp. 286
Normality and transformationp. 287
Spatial distributionp. 288
Spatial analysis: the variogramp. 288
Modelling the variogramp. 290
Spatial estimation or prediction: krigingp. 291
Mappingp. 292
GenStat Instructions for Analysisp. 293
Summary statisticsp. 293
Histogramp. 294
Cumulative distributionp. 294
Postingp. 295
The variogramp. 295
Experimental variogramp. 295
Fitting a modelp. 296
Krigingp. 297
Coregionalizationp. 297
Auto- and cross-variogramsp. 297
Fitting a model of coregionalizationp. 298
Cokrigingp. 298
Controlp. 298
Referencesp. 299
Indexp. 309
Table of Contents provided by Ingram. All Rights Reserved.

An electronic version of this book is available through VitalSource.

This book is viewable on PC, Mac, iPhone, iPad, iPod Touch, and most smartphones.

By purchasing, you will be able to view this book online, as well as download it, for the chosen number of days.

Digital License

You are licensing a digital product for a set duration. Durations are set forth in the product description, with "Lifetime" typically meaning five (5) years of online access and permanent download to a supported device. All licenses are non-transferable.

More details can be found here.

A downloadable version of this book is available through the eCampus Reader or compatible Adobe readers.

Applications are available on iOS, Android, PC, Mac, and Windows Mobile platforms.

Please view the compatibility matrix prior to purchase.