Statistical methods in soil and land resource survey

著者

書誌事項

Statistical methods in soil and land resource survey

R. Webster and M.A. Oliver

（Spatial information systems）

Oxford University Press, 1990

  • : [hard cover]
  • : pbk

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注記

Rev. ed. of: Quantitative and numerical methods in soil classification and survey, 1977

Includes bibliographical references (p. [299]-307) and indexes

内容説明・目次

内容説明

This book describes suitable statistical methods for analyzing variation in soil and for relating the soil to its environment. Sound land planning increasingly needs such quantitative information and decisions have been made against a background of variation in soil and other land resources. The authors stress sound sampling technique and show how to use the results for estimation, prediction, and efficient design. They show how classification can enhance the utility of survey data and lead to economies in sampling. Optimal methods for creating classification are described as well as alternative multivariate methods for identifying relations such as principal component and principal co-ordinate analysis. This is essentially a second edition of Dr Webster's book "Quantitative and Numerical Methods in Soil Classification and Survey". In this edition the topic of regression is tackled from the viewpoints of both statistics and natural science. There are three new chapters devoted to geostatistics which introduce regional modelling, kriging, and isarithmic mapping. However, as in the first edition, the accent throughout is on fully quantitative survey of land resources, of measurement, and of estimation. The more advanced techniques are liberally illustrated with simple diagrams.

目次

  • Quantitative description of variable material
  • sampling and estimation
  • generalization, prediction, and classification
  • relations between variables - covariance and correlation
  • regression
  • relations between individuals - similarity
  • ordination
  • analysis of dispersion and discrimination
  • numerical classification - hierarchical systems
  • numerical classification - non hierarchical methods
  • spatial dependence
  • nested sampling and analysis
  • local estimation - kriging.

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