Sampling Theory for Forest Inventory : A Teach-Yourself Course /

Sampling Theory for Forest Inventory : A Teach-Yourself Course /

Detalles Bibliográficos
Autor principal: Vries, Pieter G.de. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: eBook
Lenguaje:English
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1986.
Edición:1st ed. 1986.
Materias:
Agriculture.
Forestry.
Plant science.
Botany.
Plant Sciences.
Tabla de Contenidos:
  • 1 Simple Random Sampling without Replacement
  • 1.1 Introduction
  • 1.2 Expected Value. Estimators for Population Mean and Total
  • 1.3 Population and Sample Variance
  • 1.4 Variances of Estimated Population Mean and Total
  • 1.5 Confidence Interval and Confidence Statement
  • 1.6 Estimation of Proportions
  • 1.7 Required Sample Size
  • 1.8 Some General Remarks on Sample Plots
  • 1.9 Numerical Examples
  • 2 Stratified Random Sampling
  • 2.1 Introduction
  • 2.2 Unbiased Estimators for Population Mean and Total. Variances
  • 2.3 Some Special Cases
  • 2.4 Optimization of the Sampling Scheme
  • 2.5 Confidence Intervals. Behrens-Fisher Problem
  • 2.6 Gain in Precision Relative to Simple Random Sampling
  • 2.7 Numerical Examples
  • 3 Ratio Estimators in Simple Random Sampling
  • 3.1 Introduction. Population Ratio. Ratio Estimators for Total and Mean
  • 3.2 Variances
  • 3.3 Confidence Interval. Precision versus SRS. Required Sample Size
  • 3.4 Bias of the Ratio Estimator
  • 3.5 Ratio Estimator per Species Group in Mixed Forest
  • 3.6 Numerical Example
  • 3.7 Combining Results of Different Samples to Obtain New Information
  • 4 Ratio Estimators in Stratified Random Sampling
  • 4.1 Introduction
  • 4.2 The Separate Ratio Estimator
  • 4.3 The Combined Ratio Estimator
  • 4.4 Illustrations
  • 4.5 Numerical Example
  • 5 Regression Estimator
  • 5.1 Introduction
  • 5.2 Unbiased Estimator of Population Regression Line from Sample Data
  • 5.3 Linear Regression Estimator and its Variance
  • 5.4 Regression Estimator in Stratified Random Sampling
  • 5.5 Numerical Example
  • 6 Two-Phase Sampling or Double Sampling
  • 6.1 Introduction
  • 6.2 The Ratio Estimator in Double Sampling
  • 6.2.1 Ratio Estimator in Double Sampling — Dependent Phases
  • 6.2.2 Ratio Estimator in Double Sampling — Independent Phases
  • 6.3 The Regression Estimator in Double Sampling
  • 6.3.1 Regression Estimator in Double Sampling — Independent Phases
  • 6.3.2 Regression Estimator in Double Sampling — Dependent Phases.
  • 6.3.3 Numerical Example — Dependent Phases
  • 6.4 Optimization in Double Sampling with Ratio and Regression Estimators
  • 6.5 Double Sampling for Stratification
  • 6.5.1 Introduction
  • 6.5.2 Unbiased Estimator for Population Mean. Variance Expression
  • 6.5.3 Variance Estimator
  • 6.5.4 Optimization of the Sampling Scheme
  • 6.5.5 Numerical Example
  • 6.6 Correction for Misinterpretation in Estimating Stratum Proportions from Aerial Photographs
  • 6.6.1 Derivation of Formulas
  • 6.6.2 Numerical Example
  • 6.7 Volume Estimation with Correction for Misinterpretation
  • 6.7.1 Derivation of Formulas
  • 6.7.2 Numerical Example
  • 7 Continuous Forest Inventory with Partial Replacement of Sample Plots
  • 7.1 Introduction
  • 7.2 Definition of Symbols
  • 7.3 Most Precise Unbiased Linear Estimator for Population Mean on the Second Occasion
  • 7.4 Optimization of Sampling for Current Estimate
  • 7.5 Estimation of Change (Growth or Drain)
  • 7.6 A Compromise Sampling Scheme
  • 7.7 Numerical Example
  • 8 Single- and More-Stage Cluster Sampling
  • 8.1 Introduction
  • 8.2 Estimators in Two-Stage Sampling
  • 8.2.1 Definition of Symbols
  • 8.2.2 Unbiased Estimators for Population Total and Mean per SU
  • 8.2.3 Unbiased Estimators in Special Cases
  • 8.2.3.1 Single-Stage Cluster Sampling
  • 8.2.3.2 Primary Units of Equal Size
  • 8.2.3.3 Equal Within-Cluster Variances
  • 8.2.3.4 Relation to Stratified Random Sampling
  • 8.2.4 Ratio Estimator for Population Total and Mean per SU
  • 8.3 Optimization of the Two-Stage Sampling Scheme
  • 8.4 Three- and More-Stage Sampling
  • 8.5 Numerical Example of Two-Stage Sampling
  • 9 Single-Stage Cluster Sampling as a Research Tool
  • 9.1. Introduction
  • 9.2. Intracluster Correlation Coefficient
  • 9.3. Variance and Intracluster Correlation
  • 9.4. Measures of Heterogeneity
  • 9.4.1. The Intracluster Correlation Coefficient
  • 9.4.2. The C-Index
  • 9.4.3. The Index of Dispersion
  • 9.4.4. Numerical Example
  • 9.5. Intracluster Correlation Coefficient in Terms of Anova Quantities
  • 9.6. About the Optimum Sample Plot Size
  • 10 Area Estimation with Systematic Dot Grids
  • 10.1. Random Sampling with n Points
  • 10.2. Systematic Sampling with n Points
  • 10.3. Numerical Example
  • 11 Sampling with Circular Plots
  • 11.1. Sampling from a Fixed Grid of Squares
  • 11.2. Sampling from a Population of Fixed Circles
  • 11.3. Sampling with Floating Circular Plots
  • 11.4. Comparison of Variances
  • 12 Point Sampling
  • 12.1. General Estimator
  • 12.2. Specific Estimators
  • 12.3. Variances
  • 12.4. Sampling Near the Stand Margin
  • 12.5. Required Sample Size. Choice of K. Questionable Trees
  • 12.6. Numerical Example
  • 12.7. A More General View at PPS-Sampling, wtr
  • 13 Line Intersect Sampling
  • 13.1. Introduction
  • 13.2. BUFFON’s Needle Problem and Related Cases
  • 13.3. Total-Estimator Based on One-line Data
  • 13.4. Variance in Case of One-Line Data
  • 13.5. Sampling with More Than One Line
  • 13.6. Required Number and Length of Transects
  • 13.7. Estimating Properties of Residual Logs in Exploited Areas
  • 13.8. Estimators Based on Circular Elements
  • 13.8.1. Generalization of STRAND’s Estimator
  • 13.8.2. Density Estimation of Mobile Animal Populations
  • 13.8.3. Biomass Estimation in Arid Regions
  • 13.9. Bias in Oriented Needle Populations
  • 13.10. Generalization of LIS Theory
  • 13.10.1. KENDALL Projection and Expected Number of Intersections
  • 13.10.2. General LIS Estimator and its Variance
  • 13.10.3. Applications
  • 13.11. Line Intersect Subsampling
  • 14 List Sampling
  • 14.1. Introduction
  • 14.2. Estimation of Population Total. Variance
  • 14.3. Optimum Measure of Size. Comparison with Simple Random Sampling.
  • 14.4. Numerical Example
  • 14.5. Two-Stage List Sampling
  • 15 3-P Sampling
  • 15.1. Introduction
  • 15.2. The Principle of 3-P Sampling
  • 15.3. Variance and Expected Value of Sample Size and its Inverse
  • 15.4. Considerations about the Sample Size
  • 15.5. GROSENBAUGH’s 3-P Estimators
  • 15.6. Summary and Conclusions
  • 15.7. Numerical Example
  • 15.8. List of Equivalent Symbols
  • 1. A Family of Sampling Schemes
  • 2. Permutations, Variations, Combinations
  • 3. Stochastic Variables
  • 3.1. Stochastic Variables in General. Normal and Standard Norm. Variable
  • 3.2. The Chi-Suare Distribution
  • 3.3. STUDENT’s t-Distribution
  • 3.4. FISHER’s F-Distribution
  • 4. Stochastic Vectors and Some of their Applications
  • App1.2. Distribution of the Pooled Variance in Stratified R. S.
  • App1.3. Analysis of Variance in Stratified Random Sampling
  • App1.4. Analysis of Variance in 2-Stage Sampling
  • Appl.5. Proof of STEIN’s Method for Estimating Required Sample size
  • 5. Covariance, Correlation, Regression
  • 6. The LAGRANGE Multiplier Method of Optimization
  • 7. Expected Value and Variance in Multivariate Distributions
  • 8. Hypergeometric, Multinomial and Binomial Distributions
  • 9. The Most Precise Unbiased Linear Estimator of a Parameter X, based on a Number of Independent Unbiased Estimates of Different Precision
  • 10. Variance Formulas for Sums, Differences, Products and Ratios
  • 11. The Random Forest (POISSON FOREST)
  • 12. Derivation of the Identity used in List Sampling
  • 13. Expanding a Function in a TAYLOR Series
  • 14. About Double Sums
  • 15. Exercises
  • References.

Ejemplares similares