Sampling Spatial Units for Agricultural Surveys

Cover

Advances in Spatial Science

Sampling Spatial Units for Agricultural Surveys

Copyright
     © Springer-Verlag Berlin Heidelberg 2015
     ISSN 1430-9602
     ISSN 2197-9375 (electronic)
     ISBN 978-3-662-46007-8
     ISBN 978-3-662-46008-5 (eBook)
     DOI 10.1007/978-3-662-46008-5
     Library of Congress Control Number: 2015933847

Dedication

Preface
     Outline of the Book
     Acknowledgments

Proem

Contents

Chapter 1: Essential Statistical Concepts, Definitions, and Terminology
     1.1 Introduction
     1.2 Sampling from Finite Populations
     1.3 The Predictive Approach: The Concept of Superpopulations
     1.4 Statistics for Spatial Data
          1.4.1 Types of Spatial Data
          1.4.2 Spatial Dependence
          1.4.3 Statistical Model for Spatial Data
               1.4.3.1 Geostatistics
               1.4.3.2 Lattice Data Analysis
               1.4.3.3 Spatial Point Pattern Analysis
     Conclusions
     References

Chapter 2: Overview and Brief History
     2.1 Introduction
     2.2 The Use of Spatial Units When Sampling Natural and Environmental Resources
     2.3 Examples of Agricultural Surveys Based on Spatial Reference Frames
          2.3.1 JAS
          2.3.2 LUCAS
          2.3.3 AGRIT
          2.3.4 TER-UTI
     Conclusions
     References

Chapter 3: GIS: The Essentials
     3.1 Introduction
     3.2 Introduction to GIS Concepts and Data Models
     3.3 Spatial Analysis of GIS Data
     3.4 GRASS: An Open Source GIS
     Conclusions
     References

Chapter 4: An Introduction to Remotely Sensed Data Analysis
     4.1 Introduction
     4.2 Basic Concepts
     4.3 Geometric and Radiometric Corrections
     4.4 Image Enhancement
     4.5 Multispectral Transformations
     4.6 The Thematic Extraction of Information
          4.6.1 Unsupervised Classification
          4.6.2 Supervised Classification
          4.6.3 The Contextual Approach to the Thematic Extraction of Information
     4.7 GRASS for Analyzing Remotely Sensed Images
     Conclusion
     References

Chapter 5: Setting Up the Frame
     5.1 Introduction
     5.2 Choice of the Statistical Unit
     5.3 Main Advantages and Disadvantages of Different Frames Typologies
     5.4 Frame Construction
     Conclusions
     References

Chapter 6: Sampling Designs
     6.1 Introduction
     6.2 Simple Random Sampling
     6.3 Systematic Sampling
     6.4 Unequal Selection Probabilities
     6.5 Stratified Sampling
     6.6 Multi-stage Sampling
     6.7 Multi-phase Sampling
     6.8 Sample Coordination and Longitudinal Surveys
     6.9 Ranked Set Sampling
     6.10 Adaptive Sampling
     6.11 Cut-Off Sampling
     Conclusions
     References

Chapter 7: Spatial Sampling Designs
     7.1 Introduction
     7.2 Some Motivations for Spreading the Sample
     7.3 Sampling Plans that Exclude Adjacent Units
     7.4 Generalized Random Tessellation Sampling
     7.5 The Balanced Sampling and Cube Method
     7.6 Selection Methods Based on the Distance Between Statistical Units
     7.7 Numerical Evaluation of the Inclusion Probabilities
     7.8 Empirical Exercises
          7.8.1 Simulated Populations
          7.8.2 A Case Study: Assessing the Ecological Condition of Lakes in Northeastern USA
     Conclusions
     References

Chapter 8: Sample Size and Sample Allocation
     8.1 Introduction
     8.2 Sample Size Estimation for Simple Random Sampling
     8.3 Sample Size Estimation for Stratified Sampling
          8.3.1 Proportional Allocation
          8.3.2 Optimal Allocation
     8.4 The Multipurpose Allocation Problem
          8.4.1 Computational Aspects
     8.5 Modeling Auxiliary and Survey Variables: The Anticipated Moment Approach
     Conclusions
     References

Chapter 9: Survey Data Collection and Processing
     9.1 Introduction
     9.2 Questionnaire Design
     9.3 Data Collection, Instruction Manual, Training of Enumerators, and Field Work Management
     9.4 Data Editing
     9.5 Quality Assurance
     Conclusions
     References

Chapter 10: Advances in Sampling Estimation
     10.1 Introduction
     10.2 Using Auxiliary Information to Improve the Estimation
     10.3 Calibration Estimator
     10.4 Adjusting for Nonresponses
     10.5 Variance Estimation
     10.6 Multiple Frames
     Conclusions
     References

Chapter 11: Small Area Estimation
     11.1 Introduction
     11.2 Direct and Indirect Estimation Methods
     11.3 Small Area Models
          11.3.1 Area Level Models
          11.3.2 Unit Level Models
          11.3.3 Generalized Linear Mixed Models
     11.4 Estimation for Small Area Models
     11.5 The Spatially Augmented Approach to Small Area Estimation
     11.6 The Benchmarking Problem
     Conclusions
     References

Chapter 12: Spatial Survey Data Modeling
     12.1 Introduction
     12.2 Model-Based Inference for Finite Populations
     12.3 Spatial Interpolation as a Predictive Approach for Finite Populations
     12.4 Analysis of Spatial Survey Data
     Conclusions
     References