Assessing the accuracy of remotely sensed data principles and practices 3rd Edition by Kass Green, Russell Congalton ISBN 1498776663 9781498776660

Chapter 1: Introduction

• Why Map?
• Why Assess the Accuracy of a Map?
• Types of Map Accuracy Assessment
• Critical Steps in Accuracy Assessment
• Organization of the Book

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Chapter 2: The History of Map Accuracy Assessment

• How Maps Are Made
• History of Accuracy Assessment
• Positional Accuracy Assessment
• Thematic Accuracy Assessment

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Chapter 3: Planning for Conducting an Accuracy Assessment

• What Type of Assessment?
• Qualitative Accuracy Assessment
• Between Qualitative and Quantitative Assessment
• Similarity Analysis
• Error Budget
• Quantitative Accuracy Assessment
  • Positional Accuracy Assessment
  • Thematic Accuracy Assessment
• Why Do the Assessment?
• How Will the Assessment Be Used?
• What Special Considerations Exist?
• Conclusions

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Chapter 4: Positional Accuracy

• Introduction
• What Is Positional Accuracy?
• Common Standards for Positional Accuracy
• Designing Positional Accuracy Assessment and Sample Selection
• How Is Positional Accuracy Analyzed?
  • Review of Basic Statistics
  • Parameters and Statistics
  • Estimating the Dispersal of Variables
  • Estimating a Confidence Interval around the Estimate of the Mean
  • Statistics in Positional Accuracy Assessment
    • If Errors Are Normally Distributed
    • If Errors Are Not Normally Distributed
• How Is Positional Accuracy Reported?
• Summary

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Chapter 5: Thematic Map Accuracy Basics

• Non-site-specific Assessments
• Site-specific Assessments
• The Error Matrix
• Overall, Producer’s, and User’s Accuracies
• Mathematical Representation of the Error Matrix

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Chapter 6: Thematic Map Accuracy Assessment Considerations

• Thematic Map Classes to Be Assessed
• The Classification Scheme
• Other Data Considerations
  • Continuous vs Non-Continuous Data
  • Spatial Autocorrelation
• Appropriate Sample Unit
  • Single Pixel
  • Cluster of Pixels as a Single Sample Unit
  • Polygon as Single Sample Unit
  • Clusters of Sample Units
• How Many Samples Should Be Taken?
  • Binomial Distribution
  • Multinomial Distribution
• How Should the Samples Be Chosen?
  • Sampling Schemes
  • Sampling Scheme Considerations
  • Equal vs Minimum vs Proportional Distribution
• Conclusions

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Chapter 7: Reference Data Collection

• Source of the Reference Data
  • Using Existing vs Newly Collected Data
  • Remotely Sensed Data vs Field Visits
• How Should the Reference Data Be Collected?
• When Should the Reference Data Be Collected?
• Ensuring Objectivity and Consistency
  • Data Independence
  • Data Collection Consistency
  • Quality Control

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Chapter 8: Basic Analysis Techniques

• Kappa
• Margfit
• Conditional Kappa
• Weighted Kappa
• Compensation for Chance Agreement
• Confidence Limits
• Area Estimation/Correction

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Chapter 9: Analysis of Differences in the Error Matrix

• Errors in the Reference Data
• Sensitivity of the Classification Scheme to Observer Variability
• Inappropriateness of the Remote Sensing Data Employed to Make the Map
• Mapping Error
• Summary
• Appendix 9.1: Forest and Woodland Map Class Key of Haleakalā National Park

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Chapter 10: Fuzzy Accuracy Assessment

• Expanding the Major Diagonal of the Error Matrix
• Measuring Map Class Variability
• The Fuzzy Error Matrix Approach
• The Fuzzy Error Matrix
• Implementation of the Fuzzy Error Matrix
• Another Fuzzy Error Matrix Example
• Summary

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Chapter 11: Object-Based or Polygon Accuracy Assessment

• What Sample Unit Should Be Used?
  • Traditional Tally-based vs Area-based Error Matrix
  • Comparison of the Two Error Matrix Approaches
• Reference Data Collection/Labeling
• Computing the Accuracy
• Conclusions

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Chapter 12: An Object-based Accuracy Assessment Case Study

• Introduction
• Overview of the Case Study
• Accuracy Assessment
  • Thematic Classes to Be Assessed
  • Appropriate Sampling Unit
  • How Many Samples Should Be Taken?
  • How Samples Should Be Chosen
  • Source of Reference Data
  • How Reference Data Should Be Collected
  • Ensuring Consistency and Objectivity in Data Collection
• Analysis
  • Error Matrix and Its Use
  • Statistical Properties and Analysis Techniques
  • Fuzzy Accuracy and Fuzzy Accuracy Assessment
• Results
• Lessons Learned

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Chapter 13: The California Hardwood Rangeland Project

• Introduction
• Background
• Sample Design
  • Distribution of Map Information
  • Appropriate Sample Unit
  • Sample Size and Distribution
• Reference Data Collection
  • Source and Type of Data
  • When Data Should Be Collected
  • Quality Control
• Analysis
  • Development of Error Matrices
  • Statistical Analysis
  • Analysis of Off-diagonal Samples
  • Crown Closure Analysis
  • Crown Closure Map Results
  • Cover Type Analysis
  • Cover Type Map Results
• Discussion
• Conclusions

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Chapter 14: Advanced Topics

• Change Detection
  • Reference Data
  • Sampling
  • Change Detection Error Matrix
  • Two-Step Approach to Change Detection Accuracy Assessment
  • Case Study
    • Step 1: Accuracy of the Change Areas
    • Step 2: Change/No Change Assessment
  • Multi-layer Assessments

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Chapter 15: Summary and Conclusions