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Classification accuracy for stratification with remotely sensed dataAuthor(s): Raymond L. Czaplewski; Paul L. Patterson
Source: Forest Science. 49(3): 402-408
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionTools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy standards are developed in terms of an “error matrix,” which is familiar to remote sensing specialists. In addition, guidance is provided to determine when new remotely sensed classifications are needed to maintain acceptable levels of statistical precision with stratification.
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CitationCzaplewski, Raymond L.; Patterson, Paul L. 2003. Classification accuracy for stratification with remotely sensed data. Forest Science. 49(3): 402-408
Keywordsforest inventory and monitoring, forest statistics
- Variance approximations for assessments of classification accuracy
- Accuracy of Remotely Sensed Classifications For Stratification of Forest and Nonforest Lands
- Statistical properties of measures of association and the Kappa statistic for assessing the accuracy of remotely sensed data using double sampling
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