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Misclassification bias in areal estimatesAuthor(s): Raymond L. Czaplewski
Source: Photogrammetric Engineering and Remote Sensing. 58(2): 189-192
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionIn addition to thematic maps, remote sensing provides estimates of area in different thematic categories. Areal estimates are frequently used for resource inventories, management planning, and assessment analyses. Misclassification causes bias in these statistical areal estimates. For example, if a small percentage of a common cover type is misclassified as a rare cover type, then the area occupied by the rare type can be severely overestimated. Many categories are rare in detailed classification systems. I present an informal method to anticipate the approximate magnitude of this bias in statistical areal estimates, before a remote sensing study is conducted. If the anticipated magnitude is unacceptable, then statistical calibration methods should be used to produce unbiased areal estimates. I then discuss existing statistical methods that calibrate for misclassification bias with a sample of reference plots.
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CitationCzaplewski, Raymond L. 1992. Misclassification bias in areal estimates. Photogrammetric Engineering and Remote Sensing. 58(2): 189-192.
Keywordsremote sensing, forest inventories, misclassification, bias
- Calibration of remotely sensed proportion or area estimates for misclassification error
- Monitoring landscape level processes using remote sensing of large plots
- Proof of Concept for an Approach to a Finer Resolution Inventory
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