Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Raymond L. Czaplewski; Glenn P. Catts
    Date: 1992
    Source: Remote Sensing of Environment. 39(1): 29-43
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (1.0 MB)


    Classifications of remotely sensed data contain misclassification errors that bias areal estimates. Monte Carlo techniques were used to compare two statistical methods that correct or calibrate remotely sensed areal estimates for misclassification bias using reference data from an error matrix. The inverse calibration estimator was consistently superior to the classical estimator using a simple random sample of reference plots. The effects of sample size of reference plots, detail of the classification system, and classification accuracy on the precision of the inverse estimator are discussed. If reference plots are a simple random sample of the study area, then a total sample size of 500-1000 independent reference plots is recommended for calibration.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Czaplewski, Raymond L.; Catts, Glenn P. 1992. Calibration of remotely sensed proportion or area estimates for misclassification error. Remote Sensing of Environment. 39(1): 29-43.


    remote sensing, calibration, misclassification error

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page