Calibration of remotely sensed proportion or area estimates for misclassification error

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.