Areal Sample Units for Accuracy Evaluation of Single- date and Multi-temporal Image Classifications

Areal sample units are explored as an alternative to conventional point-based samples for map accuracy assessment. Three analyses are examined: confidence limits on estimates of the total amount of each landcover, regression of map data versus reference data, and a two-part confusion matrix approach. It is concluded that areal sample units provide some advantages compared to point-based samples, but are nonetheless subject to problems associated with providing robust accuracy assessment metrics for rare classes.