Model-assisted estimation as a unifying framework for estimating the area of land cover and land-cover change from remote sensing

Two common approaches to estimate the area of land cover or land-cover change are to use a confusion matrix to adjust the area derived from pixel counting and to use a survey sampling regression estimator that takes advantage of auxiliary variables to improve the precision of the estimated area. These two seemingly divergent approaches to area estimation are encompassed within the general framework of model-assisted estimation. The theory and methods of model-assisted estimation can be applied to expand the options for area estimators and to extend these estimators to sampling designs beyond those currently in use. The objectives of area estimation and map accuracy assessment can be addressed by the same sample data. This suggests the need for research to identify or develop sampling designs that effectively and efficiently achieve this dual-purpose use of these data.