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For a study area in the Brazilian state of Santa Catarina, the utilities of local and global forest maps in combination with poststratified and model-assisted estimators for increasing the precision of estimates of forest area were compared. Auxiliary information was in the form of local maps, the recent Global Forest Change map, and combinations of these maps. The poststratified estimators produced estimates of greater precision than the model-assisted regression estimators for maps of categorical variables, but the model assisted estimators produced estimates of greater precision for maps of continuous variables. The Global Forest Change map was the least accurate of all the maps, but it produced estimates of forest area that were similar to those for the other maps and that were more precise than if the map had not been used. Thus, the Global Forest Change map may be an attractive option if local maps are not available or cannot be constructed. The primary contributions of the study are two-fold. First, this is one of the first case studies that rigorously assess the utility of global maps for national estimation. After accumulation of a few more such studies, broader generalizations should be forthcoming. Second, a statistical basis is provided for the previously unexplained greater precision for poststratified estimators than for model-assisted estimators. [ABSTRACT FROM AUTHOR]

Abstract: Estimation of large area forest attributes, such as area of forest cover, from remote sensing-based maps is challenging because of image processing, logistical, and data acquisition constraints. In addition, techniques for estimating and compensating for misclassification and estimating uncertainty are often unfamiliar. Forest area for the state of Santa Catarina in southern Brazil was estimated from each of four satellite image-based land cover maps, and an independent estimate was obtained using observations of forest/non-forest for more than 1000 points assessed as part of the Santa Catarina Forest and Floristic Inventory. The latter data were also used as an accuracy assessment sample for evaluating the four maps. The map analyses consisted of identifying classification errors, constructing error matrices, calculating associated accuracy measures, estimating bias, and constructing 95% confidence intervals for proportion forest estimates using a model-assisted regression estimator. Overall accuracies for the maps ranged from 0.876 to 0.929. The standard errors of the estimates were all smaller than the standard error of the simple random sampling estimate by factors ranging from approximately1.23 to approximately 1.69. The model-assisted regression estimator lends itself to easy implementation for adjusting for estimated classification bias and for constructing confidence intervals. [Copyright &y& Elsevier]