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    Author(s): Ronald E. McRoberts; Alexander C. Vibrans; Christophe Sannier; Erik Næsset; Matthew C. Hansen; Brian F. Walters; Débora V Lingner
    Date: 2016
    Source: Canadian Journal of Forest Research
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (693.0 KB)


    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.

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    McRoberts, Ronald E.; Vibrans, Alexander C.; Sannier, Christophe; Næsset, Erik; Hansen, Matthew C.; Walters, Brian F.; Lingner, Débora V. 2016. Methods for evaluating the utilities of local and global maps for increasing the precision of estimates of subtropical forest area. Canadian Journal of Forest Research. 46(7): 924-932.


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    logistic regression model, inference, Santa Catarina, Brazil

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