Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data

Estimates of forest area are among the most common and useful information provided by national forest inventories. The estimates are used for local and national purposes and for reporting to international agreements such as the Montréal Process, the Ministerial Conference on the Protection of Forests in Europe, and the Kyoto Protocol. The estimates are usually based on sample plot data and are calculated using probability-based estimators. These estimators are familiar, generally unbiased, and entail only limited computational complexity, but they do not produce the maps that users are increasingly requesting, and they generally do not produce sufficiently precise estimates for small areas. Model-based estimators overcome these disadvantages, but they may be biased and estimation of variances may be computationally intensive. The study objective was to compare probability- and model-based estimators of mean proportion forest using maps based on a logistic regression model, forest inventory data, and Landsat imagery. For model-based estimators, methods for evaluating bias and reducing the computational intensity were also investigated. Four conclusions were drawn: the logistic regression model exhibited no serious lack of fit to the data; all the estimators produced comparable estimates for mean proportion forest, except for small areas; probability-based inferences enhanced using maps produced increased precision; and the computational intensity associated with estimating variances for model-based estimators can be greatly reduced with no detrimental effects.