Design-based estimation of forest volume within a model-based sample selection framework
1995
Schreuder, H.T. (USDA Forest Service, Fort Collins, CO.) | Williams, M.S.
Equations for predicting tree volume are often developed using data collected either by a model-based method such as purposive sampling or by stratified random sampling so that an "adequate" number of trees from each diameter class are sampled across the range of classes expected in populations of interest. Such equations are then used together with a design-based (probabilistic) sample such as variable radius plot sampling from a specific population to generate estimates of total volume. The probabilities of selection of the sample trees used in developing the volume equation are ignored, may not be known, or may not be appropriate for populations to which the equation are applied. Less biased and more efficient estimates of the population volume can be generated by using known frequencies or estimated frequencies of the diameter classes in the population from the probabilistic sample used for estimating total volume in the population. These frequencies are used as weighting factors in the construction of population-specific volume equations. We show a reduction in bias and increased efficiency in a simulation study for several forest populations with strong linear relationships between variables and reasonably well known error structure. A model-based sampling procedure called pscx sampling or a large-sample extension thereof is used to select sample trees for volume equations. Such bias reduction did not happen for other populations with weak linear relationships and unknown error structure
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