DESIGN-BASED ESTIMATION OF FOREST VOLUME WITHIN A MODEL-BASED SAMPLE SELECTION FRAMEWORK

Equations for predicting tree volume are often developed using data co llected either by a model-based method such as purposive sampling or b y stratified random sampling so that an ''adequate'' number of trees f rom each diameter class are sampled across the range of classes expect ed in populations of interest. Such equations are then used together w ith a design-based (probabilistic) sample such as variable radius plot sampling from a specific population to generate estimates of total vo lume. The probabilities of selection of the sample trees used in devel oping the volume equation are ignored, may not be known, or may not be appropriate for populations to which the equation are applied. Less b iased and more efficient estimates of the population volume can be gen erated by using known frequencies or estimated frequencies of the diam eter 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 rel ationships between variables and reasonably well known error structure . A model-based sampling procedure called pscx sampling or a large-sam ple extension thereof is used to select sample trees for volume equati ons. Such bias reduction did not happen for other populations with wea k linear relationships and unknown error structure.