Improving Segmented Taper Models through Generalization and Mixed Effects
Jordan, Lewis, C
AdvisorMatney, Thomas G.
CommitteeSchultz, Emily B.
Souter, Ray A.
One area of forest biometrics that continues to progress is the development of statistical models as tools to describe tree taper. Taper models allow for the prediction of multiple tree level attributes including: diameter at any height, total tree stem volume, merchantable volume and height to any upper stem diameter from any lower stem height, individual log volumes, and subsequently total tree value. In this work, we generalize segmented regression taper models to include multiple segments and compare it to the traditional 3-Segment (2-Knot) models commonly observed in the forestry literature. We then focus on predicting a future realization of diameter given previously observed data. This is accomplished by comparing a segmented taper model under both the Generalized Algebraic Difference Approach (GADA) and Nonlinear Mixed Effects Models (NLMM) methodologies. Both the GADA and NLMM allow for predictions at the individual tree level of a future realization diameter given a differing number of observed height-diameter pairs. Finally, we explore the prediction and cost/benefit of total tree volume obtained from an integrated taper equation with the incorporation of tree specific random effects given differing observed height-diameter pairs.