Graduate Thesis Or Dissertation


Effects of Subsampling Trees and Imputation Methods on the Accuracy of Height-to-Crown-Base Predictions for Douglas-fir in Southwest Oregon Public Deposited

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  • Accurate estimates of height-to-crown-base (HTCB) are essential for the reliable projections of stand structure over time required for sustainable forest management. Yet, HTCB often measured alongside total height (HT) for only for a subsample of trees in traditional stand-based inventories and must be imputed for all others. We compared the effects of subsample size, imputation method, and use of imputed rather than measured HT on the accuracy of HTCB predictions. HT and HTCB was imputed for all other sampled trees using a regional nonlinear fixed-effects model (NFEM), a NFEM with an OLS correction factor computed from the subsampled measurements, and a nonlinear mixed-effects model (NMEM) with a stand-level random intercept and best linear unbiased predictor (BLUP) to estimate the random effect for new stands. Based on mean bias (mean signed difference) and error (RMSE), our results indicated that the use of a NMEM with BLUP and subsamples of eight or more trees produced the best predictive performance for HTCB imputation. When five or more trees were subsampled, the difference in predictive error between a NFEM with an OLS correction factor and a NMEM was less than 0.5 feet. Using imputed HT estimates to predict HTCB increased predictive error for all methods and bias in particular when a NFEM and OLS correction factor were used. Overall, our study provides support for the use of NMEMs to impute HT and HTCB to improve predictive performance for Douglas-fir in southwestern Oregon.
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