A Random-Parameter Height-Dbh Model for Cherrybark Oak

Lynch, Thomas B.; Holley, A. Gordon; Stevenson, Douglas J.

doi:10.1093/sjaf/29.1.22

Cited by 28 publications

(14 citation statements)

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“…Mixed-model approaches formally incorporate the between-stand variability of the h-d relationship into the model. Estimation of h using NMEM has been previously reported by Lynch et al (2005) and Castedo et al (2006).…”

Section: Strategy 2 -Nmem With One Random Stand Effectmentioning

confidence: 97%

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

2008

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Using an extensive Douglas-fir data set from southwest Oregon, we examined the (1) performance and suitability of selected prediction strategies, (2) contribution of relative position and stand-density measures in improving tree height (h) prediction values, and (3) effect of different subsampling designs to fill in missing h values in a new stand using a regional nonlinear model. Nonlinear mixed-effects models (NMEM) substantially improved the accuracy and precision of height prediction over the conventional nonlinear fixed-effects model (NFEM) that assumes the observations are independent, particularly when a few trees are subsampled for height. The predictive performance of a correction factor on a NFEM with relative position and stand-density measures was comparable to that of a NMEM when four or more trees were subsampled for height. When two or more heights were randomly subsampled, the NMEM efficiently explained the differences in the height–diameter relationship because of the variations in relative position of trees and stand density without having to incorporate them into the model. When only one height was subsampled, selecting the largest diameter tree in the stand would result in a lower predicted root mean square error (RMSE) than randomly selecting the height, regardless of the model form or fitting strategy used.

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Section: Strategy 2 -Nmem With One Random Stand Effectmentioning

confidence: 97%

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

2008

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“…As a result, the use of non-linear ordinary least square (OLS) estimation is often not reliable, because an assumption of random samples and independent observations is violated and the presence of autocorrelation does not conform to the assumptions of OLS. Therefore, mixed-effects modelling as an alternative to OLS for repeated measurements and grouped data has been widely used in forestry growth and yield models (Lappi, 1991;Lynch et al, 2005Lynch et al, , 2012Budhathoki et al, 2008;Temesgen et al, 2014). This approach helpsto address a possible source of subject-specific variation that the OLS approach does not consider because the fixed-effect parameters represent population average responses, while random effects parameters represent response specific to each sampling unit (Lappi, 1991;Lynch et al, 2005).…”

Section: Forestryani Nternational Journal Of Forest Researchmentioning

confidence: 99%

Using quadratic mean diameter and relative spacing index to enhance height–diameter and crown ratio models fitted to longitudinal data

et al. 2016

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The inclusion of quadratic mean diameter (QMD) and relative spacing index (RSI) substantially improved the predictive capacity of height-diameter at breast height (d.b.h.) and crown ratio models (CR), respectively. Data were obtained from 208 permanent plots established in western Arkansas and eastern Oklahoma during 1985-1987 and remeasured for the sixth time (2012-2014). Existing height-d.b.h. and CR estimation models for naturally occurring shortleaf pine forests (Pinus echinata Mill.) were updated and modified for improved performance. Additionally, eight height-d.b.h. relationship models that use only d.b.h. (fundamental local models) were modified using covariates. The model performance was evaluated using fit statistics [root mean square error (RMSE), Fit index and Akaike information criteria (AIC)]. The results showed that the best model form which was an extended non-linear model with autoregressive first order AR (1) structure and power variance function performed better than extended mixed-effects models and predicted well as an ordinary least squares non-linear model. The autocorrelation within individual trees was larger for the height-d.b.h. relationship than for CR estimation. The addition of QMD to mean dominant height (H D) greatly improved height-d.b.h. relationship with a reduction of 8 per cent in RMSE, compared with the use of basal area per hectare. Similarly, multiplying a fundamental local model by using QMD raised to a parametric power reduced RMSE by 16 per cent, improved Fit index by 12 per cent and decreased the AIC value by 7 per cent. D.b.h., H D and RSI best explained the crown ratio relationship with an improved Fit index by 6.7 per cent compared with alternative non-linear models without RSI. The logistic model for CR also provided prediction accuracy similar to that of a commonly used non-linear model. A non-linear model with an application of remedial measures to enhance adherence to modelling assumptions can provide better parameter estimates than mixedeffects modelling approach.

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“…Therefore, they have fitted H-D models as fixed-effects models, ignoring the grouping of the observations into sample plots. Most of these articles have used a generalized model (e.g., Arabazis and Burkhart 1992;Houghton and Gregoire 1993;Lynch et al 2005), but there are also published simple regional models (e.g., Moore et al 1996;Huang et al 2000). These models are justified if the aim is marginal prediction, i.e., prediction of the mean height of trees with a given diameter (and given plot-specific predictors in the case of generalized model) in the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Modeling height-diameter curves for prediction

2015

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Individual tree heights are needed in many situations, including estimation of 2 tree volume, dominant height and simulation of tree growth. However, height measurements 3 are tedious compared to tree diameter, and therefore height-diameter (H-D) models are 4 commonly used for prediction of tree height. Previous studies have fitted H-D models 5 using approaches that include plot-specific predictors in the models and do not include 6 them. In both these approaches, aggregation of the observations to sample plots has usually 7 been taken into account through random effects, but this has not always been done. In this 8 paper, we discuss the resulting four alternative model formulations and report an extensive 9 comparison of 16 nonlinear functions in this context using a total of 28 datasets. The datasets 10 represent a wide range of tree species, regions and ecological zones, consisting of about 11 126 000 measured trees from 3717 sample plots. Specific R-functions for model fitting 12 and prediction were developed to enable such an extensive model fitting and comparison 13 study. Suggestions on model selection, model fitting procedures, and prediction are given 14 and interpretation of predictions from different models are discussed. No uniformly best 15 function, model formulation and model fitting procedure was found. However, a 2-parameter 16 Näslund's and Curtis' function provided satisfactory fit in most datasets for the plot-specific 17 H-D relationship. Model fitting and height imputation procedures developed for this study are 18 provided in an R-package for later use.19

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A Random-Parameter Height-Dbh Model for Cherrybark Oak

Cited by 28 publications

References 0 publications

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

Analysis and comparison of nonlinear tree height prediction strategies for Douglas-fir forests

Using quadratic mean diameter and relative spacing index to enhance height–diameter and crown ratio models fitted to longitudinal data

Modeling height-diameter curves for prediction

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