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Stand Volume Growth Modeling with Mixed-Effects Models and Quantile Regressions for Major Forest Types in the Eastern Daxing’an Mountains, Northeast China
The relative growth rate () is the standardized measurement of forest growth, whereby excluding the size differences between individuals allows their performance to be compared equally. The model was developed using the National Forest Inventory () data on the Daxing’an Mountains, in Northeast China, which contain Dahurian larch (Larix gmelinii Rupr.), white birch (Betula platyphylla Suk.), and mixed coniferous–broadleaf forests. Four predictor variables—i.e., quadratic mean diameter (), stand basal area (), average tree height (), and altitude ()—and four different methods—i.e., the nonlinear mixed-effects models (), three nonlinear quantile regression (), five nonlinear quantile regression (), and nine nonlinear quantile regression () models—were used in this study. All the models were validated using the leave-one-out method. The results showed that (1) the mixed coniferous–broadleaf forest presented the highest ; (2) the was negatively correlated with the four predictors, and the heteroscedasticity reduced significantly after the weighting function was integrated into the models; and (3) the quantile regression models performed better than , and outperformed both and . To make more accurate predictions, parameters of the adjusted mixed-effects and quantile regression models should be recalculated and localized using sampled in each region and then applied to predict all the other of plots. indicates the mean absolute percentage error. The values were stable when the sample numbers were greater than or equal to six across the three forest types, which showed relatively accurate and lowest-cost prediction results.
Stand Volume Growth Modeling with Mixed-Effects Models and Quantile Regressions for Major Forest Types in the Eastern Daxing’an Mountains, Northeast China
The relative growth rate () is the standardized measurement of forest growth, whereby excluding the size differences between individuals allows their performance to be compared equally. The model was developed using the National Forest Inventory () data on the Daxing’an Mountains, in Northeast China, which contain Dahurian larch (Larix gmelinii Rupr.), white birch (Betula platyphylla Suk.), and mixed coniferous–broadleaf forests. Four predictor variables—i.e., quadratic mean diameter (), stand basal area (), average tree height (), and altitude ()—and four different methods—i.e., the nonlinear mixed-effects models (), three nonlinear quantile regression (), five nonlinear quantile regression (), and nine nonlinear quantile regression () models—were used in this study. All the models were validated using the leave-one-out method. The results showed that (1) the mixed coniferous–broadleaf forest presented the highest ; (2) the was negatively correlated with the four predictors, and the heteroscedasticity reduced significantly after the weighting function was integrated into the models; and (3) the quantile regression models performed better than , and outperformed both and . To make more accurate predictions, parameters of the adjusted mixed-effects and quantile regression models should be recalculated and localized using sampled in each region and then applied to predict all the other of plots. indicates the mean absolute percentage error. The values were stable when the sample numbers were greater than or equal to six across the three forest types, which showed relatively accurate and lowest-cost prediction results.
Stand Volume Growth Modeling with Mixed-Effects Models and Quantile Regressions for Major Forest Types in the Eastern Daxing’an Mountains, Northeast China
Tao Wang (author) / Longfei Xie (author) / Zheng Miao (author) / Faris Rafi Almay Widagdo (author) / Lihu Dong (author) / Fengri Li (author)
2021
Article (Journal)
Electronic Resource
Unknown
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A Comparison of Models of Stand Volume in Spruce-Fir Mixed Forest in Northeast China
| DOAJ | 2022