Browsing by Author "Li, Fengri"
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Item Evaluation of the mixed effects model and quantile regression approaches for predicting tree height in larch (Larix olgensis) plantations in northeastern China(Canadian Science Publishing, 2021-09-21) Xie, Longfei; Widagdo, Faris Rafi Almay; Miao, Zheng; Dong, Lihu; Li, FengriTree height (H) is one of the most important tree variables and is widely used in growth and yield models, and its measurement is often time-consuming and costly. Hence, height-diameter (H-D) models have become a great alternative, providing easy-to-use and accurate tools for H prediction. In this study, H-D models were developed for Larix olgensis in Northeast China. The Chapman-Richards function with three predictors (diameter at breast height, dominant tree height, and relative size of individual trees) performed best. Nonlinear mixed effects (NLME) models and nonlinear quantile regressions (NQR9, 9 quantiles; NQR5, 5 quantiles; and NQR3, 3 quantiles) were further used and improved the generalized H-D model, successfully providing accurate H predictions. In addition, the H predictions were calibrated using several measurements from subsamples, which were obtained from different sampling designs and sizes. The results indicated that the predictive accuracy was higher when calibrated by using any number of height measurements for the NLME model and more than 3 height measurements for the NQR3, NQR5 and NQR9 models. The best sampling strategy for the NLME and NQR models involved sampling the medium-sized trees. Overall, the newly developed H-D models can provide highly accurate height predictions for L. olgensis.Item Meta optimization of stand management with population based methods(Canadian Science Publishing, 2018-03-11) Jin, Xingji; Pukkala, Timo; Li, FengriThe amount of different products and services obtained from forests depends on several management decisions such as thinning years, thinning intensity, thinning type and rotation length. The relationships between management actions and the various outputs obtained from forests are complicated. This makes stand management optimization challenging, especially if the number of simultaneously maximized outputs and the number of optimized variables are high. The direct search method of Hooke and Jeeves (HJ) has been used much in stand management optimization. In recent years, population-based methods have been proposed as an alternative to the HJ method. The performance of a population-based method depends on its parameters such as number iterations and population size (number of solution vectors used in the population-based method). This study used two-level meta optimization to simultaneously optimize the parameters of a population-based method and the management schedule of a stand. Four population-based methods were analysed: differential evolution (DE), particle swarm optimization (PS), evolution strategy optimization (ES) and the method of Nelder and Mead (NM). With optimal parameter values, DE and PS found the best stand management schedules, followed by ES and NM. DE and PS performed better than HJ. Therefore, DE and PS should be used more in forest management and their search algorithms should be further developed.Item Modeling net CO2 assimilation (AΝ) within the crown of young planted Larix olgensis trees(Canadian Science Publishing, 2018-06-28) Liu, Qiang; Dong, Lihu; Li, FengriNet CO2 assimilation (AN) is an important physiological indicator that reflects the photosynthetic capacity. The seasonal and spatial variations of AN play an important role in carbon uptake simulations, especially for trees. To gain a clearer understanding of the state of the branch carbon balance, it is necessary to more carefully evaluate the dynamic variation of AN over different gradients in the crown during the growing season. Gas exchange, leaf temperature (Tleaf), vapor pressure deficit (VPD), leaf mass per area (LMA) and relative depth into crown (RDINC) were measured throughout the growing season of planted Larix olgensis trees. A semiempirical model for predicting multilayered crown AN was established by incorporating Tleaf, VPD, LMA, RDINC and their combinations into a photosynthetic light-response (PLR) curve model using reparameterization. The model was assessed based on goodness of fit (adjusted coefficient of determination, Ra2; root mean square error, RMSE; and Akaike information criterion, AIC) and on the validation results (mean error, ME; mean absolute error, MAE; precision estimation, P) and performed well. The multilayered predicted model of crown AN lays the foundation for calculating the multilayered photosynthetic production within the crown and determining the range of the functional crown for individual trees.Item Modeling the Number of the First- and Second-Order Branches within the Live Tree Crown of Korean Larch Plantations in Northeast, China(Canadian Science Publishing, 2020-10-09) Miao, Zheng; Zhang, Lianjun; Widagdo, Faris Rafi Almay; Dong, Lihu; Li, FengriModeling the number of branches is fundamental for simulating other branch characteristics and crown structure. In this study, a total of 77 Korean larch trees (Larix olgensis Henry) were destructively sampled from the plantations in the Northeast of China. The number of the first- and second-order branches were modeled using seven count data models, i.e., Poisson, negative binomial (i.e. NB, including NB-1, NB-2, and NB-P) and generalized Poisson (i.e. GP, including GP-1, GP-2, and GP-P) regression models. Further, the generalized linear mixed models (GLMM) were applied to those models using the sampled trees as the random effects. The results showed that (1) the Poisson regression was preferred for modeling the number of the first-order branches; (2) the GP-1 regression was considered the optimal model for the number of the second-order branches; (3) the significant predictor variables included tree height increment, branch position, relative tree size, average dominant height, and tree age; (4) the GLMM models significantly improved both modeling fitting and prediction performance; (5) the prediction accuracy of the GLMM models increased gradually with the increasing number of sample sizes; and (6) a relatively small sample size with an appropriate sampling strategy would be adequate to provide a good estimation at a specific crown section.Item Modification of a photosynthetic light-response (PLR) model for modeling the vertical gradient in the response of crown PLR curves(Canadian Science Publishing, 2019-02-05) Liu, Qiang; Dong, Lihu; Li, FengriThe photosynthetic light-response (PLR) curve is a mathematical description of a single biochemical process that has been widely applied in many ecophysiological models. For trees, the heterogeneity of PLR curves within the crown is significant but rarely modeled by mathematical techniques. This paper establishes a modified model for estimating crown PLR curves based on PLR functions by linking the parameters of the PLR functions to leaf nitrogen (N), specific leaf area (SLA) and relative depth into the crown (RDINC). The modified models were assessed by considering the goodness of fit (adjusted coefficient of determination, Ra2; root mean square error, RMSE; and Akaike information criterion, AIC) and model structure. Significant correlations were observed between the parameters of PLR functions and N, SLA and RDINC. The optimal modified PLR model, by linking RDINC into a modified Mitscherlich function, fit well due to its simple and easily understood structure. Therefore, it is feasible to simultaneously estimate the multilayered and varied PLR curves of the tree crown.