The evidence of stand development stages in simulations by an individual-tree growth simulator

2011 ◽  
Vol 41 (1) ◽  
pp. 124-138 ◽  
Author(s):  
Markus O. Huber
Keyword(s):  
Tree Growth ◽  
Growth Models ◽  
Stand Density ◽  
Classification Model ◽  
Stand Development ◽  
Natural Forests ◽  
Individual Tree ◽  
Development Stages ◽  
Individual Tree Growth ◽  
Growth Simulator

Individual-tree growth models have often been evaluated at the stand level in terms of stand increment or the development of stand density, but rarely in conceptual terms of natural stand dynamics. This study addresses the stand development stage concept developed for natural forests and shows how individual-tree growth models can be evaluated in this respect. For this purpose, the individual-tree growth simulator PrognAus was used to simulate the development of three sites for 2500 years, and a statistical classification model and a rule-based classification scheme were used to identify development stages in the output of the simulator. A comparison of the simulated stages with stages observed in a natural forest in terms of the stand volume and a comparison of the simulated stages with hypotheses from the literature in terms of volume, change of volume, and stand density were performed to determine if the simulations are consistent with the theory.

Stochastic structure and individual-tree growth models

2001 ◽  
Vol 154 (1-2) ◽  
pp. 261-276 ◽  
Author(s):  
Julian C. Fox ◽  
Peter K. Ades ◽  
Huiquan Bi
Keyword(s):  
Tree Growth ◽  
Growth Models ◽  
Stochastic Structure ◽  
Individual Tree ◽  
Individual Tree Growth

Prevalence of individual-tree growth decline in red spruce populations of the southern Appalachian Mountains

1992 ◽  
Vol 22 (6) ◽  
pp. 905-914 ◽  
Author(s):  
David C. LeBlanc ◽  
N.S. Nicholas ◽  
S.M. Zedaker
Keyword(s):  
Tree Growth ◽  
Stand Density ◽  
Red Spruce ◽  
Slope Aspect ◽  
Individual Tree ◽  
Great Smoky Mountains ◽  
Individual Tree Growth ◽  
Growth Decline ◽  
Recreation Area ◽  
A Site

The prevalence of individual-tree growth decline was determined for red spruce (Picearubens Sarg.) populations at three locations in the southern Appalachians: Mount Rogers National Recreation Area, the Black Mountains, and Great Smoky Mountain National Park. An index of annual stemwood volume increment (AVI) was computed from dendrochronological data and a site-specific DBH–height regression equation. Individual-tree AVI time series were analyzed to identify changes in 20-year periodic mean AVI and AVI trend. The proportion of red spruce that exhibited decreasing mean AVI or negative AVI trend was determined for the most recent 20-year period, and this was compared with the estimated historical prevalence of these indications of growth decline. Also, the prevalence of growth decline was compared among subpopulations that differed with regard to various tree, stand, and site characteristics. Of 263 red spruce sampled, 25% exhibited a decrease in mean AVI during the period 1967–1986, 8% exhibited a negative AVI trend without a reduction in mean AVI, and 17% exhibited a reduction in the slope of the AVI curve. The proportion of trees that exhibited decreasing or slowed growth after 1967 was substantially greater among trees growing at 1980 m than in populations at lower elevations; no relationship was found between elevation and growth decline below 1980 m. No difference was found in prevalence of growth decline between subpopulations that differed with regard to age, DBH, competitive status, stand density, slope aspect, or site exposure. The prevalence of individual-tree growth decline for the most recent 20-year period did not exceed estimated levels for historical periods of decline in the Great Smoky Mountains population.

scholarly journals Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 187 ◽  
Author(s):  
Qiangxin Ou ◽  
Xiangdong Lei ◽  
Chenchen Shen
Keyword(s):  
Machine Learning ◽  
Tree Growth ◽  
Growth Models ◽  
Tree Size ◽  
Diameter Growth ◽  
Individual Tree ◽  
Mixed Forests ◽  
Tree Diameter ◽  
Individual Tree Growth ◽  
The Individual

Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.

scholarly journals Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1338
Author(s):  
Simone Bianchi ◽  
Mari Myllymaki ◽  
Jouni Siipilehto ◽  
Hannu Salminen ◽  
Jari Hynynen ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Tree Growth ◽  
Cross Validation ◽  
Growth Models ◽  
Forest Growth ◽  
Individual Tree ◽  
Selection Cutting ◽  
Single Tree ◽  
Individual Tree Growth ◽  
Single Tree Selection

Background and Objectives: Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting. Materials and Methods: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting. The models were compared in terms of Akaike Information Criteria (AIC), root mean square error (RMSE), and mean absolute bias (MAB), both with the training data and after cross-validation with a leave-one-out method at stand level. Results: Even though the spatial model had a lower AIC than the nonspatial model, RMSE and MAB of the two models were similar. Both models tended to underpredict growth for the highest observed values when the tree-level random effects were not used. After cross-validation, the aggregated predictions at stand level well represented the observations in both models. For most of the predictors, the use of values based on trees’ height rather than trees’ diameter improved the fit. After single-tree selection cutting, trees had a growth boost both in the first and second five-year period after cutting, however, with different predicted intensity in the two models. Conclusions: Under the research framework here considered, the spatial modeling approach was not more accurate than the nonspatial one. Regarding the single-tree selection cutting, an intervention regime spaced no more than 15 years apart seems necessary to sustain the individual tree growth. However, the model’s fixed effect parts were not able to capture the high growth of the few fastest-growing trees, and a proper estimation of site potential is needed for uneven-aged stands.

scholarly journals The SOHARC Model System for Growth and Yield of Southern Hardwoods

2008 ◽  
Vol 32 (4) ◽  
pp. 173-183 ◽  
Author(s):  
John Paul McTague ◽  
David O'Loughlin ◽  
Joseph P. Roise ◽  
Daniel J. Robison ◽  
Robert C. Kellison
Keyword(s):  
Tree Growth ◽  
Growth Models ◽  
Basal Area ◽  
Tree Height ◽  
Growth And Yield ◽  
Permanent Plots ◽  
Red Maple ◽  
Yellow Poplar ◽  
Individual Tree ◽  
Individual Tree Growth

Abstract A system of stand level and individual tree growth-and-yield models are presented for southern hardwoods. These models were developed from numerous permanent growth-and-yield plots established across 13 states in the US South on 9 site types, in even-aged (age classes from 20 to 60 years), fully stocked, naturally regenerated mixed hardwood and mixed hardwood-pine stands. Nested plots (⅕ and ac) were remeasured at 5-year intervals. The system of permanent plots was established and maintained by private and public members in the North Carolina State University Hardwood Research Cooperative. Stand level models are presented for dominant height, survival, basal area prediction and projection, and the ingrowth component. Individual tree diameter growth and tree height models were constructed for the most common species: sweetgum, tupelo, yellow-poplar, blackgum, and red maple. All other species were grouped according to growth dynamics into four species groups using cluster analysis. A ranking variable was incorporated into the individual tree growth models to account for competition.

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