scholarly journals An optimization model for Norway spruce management based on individual-tree growth models

1992 ◽  
Vol 0 (232) ◽  
Author(s):  
Lauri. Valsta
Keyword(s):  
Norway Spruce ◽  
Tree Growth ◽  
Optimization Model ◽  
Growth Models ◽  
Individual Tree ◽  
Individual Tree 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.

Comparing individual-tree growth models using principles of stand growth for Norway spruce, Scots pine, and European beech

2015 ◽  
Vol 45 (8) ◽  
pp. 1006-1018 ◽  
Author(s):  
Sonja Vospernik ◽  
Robert A. Monserud ◽  
Hubert Sterba
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Tree Growth ◽  
Growth Models ◽  
Basal Area ◽  
European Beech ◽  
Growth And Yield ◽  
Individual Tree ◽  
Individual Tree Growth ◽  
Volume Increment

We examined the relationship between thinning intensity and volume increment predicted by four commonly used individual-tree growth models in Central Europe (i.e., BWIN, Moses, Prognaus, and Silva). We replicated conditions of older growth and yield experiments by selecting 34 young, dense plots of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.). At these plots, we simulated growth, with mortality only, to obtain the maximum basal area. Maximum basal area was then decreased by 5% or 10% steps using thinning from below. Maximum density varied considerably between simulators; it was mostly in a reasonable range but partly exceeded the maximum basal area observed by the Austrian National Forest Inventory or the self-thinning line. In almost all cases, simulated volume increment was highest at maximum basal area and then decreased with decreasing basal area. Critical basal area, at which 95% of maximum volume increment can be achieved, ranged from 0.46 to 0.96. For all simulators, critical basal area was lower for the more shade-tolerant species. It increased with age, except for Norway spruce, when simulated with the BWIN model. Age, where mean annual increment culminated, compared well with yield tables.

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

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 Possibilities and limitations of individual-tree growth models – A review on model evaluations

2017 ◽  
Vol 68 (2) ◽  
pp. 103-112 ◽  
Author(s):  
Sonja Vospernik
Keyword(s):  
Tree Growth ◽  
Growth Models ◽  
Wood Quality ◽  
Rotation Period ◽  
Growth And Yield ◽  
Tree Age ◽  
Emergent Properties ◽  
Management Scenarios ◽  
Individual Tree ◽  
Individual Tree Growth

Summary Individual-tree growth models are the new standard for modeling growth and yield. Their main purpose is to simulate future forest management scenarios but they can also be used to predict wood quality, rockfall protection or habitat quality. Individual tree growth models may consist of different models but core models are diameter increment, height increment, crown ratio (often used as a predictor for increment) and mortality. The model differentiation is based on how these four models include tree age (size), competition and site. Four common growth simulators in Central Europe are BWIN, Moses, Prognaus and Silva. These four models are commonly deployed to simulate 30 years of growth, but a prospective application is the simulation of a whole rotation period (80–150 years). It is therefore crucial to understand the possibilities and limitations of these models by evaluating them. This review paper summarizes the statistical and emergent properties’ evaluation results for these models. Statistical evaluations focus on individual models of a simulator, whereas the evaluation of emergent properties evaluates the entire simulator, by testing if the models conform to known principles of stand growth. Further, the meaning of these evaluation results for the development and improvement of individual-tree growth models is discussed.

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