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

Vospernik, Sonja

doi:10.1515/boku-2017-0010

Cited by 7 publications

(7 citation statements)

References 46 publications

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“…Thus, validation of the models using the data acquired from different sampling design and tree population may be the best alternative [3,21,38,67]. Developing highly precise individual tree growth models may be challenging, because of several influential factors, which are not readily measured [10]. Any limitation experienced in the current study will be useful for improvement of the future modelling works.…”

Section: Discussionmentioning

confidence: 99%

“…where u i is a vector of the random effects, Equations (8) and (10) provide the elements of matrices R i and D, respectively. A vector ε i was obtained from fixed parts of the mixed-effects models (Equations (6) and (7)).…”

Section: Calibrated Response or Localized Diameter Increment Predictionmentioning

confidence: 99%

“…The individual tree-based growth models help explore the detailed management alternatives, as they are flexible in forecasting tree growth regardless of species, size, age, and site quality [4,[6][7][8][9][10]. The individual tree-based growth models, therefore, have been receiving the increased attention in recent years, since they are able to simulate various silvicultural alternatives and replace the traditional yield tables for more effective decision-making [10][11][12][13]. These models usually consist of a set of core models with their response variables, such as diameter increment, height increment, mortality, and crown ratio [10], and therefore are used as input models in the forest simulators-major decision-making tools in forestry [14,15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Sharma

Štefančík

Vacek

et al. 2019

Forests

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Individual tree growth and yield models precisely describe tree growth irrespective of stand complexity and are capable of simulating various silvicultural alternatives in the stands with diverse structure, species composition, and management history. We developed both age dependent and age independent diameter increment models using long-term research sample plot data collected from both monospecific and mixed stands of European beech (Fagus sylvatica L.) in the Slovak Republic. We used diameter at breast height (DBH) as a main predictor and other characteristics describing site quality (site index), stand development stage (dominant height and stand age), stand density or competition (ratio of individual tree DBH to quadratic mean diameter), species mixture (basal area proportion of a species of interest), and dummy variable describing stand management regimes as covariate predictors to develop the models. We evaluated eight versatile growth functions in the first stage using DBH as a single predictor and selected the most suitable one, i.e., Chapman-Richards function for further analysis through the inclusion of covariate predictors. We introduced the random components describing sample plot-level random effects and stochastic variations on the diameter increment, into the models through the mixed-effects modelling. The autocorrelation caused by hierarchical data-structure, which is assumed to be partially reduced by mixed-effects modelling, was removed through the inclusion of the parameter accounting for the autoregressive error-structures. The models described about two-third parts of a total variation in the diameter increment without significant trends in the residuals. Compared to the age independent mixed-effects model (conditional coefficient of determination, R c 2 = 0.6566; root mean square error, RMSE = 0.1196), the age dependent model described a significantly larger proportion of the variations in diameter increment ( R c 2 = 0.6796, RMSE = 0.1141). Diameter increment was significantly influenced differently by covariate predictors included into the models. Diameter increment decreased with the advancement of stand development stage (increased dominant height and stand age), increasing intraspecific competition (increased basal area proportion of European beech per sample plot), and diameter increment increased with increasing site quality (increased site index) and decreased competition (increased ratio of DBH to quadratic mean diameter). Our mixed-effects models, which can be easily localized with the random effects estimated from prior measurement of diameter increments of four randomly selected trees per sample plot, will provide high prediction accuracies. Our models may be used for simulating growth of European beech irrespective of its stand structural complexity, as these models have included various covariate variables describing both tree-and stand-level characteristics, thinning regimes, except the climate characteristics. Together with other forest models, our models will be used as inputs to the growth simulator to be developed in the future, which is important for decision-making in forestry.

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Section: Discussionmentioning

confidence: 99%

Section: Calibrated Response or Localized Diameter Increment Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Sharma

Štefančík

Vacek

et al. 2019

Forests

View full text Add to dashboard Cite

show abstract

“…Forest growth and yield models serve as quantitative generalizations of these dynamics, offering a means to narrow the uncertainty gap through precise predictions [2], and provide crucial insights for effective forest management [10,11]. Amidst the diverse array of forest growth and yield models, individual tree growth models have emerged as refined simulations operating at a finer scale to provide flexible stand dynamics predictions [12]. Generally, individual-tree-level models aspire to forecast stand growth dynamics by encapsulating inter-tree interactions, including one-or two-sided competition and facilitation [2,13,14].…”

Section: Introductionmentioning

confidence: 99%

Spatially Explicit Individual Tree Height Growth Models from Bi-Temporal Aerial Laser Scanning

Salekin,

Pont,

Dickinson

et al. 2024

Remote Sensing

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Individual-tree-based models (IBMs) have emerged to provide finer-scale operational simulations of stand dynamics by accommodating and/or representing tree-to-tree interactions and competition. Like stand-level growth model development, IBMs need an array of detailed data from individual trees in any stand through repeated measurement. Conventionally, these data have been collected through forest mensuration by establishing permanent sample plots or temporary measurement plots. With the evolution of remote sensing technology, it is now possible to efficiently collect more detailed information reflecting the heterogeneity of the whole forest stand than before. Among many techniques, airborne laser scanning (ALS) has proved to be reliable and has been reported to have potential to provide unparallel input data for growth models. This study utilized repeated ALS data to develop a model to project the annualized individual tree height increment (ΔHT) in a conifer plantation by considering spatially explicit competition through a mixed-effects modelling approach. The ALS data acquisition showed statistical and biological consistency over time in terms of both response and important explanatory variables, with correlation coefficients ranging from 0.65 to 0.80. The height increment model had high precision (RMSE = 0.92) and minimal bias (0.03), respectively, for model fitting. Overall, the model showed high integrity with the current biological understanding of individual tree growth in a monospecific Pinus radiata plantation. The approach used in this study provided a robust model of annualized individual tree height growth, suggesting such an approach to modelling will be useful for future forest management.

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“…Individual-based modeling is one of the most comprehensive and detailed approaches to predicting individual trees' growth. It has been applied to simulate future forest management scenarios [7], predict/explain wood quality [8], predict habitat quality [9], and plan forest management activities [10].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

Barbosa

Costa

Schons

et al. 2022

Forests

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This research aimed to develop statistical models to predict basal area increment (BAI) for Araucaria angustifolia using Artificial Neural Networks (ANNs). Tree species were measured for their biometric variables and identified at the species level. The data were subdivided into three groups: (1) intraspecific competition with A. angustifolia; (2) the first group of species that causes interspecific competition with A. angustifolia; and (3) the second group of species that causes interspecific competition with A. angustifolia. We calculated both the dependent and independent distance and the described competition indices, considering the impact of group stratification. Multi-layer Perceptron (MLP) ANN was structured for modeling. The main results were that: (i) the input variables size and competition were the most significant, allowing us to explain up to 77% of the A. angustifolia BAI variations; (ii) the spatialization of the competing trees contributed significantly to the representation of the competitive status; (iii) the separate variables for each competition group improved the performance of the models; and (iv) besides the intraspecific competition, the interspecific competition also proved to be important to consider. The ANN developed showed precision and generalization, suggesting it could describe the increment of a species common in native forests in Southern Brazil and with potential for upcoming forest management initiatives.

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

Cited by 7 publications

References 46 publications

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Generalized Nonlinear Mixed-Effects Individual Tree Diameter Increment Models for Beech Forests in Slovakia

Spatially Explicit Individual Tree Height Growth Models from Bi-Temporal Aerial Laser Scanning

Individual Tree Basal Area Increment Models for Brazilian Pine (Araucaria angustifolia) Using Artificial Neural Networks

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