Linking individual-tree and whole-stand models for forest growth and yield prediction

Cao, Quang V.

doi:10.1186/s40663-014-0018-z

Cited by 25 publications

(14 citation statements)

References 20 publications

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“…were used for stand survival prediction. Surprisingly, this tree model ranked better than the Cao (2006) stand-level model (2.57 versus 5.27), even though it produced worse values of MAD and FI than the Cao (2006) in the literature (Qin and Cao 2006, Cao 2014, Hevia et al 2015, which favor the direct prediction of stand models to the extra summation step of tree models. Consequently, disaggregation, which adjusts outputs from tree-level models to match those from stand-level models, is not justified in these cases.…”

Section: Stand-level Models Versus Tree-level Survival Modelmentioning

confidence: 82%

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A method to derive a tree survival model from any existing stand survival model

Cao

2019

Can. J. For. Res.

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This study addresses a situation in which a forest manager has been using a whole-stand model that seems to predict well for their stands and now wants to derive an individual-tree model from it to form an integrated system that can perform well at both stand and tree levels. A simple method was developed to derive tree survival models from three existing stand-level survival models. The derived tree survival models were based on the difference between the diameter of a given tree and the diameter at which tree and stand survival probabilities are equal. For stand survival prediction, each stand model performed less adequately than its derived tree model, and one of the derived tree survival models was the best overall. For tree survival prediction, the same derived tree model also performed best overall. Even though only three stand-level survival models were considered in this study, the method presented here should be applicable to any stand survival model. When no tree survival data were available, tree survival models derived from stand survival models ranked lowest in terms of performance but produced acceptable evaluation statistics for predicting tree-level survival.

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Section: Stand-level Models Versus Tree-level Survival Modelmentioning

confidence: 82%

“…The disaggregation method (Cao 2010(Cao , 2014 was also applied to compute the adjusted tree survival probability ( ) as follows:…”

Section: Methodsmentioning

confidence: 99%

A method to derive a tree survival model from any existing stand survival model

Cao

2019

Can. J. For. Res.

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“…The combination modeling technique used to model stand basal area of Norway spruce [49] attained the coefficient of determination, 0.974, and the bias, 0.1 m 2 ha −1 . For loblolly pine (Pinus taeda L.) stands disaggregation modeling technique explained 86.2% of the total variance, attained the prediction bias 0.06 m 2 ha −1 and the absolute prediction bias 2.17 m 2 ha −1 [50].…”

Section: Stand Basal Area Modelsmentioning

confidence: 99%

Modeling Dynamics of Structural Components of Forest Stands Based on Trivariate Stochastic Differential Equation

Rupšys

2019

Forests

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Research Highlights: Today’s approaches to modeling of forest stands are in most cases based on that the regression models and they are constructed as static sub-models describing individual stands variables. The disadvantages of this method; it is laborious because too many different equations need to be assessed and empirical choices of candidate equations make the results subjective; it does not relate to the stand variables dynamics against the age dimension (time); and does not consider the underlying covariance structure driving changes in the stand variables. In this study, the dynamical model defined by a fixed-and mixed effect parameters trivariate stochastic differential equation (SDE) is introduced and described how such a model can be used to model quadratic mean diameter, mean height, number of trees per hectare, self-thinning line, stand basal area, stand volume per hectare and much more. Background and Objectives: New developed marginal and conditional trivariate probability density functions, combining information generated from an age-dependent variance-covariance matrix of quadratic mean diameter, mean height and number of trees per hectare, improve stand growth prediction, and forecast (in forecast the future is completely unavailable and must only be estimated from historical patterns) accuracies. Materials and Methods: Fixed-and mixed effect parameters SDE models were harmonized to predict and forecast the dynamics of quadratic mean diameter, mean height, number of trees per hectare, basal area, stand volume per hectare, and their current and mean increments. The results and experience from applying the SDE concepts and techniques in an extensive whole stand growth and yield analysis are described using a Scots pine (Pinus sylvestris L.) experimental dataset in Lithuania. Results: The mixed effects scenario SDE model showed high accuracy, the percentage root mean square error values for quadratic mean diameter, mean height, number of trees per hectare, stand basal area and stand volume per hectare predictions (forecasts) were 3.37% (10.44%), 1.82% (2.07%), 1.76% (2.93%), 6.65% (10.41%) and 6.50% (8.93%), respectively. In the same way, the quadratic mean diameter, mean height, number of trees per hectare, stand basal area and stand volume per hectare prediction (forecast) relationships had high values of the coefficient of determination, R2, 0.998 (0.987), 0.997 (0.992), 0.997 (0.988), 0.968 (0.984) and 0.966 (0.980), respectively. Conclusions: The approach presented in this paper can be used for developing a new generation stand growth and yield models.

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“…To exploit the advantages of both model types and to improve predictions, mathematical methods have been developed to link stand-and individual-level models into a compatible system [5][6][7][8]. For instance, predicted individual tree basal areas can be adjusted so that their sum equals a predicted stand-level total basal area [4]; thus, making these predictions compatible.…”

Section: Introductionmentioning

confidence: 99%

Prediction Comparison of Stand Parameters and Two Ecosystem Services through New Growth and Yield Model System for Mixed Nothofagus Forests in Southern Chile

Moreno

Palmas

Gezan

2021

Forests

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Forest managers need tools to predict the behavior of forests not only for the main stand parameters, such as basal area and volume, but also for ecosystem services such as timber volume and carbon sequestration. Useful tools to predict these parameters are growth and yield model systems with several possible options for modeling, such as the whole stand-level model, with or without diameter distribution generation, individual tree-level model, and compatibility models. However, those tools are scarce or developed mainly for forest plantations that are mostly located in the northern hemisphere. Thus, this study focuses on analyzing predictions of several growth and yield models built for native mixed Nothofagus forests from southern Chile, using the simulator Nothopack. A dataset of 19 permanent plots with three measurements were used for comparing the different models. Individual tree-level simulation presented the best goodness-of-fit statistics for stand parameters and ecosystem services. For example, the basal area gave an R2emp of 0.97 and 0.87 at 6 and 12 years of projection. However, the stand-level simulations with a generation of diameter distribution and both compatibility models showed satisfactory performance, both in accuracy and bias control. The simulator Nothopack, which has the capability of obtaining detailed outputs, is a useful tool to support management plans for these forest ecosystems.

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Linking individual-tree and whole-stand models for forest growth and yield prediction

Cited by 25 publications

References 20 publications

A method to derive a tree survival model from any existing stand survival model

A method to derive a tree survival model from any existing stand survival model

Modeling Dynamics of Structural Components of Forest Stands Based on Trivariate Stochastic Differential Equation

Prediction Comparison of Stand Parameters and Two Ecosystem Services through New Growth and Yield Model System for Mixed Nothofagus Forests in Southern Chile

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