Mixed-effects height–diameter models for black pine (Pinus nigra Arn.) forest management

Raptis, Dimitrios; Kazana, Vassiliki; Kazaklis, Angelos; Stamatiou, Christos

doi:10.1007/s00468-021-02106-x

Cited by 33 publications

(33 citation statements)

References 51 publications

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“…DH positively and significantly correlated with the asymptote coefficient of the candidate model. Our results are consistent with those of many previous studies [5,17,21,46] that used dominant height as an additional stand-level predictor variable associated with the asymptote coefficient. This could be expected, as DH is a measure of the stand's maximum height potential, which is usually associated with the site productivity and the site index [47].…”

Section: Basic and Generalized H-d Modelssupporting

confidence: 92%

“…This could be expected, as DH is a measure of the stand's maximum height potential, which is usually associated with the site productivity and the site index [47]. DH is a result of the competition process present in the stand, the stand density [17], and the stand quality [5,46]. Although significant correlation between the coefficients and the stand-level predictor variables such as the Age, the N and the BA was detected, adding other stand-level predictor variables did not increase the model performances significantly.…”

Section: Basic and Generalized H-d Modelsmentioning

confidence: 99%

“…Until now, many H-D models with various mathematical forms have been described for different tree species in various forest types around the world [5,8,[11][12][13][14]. Although the results of these studies have indicated differences in H-D models between species and forest types, general sigmoidal or nonlinear growth curves are suitable for H-D model development [15][16][17]. Furthermore, the H-D relationship varies between stands of the same species due to stand site quality, stand age, and competition [16,18,19].…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce the H-D relationship variation between stands, various stand-level predictor variables are usually introduced into a simple basic H-D model, with the aim of developing a generalized H-D models [20,21]. Usually, generalized H-D models are developed at a regional scale, providing the necessary prediction accuracy at the stand level by reducing at the same time the sampling effort needed to build simple H-D models for each stand [17,22,23].…”

Section: Introductionmentioning

confidence: 99%

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Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Han

Ze-yong

Ciceu

et al. 2021

Forests

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Height-diameter (H-D) models are important tools for forest management practice. Sandy Mongolian pine plantations (Pinus sylvestris var. mongolica) are a major component of the Three-North Afforestation Shelterbelt in Northern China. However, few H-D models are available for Mongolian pine plantations. In this paper we compared different equations found in the literature for predicting tree height, using diameter at breast height and additional stand-level predictor variables. We tested if the additional stand-level predictor variable is necessary to produce more accurate results. The dominant height was used as a stand-level predictor variable to describe the variation of the H-D relationship among plots. We found that the basic mixed-effects H-D model provided a similar predictive accuracy as the generalized mixed-effects H-D model. Moreover, it had the advantage of reducing the sampling effort. The basic mixed-effects H-D model calibration, in which the heights of the two thickest trees in the plot were included to calibrate the random effects, resulted in accurate and reliable individual tree height estimations. Thus, the basic mixed-effects H-D model with the above-described calibration design can be an accurate and cost-effective solution for estimating the heights of Mongolian pine trees in northern China.

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Section: Basic and Generalized H-d Modelssupporting

confidence: 92%

Section: Basic and Generalized H-d Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Han

Ze-yong

Ciceu

et al. 2021

Forests

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“…(Huang et al 2009). Further, research on this topic showed that the inclusion of dominant height and dominant diameter as predictors can improved the accuracy of h-d models (Hanus et al 1999;Crecente-Campo et al 2010;Raptis et al 2021) Different combinations of mixed parameters from equation (1) were implemented (Table 2). The results of Table 2 shows that model (1) with random components 1 and 3 produced the lowest values of Akaike's information criterion (AIC) (Akaike 1974) and Bayesian information criterion (BIC) among various combinations of mixed parameters.…”

Section: Nonlinear Regression Models (Generalized Models Fixed-and Mixed-effects Models)mentioning

confidence: 99%

Investigation of parametric and artificial neural network modeling approaches for total tree height prediction in cedar plantations

Karatepe¹,

Diamantopoulou²,

Özçelık³

et al. 2021

Preprint

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Background: Height-diameter relationships are of critical importance in tree and stand volume estimation. Stand description, site quality determination and proper forest management decisions originate from reliable stem height predictions. Methods: In the context of this work, the prediction ability of the developed height-diameter models was investigated for cedar (Cedrus libani A. Rich.) plantations in Western Mediterranean Region of Turkey. Towards this direction, parametric modeling methods such as fixed-effects, generalized models, and mixed-effects were evaluated. Furthermore, in an effort to come up with the construction of more reliable stem-height prediction models, artificial neural networks were employed using two different modeling algorithms: the Levenberg-Marquardt and the resilient back-propagation. Results: Taking into account the prediction behavior of each respective modelling strategy while using a new validation data set, the mixed-effects model with calibration using 3 trees for each plot seems to be a reliable alternative to the rest standard modelling approaches given the evaluation statistics regarding the predictions of tree heights. Conclusion: Finally, as for providing the most reliable results as compared to the remaining, the resilient propagation algorithm showed its capability of providing more accurate predictions of the tree stem height and thus it can be a reliable alternative to pre-existing modelling methodologies.

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Nonlinear mixed-effect branch growth model development for planted Korean pine in Northeast China

Liu,

Feng,

Gao

et al. 2024

Trees

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Mixed-effects height–diameter models for black pine (Pinus nigra Arn.) forest management

Cited by 33 publications

References 51 publications

Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Investigation of parametric and artificial neural network modeling approaches for total tree height prediction in cedar plantations

Nonlinear mixed-effect branch growth model development for planted Korean pine in Northeast China

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