Fitting and Calibrating a Mixed-Effects Segmented Taper Model for Brutian Pine

Özçelık, Ramazan; Alkan, Onur

doi:10.1590/01047760202026032737

Cited by 3 publications

(5 citation statements)

References 32 publications

(32 reference statements)

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“…4). Previous studies showed that a mixedeffects model with random parameters may not be able to fully eliminate the error autocorrelation (Garber & Maguire 2003, Trincado & Burkhart 2006, Ozçelik & Alkan 2020. In this study, the use of the autoregressive error structure AR(1) fully removed the heteroscedasticity and autocorrelation in the residuals.…”

Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 68%

“…The parameters of Jiang et al (2005) stem taper model were estimated in this study via AR (1) autoregressive modeling to remove the autocorrelation between the data collected as time series, especially the stem analysis data. The AR (1) autoregressive modeling has been used in many studies and is recommended especially when nonlinear mixed-effects modeling does not completely remove the autocorrelation of errors (Ozçelik & Alkan 2020, Koirala et al 2021.…”

Section: Nonlinear Mixed-effects Modeling Approachmentioning

confidence: 99%

“…Equations for stem diameter and volume are important components of forest inventory, growth and yield modeling, forest management planning, and product simulation systems (Quiñonez-Barraza et al 2019, Salekin et al 2021. To describe stem taper throughout the bole with different geometric solids, compatible taper models with a set of submodels have long been used (Fang et al 2000, Ozçelik & Alkan 2020, Salekin et al 2021). According to Kozak (2004), these taper models provide estimates of: (i) diameter inside or outside bark at any height of the stem; (ii) height of any stem diameter; (iii) total stem volume; (iv) merchantable volume; (v) the volume of wood assortment; (vi) individual volume for logs between any two heights; and (vii) individual volumes for logs between any two diameters on the stem (Barrio-Anta et al 2007, Ozçelik et al 2011, Senyurt et al 2017.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, for hierarchical data structures where the assumption of data independence cannot be achieved and there is a problem of serial correlation between data, the use of the nonlinear mixed-effects (NLME) modeling approach is recommended to model the variance-covariance matrix structure (Keselman et al 1998). If the mixed model that includes random parameters does not completely eliminate autocorrelation of the errors, the autoregressive error structure AR(1) can remove the autocorrelation in the residuals (Ozçelik & Alkan 2020.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling compatible taper and stem volume of pure Scots pine stands in Northeastern Turkey

Saygili¹,

Kahriman²

2023

iForest

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Compatible taper and volume equations for pure and natural Scots pine stands in the northeastern part of Turkey (Ardahan Province) were developed using a nonlinear mixed-effects modeling approach. Experimental data were obtained from 137 felled sample trees in different diameter and height classes. The most successful model (Jiang et al. 2005) explained 98.3% of the variance in stem diameter estimation and the RMSE, ME, MAE, AIC and BIC value obtained using this model were 1.955 cm, 0.043 cm, 1.300 cm, 9783.8 and 9812.6, respectively. Considering the criterion values of AIC, BIC and -2LnL, the model with random-effects in two parameters (b1 and b3) was the most successful for Scots pine. While the mixed model including random parameters did not completely solved the problem of the autocorrelation of errors in this study, the use of the autoregressive error structure AR(1) eliminated the autocorrelation in the residuals. In addition, the best estimation results among 20 different calibration options were obtained using the option of measuring two tree diameters at d1.30 and d5.30 with validation data. The most successful model explained 99.18% of the total variance in stem volume estimation in Scots pine.

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Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 68%

Section: Nonlinear Mixed-effects Modeling Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling compatible taper and stem volume of pure Scots pine stands in Northeastern Turkey

Saygili¹,

Kahriman²

2023

iForest

View full text Add to dashboard Cite

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“…Till today, the proposed taper models included polynomial, sigmoid, principal component analysis (PCA), and linear mixed functions, while contemporary machine learning (ML) approaches being explored (SALEKIN et al, 2021). According to the use of primary data from pine trees, the Max and Burkhart segmented taper equation under the nonlinear mixed-effects modeling technique was extensively used (TRICANDO and BURKHART, 2006;ÖZÇELIK and ALKAN, 2020). NICOLΕTTI et al (2020) evaluated the accuracy of bivariate and generalized linear mixed modeling in the representation of the Pinus taeda L. trunk taper, while many researchers tested parametric and semi-parametric models for constructing reliable taper models (ÖZÇELIK et al, 2016;ALGERA et al, 2019;MARCHI et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

Diamantopoulou

2022

Folia Oecologica

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The difficulty of locating and measuring the over-bark tree bole diameters at heights that are far from the ground, is a serious problem in ground-truth data measurements in the field. This problem could be addressed through the application of intelligent systems methods. The paper explores the possibility of applying the Random Forest regression method (RFr) in order to assess, as accurately as possible, the size of the tree bole diameters at any height above the ground, considering data that can be easily measured in the field. For this purpose, diameter measurements of pine trees (Pinus brutia Ten.) from the Seich–Sou urban forest of Thessaloniki, Greece, were used. The effectiveness of the Random Forest regression technique is compared with the results of non-linear regression models that fitted to the available data and evaluated. This research has shown that the RFr method can be a reliable alternative methodology in order to receive accurate information provided by the model, saving time and effort in field.

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The shape of the trunk of Scots pine in the stands of the Ukrainian Polissia

Burianchuk¹,

Bilous²

2023

Ukr. j. for. wood sci.

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Due to the transition of Ukraine in 2019 to new regulatory documents on standardisation regarding the classification of round timber by quality classes, it became necessary to develop standards for evaluating volumes in accordance with the new requirements. The purpose of the study was to develop a mathematical model of the forming stem of Scots pine for trees of two groups of diameters. The study is based on the use of empirical material collected in different years by researchers of the Department of forest taxation and forest management of the National University of Life and Environmental Sciences of Ukraine, which is 583 model pine trees. The Kozak model was use as the basic model for modelling the forming Scots pine. A mixed effect model was calibrated to characterise the variability in the tree stem shape of Scots pine. Parameters for the role of random effects were selected based on their coefficients of variation using the bootstrap process. Data were grouped into two groups of diameters by analysing modelling residues and evaluating the quality of models based on the use of the Akaike information criterion (AIC). By dividing the data array into two groups of thickness degrees and developing models of the creator for each of the groups separately, a decrease in the indicator of the Akaike information criterion (AIC) was achieved to the level of AIC=7433.02 for a group (8-28 cm) and AIC=13154.67 for the group (32-76 cm), which increased the accuracy of the developed models. Enabling random effect parameters in model by A. Kozak for the developed mathematical equations allowed more accurately describing the variability of the tree stem of Scots pine. The obtained models give a clear and fairly accurate prediction of the determination of tree stem diameters along the entire length. The use of the developed models can serve as a basis for improving forest traction standards for estimating the volume of tree stem in the process of forestry production and predicting the yield of roundwood volumes by quality classes

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Fitting and Calibrating a Mixed-Effects Segmented Taper Model for Brutian Pine

Cited by 3 publications

References 32 publications

Modeling compatible taper and stem volume of pure Scots pine stands in Northeastern Turkey

Modeling compatible taper and stem volume of pure Scots pine stands in Northeastern Turkey

Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm

The shape of the trunk of Scots pine in the stands of the Ukrainian Polissia

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