scholarly journals Modelling the Tree Height, Crown Base Height, and Effective Crown Height of Pinus koraiensis Plantations Based on Knot Analysis

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1778
Author(s):  
Wancai Zhu ◽  
Zhaogang Liu ◽  
Weiwei Jia ◽  
Dandan Li
Keyword(s):  
Tree Height ◽  
Information Criterion ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Pinus Koraiensis ◽  
Tree Level ◽  
Model Parameters ◽  
Crown Height ◽  
Crown Base Height ◽  
Pruning Intensity

Taking 1735 Pinus koraiensis knots in Mengjiagang Forest Farm plantations in Jiamusi City, Heilongjiang Province as the research object, a dynamic tree height, effective crown height, and crown base height growth model was developed using 349 screened knots. The Richards equation was selected as the basic model to develop a crown base height and effective crown height nonlinear mixed-effects model considering random tree-level effects. Model parameters were estimated with the non-liner mixed effect model (NLMIXED) Statistical Analysis System (SAS) module. The akaike information criterion (AIC), bayesian information criterion (BIC), −2 Log likelihood (−2LL), adjusted coefficient (Ra2), root mean square error (RMSE), and residual squared sum (RSS) values were used for the optimal model selection and performance evaluation. When tested with independent sample data, the mixed-effects model tree effects-considering outperformed the traditional model regarding their goodness of fit and validation; the two-parameter mixed-effects model outperformed the one-parameter model. Pinus koraiensis pruning times and intensities were calculated using the developed model. The difference between the effective crown and crown base heights was 1.01 m at the 15th year; thus, artificial pruning could occur. Initial pruning was performed with a 1.01 m intensity in the 15th year. Five pruning were required throughout the young forest period; the average pruning intensity was 1.46 m. The pruning interval did not differ extensively in the half-mature forest period, while the intensity decreased significantly. The final pruning intensity was only 0.34 m.

scholarly journals A Mixed-Effects Model for the dbh–Height Relationship of Shortleaf Pine (Pinus echinata Mill.)

2008 ◽  
Vol 32 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Chakra B. Budhathoki ◽  
Thomas B. Lynch ◽  
James M. Guldin
Keyword(s):  
Tree Height ◽  
Ordinary Least Squares ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Tree Level ◽  
Permanent Plots ◽  
Pinus Echinata ◽  
Shortleaf Pine ◽  
Individual Tree ◽  
Total Height

Abstract Individual tree measurements were available from over 200 permanent plots established during 1985–1987 and later remeasured in naturally regenerated even-aged stands of shortleaf pine (Pinus echinata Mill.) in western Arkansas and eastern Oklahoma. The objective of this study was to model shortleaf pine growth in natural stands for the region. As a major component of the shortleaf modeling effort, an individual tree-level dbh–total height model was developed in which plot-specific random parameters were fitted using maximum-likelihood methods. The model predicts tree height on the basis of dbh and dominant stand height (which could be obtained from a site-index model). The mixed-effects model approach was found to predict the total height better than the similar models developed previously for this species using ordinary least-squares methods. Moreover, such a model has the appeal of generalization of the results over a region from which the plots were sampled; and also of calibration of parameters for newly sampled stands with minimal measurements.

Nitrogen mineralisation in sugarcane soils in Queensland, Australia: I. evaluation of soil tests for predicting nitrogen mineralisation

Soil Research ◽  
2019 ◽  
Vol 57 (7) ◽  
pp. 738 ◽  
Author(s):  
D. E. Allen ◽  
P. M. Bloesch ◽  
T. G. Orton ◽  
B. L. Schroeder ◽  
D. M. Skocaj ◽  
...  
Keyword(s):  
Soil Properties ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Co2 Production ◽  
Organic N ◽  
Model Parameters ◽  
Nitrogen Mineralisation ◽  
Short Term ◽  
Available N

We explored soil properties as indices of mineralisable nitrogen (N) in sugarcane soils and whether we could increase the accuracy of predicting N mineralisation during laboratory incubations. Utilising historical data in combination with samples collected during 2016, we: (i) measured mineralised N over the course of short-term (14 days) and long-term (301 days) laboratory incubations; (ii) compared models representing mineralisation; then (iii) related model parameters to measured soil properties. We found measures representing the labile organic N pool (Hydrolysable NaOH organic N; amino sugar Illinois soil N test) best related to short-term mineralised N (R2 of 0.50–0.57, P < 0.001), while measures of CO2 production (3, 7, 10 and 14 days) best related to longer-term mineralised N (R2 of 0.75–0.84, P < 0.001). Indices were brought together to model the active and slow pools of a two-pool mineralisation model in the statistical framework of a mixed-effects model. Of the models that relied on measurement of one soil property, cumulative CO2 production (7 days) performed the best when considering all soil types; in a cross-validation test, this model gave an external R2 of 0.77 for prediction of the 301-day mineralised N. Since the mixed-effects model accounts for the various sources of uncertainty, we suggest this approach as a framework for prediction of in-field available N, with further measurement of long-term mineralised N in other soils to strengthen predictive certainty of these soil indices.

scholarly journals Stem Damage Modifies the Impact of Wind on Norway Spruces

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 463 ◽  
Author(s):  
Guntars Snepsts ◽  
Mara Kitenberga ◽  
Didzis Elferts ◽  
Janis Donis ◽  
Aris Jansons
Keyword(s):  
Tree Height ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Wind Damage ◽  
Stand Age ◽  
Bark Stripping ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Stem Damage ◽  
The Impact

Bark stripping caused by cervids can have a long-lasting negative effect on tree vitality. Such trees of low vitality might be more susceptible to other disturbances. The amplifying effects of disturbance interactions can cause significantly more damage to forest ecosystems than the individual effects of each disturbance. Therefore, this study aimed to assess the impact of bark stripping (stem damage) on the probability of wind damage and snapping height for Norway spruces (Picea Abies (L.) H. Karst.). In this study, we used the Latvian National Forest Inventory data from the period 2004–2018. In the analysis, we used data based on 32,856 trees. To analyse the data, we implemented a Bayesian binary logistic generalised linear mixed-effects model and the linear mixed-effects model. Our results showed that stem damage significantly increased the probability of wind damage and affected the snapping height of Norway spruces. Similarly, root damage, the slenderness ratio, the stand age, the stand density, the soil type, and the dominant tree species had a significant influence on the probability of wind damage. In both periods, trees with stem damage had significantly (p < 0.05) higher probability (odd ratio 1.68) to be wind damaged than trees without stem damage. The stem damaged Norway spruce trees snapped in the first 25% of the tree height, while trees without stem damage snapped around half (50%) of the tree height. Our results show that stem damage significantly alters the effect of wind damage on Norway spruces, suggesting that such damage must be incorporated into wind-risk assessment models.

scholarly journals Development of a Mixed-Effects Individual-Tree Basal Area Increment Model for Oaks (Quercus spp.) Considering Forest Structural Diversity

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 474 ◽  
Author(s):  
Wenwen Wang ◽  
Xinyun Chen ◽  
Weisheng Zeng ◽  
Jianjun Wang ◽  
Jinghui Meng
Keyword(s):  
Basal Area ◽  
Information Criterion ◽  
Structural Diversity ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Central China ◽  
Basal Area Increment ◽  
Individual Tree ◽  
Final Model ◽  
Quercus Spp

In the context of uneven-aged mixed-species forest management, an individual-tree basal area increment model considering forest structural diversity was developed for oaks (Quercus spp.) using data collected from 11,860 observations in 845 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, south-central China. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the model. We also used the variance function and an autocorrelation structure to describe within-plot heteroscedasticity and autocorrelation. Finally, the optimal mixed-effects model was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik) and the likelihood ratio test (LRT). The results indicate that the reciprocal transformation of initial diameter at breast height (1/DBH), relative density index (RD), number of trees per hectare (NT), elevation (EL) and Gini coefficient (GC) had a significant impact on the individual-tree basal area increment. In comparison to the basic model developed using least absolute shrinkage and selection operator (LASSO) regression, the mixed-effects model performance was greatly improved. In addition, we observed that the heteroscedasticity was successfully removed by the exponent function and autocorrelation was significantly corrected by AR(1). Our final model also indicated that forest structural diversity significantly affected tree growth and hence should not be neglected. We hope that our final model will contribute to the scientific management of oak-dominated forests.

A note on estimating single-class piecewise mixed-effects models with unknown change points

2018 ◽  
Vol 42 (5) ◽  
pp. 518-524 ◽  
Author(s):  
Nidhi Kohli ◽  
Yadira Peralta ◽  
Cengiz Zopluoglu ◽  
Mark L. Davison
Keyword(s):  
Bayesian Estimation ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Mixed Effects Models ◽  
Change Points ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Residual Variance ◽  
Monte Carlo Simulation Study ◽  
Ml Estimation

Piecewise mixed-effects models are useful for analyzing longitudinal educational and psychological data sets to model segmented change over time. These models offer an attractive alternative to commonly used quadratic and higher-order polynomial models because the coefficients obtained from fitting the model have meaningful substantive interpretation. The current study thus focuses on the estimation of piecewise mixed-effects model with unknown random change points using maximum likelihood (ML) as described in Du Toit and Cudeck (2009). Previous simulation work (Wang & McArdle, 2008) showed that Bayesian estimation produced reliable parameter estimates for the piecewise model in comparison to frequentist procedures (i.e., first-order Taylor expansion and the adaptive Gaussian quadrature) across all simulation conditions. In the current article a small Monte Carlo simulation study was conducted to assess the performance of the ML approach, a frequentist procedure, and the Bayesian approach for fitting linear–linear piecewise mixed-effects model. The obtained findings show that ML estimation approach produces reliable and accurate estimates under the conditions of small residual variance of the observed variables, and that the size of the residual variance had the most impact on the quality of model parameter estimates. Second, neither ML nor Bayesian estimation procedures performed well under all manipulated conditions with respect to the accuracy and precision of the estimated model parameters.

scholarly journals A Merchantable and Total Height Model for Tree Species in Maine

2006 ◽  
Vol 23 (4) ◽  
pp. 241-249 ◽  
Author(s):  
James A. Westfall ◽  
Kenneth M. Laustsen
Keyword(s):  
Tree Height ◽  
Mixed Effects ◽  
Site Quality ◽  
Tree Level ◽  
Mixed Effects Modeling ◽  
Height Prediction ◽  
Species Groups ◽  
Total Tree ◽  
Height Model ◽  
Tree Characteristics

Abstract A model for predicting merchantable and total tree height for 18 species groups in Maine is presented. Only tree-level predictor variables are used, so stand-level attributes, such as age and site quality, are not required. A mixed-effects modeling approach accounts for the correlated within-tree measurements. Data-collection protocols encompass situations in which merchantability to a specified top diameter is not attained due to tree characteristics. The advantage of using the height prediction model over taper-derived estimates of merchantable height is demonstrated.

scholarly journals Evaluation of Different Calibration Approaches for Merchantable Volume Predictions of Norway Spruce Using Nonlinear Mixed Effects Model

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1104
Author(s):  
Zdeněk Adamec ◽  
Radim Adolt ◽  
Karel Drápela ◽  
Jiří Závodský
Keyword(s):  
Norway Spruce ◽  
Tree Height ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Wood Volume ◽  
Nonlinear Mixed Effects Model ◽  
Breast Height ◽  
Stem Taper ◽  
Total Tree

Research Highlights: Determination of merchantable wood volume is one of the key preconditions for sustainable forest management. This study explores accuracy of calibrated predictions of merchantable wood volume of Norway spruce (Picea abies (L.) H. Karst.) using stem taper curves (STC) in a form of a mixed model. Background and Objectives: The study is devoted to the determination of merchantable wood volume (over bark) of individual standing stems based on the integration of an STC model calibrated using upper diameter measurements. Various options of upper diameter measurement were tested and their impact on the accuracy of merchantable wood volume prediction was evaluated. Materials and Methods: To model stem taper curves, a Kozak 02 function was applied in a form of a nonlinear, mixed effects model. Accuracies of calibrated merchantable wood volume predictions obtained through remote (optical) upper diameter measurements were compared to accuracies corresponding to contact measurements by a caliper. The performance of two alternative methods used in the Czech National Forest Inventory (NFI) and forestry practice, involving diameter at breast height and total tree height as the only predictors, were also tested. The contact measurements were performed at identical stem positions after felling the respective sample tree. The calibration was done in order to account for factors inherent in particular location, and, optionally, also in a particular sample stem (within the respective location). Input data was sourced as part of a dedicated survey involving the entire territory of the Czech Republic. In total, 716 individual spruce trees were measured, felled and analysed at 169 locations. Results: In general, the best merchantable volume predictions were obtained by integrating the STC fitted (and calibrated) by minimising errors of stem cross-sectional areas instead of diameters. In terms of calibrated predictions, using single-directional, caliper measurement of upper diameter at 7 m (after felling) led to the best accuracy. In this case, the observed mean bias of merchantable volume prediction was only 0.63%, indicating underestimation. The best optical calibration strategy involved upper diameter measurements at two heights (5 and 7 m) simultaneously. Bias of this volume prediction approach was estimated at 2.1%, indicating underestimation. Conclusions: Concerning the prediction of merchantable stem volume of standing Norway spruce trees, STC calibration using two optical upper diameter measurements (at 5 and 7 m) was found to be practically applicable, provided a bias up to 3.7% can be accepted. This method was found to be more accurate than the existing national alternatives using diameter at breast height and the total tree height as the only predictors.

scholarly journals Verification of bi-parameter models of dependence diameter on breast height in birch stands

2020 ◽  
Author(s):  
А.В. Лебедев ◽  
В.В. Кузьмичев
Keyword(s):  
Standard Error ◽  
Research Work ◽  
Tree Height ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Statistical Point ◽  
Diameter At Breast Height ◽  
Breast Height

Важное значение как в лесохозяйственных работах, так и при проведении научных исследований имеет точность определения высоты деревьев. Высота обычно определяется с использованием конкретных моделей, где она является функцией от диаметра дерева на высоте груди. Наиболее простыми моделями зависимости высоты деревьев от диаметра на высоте груди являются двухпараметрические, и они находят широкое применение в лесотаксационных работах. На материалах 23 пробных площадей с измерением модельных деревьев, заложенных в березовых древостоях Лесной опытной дачи Тимирязевской сельскохозяйственной академии, проводилось определение параметров для 14 двухпараметрических моделей, наиболее часто встречающихся в литературных источниках. Параметры моделей вычислялись путем минимизации среднеквадратической ошибки. Качество моделей оценивалось по следующим метрикам: квадратный корень из среднеквадратической ошибки, коэффициент детерминации, информационный критерий Акаике, информационный критерий Байеса. Полученные результаты подтвердили целесообразность использования на практике ряда уравнений, которые среди двухпараметрических моделей показывают приемлемое качество. Результаты анализа данных показывают, что со статистической точки зрения полученные различия в качестве моделей не являются значимыми на 5-м уровне (t-тест). Обоснованные в качестве лучших модели зависимости высоты от диаметра на высоте груди могут быть использованы на практике при выполнении лесохозяйственных и научно-исследовательских работ в березовых древостоях, произрастающих в центральных регионах европейской части России. Методика проведенного исследования позволяет повторить аналогичную работу для древесных пород и лесорастительных условий, для которых информация о характере связи высоты с таксационным диаметром является неполной или отсутствует. The accuracy of determining the height of trees is important both in forestry and in scientific research. Height is usually determined using specific models, where it is a function of the diameter at breast height. The simplest models of the dependence of tree height on diameter at breast height are two-parameter, and they are widely used in forest taxation researches. On the materials of 23 sample plots with the measurement of model trees laid in birch stands of the Forest Experimental District of the Timiryazev Agricultural Academy, the parameters were determined for the 14 bi-parameter models that are most often found in literary sources. Model parameters were calculated by minimizing the standard error. The quality of the models was evaluated by the following metrics: the square root of the standard error, the coefficient of determination, the Akaike information criterion, the Bayes information criterion. The obtained results confirmed the feasibility of using in practice several equations, which among the biparameter models show acceptable quality. The results of data analysis show that, from a statistical point of view, the differences obtained in the quality of models are not significant at the 5 level (t-test). Substantiated as the best models of the dependence of height on diameter at breast height can be used in practice when performing forestry and research work in birch stands growing in the central regions of the European part of Russia. The methodology of the study allows you to repeat the same work for tree species and forest conditions, for which information about the nature of the relationship of height with the diameter at breast height is incomplete or absent.

Imputing missing height measures using a mixed-effects modeling strategy

2004 ◽  
Vol 34 (12) ◽  
pp. 2492-2500 ◽  
Author(s):  
Andrew P Robinson ◽  
William R Wykoff
Keyword(s):  
Mean Squared Error ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Tree Level ◽  
Root Mean Squared Error ◽  
Mixed Effects Modeling ◽  
Squared Error ◽  
Best Linear Unbiased ◽  
Modeling Strategy ◽  
Point Level

This paper proposes a method whereby height–diameter regression from an inventory can be incorporated into a height imputation algorithm. Point-level subsampling is often employed in forest inventory for efficiency. Some trees will be measured for diameter and species, while others will be measured for height and 10-year increment. Predictions of these missing measures would be useful for estimating volume and growth, respectively, so they are often imputed. We present and compare three imputation strategies: using a published model, using a localized version of a published model, and using best linear unbiased predictions from a mixed-effects model. The bases of our comparison are four-fold: minimum fitted root mean squared error and minimum predicted root mean squared error under a 2000-fold cross-validation for tree-level height and volume imputations. In each case the mixed-effects model proved superior. This result implies that substantial environmental variation existed in the height–diameter relationship for our data and that its representation in the model by means of random effects was profitable.

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