Comparison of stem taper models for the four tropical tree species in Mount Makiling, Philippines

Lumbres, Roscinto Ian C.; Abino, Azyleah C.; Pampolina, Nelson M.; Calora, Feliciano G.; Lee, Young Jin

doi:10.1007/s11629-015-3546-2

Cited by 12 publications

(6 citation statements)

References 29 publications

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“…Among the models with different kernels, the supportFor each model, we first randomly selected 70% of the data for training and 30% for validation/testing (see also Bayat et al 2019a;Benali et al 2019). Common criteria for assessing the goodness-of-fit of model predictions have historically been the coefficient of determination (R 2 ), the root-mean-squared error (RMSE), the relative RMSE, BIAS, and the relative BIAS (Lumbres et al 2016). An important advantage of the relative RMSE (in %) is that it enables comparisons among predictions produced by different models (Pulido-Calvo et al 2007).…”

Section: Study Methodsmentioning

confidence: 99%

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest

Hamidi

Zenner

Bayat

et al. 2021

Annals of Forest Science

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Key messageWe modeled 10-year netstand volume growth with four machine learning (ML) methods, i.e., artificialneural networks (ANN), support vector machines (SVM), random forests (RF), andnearest neighbor analysis (NN), and with linear regression analysis.Incorporating interactions of multiple variables, the ML methods ANN and SVMpredicted nonlinear system behavior and unraveled complex relations withgreater accuracy than regression analysis. Context Investigating the quantitative and qualitative characteristics of short-term forest dynamics is essential for testing whether the desired goals in forest-ecosystem conservation and restoration are achieved. Inventory data from the Jojadeh section of the Farim Forest located in the uneven-aged, mixed Hyrcanian Forest were used to model and predict 10-year net annual stand volume increment with new machine learning technologies. Aims The main objective of this study was to predict net annual stand volume increment as the preeminent factor of forest growth and yield models. Methods In the current study, volume increment was modeled from two consecutive inventories in 2003 and 2013 using four machine learning techniques that used physiographic data of the forest as input for model development: (i) artificial neural networks (ANN), (ii) support vector machines (SVM), (iii) random forests (RF), and (iv) nearest neighbor analysis (NN). Results from the various machine learning technologies were compared against results produced with regression analysis. Results ANNs and SVMs with a linear kernel function that incorporated field-measurements of terrain slope and aspect as input variables were able to predict plot-level volume increment with a greater accuracy (94%) than regression analysis (87%). Conclusion These results provide compelling evidence for the added utility of machine learning technologies for modeling plot-level volume increment in the context of forest dynamics and management.

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Section: Study Methodsmentioning

confidence: 99%

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest

Hamidi

Zenner

Bayat

et al. 2021

Annals of Forest Science

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“…Ample material is available on manifold functions and procedures for the precise estimation of diameter at any position along the trunk (Crecente-Campo et al 2009;Heidarsson and Pukkala 2011;Hjelm 2013;Özcelik and Göceri 2015;Lumbres et al 2016;Tang et al 2017). In this study, three stem taper functions from two groups, segmented and variable exponent, were fitted to estimate stem diameters of white birch with the best possible accuracy.…”

Section: Discussionmentioning

confidence: 99%

Stem taper functions for Betula platyphylla in the Daxing’an Mountains, northeast China

et al. 2020

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Accurate prediction of stem diameter is an important prerequisite of forest management. In this study, an appropriate stem taper function was developed for upper stem diameter estimation of white birch (Betula platyphylla Sukaczev) in ten sub-regions of the Daxing’an Mountains, northeast China. Three commonly used taper functions were assessed using a diameter and height dataset comprising 1344 trees. A first-order continuous-time error structure accounted for the inherent autocorrelation. The segmented model of Max and Burkhart (For Sci 22:283–289, 1976. 10.1093/forestscience/22.3.283) and the variable exponent taper function of Kozak (For Chron 80:507–515, 2004. 10.5558/tfc80507-4) described the data accurately. Owing to its lower multicollinearity, the Max and Burkhart (1976) model is recommended for diameter estimation at specific heights along the stem for the ten sub-regions. After comparison, the Max and Burkhart (1976) model was refitted using nonlinear mixed-effects techniques. Mixed-effects models would be used only when additional upper stem diameter measurements are available for calibration. Differences in region-specific taper functions were indicated by the method of the non-linear extra sum of squares. Therefore, the particular taper function should be adjusted accordingly for each sub-region in the Daxing’an Mountains.

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“…In modelling the stem taper of Betula pubescens in north-west Spain, the function received recommendations [25]; it also became the option of choice for Quercus glauca in South Korea [50]. It was recommended for modelling stem taper of several tropical species in the area of the Philippines [51], as well as for estimating stem diameter of Tectona grandis in mid-western Brazil [52]. The range of species for which the Kozak function has been recommended, as well as the number of regions for which it has been used, both make it possible to deduce that the taper function is a species-specific function.…”

Section: Discussionmentioning

confidence: 99%

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

Adamec

Adolt

Drápela

et al. 2019

Forests

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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.

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Comparison of stem taper models for the four tropical tree species in Mount Makiling, Philippines

Cited by 12 publications

References 29 publications

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest

Analysis of plot-level volume increment models developed from machine learning methods applied to an uneven-aged mixed forest

Stem taper functions for Betula platyphylla in the Daxing’an Mountains, northeast China

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

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