A generalized nonlinear mixed-effects height–diameter model for Norway spruce in mixed-uneven aged stands

Ciceu, Albert; García-Duro, Juan; Seceleanu, Ioan; Badea, Ovidiu

doi:10.1016/j.foreco.2020.118507

Cited by 37 publications

(29 citation statements)

References 55 publications

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“…Several nonlinear single predictor height-diameter functions have been used to describe tree height and diameter relationships in both even-aged and uneven-aged stands (Mehtätalo et al 2015;Corral-Rivas et al 2019;Bronisz and Mehtätalo 2020;Ciceu et al 2020;Ercanli 2020a;Xie et al 2020), and in complex natural forests (Feldpausch et al 2011;Temesgen et al 2014;Kearsley et al 2017;Ogana 2019;Chenge 2021). To select the base model for the complex tropical forests, 18 single predictor h-d models were initially evaluated.…”

Section: Models Based On Classical Methods: Nls and Nlmementioning

confidence: 99%

“…1007/ s11676-021-01373-1. of tree height is costly, often difficult and time-consuming (Özçelik et al 2018;Ciceu et al 2020;Magnussen et al 2020), especially in complex forest ecosystems with closedcanopies (Larjavaara and Muller-Landau 2013), and as such, foresters find it more acceptable to estimate this variable (Temesgen et al 2014). To do this, a few heights are measured and an appropriate height-diameter (h-d) function is then used to estimate other tree heights for which diameters have been measured (Kalbi et al 2018).…”

Section: Supplementary Informationmentioning

confidence: 99%

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Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

Ogana

Ercanlı

2021

J. For. Res.

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Modelling tree height-diameter relationships in complex tropical rain forest ecosystems remains a challenge because of characteristics of multi-species, multi-layers, and indeterminate age composition. Effective modelling of such complex systems required innovative techniques to improve prediction of tree heights for use for aboveground biomass estimations. Therefore, in this study, deep learning algorithm (DLA) models based on artificial intelligence were trained for predicting tree heights in a tropical rain forest of Nigeria. The data consisted of 1736 individual trees representing 116 species, and measured from 52 0.25 ha sample plots. A K-means clustering was used to classify the species into three groups based on height-diameter ratios. The DLA models were trained for each species-group in which diameter at beast height, quadratic mean diameter and number of trees per ha were used as input variables. Predictions by the DLA models were compared with those developed by nonlinear least squares (NLS) and nonlinear mixed-effects (NLME) using different evaluation statistics and equivalence test. In addition, the predicted heights by the models were used to estimate aboveground biomass. The results showed that the DLA models with 100 neurons in 6 hidden layers, 100 neurons in 9 hidden layers and 100 neurons in 7 hidden layers for groups 1, 2, and 3, respectively, outperformed the NLS and NLME models. The root mean square error for the DLA models ranged from 1.939 to 3.887 m. The results also showed that using height predicted by the DLA models for aboveground biomass estimation brought about more than 30% reduction in error relative to NLS and NLME. Consequently, minimal errors were created in aboveground biomass estimation compared to those of the classical methods.

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Section: Models Based On Classical Methods: Nls and Nlmementioning

confidence: 99%

Section: Supplementary Informationmentioning

confidence: 99%

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

Ogana

Ercanlı

2021

J. For. Res.

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“…Our study's aim was to build an appropriate H-D model for the Mongolian pine stands in Zhanggutai, where the oldest introduced sandy Mongolian pine plantations in China are located [44]. We developed a mixed-effects H-D model and determined an economical method and a simple but effective calibration design for estimating the heights of Mongolian pines to aid forest management planning [6,21].…”

Section: Basic and Generalized H-d Modelsmentioning

confidence: 99%

“…Tree height and diameter at breast height are two elementary input variables used in forest management practice [5,6], with tree height being an input variable needed in Forests 2021, 12, 1144 2 of 14 most allometric equations that estimate tree biomass and volume [7][8][9]. Furthermore, tree height estimates are needed in growth and yield modelling.…”

Section: Introductionmentioning

confidence: 99%

“…Mixed-effects models can easily account for the variations in H-D relationships among stands by including stand-level random effect parameters [27,28]. Furthermore, less sampling effort is required and similar accuracy is achieved by calibrating H-D mixed-effects models compared with the sampling effort necessary to build a local H-D model [6,15]. However, stand-level predictor variables may not always be necessary in mixed-effects H-D models' modelling framework.…”

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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Age-independent diameter increment models for mixed mountain forests

Ciceu

Bronisz²,

García-Duro³

et al. 2022

Eur J Forest Res

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Mixed mountain forests with an uneven-aged structure are characterized by a high tree-growth variability making traditional age-dependent growth models inapplicable. Estimating site productivity is yet another impediment for modelling tree growth in such forests. Uneven-aged mixed-stand forests are known for their high resilience, resistance and productivity, and are being promoted as a suitable alternative to even-aged, pure plantations for climate change adaptation and mitigation. However, their growth must be accurately measured and predicted, but diameter at the breast height (dbh) increment models specifically designed for uneven-aged mixed mountain forests are still rare. Using permanent sampling network data and 465 increment cores, we built two age-independent dbh increment ($${i}_{d}$$ i d ) models for the main species of the study area, namely Norway spruce (Picea abies (L.) Karst.), silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.). Mixed effects models and the algebraic difference approach were employed to develop $${i}_{d}$$ i d models based on empirical and commonly used theoretical growth functions. A past growth index was further developed and introduced in the model in order to explain the $${i}_{d}$$ i d variability. Several mixed effects calibration strategies were assessed in order to obtain the most accurate localized curve for new plots. Tree size, competition and biogeoclimatic variables were found to explain the $${i}_{d}$$ i d through the empirical growth function, while the growth index significantly improved the theoretical growth function for Norway spruce. The optimization of the calibration strategy for the mixed effects modelling framework enables the growth index implementation in forest practice as an accurate method for estimating site productivity. The accuracy of the two $${i}_{d}$$ i d models was similar: the root mean squared error of the empirical growth function varied between 0.940 and 1.042 cm for spruce, beech and fir, while the root mean squared error obtained through the theoretical growth function for spruce only was 1.105 cm. The basal area increment prediction at the plot level based on the theoretical growth function reached a root mean squared error of 0.043 m2 while using the empirical growth function the root mean squared error is 0.047 m2. The high accuracy obtained using age-independent models underlines their suitability for predicting growth in mixed uneven-aged forests. The developed models can be easily integrated into forest practice to accurately obtain $${i}_{d}$$ i d estimates.

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A generalized nonlinear mixed-effects height–diameter model for Norway spruce in mixed-uneven aged stands

Cited by 37 publications

References 55 publications

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm

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

Age-independent diameter increment models for mixed mountain forests

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