NONLINEAR MIXED-EFFECT HEIGHT-DIAMETER MODEL FOR Pinus pinaster AIT. AND Pinus radiata D. DON

Ogana, Friday Nwabueze; Corral-Rivas, Sacramento; Gorgoso-Varela, José Javier

doi:10.1590/01047760202026012695

Cited by 14 publications

(22 citation statements)

References 23 publications

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“…As recommended by Sharma and Parton (2007) and the first step was try to fit the mixed-effects model using the three parameters expanded with random effects, however, convergence was not achieved in any case, so it was decided to reduce the number and systematically combine the parameters until convergence was achieved. Other studies have pointed out convergence problems, specially in models with more than two parameters to expand Ogana et al, 2020;. In our study, in all the proposed generalized models the convergence was achieved with two parameters.…”

Section: Parameter Expansion With Random Effectssupporting

confidence: 49%

“…religiosa, Pinus, and Quercus groups) we tested 12 non-linear functions frequently used in forestry (López et al, 2012;Hernández-Ramos et al, 2015;Ogana et al, 2020)(Table 3.2). While the 6 local models were fitted using the ordinary nonlinear least squares (ONLS) method with the function 'nls' of R (R Core Team, 2019), the other 6 generalized models were fitted using only the mixed-effects modeling approximation.…”

Section: Tree Height-diameter Functionsmentioning

confidence: 99%

“…In the study of Corral-Rivas et al ( 2019) different sub-sample options were analyzed, from one to three trees, the best calibration option was to randomly select the three closest trees to the second quantile of the diameter distribution. In the study of Ogana et al (2020), the best strategy was to randomly select just one tree. used a sub-sample of four trees.…”

Section: Random Effects Calibrationmentioning

confidence: 99%

“…Several functions, both linear and nonlinear, have been used to fit allometric tree h-d relationships, a compilation of which can be found in and Ogana et al (2020). Functions that only include tree diameter as predictor variable are known as local models, and can be applied to accurately predict tree height, but they are not inherently useful in all forest conditions .…”

Section: Introductionmentioning

confidence: 99%

“…approach toOgana et al (2020) and, i.e. we tested different subsample combinations based on the diameter distribution.…”

mentioning

confidence: 99%

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Models for Mexican Temperate Forests: A Sustainable Management Approach

Alonso¹

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Section: Parameter Expansion With Random Effectssupporting

confidence: 49%

Section: Tree Height-diameter Functionsmentioning

confidence: 99%

Section: Random Effects Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…approach toOgana et al (2020) and, i.e. we tested different subsample combinations based on the diameter distribution.…”

mentioning

confidence: 99%

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Models for Mexican Temperate Forests: A Sustainable Management Approach

Alonso¹

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Removal of wood dyes from aqueous solutions by sorption on untreated pine (Pinus radiata) sawdust

et al. 2023

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In this work, the possibility of using un-treated pine (Pinus radiata) sawdust as biosorbent for the removal of three wood dyes (blue, red and black) from aqueous solutions was investigated. The BET surface area of the material was 1.55 ± 0.81 m2 g−1 using N2 at − 196 °C and 17.83 ± 0.032 m2 g−1 using CO2 at 0 °C. The point of zero charge was 4.8. In addition, the material was characterized before and after adsorption by surface analytical techniques. Assays were performed to analize the stability of wood dyes with time and pH and also to estimate the pKa values. Batch adsorption experiments were performed and the influence of experimental parameters such as contact time (up to equilibrium), stirring rate (100 and 210 rpm), adsorbent dose (2–10 g L−1), pH (2–9) and initial dye concentration (5 and 300 mg L−1) were studied. The optimum conditions were pH 2 and adsorbent dose of 6 g L−1 and the highest adsorption percentages achieved at 5 mg L−1 were: 100.0% for the blue, 99.7% for the black and 92.4% for the red. For 300 mg L−1 adsorption efficiency decreased to 85.6, 57.0 and 63.8% for the blue, black and red ones, respectively. Kinetic data were best fitted to the pseudo-second order model, suggesting a chemisorption process. Freundlich and Dubidin-Radushkevich were the best isotherm models. Regeneration study showed that un-treated pine sawdust could be reused efficiently for red dye removal at least in up to four repeated adsorption–desorption cycles using NaOH as desorption agent.

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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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NONLINEAR MIXED-EFFECT HEIGHT-DIAMETER MODEL FOR Pinus pinaster AIT. AND Pinus radiata D. DON

Cited by 14 publications

References 23 publications

Models for Mexican Temperate Forests: A Sustainable Management Approach

Models for Mexican Temperate Forests: A Sustainable Management Approach

Removal of wood dyes from aqueous solutions by sorption on untreated pine (Pinus radiata) sawdust

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

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