Modeling diameter distributions of<i>Gmelina arborea</i>plantation in Omo Forest Reserve, Nigeria with Johnson’s S<sub>B</sub>

Ogana, Friday Nwabueze; Itam, E S; Osho, J.S.A.

doi:10.1080/10549811.2016.1263575

Cited by 13 publications

(8 citation statements)

References 15 publications

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“…Heavily skewed or asymmetric structure for plantations could be due to thinning, poor forest management, illegal exploitation and other forms of disturbances. The E. camaldulensis and G. arborea stands have been previously reported to have suffered from severe disturbanceboth anthropogenic and wind damage (Ogana et al 2017(Ogana et al , 2018. Though silvicultural practices such as selection cutting can be used to convert even-aged stand to uneven-aged (reverse J-shaped), it is not a common practice in Nigeria.…”

Section: Discussionmentioning

confidence: 99%

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

Ogana

2020

Eurasian Journal of Forest Science

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The quality fit produce by distribution function such as the Weibull depends to an extent the type of estimator used to derive its parameters. Inappropriate choice of estimator could affect management decision. Though several estimators have been developed for the Weibull function, their application to forestry have been relatively few. Therefore, this study evaluated ten estimators of the Weibull parameters using tree diameter data from five production forest plantations in Nigeria. The estimators were generalized least type I and type II, L-moment, moments, maximum likelihood, percentiles, rank correlation, least squares, U-statistics and weighted least squares. The quality of fits of the Weibull function were evaluated with Kolmogorov-Smirnov, Anderson-Darling, Cramervon Mises, Akaike information criterion and Bayesian information criterion. Relative rank sum from the evaluation statistics of the methods was analysed using One-way analysis of variance. The results showed that weighted least square had the smallest statistics and relative rank, but not significantly different from L-moment, moments and maximum likelihood (p > 0.05). The performances of least squares, generalized least type I and type II, percentiles and U-statistics were relatively poor. Thus, either the weighted least squares, moments-based or MLE could be used for the Weibull function in the diameter distribution of forest stands in Nigeria.

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Section: Discussionmentioning

confidence: 99%

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

Ogana

2020

Eurasian Journal of Forest Science

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“…Adeniyi (unpubl.) and Ogana et al (2015Ogana et al ( , 2017, and Chukwu & Osho (2018), respectively. The descriptive statistics of the Dbh, total height (Ht), quadratic mean diameter (dg), dominant height (i.e., average height of the 100 tallest trees per ha, H o ), basal area per ha (G), density (N) and stand age (A) are presented in Table 1.…”

Section: Datamentioning

confidence: 92%

“…Statistical functions are often used to model diameter distributions of forest stands (e.g., Poudel & Cao, 2013;Gorgoso-Varela & Rojo-Alboreca, 2014;Ogana et al, 2017), and have become an important part of forest management and planning. Diameter distribution forms the link between stand level and tree level models (Nord-Larsen & Cao, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…Diameter distribution forms the link between stand level and tree level models (Nord-Larsen & Cao, 2006). It can be used to provide information on the growth, value and volume production of forest stands (Gorgoso et al, 2012;Ogana et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…stands in Northwest Spain. In Nigeria, Ogana et al (2017 modelled the diameter distributions of Gmelina arborea Roxb. and Eucalyptus camaldulensis Dehn.…”

Section: Introductionmentioning

confidence: 99%

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A percentile-based estimator for the log-logistic function: Application to forestry

Ogana

2020

Forestry Studies

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Developing a simplified estimation method without compromising the performance of the distribution is germane to forest modelling. Few estimation methods exist for the Log-Logistic distribution and are relatively complex. A simplified estimator for the Log-Logistic parameters will increase its application in diameter distribution yield systems. Therefore, in this study, a percentile-based estimator was applied for the Log-Logistic distribution. The Kolmogorov-Smirnov, Anderson-Darling and Cramer-von Mises statistics were used to evaluate the method in two natural forest stands and two monospecific plantations of Gmelina arborea Roxb. and Tectona grandis Linn. f. in Nigeria. The parameter recovery model (PRM) and parameter prediction model (PPM) were used to predict the diameter distributions of independent stands of G. arborea and T. grandis. The results showed that the percentile estimator did not compromise the quality of fits of the Log-Logistic function across the four forest stands and are comparable to the maximum likelihood estimator. The 25th and 75th, and 40th and 80th were the best sample percentiles for the estimator. The predicted diameter distributions of G. arborea and T. grandis stands from the PRM and PPM were reasonable and compare well with the observed distribution. Thus, either of the models can be incorporated into the growth and yield system of forest stand management.

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Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique

2023

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The diameter distribution of trees in a stand provides the basis for determining the stand’s ecological and economic value, its structure and stability and appropriate management practices. Scots pine (Pinus sylvestris L.) is one of the most common and important conifers in Turkey, so a well-planned management schedule is critical. Diameter distribution models to accurately describe the stand structure help improve management strategies, but developing reliable models requires a deep understanding of the growth, output and constraints of the forests. The most important information derived by diameter distribution models is primary data on horizontal stand structure for each diameter class of trees: basal area and volume per unit area. These predictions are required to estimate the range of products and predicted volume and yield from a forest stand. Here, to construct an accurate, reliable diameter distribution model for natural Scots pine stands in the Türkmen Mountain region, we used Johnson’s SB distribution to represent the empirical diameter distributions of the stands using ground-based measurements from 55 sample plots that included 1219 trees in natural distribution zones of the forests. As an alternative, nonparametric approach, which does not require any predefined function, an artificial intelligence model was constructed based on support vector machine methodology. An error index was calculated to evaluate the results. Overall, both Johnson’s SB probability density function with a three-parameter recovery approach and the support vector regression methodology provided reliable estimates of the diameter distribution of these stands.

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Modeling diameter distributions ofGmelina arboreaplantation in Omo Forest Reserve, Nigeria with Johnson’s S_B

Cited by 13 publications

References 15 publications

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

A percentile-based estimator for the log-logistic function: Application to forestry

Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique

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Modeling diameter distributions ofGmelina arboreaplantation in Omo Forest Reserve, Nigeria with Johnson’s SB

Cited by 13 publications

References 15 publications

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

An evaluation of ten estimators for fitting two-parameter weibull function to Nigerian forest stands

A percentile-based estimator for the log-logistic function: Application to forestry

Diameter distributions in Pinus sylvestris L. stands: evaluating modelling approaches including a machine learning technique

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Modeling diameter distributions ofGmelina arboreaplantation in Omo Forest Reserve, Nigeria with Johnson’s S_B