Appropriate Volume Functions for Leguminosae Family in Two Tropical Rainforests in Cross River State, Nigeria

Igbinosa, Aigbe Humphrey; Amoo, Oyebade Bukola

doi:10.5296/jee.v5i2.6742

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…The estimation errors in larger trees were typical of heterogeneous natural forests (Brandeis et al 2006). The Schumacher and Hall's model is often cited as one of the most appropriate to estimate volume of trees in tropical forests (Akindele & LeMay 2006, Igbinosa & Amoo 2014. Thus, the classification approach based on the regression coefficients would seem appropriate, since it effectively reflects tree taper.…”

Section: Discussionmentioning

confidence: 99%

Cluster and Discriminant Analyses for Stem

Cysneiros¹,

Pelissari²,

Machado³

et al. 2017

JTFS

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The diversity of species in native tropical forests causes difficulty in the interpretation of data that support their management and conservation. Species grouping, based on characteristics of interest, reduces significantly the number of volume equations and helps solve the problem of undersampling rare species. This study aims to group 32 Amazonian trees species of commercial interest based on regression coefficients of the Schumacher and Hall's model and their fit statistics. To accomplish this, we employ a two-stage approach, in which we first applied cluster analysis to classify species with higher sampling intensity (n > 30). This phase allowed us to allocate poorly sampled species (n < 30) to groups created by discriminant analysis, resulting in the second stage. This proposed approach has proven adequate for grouping timber species in the Amazon forest, and so the stem volume can be modelled on consistent groups of species. The grouping of Amazon rainforest commercial species, based on the regression coefficients and fit statistics, performs better in aggregation for the stem volume modelling, providing stabilisation of estimation error and supplying few equations for the evaluation of standing stock.

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Section: Discussionmentioning

confidence: 99%

Cluster and Discriminant Analyses for Stem

Cysneiros¹,

Pelissari²,

Machado³

et al. 2017

JTFS

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“…The main advantage of this model is the ponderation factor d².h, being appropriate for the reduction of heteroscedasticity and improvement of estimates (AKINDELE and LEMAY, 2006;IGBINOSA and AMOO, 2014). Posteriorly, estimations per plot were converted to hectare by the proportionality factor of 0.8.…”

Section: Data Basementioning

confidence: 99%

“…However, the abundance of these tree species in many forms and dimensions, added to the high floristic and structural diversity, are the main characteristics of tropical rainforests that make difficult to obtain precise approximations of individual volumes (AKINDELE and LEMAY, 2006;IGBINOSA and AMOO, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modeling of the Commercial Volume Stock in an Ombrophilous Forest in the Southwest of the Amazon

Cysneiros

Machado

Pelissari

et al. 2016

CERNE

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ABSTRACT:The abundance of tree species in tropical rainforests with different shapes and dimensions, as well as the great structural diversity, makes difficult the employment of form factors or equations to estimate individual volumes. However, the employment of equations at the population level makes possible to predict the volumetric stock per unit of area from forest attributes, excluding the necessity of individual tree volume evaluation. Thereby, the aim of this study was to select variables, fit statistical models and propose stand equations to estimate total and exploitable commercial volumes in an Amazon forest under concession regime. For this, variables commonly measured, like density and basal area, were inserted on traditional commercial volume models; besides the application of variables that considered the diversity and forest structure on models generated by the Stepwise process. After analysis, it was observed that the models obtained through Stepwise propitiated more precise estimation of the volumetric stock, reducing estimation errors and reducing problems with heteroscedasticity of residuals. The insertion of variables that express diversity and forest structure on the equations, like Shannon's and diametric variation indices and the commercial trees ratio, contribute to predictions' improvement, especially for the exploitable commercial volume, being recommended for the precise evaluation of timber potential of areas under Amazon forest concession. MODELAGEM DO ESTOQUE VOLUMÉTRICO COMERCIAL EM UMAFLORESTA OMBRÓFILA NO SUDOESTE DA AMAZÔNIA RESUMO: Nas florestas tropicais, a abundância de espécies arbóreas em diferentes formas e dimensões, bem como a elevada diversidade estrutural, dificulta o emprego de fatores de forma ou de equações para a estimativa de volumes individuais. Contudo, o emprego de equações de povoamento possibilita predizer o estoque volumétrico por unidade de área a partir de atributos da floresta, excluindo a necessidade da avaliação individual do volume das árvores. Com isso, o objetivo deste estudo foi selecionar variáveis, ajustar modelos estatísticos e propor equações de povoamento para a estimativa do volume comercial total e explorável em uma floresta sob regime de concessão na Amazônia. Para isso, medidas comumente mensuradas, como a densidade e a área basal, foram empregadas em modelos tradicionais da literatura; além da aplicação de variáveis que consideraram a diversidade e a estrutura florestal em modelos gerados pelo processo Stepwise. Desse modo, os modelos obtidos por Stepwise propiciaram estimativas mais precisas dos estoques volumétricos, reduzindo os erros de estimativa e reduzindo problemas de heterocedasticidade dos resíduos. Além disso, a inserção de variáveis que expressam a diversidade e a estrutura florestal nas equações, como os índices de Shannon e de variação diamétrica e a razão de indivíduos comerciais, contribuiu para a melhoria das predições, sobretudo para o volume comercial explorável, sendo então recomendadas para a avaliação precisa do pote...

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