Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest

Detalhes bibliográficos
Autor(a) principal: OLIVEIRA,LAIO Z.
Data de Publicação: 2018
Outros Autores: KLITZKE,ALINE R., FANTINI,ALFREDO C., ULLER,HEITOR F., CORREIA,JEAN, VIBRANS,ALEXANDER C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703729
Resumo: Abstract The majority of Atlantic Forest fragments in Southern Brazil are second-growth forests dominated by fast-growing species with considerable market-value timber. Nevertheless, volume prediction models are scarce, especially to estimate tree total volume (i.e., stem plus branches). This study approached the issue through the following aims: to fit and select stem and total volume models (generic and species-specific) using data from 288 harvested trees in a management operation, and to fit generic and species-specific bark factors. The power model embedding diameter at breast height (D) and tree stem or total height (H) presented the greatest prediction strength for both stem and total tree volume. Models including only D to predict total tree volume were similar to double-entry models regarding goodness-of-fit. Therefore, they may be useful in the context of subtropical closed-canopy forests, where the difficulty and uncertainty in H measurements are not trivial. Species-specific models fitted for Miconia cinnamomifolia (DC) Naudin. and Hyeronima alchorneoides Allemão surpassed generic models only for the former species. Nevertheless, the prediction improvement should offset the eventual extra efforts implied in the collection of reliable samples of these species. Finally, bark factors stood as a satisfactory tool for inside bark mean volume estimation.
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spelling Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic ForestForest inventoryMata AtlânticamodellingregressionAbstract The majority of Atlantic Forest fragments in Southern Brazil are second-growth forests dominated by fast-growing species with considerable market-value timber. Nevertheless, volume prediction models are scarce, especially to estimate tree total volume (i.e., stem plus branches). This study approached the issue through the following aims: to fit and select stem and total volume models (generic and species-specific) using data from 288 harvested trees in a management operation, and to fit generic and species-specific bark factors. The power model embedding diameter at breast height (D) and tree stem or total height (H) presented the greatest prediction strength for both stem and total tree volume. Models including only D to predict total tree volume were similar to double-entry models regarding goodness-of-fit. Therefore, they may be useful in the context of subtropical closed-canopy forests, where the difficulty and uncertainty in H measurements are not trivial. Species-specific models fitted for Miconia cinnamomifolia (DC) Naudin. and Hyeronima alchorneoides Allemão surpassed generic models only for the former species. Nevertheless, the prediction improvement should offset the eventual extra efforts implied in the collection of reliable samples of these species. Finally, bark factors stood as a satisfactory tool for inside bark mean volume estimation.Academia Brasileira de Ciências2018-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703729Anais da Academia Brasileira de Ciências v.90 n.4 2018reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201820180111info:eu-repo/semantics/openAccessOLIVEIRA,LAIO Z.KLITZKE,ALINE R.FANTINI,ALFREDO C.ULLER,HEITOR F.CORREIA,JEANVIBRANS,ALEXANDER C.eng2019-01-15T00:00:00Zoai:scielo:S0001-37652018000703729Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-01-15T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
title Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
spellingShingle Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
OLIVEIRA,LAIO Z.
Forest inventory
Mata Atlântica
modelling
regression
title_short Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
title_full Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
title_fullStr Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
title_full_unstemmed Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
title_sort Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
author OLIVEIRA,LAIO Z.
author_facet OLIVEIRA,LAIO Z.
KLITZKE,ALINE R.
FANTINI,ALFREDO C.
ULLER,HEITOR F.
CORREIA,JEAN
VIBRANS,ALEXANDER C.
author_role author
author2 KLITZKE,ALINE R.
FANTINI,ALFREDO C.
ULLER,HEITOR F.
CORREIA,JEAN
VIBRANS,ALEXANDER C.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv OLIVEIRA,LAIO Z.
KLITZKE,ALINE R.
FANTINI,ALFREDO C.
ULLER,HEITOR F.
CORREIA,JEAN
VIBRANS,ALEXANDER C.
dc.subject.por.fl_str_mv Forest inventory
Mata Atlântica
modelling
regression
topic Forest inventory
Mata Atlântica
modelling
regression
description Abstract The majority of Atlantic Forest fragments in Southern Brazil are second-growth forests dominated by fast-growing species with considerable market-value timber. Nevertheless, volume prediction models are scarce, especially to estimate tree total volume (i.e., stem plus branches). This study approached the issue through the following aims: to fit and select stem and total volume models (generic and species-specific) using data from 288 harvested trees in a management operation, and to fit generic and species-specific bark factors. The power model embedding diameter at breast height (D) and tree stem or total height (H) presented the greatest prediction strength for both stem and total tree volume. Models including only D to predict total tree volume were similar to double-entry models regarding goodness-of-fit. Therefore, they may be useful in the context of subtropical closed-canopy forests, where the difficulty and uncertainty in H measurements are not trivial. Species-specific models fitted for Miconia cinnamomifolia (DC) Naudin. and Hyeronima alchorneoides Allemão surpassed generic models only for the former species. Nevertheless, the prediction improvement should offset the eventual extra efforts implied in the collection of reliable samples of these species. Finally, bark factors stood as a satisfactory tool for inside bark mean volume estimation.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703729
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000703729
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765201820180111
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.90 n.4 2018
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
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