Robust volumetric models for supporting the management of secondary forest stands in the Southern Brazilian Atlantic Forest
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
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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Anais da Academia Brasileira de Ciências (Online) |
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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 |
format |
article |
status_str |
publishedVersion |
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 |
dc.format.none.fl_str_mv |
text/html |
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) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
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1754302866909036544 |