Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Floresta e Ambiente |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106 |
Resumo: | ABSTRACT Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palustris stump-biomass. Biomass data were collected at different locations in the southeastern United States. A total of 119 allometric equations were fitted from the combination of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G), number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE) was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass prediction for trees from different sites. |
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Floresta e Ambiente |
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Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United Statesforest managementmodelingregressionABSTRACT Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palustris stump-biomass. Biomass data were collected at different locations in the southeastern United States. A total of 119 allometric equations were fitted from the combination of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G), number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE) was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass prediction for trees from different sites.Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106Floresta e Ambiente v.26 n.spe1 2019reponame:Floresta e Ambienteinstname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJ10.1590/2179-8087.040318info:eu-repo/semantics/openAccessFarias,Aline AraújoGezan,Salvador A.de Carvalho,Melissa PisarogloFerraz Filho,Antonio CarlosSoares,Carlos Pedro Boechateng2019-08-26T00:00:00Zoai:scielo:S2179-80872019005000106Revistahttps://www.floram.org/PUBhttps://old.scielo.br/oai/scielo-oai.phpfloramjournal@gmail.com||floram@ufrrj.br||2179-80871415-0980opendoar:2019-08-26T00:00Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
title |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
spellingShingle |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States Farias,Aline Araújo forest management modeling regression |
title_short |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
title_full |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
title_fullStr |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
title_full_unstemmed |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
title_sort |
Allometric Equations to Predict Pinus palustris Biomass in the Southeastern United States |
author |
Farias,Aline Araújo |
author_facet |
Farias,Aline Araújo Gezan,Salvador A. de Carvalho,Melissa Pisaroglo Ferraz Filho,Antonio Carlos Soares,Carlos Pedro Boechat |
author_role |
author |
author2 |
Gezan,Salvador A. de Carvalho,Melissa Pisaroglo Ferraz Filho,Antonio Carlos Soares,Carlos Pedro Boechat |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Farias,Aline Araújo Gezan,Salvador A. de Carvalho,Melissa Pisaroglo Ferraz Filho,Antonio Carlos Soares,Carlos Pedro Boechat |
dc.subject.por.fl_str_mv |
forest management modeling regression |
topic |
forest management modeling regression |
description |
ABSTRACT Pinus palustris Mill. ecosystem is considered one of the most threatened of North America. In this context, studies on biomass quantification are fundamental for forest management plans. Thus, the objective of this study was to develop a set of allometric equations to predict total P. palustris stump-biomass. Biomass data were collected at different locations in the southeastern United States. A total of 119 allometric equations were fitted from the combination of explanatory variables: diameter at breast height (DBH), height (H), age (I), basal area (G), number of trees per hectare (N), site index (S) and quadratic diameter (Dq). One of the models that presented the lowest residual standard error (Sy.x) and root mean square error (RMSE) was ln(W) = -0.9978+0.7082.(H)+0.1009.ln(H.DBH)-0.5310.(N)-0.0003.ln(Dq). Therefore, the insertion of dendrometric variables characteristic of forest stands has great efficacy in biomass prediction for trees from different sites. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
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=S2179-80872019005000106 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872019005000106 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2179-8087.040318 |
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 |
Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro |
publisher.none.fl_str_mv |
Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro |
dc.source.none.fl_str_mv |
Floresta e Ambiente v.26 n.spe1 2019 reponame:Floresta e Ambiente instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
reponame_str |
Floresta e Ambiente |
collection |
Floresta e Ambiente |
repository.name.fl_str_mv |
Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ) |
repository.mail.fl_str_mv |
floramjournal@gmail.com||floram@ufrrj.br|| |
_version_ |
1750128143226634240 |