Measurement errors in forest inventories and comparison of biomass estimation methods
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-018X2018000300030 |
Resumo: | Accurate quantification of above-ground biomass (AGB) in managed forests requires: consideration of inventory errors and the use of local or large-scale allometric models. In this study we focus on the measurement errors, data collection errors and we compared different methods to estimate AGB in managed tropical forest. The data were collected in 15 plots of 100 x 100 m. We evaluated the errors of the forest inventory of 8.898 trees. We used four methods to estimate AGB: three methods which use a pan-tropical equation, which depends on wood density data, with different ways of integrating the wood density data (obtained from dataset of the Brazilian Forest Service, Jari and Global Wood Density Database - GWDD); and one local equation. The main inventory errors were: problems with the same tree being identified as a different tree in consecutive measurements (16% of the trees). AGB estimates using each of the four methods were significantly different. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Measurement errors in forest inventories and comparison of biomass estimation methodsrainforest managementforest inventory of companieslocal and pan-tropical allometric modelsAccurate quantification of above-ground biomass (AGB) in managed forests requires: consideration of inventory errors and the use of local or large-scale allometric models. In this study we focus on the measurement errors, data collection errors and we compared different methods to estimate AGB in managed tropical forest. The data were collected in 15 plots of 100 x 100 m. We evaluated the errors of the forest inventory of 8.898 trees. We used four methods to estimate AGB: three methods which use a pan-tropical equation, which depends on wood density data, with different ways of integrating the wood density data (obtained from dataset of the Brazilian Forest Service, Jari and Global Wood Density Database - GWDD); and one local equation. The main inventory errors were: problems with the same tree being identified as a different tree in consecutive measurements (16% of the trees). AGB estimates using each of the four methods were significantly different.Sociedade de Ciências Agrárias de Portugal2018-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articletext/htmlhttp://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-018X2018000300030Revista de Ciências Agrárias v.41 n.3 2018reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttp://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-018X2018000300030Castelo,AdrianoGuedes,MarcelinoSotta,EleneideBlanc,Lilianinfo:eu-repo/semantics/openAccess2024-02-06T17:02:35Zoai:scielo:S0871-018X2018000300030Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:17:35.450110Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Measurement errors in forest inventories and comparison of biomass estimation methods |
title |
Measurement errors in forest inventories and comparison of biomass estimation methods |
spellingShingle |
Measurement errors in forest inventories and comparison of biomass estimation methods Castelo,Adriano rainforest management forest inventory of companies local and pan-tropical allometric models |
title_short |
Measurement errors in forest inventories and comparison of biomass estimation methods |
title_full |
Measurement errors in forest inventories and comparison of biomass estimation methods |
title_fullStr |
Measurement errors in forest inventories and comparison of biomass estimation methods |
title_full_unstemmed |
Measurement errors in forest inventories and comparison of biomass estimation methods |
title_sort |
Measurement errors in forest inventories and comparison of biomass estimation methods |
author |
Castelo,Adriano |
author_facet |
Castelo,Adriano Guedes,Marcelino Sotta,Eleneide Blanc,Lilian |
author_role |
author |
author2 |
Guedes,Marcelino Sotta,Eleneide Blanc,Lilian |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Castelo,Adriano Guedes,Marcelino Sotta,Eleneide Blanc,Lilian |
dc.subject.por.fl_str_mv |
rainforest management forest inventory of companies local and pan-tropical allometric models |
topic |
rainforest management forest inventory of companies local and pan-tropical allometric models |
description |
Accurate quantification of above-ground biomass (AGB) in managed forests requires: consideration of inventory errors and the use of local or large-scale allometric models. In this study we focus on the measurement errors, data collection errors and we compared different methods to estimate AGB in managed tropical forest. The data were collected in 15 plots of 100 x 100 m. We evaluated the errors of the forest inventory of 8.898 trees. We used four methods to estimate AGB: three methods which use a pan-tropical equation, which depends on wood density data, with different ways of integrating the wood density data (obtained from dataset of the Brazilian Forest Service, Jari and Global Wood Density Database - GWDD); and one local equation. The main inventory errors were: problems with the same tree being identified as a different tree in consecutive measurements (16% of the trees). AGB estimates using each of the four methods were significantly different. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-018X2018000300030 |
url |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-018X2018000300030 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0871-018X2018000300030 |
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 |
Sociedade de Ciências Agrárias de Portugal |
publisher.none.fl_str_mv |
Sociedade de Ciências Agrárias de Portugal |
dc.source.none.fl_str_mv |
Revista de Ciências Agrárias v.41 n.3 2018 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799137269218541568 |