SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cerne (Online) |
Texto Completo: | https://cerne.ufla.br/site/index.php/CERNE/article/view/2529 |
Resumo: | The objective of this study was to analyze the performance of species-specific equations (SSEs) concerning generic ones in Annual Production Units (GEAPUs) and in a Forest Management Area (GEFMA) in the Brazilian Amazon. A total of 29,119 trees from 43 species were inventoried, harvested, and volumetric measurements were taken in ten APUs, with 10% of this total being separated for validation and comparison of the selected equations. After selection and validation of the equations (GEFMA, GEAPUs and SSEs) they were compared using precision statistics, by contrasting estimated and observed volumes and by residual analysis. Precision statistics were clearly lower for the SSEs. Trend lines near the average observed volume were shown for the SSEs when the estimates were contrasted with the observations. The residuals generated by the SSEs were smaller and statistically different than those of GEFMA and GEAPUs for the majority of cases. The most important commercial species (M. huberi) had its volume overestimated by 10.6, 9.3 and 3.0% when the GEFMA, the GEAPUs, and the SSEs were applied, respectively. Among the species that generally had very large trees, H. petraeumhad its volume underestimated by 15.7, 16.6 and 4.4% by the GEFMA, GEAPUs and SSEs, respectively. The greater precision of the SSEs is reflected in better forest management planning decisions with respect to operational and economic aspects. These results show that besides being statistically valid, the SSEs are recommended for obtaining more precise estimates of commercial volume, especially since there is a great demand for reliable estimates for each individual species in forest management areas in the Amazon. |
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SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZONSPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZONcommercial species, dendrometry, forest management, tropical forest, volumetric models.The objective of this study was to analyze the performance of species-specific equations (SSEs) concerning generic ones in Annual Production Units (GEAPUs) and in a Forest Management Area (GEFMA) in the Brazilian Amazon. A total of 29,119 trees from 43 species were inventoried, harvested, and volumetric measurements were taken in ten APUs, with 10% of this total being separated for validation and comparison of the selected equations. After selection and validation of the equations (GEFMA, GEAPUs and SSEs) they were compared using precision statistics, by contrasting estimated and observed volumes and by residual analysis. Precision statistics were clearly lower for the SSEs. Trend lines near the average observed volume were shown for the SSEs when the estimates were contrasted with the observations. The residuals generated by the SSEs were smaller and statistically different than those of GEFMA and GEAPUs for the majority of cases. The most important commercial species (M. huberi) had its volume overestimated by 10.6, 9.3 and 3.0% when the GEFMA, the GEAPUs, and the SSEs were applied, respectively. Among the species that generally had very large trees, H. petraeumhad its volume underestimated by 15.7, 16.6 and 4.4% by the GEFMA, GEAPUs and SSEs, respectively. The greater precision of the SSEs is reflected in better forest management planning decisions with respect to operational and economic aspects. These results show that besides being statistically valid, the SSEs are recommended for obtaining more precise estimates of commercial volume, especially since there is a great demand for reliable estimates for each individual species in forest management areas in the Amazon.The objective of this study was to analyze the performance of species-specific equations (SSEs) concerning generic ones in Annual Production Units (GEAPUs) and in a Forest Management Area (GEFMA) in the Brazilian Amazon. A total of 29,119 trees from 43 species were inventoried, harvested, and volumetric measurements were taken in ten APUs, with 10% of this total being separated for validation and comparison of the selected equations. After selection and validation of the equations (GEFMA, GEAPUs and SSEs) they were compared using precision statistics, by contrasting estimated and observed volumes and by residual analysis. Precision statistics were clearly lower for the SSEs. Trend lines near the average observed volume were shown for the SSEs when the estimates were contrasted with the observations. The residuals generated by the SSEs were smaller and statistically different than those of GEFMA and GEAPUs for the majority of cases. The most important commercial species (M. huberi) had its volume overestimated by 10.6, 9.3 and 3.0% when the GEFMA, the GEAPUs, and the SSEs were applied, respectively. Among the species that generally had very large trees, H. petraeumhad its volume underestimated by 15.7, 16.6 and 4.4% by the GEFMA, GEAPUs and SSEs, respectively. The greater precision of the SSEs is reflected in better forest management planning decisions with respect to operational and economic aspects. These results show that besides being statistically valid, the SSEs are recommended for obtaining more precise estimates of commercial volume, especially since there is a great demand for reliable estimates for each individual species in forest management areas in the Amazon.CERNECERNE2020-11-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/2529CERNE; Vol 26 No 3 (2020); 315-330CERNE; Vol 26 No 3 (2020); 315-3302317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/2529/1202Copyright (c) 2020 CERNEinfo:eu-repo/semantics/openAccessSantos, Misael Freitas dosFigueiredo Filho, AfonsoGama, João Ricardo VasconcellosRetslaff, Fabiane Aparecida de SouzaCosta, Daniele Lima da2021-01-12T03:12:26Zoai:cerne.ufla.br:article/2529Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:45.578673Cerne (Online) - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
title |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
spellingShingle |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON Santos, Misael Freitas dos commercial species, dendrometry, forest management, tropical forest, volumetric models. |
title_short |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
title_full |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
title_fullStr |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
title_full_unstemmed |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
title_sort |
SPECIES-SPECIFIC EQUATIONS: GREATER PRECISION IN COMMERCIAL VOLUME ESTIMATION IN MANAGED FORESTS IN THE AMAZON |
author |
Santos, Misael Freitas dos |
author_facet |
Santos, Misael Freitas dos Figueiredo Filho, Afonso Gama, João Ricardo Vasconcellos Retslaff, Fabiane Aparecida de Souza Costa, Daniele Lima da |
author_role |
author |
author2 |
Figueiredo Filho, Afonso Gama, João Ricardo Vasconcellos Retslaff, Fabiane Aparecida de Souza Costa, Daniele Lima da |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Santos, Misael Freitas dos Figueiredo Filho, Afonso Gama, João Ricardo Vasconcellos Retslaff, Fabiane Aparecida de Souza Costa, Daniele Lima da |
dc.subject.por.fl_str_mv |
commercial species, dendrometry, forest management, tropical forest, volumetric models. |
topic |
commercial species, dendrometry, forest management, tropical forest, volumetric models. |
description |
The objective of this study was to analyze the performance of species-specific equations (SSEs) concerning generic ones in Annual Production Units (GEAPUs) and in a Forest Management Area (GEFMA) in the Brazilian Amazon. A total of 29,119 trees from 43 species were inventoried, harvested, and volumetric measurements were taken in ten APUs, with 10% of this total being separated for validation and comparison of the selected equations. After selection and validation of the equations (GEFMA, GEAPUs and SSEs) they were compared using precision statistics, by contrasting estimated and observed volumes and by residual analysis. Precision statistics were clearly lower for the SSEs. Trend lines near the average observed volume were shown for the SSEs when the estimates were contrasted with the observations. The residuals generated by the SSEs were smaller and statistically different than those of GEFMA and GEAPUs for the majority of cases. The most important commercial species (M. huberi) had its volume overestimated by 10.6, 9.3 and 3.0% when the GEFMA, the GEAPUs, and the SSEs were applied, respectively. Among the species that generally had very large trees, H. petraeumhad its volume underestimated by 15.7, 16.6 and 4.4% by the GEFMA, GEAPUs and SSEs, respectively. The greater precision of the SSEs is reflected in better forest management planning decisions with respect to operational and economic aspects. These results show that besides being statistically valid, the SSEs are recommended for obtaining more precise estimates of commercial volume, especially since there is a great demand for reliable estimates for each individual species in forest management areas in the Amazon. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-11-17 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2529 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2529 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/2529/1202 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 CERNE info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 CERNE |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
CERNE CERNE |
publisher.none.fl_str_mv |
CERNE CERNE |
dc.source.none.fl_str_mv |
CERNE; Vol 26 No 3 (2020); 315-330 CERNE; Vol 26 No 3 (2020); 315-330 2317-6342 0104-7760 reponame:Cerne (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Cerne (Online) |
collection |
Cerne (Online) |
repository.name.fl_str_mv |
Cerne (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
cerne@dcf.ufla.br||cerne@dcf.ufla.br |
_version_ |
1799874944185663488 |