Estimation of total tree height in plantations of different species through artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Campos, Bráulio Pizziolo Furtado
Data de Publicação: 2016
Outros Autores: Silva, Gilson Fernandes da, Binoti, Daniel Henrique Breda, Mendonça, Adriano Ribeiro de, Leite, Helio Garcia
Tipo de documento: Artigo
Idioma: por
Título da fonte: Pesquisa Florestal Brasileira (Online)
Texto Completo: https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1166
Resumo: The objective of this study was to analyze the ability of an artificial neural network (ANN) to estimate the total height of two tree species in different growing conditions. For comparison purposes, it was also adjusted Campos hypsometric model, applied by stratum as genus, species, rotation, spacing and age classes. The evaluation of artificial neural networks and Campos model was based on the correlation coefficient between the observed and estimated heights, the square root of the mean square percentage error (RMSE) and graphical analysis. The results of this study showed that trees height of different species, in different growing conditions and locations can be estimated using a single neural network with the same efficiency and accuracy usually obtained with regression equations.
id EMBRAPA-5_b740ad52edcd209cd0aa9a1555e38689
oai_identifier_str oai:pfb.cnpf.embrapa.br/pfb:article/1166
network_acronym_str EMBRAPA-5
network_name_str Pesquisa Florestal Brasileira (Online)
repository_id_str
spelling Estimation of total tree height in plantations of different species through artificial neural networksPredição da altura total de árvores em plantios de diferentes espécies por meio de redes neurais artificiaisRelação hipsométricaInventário florestalFloresta de produçãoHypsometric relationForest inventoriesCommercial forestryThe objective of this study was to analyze the ability of an artificial neural network (ANN) to estimate the total height of two tree species in different growing conditions. For comparison purposes, it was also adjusted Campos hypsometric model, applied by stratum as genus, species, rotation, spacing and age classes. The evaluation of artificial neural networks and Campos model was based on the correlation coefficient between the observed and estimated heights, the square root of the mean square percentage error (RMSE) and graphical analysis. The results of this study showed that trees height of different species, in different growing conditions and locations can be estimated using a single neural network with the same efficiency and accuracy usually obtained with regression equations.O objetivo deste trabalho foi analisar a capacidade de uma rede neural artificial (RNA) em estimar a altura total de árvores de duas espécies em diferentes condições de crescimento. Para fins de comparação, também foi ajustado o modelo hipsométrico de Campos, aplicado por estrato, conforme o gênero, espécie, rotação, espaçamento e classe de idade das árvores. A avaliação das redes neurais artificiais e do modelo de Campos foi realizada com base no coeficiente de correlação entre as alturas observadas e estimadas, a raiz quadrada do erro quadrático médio percentual e de análises gráficas. Observou-se que a altura de árvores de diferentes espécies, em distintas condições de crescimento e locais, pode ser estimada utilizando uma única rede neural, com a mesma eficiência e exatidão normalmente obtida com o emprego de equações de regressão.Embrapa Florestas2016-12-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/116610.4336/2016.pfb.36.88.1166Pesquisa Florestal Brasileira; v. 36 n. 88 (2016): out./dez.; 375-385Pesquisa Florestal Brasileira; Vol. 36 No. 88 (2016): out./dez.; 375-3851983-26051809-3647reponame:Pesquisa Florestal Brasileira (Online)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPAporhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1166/529Copyright (c) 2016 Bráulio Pizziolo Furtado Campos, Gilson Fernandes da Silva, Daniel Henrique Breda Binoti, Adriano Ribeiro de Mendonça, Helio Garcia Leitehttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessCampos, Bráulio Pizziolo FurtadoSilva, Gilson Fernandes daBinoti, Daniel Henrique BredaMendonça, Adriano Ribeiro deLeite, Helio Garcia2017-04-28T14:13:33Zoai:pfb.cnpf.embrapa.br/pfb:article/1166Revistahttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/PUBhttps://pfb.cnpf.embrapa.br/pfb/index.php/pfb/oaipfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br1983-26051809-3647opendoar:2017-04-28T14:13:33Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Estimation of total tree height in plantations of different species through artificial neural networks
Predição da altura total de árvores em plantios de diferentes espécies por meio de redes neurais artificiais
title Estimation of total tree height in plantations of different species through artificial neural networks
spellingShingle Estimation of total tree height in plantations of different species through artificial neural networks
Campos, Bráulio Pizziolo Furtado
Relação hipsométrica
Inventário florestal
Floresta de produção
Hypsometric relation
Forest inventories
Commercial forestry
title_short Estimation of total tree height in plantations of different species through artificial neural networks
title_full Estimation of total tree height in plantations of different species through artificial neural networks
title_fullStr Estimation of total tree height in plantations of different species through artificial neural networks
title_full_unstemmed Estimation of total tree height in plantations of different species through artificial neural networks
title_sort Estimation of total tree height in plantations of different species through artificial neural networks
author Campos, Bráulio Pizziolo Furtado
author_facet Campos, Bráulio Pizziolo Furtado
Silva, Gilson Fernandes da
Binoti, Daniel Henrique Breda
Mendonça, Adriano Ribeiro de
Leite, Helio Garcia
author_role author
author2 Silva, Gilson Fernandes da
Binoti, Daniel Henrique Breda
Mendonça, Adriano Ribeiro de
Leite, Helio Garcia
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Campos, Bráulio Pizziolo Furtado
Silva, Gilson Fernandes da
Binoti, Daniel Henrique Breda
Mendonça, Adriano Ribeiro de
Leite, Helio Garcia
dc.subject.por.fl_str_mv Relação hipsométrica
Inventário florestal
Floresta de produção
Hypsometric relation
Forest inventories
Commercial forestry
topic Relação hipsométrica
Inventário florestal
Floresta de produção
Hypsometric relation
Forest inventories
Commercial forestry
description The objective of this study was to analyze the ability of an artificial neural network (ANN) to estimate the total height of two tree species in different growing conditions. For comparison purposes, it was also adjusted Campos hypsometric model, applied by stratum as genus, species, rotation, spacing and age classes. The evaluation of artificial neural networks and Campos model was based on the correlation coefficient between the observed and estimated heights, the square root of the mean square percentage error (RMSE) and graphical analysis. The results of this study showed that trees height of different species, in different growing conditions and locations can be estimated using a single neural network with the same efficiency and accuracy usually obtained with regression equations.
publishDate 2016
dc.date.none.fl_str_mv 2016-12-30
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://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1166
10.4336/2016.pfb.36.88.1166
url https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1166
identifier_str_mv 10.4336/2016.pfb.36.88.1166
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://pfb.cnpf.embrapa.br/pfb/index.php/pfb/article/view/1166/529
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Embrapa Florestas
publisher.none.fl_str_mv Embrapa Florestas
dc.source.none.fl_str_mv Pesquisa Florestal Brasileira; v. 36 n. 88 (2016): out./dez.; 375-385
Pesquisa Florestal Brasileira; Vol. 36 No. 88 (2016): out./dez.; 375-385
1983-2605
1809-3647
reponame:Pesquisa Florestal Brasileira (Online)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Pesquisa Florestal Brasileira (Online)
collection Pesquisa Florestal Brasileira (Online)
repository.name.fl_str_mv Pesquisa Florestal Brasileira (Online) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv pfb@embrapa.br || revista.pfb@gmail.com || patricia.mattos@embrapa.br
_version_ 1783370935844208640

Registros relacionados