Estimation of total tree height in plantations of different species through artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , |
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. |
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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 |
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1783370935844208640 |