Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência Florestal (Online) |
Texto Completo: | https://periodicos.ufsm.br/cienciaflorestal/article/view/25808 |
Resumo: | The modeling of recruitment in tropical forests is important for studies of forest management sustainability, for giving adequate subsidies to the recovery of wood stock. The objective of the work was to estimate the recruitment after wood harvest, using a model of artificial neural network (ANN). The study area is located in the Tapajós National Forest (55° 00' W, 2° 45' S), Pará. In 64 ha of the study area, in 1979, an intensive harvest of 72.5 m3 ha-1 was carried out. In 1981, 36 permanent plots of 50 m x 50 m were randomly installed. These plots were measured in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010 and 2012. To model the recruitment the variables of target subplot and its neighborhood were considered. The estimates obtained in the training and generalization of ANN were evaluated by statistics: correlation ( R) and root mean square error (RMSE) being obtained RMSE 35.6% and 0.89. It was possible to model the recruitment tendency over the time in tropical forests, after the wood harvest. |
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Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forestModelagem do recrutamento de árvores por redes neurais artificiais após a colheita de madeiras em floresta no leste da AmazôniaIngrowthArtificial intelligenceForest managementIngressoInteligência artificialManejo florestalThe modeling of recruitment in tropical forests is important for studies of forest management sustainability, for giving adequate subsidies to the recovery of wood stock. The objective of the work was to estimate the recruitment after wood harvest, using a model of artificial neural network (ANN). The study area is located in the Tapajós National Forest (55° 00' W, 2° 45' S), Pará. In 64 ha of the study area, in 1979, an intensive harvest of 72.5 m3 ha-1 was carried out. In 1981, 36 permanent plots of 50 m x 50 m were randomly installed. These plots were measured in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010 and 2012. To model the recruitment the variables of target subplot and its neighborhood were considered. The estimates obtained in the training and generalization of ANN were evaluated by statistics: correlation ( R) and root mean square error (RMSE) being obtained RMSE 35.6% and 0.89. It was possible to model the recruitment tendency over the time in tropical forests, after the wood harvest.A modelagem do recrutamento em florestais tropicais é importante para estudos de sustentabilidade do manejo florestal, por dar subsídio adequado à recuperação do estoque de madeira. O objetivo do trabalho foi estimar o recrutamento após a colheita de madeira, empregando um modelo de rede neural artificial (RNA). A área de estudo está localizada na Floresta Nacional do Tapajós (55°00’ W, 2°45’ S), Pará. Em 64 ha da área de estudo, em 1979, foi realizada colheita intensiva de 72,5 m3 ha-1. Em 1981 foram instaladas, aleatoriamente, 36 parcelas permanentes de 50 m x 50 m. Essas parcelas foram mensuradas em 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010 e 2012. Para modelar o recrutamento foram consideradas as variáveis da subparcela-alvo e a sua vizinhança. As estimativas obtidas no treino e na generalização da RNA foram avaliadas pelas estatísticas: correlação () e raiz quadrada do erro quadrático médio (RQRQM), sendo obtido RQRQM 35,6% e 0,89. Foi possível modelar a tendência do recrutamento ao longo do tempo em florestas tropicais, após a colheita de madeira.Universidade Federal de Santa Maria2019-06-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.ufsm.br/cienciaflorestal/article/view/2580810.5902/1980509825808Ciência Florestal; Vol. 29 No. 2 (2019); 583-594Ciência Florestal; v. 29 n. 2 (2019); 583-5941980-50980103-9954reponame:Ciência Florestal (Online)instname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMenghttps://periodicos.ufsm.br/cienciaflorestal/article/view/25808/pdfCopyright (c) 2019 Ciência Florestalinfo:eu-repo/semantics/openAccessReis, Leonardo PequenoSouza, Agostinho Lopes deMazzei, LucasReis, Pamella Carolline Marques dos ReisLeite, Helio GarciaSoares, Carlos Pedro BoechatTorres, Carlos Moreira Miquelino EletoRuschel, Ademir RobertoSilva, Liniker Fernandes daRêgo, Lyvia Julienne Sousa2019-09-05T20:20:24Zoai:ojs.pkp.sfu.ca:article/25808Revistahttp://www.ufsm.br/cienciaflorestal/ONGhttps://old.scielo.br/oai/scielo-oai.php||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br1980-50980103-9954opendoar:2019-09-05T20:20:24Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest Modelagem do recrutamento de árvores por redes neurais artificiais após a colheita de madeiras em floresta no leste da Amazônia |
title |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest |
spellingShingle |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest Reis, Leonardo Pequeno Ingrowth Artificial intelligence Forest management Ingresso Inteligência artificial Manejo florestal |
title_short |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest |
title_full |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest |
title_fullStr |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest |
title_full_unstemmed |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest |
title_sort |
Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest |
author |
Reis, Leonardo Pequeno |
author_facet |
Reis, Leonardo Pequeno Souza, Agostinho Lopes de Mazzei, Lucas Reis, Pamella Carolline Marques dos Reis Leite, Helio Garcia Soares, Carlos Pedro Boechat Torres, Carlos Moreira Miquelino Eleto Ruschel, Ademir Roberto Silva, Liniker Fernandes da Rêgo, Lyvia Julienne Sousa |
author_role |
author |
author2 |
Souza, Agostinho Lopes de Mazzei, Lucas Reis, Pamella Carolline Marques dos Reis Leite, Helio Garcia Soares, Carlos Pedro Boechat Torres, Carlos Moreira Miquelino Eleto Ruschel, Ademir Roberto Silva, Liniker Fernandes da Rêgo, Lyvia Julienne Sousa |
author2_role |
author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Reis, Leonardo Pequeno Souza, Agostinho Lopes de Mazzei, Lucas Reis, Pamella Carolline Marques dos Reis Leite, Helio Garcia Soares, Carlos Pedro Boechat Torres, Carlos Moreira Miquelino Eleto Ruschel, Ademir Roberto Silva, Liniker Fernandes da Rêgo, Lyvia Julienne Sousa |
dc.subject.por.fl_str_mv |
Ingrowth Artificial intelligence Forest management Ingresso Inteligência artificial Manejo florestal |
topic |
Ingrowth Artificial intelligence Forest management Ingresso Inteligência artificial Manejo florestal |
description |
The modeling of recruitment in tropical forests is important for studies of forest management sustainability, for giving adequate subsidies to the recovery of wood stock. The objective of the work was to estimate the recruitment after wood harvest, using a model of artificial neural network (ANN). The study area is located in the Tapajós National Forest (55° 00' W, 2° 45' S), Pará. In 64 ha of the study area, in 1979, an intensive harvest of 72.5 m3 ha-1 was carried out. In 1981, 36 permanent plots of 50 m x 50 m were randomly installed. These plots were measured in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010 and 2012. To model the recruitment the variables of target subplot and its neighborhood were considered. The estimates obtained in the training and generalization of ANN were evaluated by statistics: correlation ( R) and root mean square error (RMSE) being obtained RMSE 35.6% and 0.89. It was possible to model the recruitment tendency over the time in tropical forests, after the wood harvest. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-06-30 |
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://periodicos.ufsm.br/cienciaflorestal/article/view/25808 10.5902/1980509825808 |
url |
https://periodicos.ufsm.br/cienciaflorestal/article/view/25808 |
identifier_str_mv |
10.5902/1980509825808 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://periodicos.ufsm.br/cienciaflorestal/article/view/25808/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2019 Ciência Florestal info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2019 Ciência Florestal |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria |
dc.source.none.fl_str_mv |
Ciência Florestal; Vol. 29 No. 2 (2019); 583-594 Ciência Florestal; v. 29 n. 2 (2019); 583-594 1980-5098 0103-9954 reponame:Ciência Florestal (Online) instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Ciência Florestal (Online) |
collection |
Ciência Florestal (Online) |
repository.name.fl_str_mv |
Ciência Florestal (Online) - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
||cienciaflorestal@ufsm.br|| cienciaflorestal@gmail.com|| cf@smail.ufsm.br |
_version_ |
1799944131778183168 |