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| Registros recuperados : 68 | |
| 23. |  | RODRIGUES, F. L.; SILVA, G. F. da; SANTOS, H. do N.; SOARES, C. P. B. Planejamento florestal otimizado: uma abordagem sob múltiplos critérios. In: SIMPÓSIO LATINO-AMERICANO SOBRE MANEJO FLORESTAL, 2., 2001, Santa Maria. [Anais]. Santa Maria: Universidade Federal de Santa Maria, Centro de Ciências Rurais, Departamento de Ciências Florestais, 2001. p. 287-304. Trabalhos Voluntários.| Biblioteca(s): Embrapa Florestas. |
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| 38. | | MARTINS, F. B.; SOARES, C. P. B.; LEITE, H. G.; SOUZA, A. L. de; CASTRO, R. V. O. Índices de competição em árvores individuais de eucalipto. Pesquisa agropecuária Brasileira, Brasília, DF, v. 46, n. 9, p. 1089-1098, set. 2011 Título em inglês: Competition indexes for individual eucalyptus trees.| Biblioteca(s): Embrapa Unidades Centrais. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Amazônia Oriental. Para informações adicionais entre em contato com cpatu.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Amazônia Oriental. |
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Data corrente: |
26/10/2016 |
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Data da última atualização: |
23/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
REIS, L. P.; SOUZA, A. L. de; MAZZEI, L.; REIS, P. C. M. dos; LEITE, H. G.; SOARES, C. P. B.; TORRES, C. M. M. E.; SILVA, L. F. da; RUSCHEL, A. R. |
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Afiliação: |
Leonardo Pequeno Reis, UFV; Agostinho Lopes de Souza, UFV; LUCAS JOSE MAZZEI DE FREITAS, CPATU; Pamella Carolline Marques dos Reis, UFV; Hélio Garcia Leite, UFV; Carlos Pedro Boechat Soares, UFV; Carlos Moreira Miquelino Eleto Torres, UFV; Liniker Fernandes da Silva, UFV; ADEMIR ROBERTO RUSCHEL, CPATU. |
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Título: |
Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Forest Ecology and Management, v. 382, p. 161-167, Dec. 2016. |
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DOI: |
http://dx.doi.org.ez103.periodicos.capes.gov.br/10.1016/j.foreco.2016.10.022 |
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Idioma: |
Inglês |
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Conteúdo: |
The prognosis of forest structure along the cutting cycle, using models of individual trees, is one of the alternatives to manage tropical forests aiming at sustainability. Currently, in forest management practiced in the Amazon Region, growth and production models are not used to predict the future stock of the forest. Thus, the sustainable economic and environmental aspects of this activity remain uncertain. The aim of this present work was to model the growth of individual trees in a forest managed in the Amazon Region, by using artificial neural networks (ANN) to serve as subsidy to the wielder in obtaining future stock after logging, thus reducing uncertainty on forest management sustainability. Selective harvest was carried out in 1979 with an intensity of 72.5 m3 ha−1 in a 64 ha area in the Tapajós National Forest - PA. In 1981, 36 permanent plots (50 m × 50 m) were installed at random and inventoried. There were nine successive measurements in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010, and 2012. In the modeling of the future diameter, training and testing of ANN were carried out, including different semi-independent competition indexes (DSICI). All ANN, with and without DSICI, presented correlation above 99%, RMSE below 11%, and EF above 0.98. Based on the prognosis of tree growth, we were able to conclude that ANN can be effectively used to assist in the management of tropical forests and, thus, allow for the most suitable cutting intensity and cutting cycle per species, ensuring environmental and economic sustainability of forest management. MenosThe prognosis of forest structure along the cutting cycle, using models of individual trees, is one of the alternatives to manage tropical forests aiming at sustainability. Currently, in forest management practiced in the Amazon Region, growth and production models are not used to predict the future stock of the forest. Thus, the sustainable economic and environmental aspects of this activity remain uncertain. The aim of this present work was to model the growth of individual trees in a forest managed in the Amazon Region, by using artificial neural networks (ANN) to serve as subsidy to the wielder in obtaining future stock after logging, thus reducing uncertainty on forest management sustainability. Selective harvest was carried out in 1979 with an intensity of 72.5 m3 ha−1 in a 64 ha area in the Tapajós National Forest - PA. In 1981, 36 permanent plots (50 m × 50 m) were installed at random and inventoried. There were nine successive measurements in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010, and 2012. In the modeling of the future diameter, training and testing of ANN were carried out, including different semi-independent competition indexes (DSICI). All ANN, with and without DSICI, presented correlation above 99%, RMSE below 11%, and EF above 0.98. Based on the prognosis of tree growth, we were able to conclude that ANN can be effectively used to assist in the management of tropical forests and, thus, allow for the most suitable cutting intensity and cutting cy... Mostrar Tudo |
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Palavras-Chave: |
Ciclo de corte; Estrutura florestal; Sustentabilidade. |
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Thesagro: |
Árvore; Diâmetro; Floresta Tropical. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
|
Marc: |
LEADER 02548naa a2200301 a 4500
001 2055417
005 2022-05-23
008 2016 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org.ez103.periodicos.capes.gov.br/10.1016/j.foreco.2016.10.022$2DOI
100 1 $aREIS, L. P.
245 $aPrognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks.$h[electronic resource]
260 $c2016
520 $aThe prognosis of forest structure along the cutting cycle, using models of individual trees, is one of the alternatives to manage tropical forests aiming at sustainability. Currently, in forest management practiced in the Amazon Region, growth and production models are not used to predict the future stock of the forest. Thus, the sustainable economic and environmental aspects of this activity remain uncertain. The aim of this present work was to model the growth of individual trees in a forest managed in the Amazon Region, by using artificial neural networks (ANN) to serve as subsidy to the wielder in obtaining future stock after logging, thus reducing uncertainty on forest management sustainability. Selective harvest was carried out in 1979 with an intensity of 72.5 m3 ha−1 in a 64 ha area in the Tapajós National Forest - PA. In 1981, 36 permanent plots (50 m × 50 m) were installed at random and inventoried. There were nine successive measurements in 1982, 1983, 1985, 1987, 1992, 1997, 2007, 2010, and 2012. In the modeling of the future diameter, training and testing of ANN were carried out, including different semi-independent competition indexes (DSICI). All ANN, with and without DSICI, presented correlation above 99%, RMSE below 11%, and EF above 0.98. Based on the prognosis of tree growth, we were able to conclude that ANN can be effectively used to assist in the management of tropical forests and, thus, allow for the most suitable cutting intensity and cutting cycle per species, ensuring environmental and economic sustainability of forest management.
650 $aÁrvore
650 $aDiâmetro
650 $aFloresta Tropical
653 $aCiclo de corte
653 $aEstrutura florestal
653 $aSustentabilidade
700 1 $aSOUZA, A. L. de
700 1 $aMAZZEI, L.
700 1 $aREIS, P. C. M. dos
700 1 $aLEITE, H. G.
700 1 $aSOARES, C. P. B.
700 1 $aTORRES, C. M. M. E.
700 1 $aSILVA, L. F. da
700 1 $aRUSCHEL, A. R.
773 $tForest Ecology and Management$gv. 382, p. 161-167, Dec. 2016.
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