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| 22. |  | TOMAS, W. M.; CAMILO, A. R.; RIBAS, C.; LEUCHTENBERGER, C; BORGES, P. A. L; MOURAO, G.; PELLEGRIN, L. A. Distribution and conservation status of giant otter Pteronura brasiliensis in the Pantanal wetland, Brazil. The Latin American Journal of Aquatic Mammals, v. 10, n. 2, p. 107-114, dez. 2015.| Biblioteca(s): Embrapa Pantanal. |
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| 23. | | LEUCHTENBERGER, C.; BAROCAS, A.; THOISY, B. de; WARD, C.; EVANGELISTA, E.; MICHALSKI, F.; TRUJILLO, F.; GEORGIADIS, G.; MOURAO, G. de M.; GIL, G.; GROENENDIJK, J.; MENDOZA OBLITAS, J.; RHEINGANTZ, M. L; BUSCHIAZZO, M.; MARMONTEL, M.; VAN DAMME, P.; WALLACE, R.; BOHER, S.; MARTINO, S.; PEREIRA, T. S.; UTRERAS, V. South America: Giant otter Pteronura brasiliensis. In: Nicole Duplaix; Melissa Savage. (Org.). The Global Otter Conservation Strategy. Salem, Oregon: IUCN/SSC Otter Specialist Group, 2018, v. 1, p. 74-81.| Biblioteca(s): Embrapa Pantanal. |
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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: |
31/01/2018 |
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Data da última atualização: |
02/05/2018 |
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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; REIS, P. C. M. dos; FREITAS, L. J. M. de; SOARES, C. P. B.; TORRES, C. M. M. E.; SILVA, L. F. da; RUSCHEL, A. R.; RÊGO, L. J. S.; LEITE, H. G. |
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Afiliação: |
Leonardo Pequeno Reis, Instituto de Desenvolvimento Sustentável Mamirauá; Agostinho Lopes de Souza, UFV; Pamella Carolline Marques dos Reis, UFV; LUCAS JOSE MAZZEI DE FREITAS, CPATU; Carlos Pedro Boechat Soares, UFV; Carlos Moreira Miquelino Eleto Torres, UFV; Liniker Fernandes da Silva, Universidade Federal do Recôncavo da Bahia; ADEMIR ROBERTO RUSCHEL, CPATU; Lyvia Julienne Sousa Rêgo, UFV; Helio Garcia Leite, UFV. |
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Título: |
Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Ecological Engineering, v. 112, p. 140-147, Mar. 2018. |
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DOI: |
https://doi.org/10.1016/j.ecoleng.2017.12.014 |
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Idioma: |
Inglês |
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Conteúdo: |
Modeling individual trees in tropical rain forests in the Amazon allows for the safe use of scarce resources in a sustainable way. Unfortunately, in the Brazilian Amazon, rain forest growth and production models are not yet used to estimate future forest stock. Thus, forest management plans do not present technical-scientific support that guarantees sustainable production of wood throughout the cutting cycle. Therefore, this work aims to estimate the survival and mortality of individual trees in a selectively harvested forest using Artificial Neural Networks (ANN) to support silvicultural decisions in forest management in the Amazon rain forest. In 1979, a selective harvest was carried out, with 72.5 m3 ha-1 in an area of 64 ha in Floresta Nacional do Tapajós, in the state of Pará, Brazil. In 1981, 36 permanent plots were installed at random and inventoried. Nine successive measurements were carried from 1982 to 2012. In the modeling, classification, survival, and mortality, training and ANN testing were performed, using input variables such as: different semi-distance-independent competition indices (DSICI), diameter measured (dbh), forest class (FC), trunk identification class (TIC), competition index (CI), growth groups (GG), liana infestation intensity (liana); and crown lighting (CL); Damage to tree (D) and tree rotting (R). The categorical output variables (Classification) were Dead or Surviving tree. Overall efficiency of the classification was above 89% in training and above 90% in the test for all ANNs. Survival classification hit rate was above 99% in the test and training for all ANNs but the mortality score was low, with hit rates below 6%. The overall Kappa coefficient was below 8% for all ANNs (ranked ?poor?) but all ANNs were above 55% in the survival classification (ranked ?good?). ANN estimates the individual survival of trees more accurately but this does not occur with mortality, which is a rarer event than survival. MenosModeling individual trees in tropical rain forests in the Amazon allows for the safe use of scarce resources in a sustainable way. Unfortunately, in the Brazilian Amazon, rain forest growth and production models are not yet used to estimate future forest stock. Thus, forest management plans do not present technical-scientific support that guarantees sustainable production of wood throughout the cutting cycle. Therefore, this work aims to estimate the survival and mortality of individual trees in a selectively harvested forest using Artificial Neural Networks (ANN) to support silvicultural decisions in forest management in the Amazon rain forest. In 1979, a selective harvest was carried out, with 72.5 m3 ha-1 in an area of 64 ha in Floresta Nacional do Tapajós, in the state of Pará, Brazil. In 1981, 36 permanent plots were installed at random and inventoried. Nine successive measurements were carried from 1982 to 2012. In the modeling, classification, survival, and mortality, training and ANN testing were performed, using input variables such as: different semi-distance-independent competition indices (DSICI), diameter measured (dbh), forest class (FC), trunk identification class (TIC), competition index (CI), growth groups (GG), liana infestation intensity (liana); and crown lighting (CL); Damage to tree (D) and tree rotting (R). The categorical output variables (Classification) were Dead or Surviving tree. Overall efficiency of the classification was above 89% i... Mostrar Tudo |
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Palavras-Chave: |
Gestão florestal; Inteligência artificial; Modelagem. |
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Thesagro: |
Floresta. |
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Categoria do assunto: |
K Ciência Florestal e Produtos de Origem Vegetal |
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Marc: |
LEADER 02910naa a2200289 a 4500
001 2086820
005 2018-05-02
008 2018 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.ecoleng.2017.12.014$2DOI
100 1 $aREIS, L. P.
245 $aEstimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest.$h[electronic resource]
260 $c2018
520 $aModeling individual trees in tropical rain forests in the Amazon allows for the safe use of scarce resources in a sustainable way. Unfortunately, in the Brazilian Amazon, rain forest growth and production models are not yet used to estimate future forest stock. Thus, forest management plans do not present technical-scientific support that guarantees sustainable production of wood throughout the cutting cycle. Therefore, this work aims to estimate the survival and mortality of individual trees in a selectively harvested forest using Artificial Neural Networks (ANN) to support silvicultural decisions in forest management in the Amazon rain forest. In 1979, a selective harvest was carried out, with 72.5 m3 ha-1 in an area of 64 ha in Floresta Nacional do Tapajós, in the state of Pará, Brazil. In 1981, 36 permanent plots were installed at random and inventoried. Nine successive measurements were carried from 1982 to 2012. In the modeling, classification, survival, and mortality, training and ANN testing were performed, using input variables such as: different semi-distance-independent competition indices (DSICI), diameter measured (dbh), forest class (FC), trunk identification class (TIC), competition index (CI), growth groups (GG), liana infestation intensity (liana); and crown lighting (CL); Damage to tree (D) and tree rotting (R). The categorical output variables (Classification) were Dead or Surviving tree. Overall efficiency of the classification was above 89% in training and above 90% in the test for all ANNs. Survival classification hit rate was above 99% in the test and training for all ANNs but the mortality score was low, with hit rates below 6%. The overall Kappa coefficient was below 8% for all ANNs (ranked ?poor?) but all ANNs were above 55% in the survival classification (ranked ?good?). ANN estimates the individual survival of trees more accurately but this does not occur with mortality, which is a rarer event than survival.
650 $aFloresta
653 $aGestão florestal
653 $aInteligência artificial
653 $aModelagem
700 1 $aSOUZA, A. L. de
700 1 $aREIS, P. C. M. dos
700 1 $aFREITAS, L. J. M. de
700 1 $aSOARES, C. P. B.
700 1 $aTORRES, C. M. M. E.
700 1 $aSILVA, L. F. da
700 1 $aRUSCHEL, A. R.
700 1 $aRÊGO, L. J. S.
700 1 $aLEITE, H. G.
773 $tEcological Engineering$gv. 112, p. 140-147, Mar. 2018.
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