Data/hora: 16/06/2024 17:26:05

Biblioteca(s): | Embrapa Amazônia Oriental.
Data corrente: | 31/01/2018
Data da última atualização: | 02/05/2018
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.
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.
Título: | Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest.
Ano de publicação: | 2018
Fonte/Imprenta: | Ecological Engineering, v. 112, p. 140-147, Mar. 2018.
DOI: | https://doi.org/10.1016/j.ecoleng.2017.12.014
Idioma: | Inglês
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&#8239;m3 ha-1 in an area of 64&#8239;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.
Palavras-Chave: | Gestão florestal; Inteligência artificial; Modelagem.
Thesagro: | Floresta.
Categoria do assunto: | K Ciência Florestal e Produtos de Origem Vegetal
Registro original: | Embrapa Amazônia Oriental (CPATU)
Exemplares: | CPATU - (54653-1) - UPC - AP - DD -  -  -  - DIS