Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest
AUTOR(ES)
Reis, Leonardo Pequeno
FONTE
Ciênc. Florest.
DATA DE PUBLICAÇÃO
30/09/2019
RESUMO
Resumo 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.Abstract Recruitment models in tropical forests are important for studies on forest management sustainability because they provide adequate support to recovery of wood stocks. The objective of this work was to estimate recruitment after wood harvesting by using an artificial neural network (ANN) model. The study area is located at Tapajós National Forest (55° 00’ W, 2° 45’ S), Pará state. 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. For recruitment modeling, the variables of the target subplot and its vicinity were considered. The estimates obtained in ANN training and generalization were evaluated by statistics: correlation () and root mean square error (RMSE) were determined: RMSE 35.6% and 0.89. Recruitment tendency could be modeled over time in tropical forests after wood harvesting.
Documentos Relacionados
- Modeling of an industrial drying process by artificial neural networks
- Modeling residual biomass from mechanized wood harvesting with data measured by forest harvester
- ARTIFICIAL NEURAL NETWORKS APPLIED IN FOREST BIOMETRICS AND MODELING: STATE OF THE ART (JANUARY/2007 TO JULY/2018)
- PRELIMINARY MODELING OF AN INDUSTRIAL RECOMBINANT HUMAN ERYTHROPOIETIN PURIFICATION PROCESS BY ARTIFICIAL NEURAL NETWORKS
- Artificial neural networks modeling of kinetic curves of celeriac (Apium graveolens L.) in vacuum drying