Modeling of tree recruitment by artificial neural networks after wood harvesting in a forest in eastern Amazon rain forest

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.

Bibliographic Details

Main Authors:	Reis,Leonardo Pequeno, Souza,Agostinho Lopes de, Reis,Pamella Carolline Marques dos Reis, Mazzei,Lucas, Leite,Helio Garcia, Soares,Carlos Pedro Boechat, Torres,Carlos Moreira Miquelino Eleto, Silva,Liniker Fernandes da, Ruschel,Ademir Roberto, Rêgo,Lyvia Julienne Sousa
Format:	Digital revista
Language:	English
Published:	Universidade Federal de Santa Maria 2019
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1980-50982019000200583
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