Estimation of mortality and survival of individual trees after harvesting wood using artificial neural networks in the amazon rain forest

[1]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[2]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[3]  J. N. Hool,et al.  A Basal Area Ratio Predictor of Loblolly Pine Plantation Mortality , 1979 .

[4]  G. Gertner,et al.  Modeling red pine tree survival with an artificial neural network , 1991 .

[5]  George Z. Gertner,et al.  Using a Parallel Distributed Processing System to Model Individual Tree Mortality , 1991 .

[6]  O. R. Aguiar,et al.  Exploração mecanizada da floresta tropical úmida sem babaçu. , 1993 .

[7]  Jerome K. Vanclay,et al.  Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests , 1994 .

[8]  Kristin P. Bennett,et al.  Modeling noncatastrophic individual tree mortality using logistic regression, neural networks, and support vector methods , 2000 .

[9]  Gretchen G. Moisen,et al.  Comparing five modelling techniques for predicting forest characteristics , 2002 .

[10]  P. R. van Gardingen,et al.  An individual-based spatially explicit tree growth model for forests in East Kalimantan (Indonesian Borneo) , 2003 .

[11]  P. R. van Gardingen,et al.  An individual-based spatially explicit simulation model for strategic forest management planning in the eastern Amazon , 2004 .

[12]  Maria J. Diamantopoulou,et al.  Artificial neural networks as an alternative tool in pine bark volume estimation , 2005 .

[13]  D. Faber-Langendoen,et al.  Diameter distributions and structural sustainability in forests , 2006 .

[14]  Denis Valle,et al.  Adaptation of a spatially explicit individual tree-based growth and yield model and long-term comparison between reduced-impact and conventional logging in eastern Amazonia, Brazil , 2007 .

[15]  P. Campanello,et al.  Lianas in a subtropical Atlantic Forest: Host preference and tree growth , 2007 .

[16]  Guillaume Cornu,et al.  Impact of uncertainty in tree mortality on the predictions of a tropical forest dynamics model , 2008 .

[17]  S. Machado,et al.  Simulação de estratégias de manejo florestal na Amazônia com o uso do modelo SYMFOR , 2008 .

[18]  Matt Aitkenhead,et al.  Optimisation of competition indices using simulated annealing and artificial neural networks , 2008 .

[19]  R. C. Martins-Da-Silva,et al.  Avaliação do potencial madeireiro na Floresta Nacional do Tapajós após 28 anos da exploração florestal , 2010 .

[20]  J. Stape,et al.  Köppen's climate classification map for Brazil , 2013 .

[21]  Lori D. Daniels,et al.  Growth releases across a natural canopy gap-forest gradient in old-growth forests , 2014 .

[22]  C. Bourque,et al.  A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change , 2015, PloS one.

[23]  Gilciano Saraiva Nogueira,et al.  Artificial Neural Networks Effectiveness to Estimate Mortality in a Semi-Deciduous Seasonal Forest , 2015 .

[24]  A. L. Souza,et al.  Forest dynamics in the eastern Amazon with special reference to sapotaceae species , 2015 .

[25]  L. Gustafsson,et al.  Life history traits predict the response to increased light among 33 tropical rainforest tree species , 2016 .

[26]  Stephanie A. Bohlman,et al.  Dominance of the suppressed: Power-law size structure in tropical forests , 2016, Science.

[27]  C. P. B. Soares,et al.  Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks , 2016 .