Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks

Reis, Leonardo Pequeno; Souza, Agostinho Lopes de; Mazzei, Lucas; Reis, Pamella Carolline Marques dos Reis; Leite, Hélio Garcia; Soares, Carlos Pedro Boëchat; Torres, Carlos Moreira Miquelino Eleto; Silva, Liniker Fernandes da; Ruschel, Ademir Roberto

doi:10.1016/j.foreco.2016.10.022

Cited by 34 publications

(34 citation statements)

References 28 publications

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“…This type of technique, especially the Artificial Neural Networks (ANN), has been applied in several studies in forestry (e.g. BINOTI et al, 2013;CASTRO et al, 2013;BINOTI et al, 2015;DIAMANTOPOULOU et al, 2015;REIS et al, 2016). This methods allows to extract information from a massive database consisting of continuous and categorical variables in order to recognize patterns between the input variables and a variable of interest.…”

Section: Introductionmentioning

confidence: 99%

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

CERNE

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Several methods have been proposed to perform site classification for timber production. However, there is frequent need to assess site productive capacity before forest establishment. This has motivated the application of Artificial Neural Networks (ANN) for site classification. Hereby, the traditional guide curve (GC) procedure was compared to the ANN with no stand measures as input. In addition, different ANN settings were tested to assess the best setting. The variables used to train the ANN were: climatic variables, soil types, spacing and genetic material. The results from the ANN and the GC methods were compared to the observed classes, which were defined using the observed dominant high at the age of seven years. The comparison was performed using the Kappa coefficient (K) and descriptive analysis. The results showed that the cost function “Cross Entropy” and the output activation function “Softmax” were the best for this purpose. The ANN classification resulted in substantial agreement with the observed indices against a moderate agreement of the GC procedure. The change in growth patterns throughout the rotation may have hindered the proper classification by the CG method, which does not happen with the ANN. Moreover, the GC method shows efficiency on classification in cases which data from stands at the age close to the reference age are available. Also, it could be possible to improve its accuracy if another advanced regression techniques were applied. However, the ANN method presented here is not sensible to growth instability and allows classifying sites with no plantation history.

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Section: Introductionmentioning

confidence: 99%

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Cosenza

Soares

Alcântara³

et al. 2017

CERNE

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“…Outputs: Volume and diameter." Reis et al (2016) The study's purpose was to model the projection of the future diameter of individual trees in a forest managed in the Amazon, using ANN as a source of subsidy for decisionmaking.…”

Section: Lmann Network (Neuralmentioning

confidence: 99%

“…The Brazilian researchers who stood out during the period in question were those from the Federal University of Viçosa (8 publications). The authors (cited here individually and in chronological order) were: Silva (2009); Binoti (2013a); Binoti (2013b); Bhering (2015); Cosenza (2015); ; Reis (2016) and Binoti (2017). The themes addressed in their studies were eucalyptus clone plantation sites (7 publications) and managed forests in the Amazon (1 publication), as shown in Table 3.…”

Section: Reis Et Al (2018a)mentioning

confidence: 99%

Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

Chiarello

Steiner

Oliveira

et al. 2019

CERNE

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“…Para uma Floresta Ombrófila Densa de Terra Firme, no Estado do Amazonas, Braz et al (2012), encontraram um q = 1,58 e q = 1,77, antes e após corte, respectivamente. Reis et al (2016), em área manejada de Floresta Ombrófila Densa, na FLONA Tapajós, encontraram q = 1,85 antes da exploração e q = 1,98 após 13 anos do corte. Os autores chamaram a atenção para o fato de que no Brasil, existem poucos estudos em florestas tropicais que tratam da distribuição de De Liocourt.…”

Section: Distribuição Diamétricaunclassified

Monitoramento da estrutura e caracterização ecológica em floresta tropical manejada na Amazônia Brasileira

Oliveira

Rezende

Freitas

et al. 2019

Agraria

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RESUMO: Neste estudo foram discutidos aspectos da composição florística, diversidade de espécies e estrutura diamétrica de uma floresta manejada, no município de Paragominas, Pará. Os dados foram obtidos em dois transectos de 100 x 900 m equivalendo a uma área amostral de 18 ha, mensurados em sete ocasiões anuais. Os indivíduos arbóreos foram classificados em dois níveis de inclusão, Nível 1: DAP ≥ 20 cm em parcelas de 100 x 100 m e Nível 2: 10 cm < DAP ≤ 20 cm em subparcelas de 25 x 50 m. O índice de Shannon-Weaver variou entre 3,97 e 4,03 nats ind -1 para o Nível 1 e 3,80 e 4,03 nats ind -1 para o Nível 2. Os grupos ecológicos compostos por espécies não pioneiras dominaram a floresta, tanto em termos de riqueza quanto de densidade de árvores. Os resultados obtidos indicaram que 10 anos após a exploração, não foram constatadas mudanças expressivas na riqueza, diversidade e estrutura diamétrica da vegetação, indicando possivelmente, que a floresta quando submetida ao manejo florestal sustentável pode manter suas características próximas à floresta não perturbada pela exploração madeireira. Palavras-chave: Floresta Amazônica; manejo florestal; sucessão ecológicaMonitoring the structure and ecological characterization in rainforest managed in the Brazilian Amazon ABSTRACT: In this study aspects of floristic composition were discussed, species diversity and diameter structure of a managed forest, in the county of Paragominas, Pará. The data were obtained in two transects of 100 x 900 m, corresponding to a sample area of 18 ha, measured on seven annual occasions. The trees were classified into two inclusion levels, Level 1: DBH ≥ 20 cm in plots of 100 x 100 m and Level 2: 10 cm < DBH ≤ 20 cm in subplots of 25 x 50 m. Shannon-Weaver's index ranged from 3.97 to 4.03 nats ind -1 for Level 1 and 3.80 and 4.03 nats ind -1 for Level 2. The ecological groups composed of non-pioneer dominated the forest, both in terms of richness and density of trees. The results indicated that 10 years after the exploration, no significant changes were observed in the richness, diversity and diametric structure of the vegetation, possibly indicating that the forest when submitted to the sustainable forest management should maintain its characteristics near the forest not disturbed by the logging.Monitoramento da estrutura e caracterização ecológica em floresta tropical manejada na Amazônia Brasileira Rev. Bras.

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Prognosis on the diameter of individual trees on the eastern region of the amazon using artificial neural networks

Cited by 34 publications

References 28 publications

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Site Classification for Eucalypt Stands Using Artificial Neural Network Based on Environmental and Management Features

Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

Monitoramento da estrutura e caracterização ecológica em floresta tropical manejada na Amazônia Brasileira

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