Volume estimation of<i>Cryptomeria japonica</i>logs in southern Brazil using artificial intelligence models

Sanquetta, Carlos Roberto; Piva, Luani R.O.; Wojciechowski, Jaime; Côrte, Ana Paula Dalla; Schikowski, Ana Beatriz

doi:10.2989/20702620.2016.1263013

Cited by 22 publications

(9 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…GÖRGENS et al, 2009;AERTSEN et al, 2010;LEITE et al, 2011;SANQUETTA et al, 2017). This can be done without the problem of multicollinearity, regression assumptions or even without well-known processes (a priori) or cause-effect relationships involving these variables as in ecophysiological models, so that variable as dominant height could be replaced by another related one do predict site quality.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Sanquetta et al (2015) mentioned an increase up to 16.5% in the reduction of the standard error of estimation for models based on the famous Schumacher-Hall model in trees from environmental restoration plantations in the Brazilian Atlantic Forest biome. Sanquetta et al (2018) reported that the k-NN approach improved the volume estimates of Cryptomeria japonica in southern Brazil, providing a more accurate estimate of total and commercial volumes.…”

Section: Discussionmentioning

confidence: 99%

“…Parametric approaches such as linear regression have been the main focus of many modeling studies of the form factor of trees in forests of unequal ages and of the same age (e.g., DRESCHER et al, 2001;DRESCHER et al, 2010;BOTH et al, 2011;ADEKUNLE et al, 2013;CUNHA NETO et al, 2016, TENZIN et al, 2017. However, nonparametric approaches, such as artificial neural networks, have been shown to be appropriate in modeling forest attributes (e.g., DIAMANTOPOULOU;MILIOS, 2010;MARTINS et al, 2016, SANQUETTA et al, 2018. In this context, recent initiatives have revealed the potential of the Nearest Neighbor (k-NN) method in the prediction of biometric variables, such as volume, biomass and carbon stock in trees (SANQUETTA et al, 2013, SANQUETTA et al, 2015, SOUZA et al, 2019.…”

Section: Introductionmentioning

confidence: 99%

k-NEAREST NEIGHBOR AND LINEAR REGRESSION IN THE PREDICTION OF THE ARTIFICIAL FORM FACTOR

Souza

Nievola

Corte

et al. 2020

View full text Add to dashboard Cite

Resumo k-vizinhos mais próximos e regressão linear na predição do fator de forma artificial. A proposta deste estudo foi testar se a abordagem não-paramétrica, conhecida como k-Nearest-Neighbor (k-NN), poderia melhorar as estimativas do fator de forma artificial (f1,3) individual de árvores do híbrido Eucalyptus urophylla x Eucalyptus grandis, em comparação ao método de Mínimos Quadrados Ordinário. Foram selecionadas e derrubadas 149 árvores-amostras e medidos ao longo do fuste os diâmetros a 10% (d0,1), 30% (d0,3), 50% (d0,5) e 70% (d0,7) da altura do fuste comercial e, posteriormente, a cada 2m. Modelos matemáticos reconhecidos na literatura para predição do fator de forma foram ajustados para comparação. O hiperparâmetro k de ajuste ótimo para o estimador k-NN foi obtido através de repetidas validações cruzadas. Os dados de treinamento do modelo de regressão k-NN foram idênticos aos utilizados no ajuste dos modelos de regressão linear. A maior parte dos modelos de regressão linear múltipla apresentou problemas de colinearidade ou multicolinearidade. O uso da covariável (0.3. 0.7)/ 1.3 2 e k = 15 possibilitou a construção de modelos k-NN com melhor capacidade de generalização. O potencial do estimador k-NN para predizer o fator de forma artificial e, por conseguinte, obter estimativas menos viesadas dos volumes individuais de árvores foi admitido e, considerado superior ao uso da regressão linear e fatores de forma médios. A abordagem k-NN pode ser considerada mais genérica para predizer o fator de forma de árvores, e seu uso pode ser aconselhado quando modelos de regressão linear clássicos, ou outros métodos mais simples, não mostrarem bons resultados. Palavras-chave: Eucalyptus, aprendizado de máquina, vizinho mais próximo, regressão linear, fator de forma 0,7

show abstract

“…Output: Diametric distribution." Sanquetta et al (2018) The article sought to examine the performance of some Artificial Intelligence models (k-neighbors variant, one and three nearest neighbors and ANN) in estimating the tradable volume of Cryptomeria japonica logs in an experimental plantation in southern Brazil.…”

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

View full text Add to dashboard Cite

Volume estimation ofCryptomeria japonicalogs in southern Brazil using artificial intelligence models

Cited by 22 publications

References 37 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

k-NEAREST NEIGHBOR AND LINEAR REGRESSION IN THE PREDICTION OF THE ARTIFICIAL FORM FACTOR

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

Contact Info

Product

Resources

About