Wood density estimation using dendrometric and edaphoclimatic data in artificial neural networks

Autor: Mauro Antônio Pereira Werneburg, Mayra Luiza Marques da Silva, Helio Garcia Leite, Antonilmar Araújo Lopes da Silva, José Marinaldo Gleriani, Jeferson Pereira Martins Silva, Tais Rizzo Moreira, Sofia Maria Gonçalves Rocha, Nívea Maria Mafra Rodrigues
Jazyk: English<br />Spanish; Castilian<br />Portuguese
Rok vydání: 2023
Předmět:
Zdroj: Revista de Ciências Agroveterinárias, Vol 22, Iss 4 (2023)
Druh dokumentu: article
ISSN: 22381171
2238-1171
DOI: 10.5965/223811712242023685
Popis: Forestry measurement is aimed at volumetric production of wood; however, for the pulp processing industry, the main interest is productivity in wood biomass and, to know this variable, it is necessary to determine the basic wood density (BWD) beforehand. Artificial neural networks (ANN) have been used in the forestry sector quite successfully to describe the dynamics of forest characteristics, such as estimating wood volume. In this context, the objective of this study was to assess the accuracy of the basic wood density estimates by means of ANN’s with Continuous Forest Inventory (CFI) and edaphoclimatic input variables. The database consisted of 3,797 data, from permanent plots of the CFI conducted in Eucalyptus sp stands and edaphoclimatic data from the planting sites. The five best ANNs were selected and the analysis of the estimates was carried out through the correlation between the estimated and BWD, the relative root mean square error (RMSE%) and graphical information. It was observed that both the CFI, edaphoclimatic information and the combination of both are potential and present similar results for the basic wood density estimate, and the errors associated with the estimates are between 3.9% to 3.5%. The ANNs based only on the CFI information presented higher RMSE. The use of ANN’s is feasible for estimating BWD and allows for excellent accuracy statistics.
Databáze: Directory of Open Access Journals
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