| Campo DC | Valor | Idioma |
|---|
| dc.contributor.author | Almeida, Mariana Pacheco de | - |
| dc.contributor.author | Miguel, Eder Pereira | - |
| dc.contributor.author | Santos, Mario Lima dos | - |
| dc.contributor.author | Gaspar, Ricardo de Oliveira | - |
| dc.contributor.author | Santos, Cassio Rafael Costa dos | - |
| dc.contributor.author | Raddatz, Dione Dambrós | - |
| dc.contributor.author | Martin, Walmer Bruno Rocha | - |
| dc.contributor.author | Matricardi, Eraldo Aparecido Trondoli | - |
| dc.date.accessioned | 2025-11-17T15:38:21Z | - |
| dc.date.available | 2025-11-17T15:38:21Z | - |
| dc.date.issued | 2022-11 | - |
| dc.identifier.citation | ALMEIDA, Mariana Pacheco de et al. Predicting teak tree (Tectona grandis Linn F.) height using generic models and artificial neural networks. Australian Journal of Crop Science, [S.l], v. 16, n. 11, p. 1243-1252, 2022. DOI: 10.21475/ajcs.22.16.11.p3736. Disponível em: https://www.cropj.com/november2022.html. Acesso em: 16 jul. 2025. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/53099 | - |
| dc.language.iso | eng | pt_BR |
| dc.publisher | Southern Cross Publishing | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Predicting teak tree (Tectona grandis Linn F.) height using generic models and artificial neural networks | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Redes neurais artificiais | pt_BR |
| dc.subject.keyword | Tectona grandis | pt_BR |
| dc.subject.keyword | Estimativas de altura | pt_BR |
| dc.subject.keyword | Manejo florestal | pt_BR |
| dc.subject.keyword | Amazônia | pt_BR |
| dc.subject.keyword | Plantio (Cultivo de plantas) | pt_BR |
| dc.rights.license | All the contents of this journal is licensed under a CC-BY-NC. AJCS does not have any commercial interest in the scientific contents of the journal. Fonte: https://www.cropj.com/about.html. Acesso em: 11 mar. 2025. | pt_BR |
| dc.identifier.doi | 10.21475/ajcs.22.16.11.p3736 | pt_BR |
| dc.description.abstract1 | The continuous monitoring of dendrometric variables provides estimates that assist in conducting fast-growing stands. In this
study, we aimed to investigate the performance of generic models and artificial neural networks to estimate total height of
Tectona grandis in a forest stand in the Eastern Amazon. Continuous forest inventory was performed in this population, where
measured of total height and diameter at breast height. These variables, age and the square root of the average diameter (dg) of
the plots, were used to compose the methods adopted to estimate the height of the trees. The accuracy of these methods was
assessed using the residual standard error of the estimate, the coefficient of correlation, and the graphical analysis of residues. The
aggregated difference and ANOVA were calculated to compare the methods. The independent variables mentioned were able to
describe the behavior of individuals at height. We concluded that the methods showed good residual dispersion, normal
distribution of errors and little tendency to overestimate height. It was found that the generic models and the ANNs do not differ
significantly from each other and are efficient to estimate the height of individuals. We also concluded that the ANNs, especially
those that included dg, presented superior statistical indicators. | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-6259-4594 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-2035-2180 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-9356-0186 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0001-6538-5763 | pt_BR |
| dc.identifier.orcid | https://orcid.org/0000-0002-5323-6100 | pt_BR |
| dc.contributor.affiliation | Federal University of Lavras, Department of Forest Engineering | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Department of Forest Engineering | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Department of Forest Engineering | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Department of Forest Engineering | pt_BR |
| dc.contributor.affiliation | Federal Rural University of Amazon | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Department of Forest Engineering | pt_BR |
| dc.contributor.affiliation | Federal Rural University of Amazon | pt_BR |
| dc.contributor.affiliation | University of Brasilia, Department of Forest Engineering | pt_BR |
| dc.description.unidade | Faculdade de Tecnologia (FT) | pt_BR |
| dc.description.unidade | Departamento de Engenharia Florestal (FT EFL) | pt_BR |
| dc.description.ppg | Programa de Pós-Graduação em Ciências Florestais | pt_BR |
| Aparece nas coleções: | Artigos publicados em periódicos e afins
|
