Use este identificador para citar o ir al link de este elemento: http://hdl.handle.net/1843/43256
Tipo: Artigo de Periódico
Título: Computational techniques applied to volume and biomass estimation of trees in brazilian savanna
Autor(es): Jeferson Pereiramartins Silva
Maria Naruna Felix de Almeida
Márcia Rodrigues de Moura Fernandes
Mayra Luiza Marques da Silva
Evandro Ferreira da Silva
Gilson Fernandes da Silva
Adriano Ribeiro de Mendonça
Christian Dias Cabacinha
Emanuel França Araújo
Jeangelis Silva Santos
Giovanni Correia Vieira
Resumen: The Brazilian Savannah, known as Cerrado, has the richest flora in the world among the savannas, with a high degree of endemic species. Despite the global ecological importance of the Cerrado, there are few studies focused on the modeling of the volume and biomass of this forest formation. Volume and biomass estimation can be performed using allometric models, artificial intelligence (AI) techniques and mixed regression models. Thus, the aim of this work was to evaluate the use of AI techniques and mixed models to estimate the volume and biomass of individual trees in vegetation of Brazilian central savanna. Numerical variables (diameter at height of 1.30 m of ground, total height, volume and biomass) and categorical variables (species) were used for the training and fitting of AI techniques and mixed models, respectively. The statistical indicators used to evaluate the training and the adjustment were the correlation coefficient, bias and Root mean square error relative. In addition, graphs were elaborated as complementary analysis. The results obtained by the statistical indicators and the graphical analysis show the great potential of AI techniques and mixed models in the estimation of volume and biomass of individual trees in Brazilian savanna vegetation. In addition, the proposed methodologies can be adapted to other biomes, forest typologies and variables of interest.
Asunto: Plantas dos cerrados
Inteligência artificial
Florestas -- Administração
Máquinas
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Institución: UFMG
Departamento: ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
Tipo de acceso: Acesso Aberto
Identificador DOI: https://doi.org/10.1016/j.jenvman.2019.109368
URI: http://hdl.handle.net/1843/43256
Fecha del documento: 2019
metadata.dc.url.externa: https://www.sciencedirect.com/science/article/pii/S0301479719310771
metadata.dc.relation.ispartof: Journal of Environmental Management
Aparece en las colecciones:Artigo de Periódico

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