Computational techniques applied to volume and biomass estimation of trees in Brazilian savanna

• Published in: Journal of environmental management Vol. 249; p. 109368
• Main Authors: Martins Silva, Jeferson Pereira, Marques da Silva, Mayra Luiza, Ferreira da Silva, Evandro, Fernandes da Silva, Gilson, Ribeiro de Mendonça, Adriano, Cabacinha, Christian Dias, Araújo, Emanuel França, Santos, Jeangelis Silva, Vieira, Giovanni Correia, Felix de Almeida, Maria Naruna, Fernandes, Márcia Rodrigues de Moura
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.11.2019
• Subjects: Artificial intelligence techniques; Cerrado; Forest management; Machine learning; Model mixed effect; Artificial intelligence techniques; Forest management; Model mixed effect; Cerrado; Machine learning
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2019.109368

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. [Display omitted] •Artificial intelligence and mixed models to estimate volume and biomass.•Good of estimation accuracy of volume and biomass.•All techniques used are viable to estimate complex grow vegetation.