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

JOURNAL OF ENVIRONMENTAL MANAGEMENT

卷:249

Article

Martins Silva, Jeferson Pereira¹  Marques da Silva, Mayra Luiza²  da Silva, Evandro Ferreira¹  da Silva, Gilson Fernandes¹  de Mendonca, Adriano Ribeiro¹  Cabacinha, Christian Dias³  Araujo, Emanuel Franca¹  Santos, Jeangelis Silva¹  Vieira, Giovanni Correia⁴  Felix de Almeida, Maria Naruna¹  de Moura Fernandes, Marcia Rodrigues¹ 

[1] Fed Univ Espirito Santo UFES, Dept Forestry & Wood Sci, Ave Governor Lindemberg 316, BR-29550000 Jeronimo Monteiro, ES, Brazil

[2] Fed Univ Sao Joao Del Rei UFSJ, Setimo Moreira Martins 188, BR-35702031 Sete Lagoas, MG, Brazil

[3] Fed Univ Minas Gerais UFMG, Inst Agr Sci, Ave Univ 1000, BR-39404547 Montes Claros, MG, Brazil

[4] Fed Inst Rondonia IFRO, Campus Ji Parana,Rio Amazonas 151, BR-78900730 Ji Parana, RO, Brazil

关键词: Machine learning;    Model mixed effect;    Forest management;    Artificial intelligence techniques;    Cerrado;   

DOI  :  10.1016/j.jenvman.2019.109368

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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