Open AccessVOLUME ESTIMATE FOR THREE TIMBER SPECIES WITH COMMERCIAL INTEREST FROM THE DIAMETER OF THE STUMP
Author(s) -
Quelyson Souza De Lima,
Marco Antônio Amaro,
Thiago Augusto da Cunha,
E. O. Figueiredo,
Paulo Roberto Parente
Publication year - 2021
Publication title -
floresta
Language(s) - English
Resource type - Journals
eISSN - 1982-4688
pISSN - 0015-3826
DOI - 10.5380/rf.v51i3.72945
Subject(s) - mathematics , forestry , volume (thermodynamics) , regression analysis , statistics , environmental science , agroforestry , geography , physics , quantum mechanics
The commercial volume of trees can be calculated directly and indirectly. However, in areas of illegal deforestation, the stem is often not available for dendrometric measurements and, consequently, calculation of the commercial volume and the use of the direct method. On the other hand, the use of the indirect method is impossible due to the lack of equations with possible measurement variables. In order to make the volume estimate feasible in these cases, the objective of this work was to estimate the commercial volume of wood from individual trees using regression equations, using measurements made on the stump for three species of commercial interest, in areas of timber forest management, namely: Dipteryx odorata (Aubl.) Willd., Apuleia leiocarpa (Vogel) JFMacbr. and Amburana acreana (Ducke) A.C.Sm. Data were collected in two management areas, located in the municipalities of Sena Madureira and Rio Branco, with a sample of 250 stumps. Statistical tests were carried out to analyze regression and choose the best equations that would estimate the volume of the species selected for the study, based on the diameter of the stump. Tests of accuracy, precision, assumptions, validation and identity model were applied for choice. Models that met the research objectives for both species were tested with R²adj = 87.7% for Dipteryx odorata (Aubl.) Willd., R²adj = 81.8% for Apuleia leiocarpa (Vogel) J.F.Macbr. and R²adj = 70.5% for Amburana acreana (Ducke) A.C.Sm., as well as meeting all the assumptions of the regression resulting in the validation of the equations adjusted by the selected models.