Please use this identifier to cite or link to this item: https://repositorio.inpa.gov.br/handle/1/15419
Title: Volume equations for merchantable timber species of Southern Roraima state
Other Titles: Equações volumétricas para espécies comerciais madeireiras do sul do estado de Roraima
Authors: Gimenez, Bruno Oliva
Danielli, Filipe Eduardo
Oliveira, Criscian Kellen Amaro de
Santos, Joaquim dos
Higuchi, Niro
Keywords: Approximation Theory
Amazon
Field Data Collection
Forest Inventory
Independent Variables
Observed Data
Timber Species
Volume Equations
Volume Models
Forestry
Forest Management
Forests
Inventory Control
Issue Date: 2015
metadata.dc.publisher.journal: Scientia Forestalis/Forest Sciences
metadata.dc.relation.ispartof: Volume 43, Número 106, Pags. 291-301
Abstract: Eight volume models were tested to fit observed data from merchantable tree species of Southern Roraima; four simple entry models with DBH as independent variable and four double entry models with DBH and merchantable height (Hc) as independent variables. Among those, the best model was compared with the one which uses form factor equal to 0.7. Additionally, the same models were tested with diameter at stump height (Dtoco) instead of DBH in order to develop an equation to estimate the volume removed by logging. To carry out this study, 54 sample trees with DBH ≥ 30 cm were taken from a clear cut area for agriculture projects in Rorainópolis, Southern Roraima. The model that best fit the observed data was V = 0.000503 ∗ DBHΛ2.157162 (R2 adj = 0.899 and Syx = 1.38 m3), because it does not have Hc as independent variable. To estimate the volume from the independent variable Dtoco the model that presented the best results was V = 0.002603 ∗ DtocoΛ1.761132 (R2 adj. = 0.789 and Syx = 1.93 m3). In the clear cut area, merchantable height could be measured precisely; in this case, double entry model could be very convenient as well, such as V = 0.000263 ∗ Dtoco Λ1.782244 ∗ Hc Λ 0.765729 (R2 adj. = 0.872 and Syx = 1.51 m3). Models with a universal form factor (0.7, for example) are not reliable mainly because Hc is not obtained precisely during the field data collection for forest inventory.
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