MODELS TO ESTIMATIVE VOLUME OF INDIVIDUAL TREES BY MORPHOMETRY OF CROWNS OBTAINED WITH LIDAR

Main Article Content

Evandro Orfanó Figueiredo Marcus Vinicio Neves d´Oliveira Philip Martin Fearnside Daniel de Almeida Papa

Abstract

The volumetric estimate from digital scanning of the forests through the use of LIDAR increases the precision of forest management techniques in planning tropical forest logging operations. The use of this remote detection technology allows the incorporation of crown morphometric variables which are still little known and little used due to the difficulty of collecting field data for volume equations. The objective of this study was to build equations capable of estimating the stem volume of dominant and codominant individual trees from the crown’s morphometry obtained by airborne LIDAR, considering two forest inventory situations: a) with the collection of diameter at breast height (DBH), and crown morphometric variables obtained from LIDAR data and b) using only the crown morphometry variables. For the selection of models the factors considered were: the correlation matrix of predictor variables and the combination of variables that generates the best results by statistical criteria Syx, Syx (%) and Pressp, and that were homoscedastic and had a normal and independent distribution of errors. The influence analysis was performed for the best equations. The results for the statistical fit of the equations to the two situations allowed the selection of models with and without DBH, with R2 aj.(%) values of a) 92.92 and b) 79.44, Syx(%) values of a) 16.73 and b) 27.47, and, Pressp criterion values of a) 201.15 m6 and b) 537.47 m6, respectively. Through morphometric variables it was possible to develop equations capable of accurately estimating the stem volume of dominant and codominant trees in tropical forests.

Article Details

How to Cite
FIGUEIREDO, Evandro Orfanó et al. MODELS TO ESTIMATIVE VOLUME OF INDIVIDUAL TREES BY MORPHOMETRY OF CROWNS OBTAINED WITH LIDAR. CERNE, [S.l.], v. 20, n. 4, p. 621-628, apr. 2016. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/1029>. Date accessed: 16 sep. 2019.
Keywords
Laser profiling, regression analysis, precision forestry, Amazon.
Section
Article