Evaluation of multiple linear regression model to obtain dbh of trees using data from a lightweight laser scanning system on-board a uav
Nenhuma Miniatura disponível
Data
2019-06-04
Autores
MacHado, M. V. [UNESP]
Tommaselli, A. M.G. [UNESP]
Tachibana, V. M. [UNESP]
Martins-Neto, R. P. [UNESP]
Campos, M. B. [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
Vegetation mapping requires information about trees and underlying vegetation to ensure proper management of the urban and forest environments. This information can be obtained using remote sensors. For instance, lightweight systems composed of Unmanned Aerial Vehicles (UAVs) as a platform, low-cost laser units and the recent miniaturized navigation sensors (positioning and orientation) have become a very feasible and flexible alternative. Low-cost UAV-ALS systems usually provide centimetric accuracy in altimetry, according to flight data configuration and quality of observations. This paper presents a feasibility study of a lightweight ALS system on-board a UAV to estimate the diameters at breast height (DBH) of urban trees using LiDAR data and linear regression model. A mathematical model correlating the crown diameter and height of the tree to estimate the DBH was developed based on a linear regression with stepwise method. The stepwise linear regression method enables the addition and the removal of predictor variables through statistical tests. The tree samples were separated in two classes (A and B), according to the diametric distribution. These sample classes were used to define two linear regression models. The regression models that best fit the samples achieved an R2 adj value above 94% for class A and B, which demonstrates the closeness between the samples and the developed mathematical models. The quality control of the proposed regression models was performed comparing the DBH values estimated and directly measured (reference). DBH of the trees were estimated with an average discrepancy of 8.7 cm.
Descrição
Palavras-chave
Airborne Laser Scanning, forest application, multiple linear regression, Unmanned Aerial Vehicles
Como citar
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v. 42, n. 2/W13, p. 449-454, 2019.