The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 449–454, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-449-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 449–454, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-449-2019

  04 Jun 2019

04 Jun 2019

EVALUATION OF MULTIPLE LINEAR REGRESSION MODEL TO OBTAIN DBH OF TREES USING DATA FROM A LIGHTWEIGHT LASER SCANNING SYSTEM ON-BOARD A UAV

M. V. Machado1, A. M. G. Tommaselli2, V. M. Tachibana3, R. P. Martins-Neto1, and M. B. Campos1 M. V. Machado et al.
  • 1Post Graduate Program in Cartographic Science, São Paulo State University (UNESP), Presidente Prudente-SP, Brazil
  • 2Dept. of Cartography, São Paulo State University (UNESP), Presidente Prudente -SP, Brazil
  • 3Dept. of Statistics, São Paulo State University (UNESP), Presidente Prudente -SP, Brazil

Keywords: Airborne Laser Scanning, Unmanned Aerial Vehicles, multiple linear regression, forest application

Abstract. 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.