Open AccessEstimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection
Author(s) -
Ângela Maria Klein Hentz,
Carlos Alberto Silva,
Ana Paula Dalla Corte,
Sylvio Péllico Netto,
Michael P. Strager,
Carine Klauberg
Publication year - 2018
Publication title -
forest systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.455
H-Index - 25
eISSN - 2171-9845
pISSN - 2171-5068
DOI - 10.5424/fs/2018272-11713
Subject(s) - point cloud , eucalyptus , terrain , environmental science , forest inventory , watershed , remote sensing , pinus <genus> , forestry , tree (set theory) , mathematics , geography , computer science , forest management , ecology , cartography , biology , botany , mathematical analysis , machine learning , computer vision
Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years.Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index.Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%).Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.