Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners

• Published in: Open Geosciences
• Main Author: Mohan M.; Leite R.V.; Broadbent E.N.; Wan Mohd Jaafar W.S.; Srinivasan S.; Bajaj S.; Dalla Corte A.P.; Do Amaral C.H.; Gopan G.; Saad S.N.M.; Muhmad Kamarulzaman A.M.; Prata G.A.; Llewelyn E.; Johnson D.J.; Doaemo W.; Bohlman S.; Almeyda Zambrano A.M.; Cardil A.
• Format: Article
• Language: English
• Published: De Gruyter Open Ltd 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115406510&doi=10.1515%2fgeo-2020-0290&partnerID=40&md5=090c226a08e37d7712a0c92311e068e9

Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpretation from high-resolution imagery and field-data-based accuracy assessment. As the intended audience are beginners in remote sensing, we have adopted a very simple methodology and chosen study plots that have relatively open canopies to demonstrate our proposed approach; the respective R codes and sample plot data are available as supplementary materials. © 2021 Midhun Mohan et al., published by De Gruyter.