Publication
 

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

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.

Authors: 
Midhun Mohan, Rodrigo Vieira Leite, Eben North Broadbent, Wan Shafrina Wan Mohd Jaafar, Shruthi Srinivasan, Shaurya Bajaj, Ana Paula Dalla Corte, Cibele Hummel do Amaral, Gopika Gopan, Siti Nor Maizah Saad, Aisyah Marliza Muhmad Kamarulzaman, Gabriel Atticciati Prata, Emma Llewelyn, Daniel J. Johnson, Willie Doaemo, Stephanie Bohlman, Angelica Maria Almeyda Zambrano, & Adrián Cardil
Journal: 
Open Geosciences
Year: 
2021
Volume: 
13
Issue: 
1
Pages: 
1028-1039
DOI: 
10.1515/geo-2020-0290
Site: 
Ordway Swisher