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

Abstract

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.

DIMASS: A Delaunay-Inspired, Hybrid Approach to a Team of Agents Search Strategy

“…That is, each of the functions returns the velocity vector of the target (fire) mobility rate for each dimension based on the environmental dynamic variables configuration vector taken as passing parameter, e.g., fuel type, wind speed, location relation ground surface (uphill or downhill, etc). Parameters values for the variables were obtained from documented standard operating procedure (SOP), UAV images analysis works ( Fernández-Hernandez et al, 2015 ; Haddadi et al, 2020 ; Dalla Corte et al, 2020 ; Corte et al, 2020 ; da Costa et al, 2021 ; Mohan et al, 2021 ; Neto et al, 2021 ), and arranged physical experiments as described in Figure 6 and Table 4 . For example, the contribution to fire spread weight (w) from dried shrubs is higher than the wet ones (fire spreads faster in dried shrubs than in marshland).…”

Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow

“…Previous studies have already shown the value of lidar data from drone platforms, such as Dalla Corte et al, 2020, who estimated tree diameter and height from drone-based lidar using R software packages [47]. Processing these individual tree metrics from lidar datasets is becoming more accessible using free and open-source R software packages [48]. To process large-extent lidar datasets using an individual tree approach efficiently will require the creation of workflows such as that presented in this study.…”

Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data

“…As emphasized by several of these studies, detection and delineation of tree crowns in complex forest structures with dense canopy closure and overlapping tree crowns remains challenging [35,51]. Some studies focus on ITCD in forest with low to moderate canopy closure or open forest stands [42,48,53], as well as on forest plantations characterized by a more regular tree spacing and structure [44,46]. These types of forest tend to facilitate the detection of crown boundaries and the number of trees, as most algorithms perform better in homogeneous forest stands with lower canopy closure [19].…”