Single-tree detection in high-density LiDAR data from UAV-based survey

Abstract

Unmanned aerial vehicle-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, as well as demanding high computational load. Single-tree detection is of great interest for forest management and ecology purposes, and the task is relatively well solved for forests made of single or largely dominant species, and trees having a very evident pointed shape in the upper part of the canopy (in particular conifers). Most authors proposed methods based totally or partially on search of local maxima in the canopy, which has poor performance for species that have flat or irregular upper canopy, and for mixed forests, especially where taller trees hide smaller ones. Such considerations apply in particular to Mediterranean hardwood forests. In such context, it is imperative to use the whole volume of the point cloud, however keeping computational load tractable. The authors propose the use of a methodology based on modelling the 3D-shape of the tree, which improves performance with respect to maxima-based models. A case study, performed on a hazel grove, is provided to document performance improvement on a relatively simple, but significant, case. ARTICLE HISTORY

Acquisition of Forest Attributes for Decision Support at the Forest Enterprise Level Using Remote-Sensing Techniques—A Review

“…Compared to ALS, UAV data provides more geometrical details, which result in improved tree detection; that was demonstrated by Thiel and Schmullius [152] who reported detection rate in ALS 78%, and in UAV DAP point cloud 93%. Balsi et al [153] utilized RANSAC algorithm followed by k-means clustering to detect and segment individual crowns from LiDAR data acquired with an UAV. They successfully applied the method on a plantation of hazel trees characterized by irregularly shaped crowns and no evident apex, as a model of broadleaf forest, which is a challenging task for tree detection algorithms.…”

Detecting shrub encroachment in seminatural grasslands using UAS LiDAR

“…UAS LiDAR systems have been used to detect individual trees and to measure metrics such as height and stem diameter (Wallace, Lucieer, Watson, & Turner, 2012; Wieser et al, 2017). The ability to detect single trees has been found to increase with higher point densities (Wallace, Lucieer, & Watson, 2014), and Balsi, Esposito, Fallavollita, & Nardinocchi, 2018 could contrast different shapes of horizontal overlapping trees using information from the whole volume of points in the point cloud. Furthermore, Moeslund et al, 2019 analyzed the potential of using airborne LiDAR‐derived metrics, including a biomass measure, to assess the diversity of different organisms (i.e., vascular plants, fungi, lichens, and bryophytes).…”

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

“…Similarly, UAVs have been used to tackle the problem of tree detection from many perspectives. For example, LiDAR-based methods model the 3D-shape of trees for detection with accuracy values ranging from 86% to 98% [11,12]; however, the high cost of LiDAR for UAVs represents an important limitation. The same limitation occurs with hyperspectral-based methods, such as [13], which uses a hyperspectral frame format camera and an RGB camera along with 3D modelling and Multilayer Perceptron (MLP) neural networks, and obtains accuracy values ranging from 40% to 95% depending on the conditions of the area.…”