Automatic tree parameter extraction by a Mobile LiDAR System in an urban context.

Mónica Herrero-Huerta,Pablo Rodríguez-Gonzálvez,Roderik Lindenbergh,Steven Arthur Loiselle

doi:10.1371/journal.pone.0196004

Abstract

In an urban context, tree data are used in city planning, in locating hazardous trees and in environmental monitoring. This study focuses on developing an innovative methodology to automatically estimate the most relevant individual structural parameters of urban trees sampled by a Mobile LiDAR System at city level. These parameters include the Diameter at Breast Height (DBH), which was estimated by circle fitting of the points belonging to different height bins using RANSAC. In the case of non-circular trees, DBH is calculated by the maximum distance between extreme points. Tree sizes were extracted through a connectivity analysis. Crown Base Height, defined as the length until the bottom of the live crown, was calculated by voxelization techniques. For estimating Canopy Volume, procedures of mesh generation and α-shape methods were implemented. Also, tree location coordinates were obtained by means of Principal Component Analysis. The workflow has been validated on 29 trees of different species sampling a stretch of road 750 m long in Delft (The Netherlands) and tested on a larger dataset containing 58 individual trees. The validation was done against field measurements. DBH parameter had a correlation R2 value of 0.92 for the height bin of 20 cm which provided the best results. Moreover, the influence of the number of points used for DBH estimation, considering different height bins, was investigated. The assessment of the other inventory parameters yield correlation coefficients higher than 0.91. The quality of the results confirms the feasibility of the proposed methodology, providing scalability to a comprehensive analysis of urban trees.

Highlights

Tree inventory information and monitoring tree changes over time are critical input for tree management systems, ecosystem services and aboveground biomass estimation
The first step involves the determination of the CBH (Crown Base Height) to separate the canopy from the trunk by means of histogram analysis of the individual tree voxelization
The manuscript presents an efficient and non-invasive method for urban tree inventory based on point clouds acquired by Mobile LiDAR Systems (MLS)

Summary

Introduction

Tree inventory information and monitoring tree changes over time are critical input for tree management systems, ecosystem services and aboveground biomass estimation. This information could show the ecosystem influence on climate change, carbon and water cycling [1]. Automatic tree parameter extraction by a Mobile LiDAR System in an urban context and for biodiversity monitoring [2]. The municipality of Delft (The Netherlands) has opened a data-base where all the trees from the urban area of Delft are listed with properties as location, DBH range, dimension range, species health conditions, year of plantation and inspection date [3]

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Conclusion