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Extracting trees in an urban environment using airborne LiDAR Plowright, Andrew

Abstract

Urban forests represent a considerable financial investment for cities. Despite the efforts and resources expended on the maintenance of trees, cities often lack comprehensive information on their condition. Light detection and ranging (LiDAR), a remote sensing technology already employed in commercial forest management, shows significant potential as a tool for monitoring urban forests. Automated data processing algorithms are required for extracting information at an individual tree basis from LiDAR data. Here, two methods for detecting and delineating trees, variable window filtering (VWF) and multi-scale segment integration (MSI), are applied to two urban plots. The accuracy of both methods is reported in terms of the frequency of errors of omission and commission. Results show a broad variation in the performance between the two methods depending on tree age, species and location. On average, the MSI approach produced fewer errors, which make it a potentially stronger candidate for applications in urban forest management.

Item Metadata

Title
Extracting trees in an urban environment using airborne LiDAR
Creator
Plowright, Andrew
Date Issued
2015-03-09
Description
Urban forests represent a considerable financial investment for cities. Despite the efforts and resources expended on the maintenance of trees, cities often lack comprehensive information on their condition. Light detection and ranging (LiDAR), a remote sensing technology already employed in commercial forest management, shows significant potential as a tool for monitoring urban forests. Automated data processing algorithms are required for extracting information at an individual tree basis from LiDAR data. Here, two methods for detecting and delineating trees, variable window filtering (VWF) and multi-scale segment integration (MSI), are applied to two urban plots. The accuracy of both methods is reported in terms of the frequency of errors of omission and commission. Results show a broad variation in the performance between the two methods depending on tree age, species and location. On average, the MSI approach produced fewer errors, which make it a potentially stronger candidate for applications in urban forest management.
Subject
Urban forestry; Tree detection
Genre
Report
Type
Text
Language
eng
Series
University of British Columbia. FRST 538
Date Available
2015-03-13
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI
10.14288/1.0075836
URI
http://hdl.handle.net/2429/52384
Affiliation
Forestry, Faculty of; Forest Resources Management, Department of
Campus
UBCV
Peer Review Status
Unreviewed
Scholarly Level
Graduate
Rights URI
http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
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Attribution-NonCommercial-NoDerivs 2.5 Canada