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Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification

Marselis, Suzanne M.; Yebra, Marta; Jovanovic, Tom; van Dijk, Albert I.J.M.

Description

The advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a...[Show more]

dc.contributor.authorMarselis, Suzanne M.
dc.contributor.authorYebra, Marta
dc.contributor.authorJovanovic, Tom
dc.contributor.authorvan Dijk, Albert I.J.M.
dc.date.accessioned2016-09-30T02:07:26Z
dc.date.available2016-09-30T02:07:26Z
dc.identifier.issn1364-8152
dc.identifier.urihttp://hdl.handle.net/1885/109114
dc.description.abstractThe advent of mobile laser scanning has enabled time efficient and cost effective collection of forest structure information. To make use of this technology in calibrating or evaluating models of forest and landscape dynamics, there is a need to systematically and reproducibly automate the processing of LiDAR point clouds into quantities of forest structural components. Here we propose a method to classify vegetation structural components of an open-understorey eucalyptus forest, scanned with a ‘Zebedee’ mobile laser scanner. It detected 98% of the tree stems (N = 50) and 80% of the elevated understorey components (N = 15). Automatically derived DBH values agreed with manual field measurements with r² = 0.72, RMSE = 3.8 cm, (N = 27), and total basal area agreed within 1.5%. Though this methodological study was restricted to one ecosystem, the results are promising for use in applications such as fuel load, habitat structure, and biomass estimations.
dc.description.sponsorshipThe support of the Commonwealth of Australia through the Cooperative Research Centre program is gratefully acknowledged. The authors thank CSIRO for lending the Zebedee unit, Robert Zlot (CSIRO) for pre-processing the data and Peter Hairsine for his help with revisions.
dc.publisherElsevier
dc.rights© 2016 Elsevier Ltd
dc.sourceEnvironmental Modelling & Software
dc.subjectGround-based LiDAR
dc.subjectAutomatic classification
dc.subjectVegetation components
dc.subjectStem diameter
dc.titleDeriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification
dc.typeJournal article
local.identifier.citationvolume82
dc.date.issued2016
local.publisher.urlhttp://www.elsevier.com/
local.type.statusPublished Version
local.contributor.affiliationMarselis, S. M., Fenner School of Environment and Society, The Australian National University
local.contributor.affiliationYebra, M., Fenner School of Environment and Society, The Australian National University
local.contributor.affiliationvan Dijk, A. I. J. M., Fenner School of Environment and Society, The Australian National University
local.bibliographicCitation.startpage142
local.bibliographicCitation.lastpage151
local.identifier.doi10.1016/j.envsoft.2016.04.025
CollectionsANU Research Publications

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