Statistical analysis of airborne LiDAR data for forest classification in the Strzelecki Ranges, Victoria, Australia

Paper


Zhang, Z., Liu, X., Peterson, J. and Wright, W.. 2011. "Statistical analysis of airborne LiDAR data for forest classification in the Strzelecki Ranges, Victoria, Australia." Chan, F., Marinova, D. and Anderssen, R. S. (ed.) 19th International Congress on Modelling and Simulation (MODSIM2011). Perth, Australia 12 - 16 Dec 2011 Australia. Modelling and Simulation Society of Australia and New Zealand .
Paper/Presentation Title

Statistical analysis of airborne LiDAR data for forest classification in the Strzelecki Ranges, Victoria, Australia

Presentation TypePaper
AuthorsZhang, Z. (Author), Liu, X. (Author), Peterson, J. (Author) and Wright, W. (Author)
EditorsChan, F., Marinova, D. and Anderssen, R. S.
Journal or Proceedings TitleProceedings of the 19th International Congress on Modelling and Simulation (MODSIM2011)
ERA Conference ID44996
Number of Pages7
Year2011
PublisherModelling and Simulation Society of Australia and New Zealand
Place of PublicationAustralia
ISBN9780987214317
Web Address (URL) of Paperhttp://www.mssanz.org.au/modsim2011/index.htm
Conference/Event19th International Congress on Modelling and Simulation (MODSIM2011)
International Congress on Modelling and Simulation
Event Details
International Congress on Modelling and Simulation
MODSIM
Rank
C
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C
C
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Event Details
19th International Congress on Modelling and Simulation (MODSIM2011)
Parent
International Congress on Modelling and Simulation
Delivery
In person
Event Date
12 to end of 16 Dec 2011
Event Location
Perth, Australia
Abstract

Although remotely sensed data have been widely explored for forest applications, passive remote sensing techniques are limited in their ability to capture forest structural complexity, particularly in uneven-aged, mixed species forests with multiple canopy layers. Generally, these techniques are only able to provide information on horizontal (two-dimensional) forest extent. The vertical forest structure (or the interior of the canopy and understorey vegetation) cannot be mapped using these passive remote sensing techniques. Fortunately, it has been shown that active remote sensing techniques via airborne LiDAR (light detection and ranging) with capability of canopy penetration yields such high density sampling that detailed description of the forest structure in three-dimensions can be obtained. Accordingly, much interest is attached to exploring the application of this approach for identifying the distribution of designated vegetation
communities. However, the suitability of LiDAR data for the classification of forests with complex structures, particularly for cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This study applied airborne LiDAR data for the classification of cool temperate rainforest dominated by Myrtle Beech (Nothofagus cunninghamii) and adjacent forests including naturally regenerated Mountain Ash (Eucalyptus regnans), mixed forest consisting of overstorey Mountain Ash and understorey Myrtle Beech, Silver Wattle (Acacia dealbata), and hardwood plantation dominated by Shining Gum (Eucalyptus nitens) in
the Strzelecki Ranges, Victoria, Australia. LiDAR data were extracted within each of the forest plots. Nonground laser returns were used to generate forest height profiles for the analysis of the spatial distribution of vertical forest structure for the plots dominated by different forest types. The k-means clustering algorithm was performed on each of the plots to stratify the vertical forest structure into three layers, representing the overstorey, mid-storey and lower storey of the plot-level forests. Variables were then calculated from the LiDAR data based on the three-layered structure for each plot. The statistical analyses, which included oneway ANOVA (analysis of variance) and the post hoc tests, identified effective variables for forest type classifications. Linear discriminant analysis with cross-validation was carried out to classify the forest types and assess the classification accuracy using error matrixes. This study demonstrated the applicability of
airborne LiDAR for the classification of the Australian cool temperate rainforest and adjacent forests in the study area.

KeywordsLiDAR; cool temperate rainforest; forest classification; statistical analysis; Strzelecki Ranges
ANZSRC Field of Research 2020300707. Forestry management and environment
401304. Photogrammetry and remote sensing
401302. Geospatial information systems and geospatial data modelling
Public Notes

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Byline AffiliationsAustralian Centre for Sustainable Catchments
Monash University
Institution of OriginUniversity of Southern Queensland
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