Events2nd International Electronic Conference on Remote Sensing
Published
This submission belongs to the session D. Applications of the event 2nd International Electronic Conference on Remote Sensing
Published date
22 Mar, 2018
Citation
John Tasker, Stuart Phinn, Vertical Segmentation of Airborne LiDAR for Select Australian Vegetation Communities, in Proceedings of 2nd International Electronic Conference on Remote Sensing, 22 March–5 April 2018, MDPI: Basel, Switzerland, doi: 10.3390/ecrs-2-05151
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Vertical Segmentation of Airborne LiDAR for Select Australian Vegetation Communities

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1. Remote Sensing Research Centre, School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Queensland, Australia
Abstract

A quantitative understanding of vegetation structure is vital to inform long-term protection and management of Australia’s vegetation communities. Although airborne light detection and ranging (LiDAR) systems are increasingly utilised to provide three-dimensional measures of vegetation structure at high spatial resolutions (1 – 10 m2), only limited studies characterise vertical vegetation structure using these datasets. This study assesses the capacity of high spatial resolution LiDAR data to accurately characterise the structural forms of Australian vegetation communities. Four study sites, each covering approximately 25 km2, were selected to provide examples across a range of vegetation structural forms, from shrubland to tall closed forest. A novel vertical segmentation methodology was developed to process airborne LiDAR data from each study site at 1 or 2 m vertical and horizontal spatial resolutions. Ratios were applied to standardise point density values, prior to exploratory analysis utilising multi-dimensional clustering algorithms to classify distinct vertical structure patterns. Comparisons were subsequently performed between the exploratory analysis results and established structural classifications for Australian vegetation communities. The use of the vertical segmentation technique was found to improve the identification of sub-canopy features in multi-story vegetation communities, particularly shrubs and herbaceous ground covers 0.5 - 4 m tall. Exploratory analysis results saw increased noise in structurally complex and dense vegetation communities due to reduced sub-canopy returns. Further development and application of vertical segmentation methods in multi-story vegetation communities should be evaluated due to the potential for targeted management and monitoring of vegetation communities and wildlife populations.

Keywords
LiDAR
ALS
point cloud
vertical segmentation
vegetation structure
Manuscript
Poster
ECRS_TASKER_Presentation.pdf
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