Terrain and canopy surface modelling from LiDAR data for tree species classification

Paper


Zhang, Zhenyu, Liu, Xiaoye, Peterson, Jim and Wright, Wendy. 2012. "Terrain and canopy surface modelling from LiDAR data for tree species classification." Ruzicka, Jan and Ruzickova, Katerina (ed.) Symposium GIS Ostrava 2012: Surface Models for Geosciences. Ostrava, Czech Republic 23 - 25 Jan 2012 Ostrava, Czech Republic.
Paper/Presentation Title

Terrain and canopy surface modelling from LiDAR data for tree species classification

Presentation TypePaper
AuthorsZhang, Zhenyu (Author), Liu, Xiaoye (Author), Peterson, Jim (Author) and Wright, Wendy (Author)
EditorsRuzicka, Jan and Ruzickova, Katerina
Journal or Proceedings TitleSymposium GIS Ostrava 2012: Surface Models for Geosciences Proceedings
Number of Pages13
Year2012
Place of PublicationOstrava, Czech Republic
ISBN9788024826677
Web Address (URL) of Paperhttp://gis.vsb.cz/GIS_Ostrava/GIS_Ova_2012/sbornik/papers/zhang.pdf
Conference/EventSymposium GIS Ostrava 2012: Surface Models for Geosciences
Event Details
Symposium GIS Ostrava 2012: Surface Models for Geosciences
Event Date
23 to end of 25 Jan 2012
Event Location
Ostrava, Czech Republic
Abstract

It has been recognised that airborne LiDAR (light detection and ranging) offers advantages over the interpretation of aerial photographs and processing of multi-spectral and/or hyperspectral remote sensing data in forest classification. LiDAR with capability of canopy penetration yields such high density sampling that detailed terrain and canopy surface models can be derived. Recent success in forest classification using LiDAR derived products including terrain and canopy surface models has been reported in many studies. However, there is still considerable scope for further improvement in classification accuracy by taking
maximum advantage of the information extracted from LiDAR data and by employing more efficient classifiers such as support vector machines (SVMs). This study aims to use LiDAR
data to generate digital terrain and canopy surface models to identify the location and crown size of individual trees for the species classification of Australian cool temperate rainforest dominated by the Myrtle Beech (Nothofagus cunninghamii) and neighbouring Silver Wattle (Acacia dealbata). The tree species classification was achieved by employing LiDAR-derived structure and intensity variables via linear discriminant analysis (LDA) and SVMs. The results showed that the inclusion of LiDAR-derived intensity variables improved the accuracy of the classification of the Myrtle Beech and the Silver Wattle species in the study area. It demonstrated that the SVMs have significant advantages over the traditional classification methods such as the LDA methods in terms of classification accuracy.

KeywordsLiDAR, LiDAR intensity, canopy surface model, support vector machines, forest classification
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
Public Notes

© VSB - Technical University of Ostrava.

Byline AffiliationsAustralian Centre for Sustainable Catchments
Monash University
Institution of OriginUniversity of Southern Queensland
Book TitleGIS Ostrava 2012: Surface Models for Geosciences
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