Object-based image analysis for forest species classification using Worldview-2 satellite imagery and airborne LiDAR data

Paper

Zhang, Zhenyu, Liu, Xiaoye and Wright, Wendy. 2012. "Object-based image analysis for forest species classification using Worldview-2 satellite imagery and airborne LiDAR data." International Symposium on Remote Sensing (ISRS 2012), 8th International Conference on Space, Aeronautical and Navigational Electronics (ICSANE 2012). Incheon, Korea 10 - 12 Oct 2012
Paper/Presentation Title

Object-based image analysis for forest species classification using Worldview-2 satellite imagery and airborne LiDAR data

Presentation TypePaper
AuthorsZhang, Zhenyu (Author), Liu, Xiaoye (Author) and Wright, Wendy (Author)
Journal or Proceedings TitleProceedings of the 2012 International Symposium on Remote Sensing (ISRS 2012)
Number of Pages4
Year2012
Web Address (URL) of Paperhttp://www.ieice.org/cs/sane/ICSANE2012/index.html
Conference/EventInternational Symposium on Remote Sensing (ISRS 2012), 8th International Conference on Space, Aeronautical and Navigational Electronics (ICSANE 2012)
Event Details
International Symposium on Remote Sensing (ISRS 2012), 8th International Conference on Space, Aeronautical and Navigational Electronics (ICSANE 2012)
Event Date
10 to end of 12 Oct 2012
Event Location
Incheon, Korea
Abstract

It has been shown that new remote sensing technologies have the potential to complement deficiencies of conventional methods such as aerial photograph interpretation and field sampling as well as improve the accuracy, reduce costs, and increase the number of applications within various forest environments. Newly available high resolution spatial data such as small footprint, multiple-return, discrete airborne LiDAR data and WorldView-2 satellite imagery offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. However, the development of a comprehensive procedure for deployment of these new remote sensing data to create forest mapping products that are comparable and/or superior in accuracy to conventional photo-interpreted maps poses big challenges. Proper use of high spatial resolution data with object-based image analysis approach and nonparametric classification method such as decision trees may offer an alternative to aerial photograph interpretation in support of forest classification and mapping. This study presented ways of processing airborne LiDAR data and WorldView-2 satellite imagery for object-based forest species classification using decision trees in the Strzelecki Ranges, one of the four major Victorian areas of cool temperate rainforest in Australia. The results showed the contribution of four new WorldView-2 image bands to forest classifications, and demonstrated that the integration of airborne LiDAR and eight WorldView-2 bands significantly improved the classification accuracy.

Keywordsobject-based image analysis; WorldView-2; LiDAR; decision tree; forest classification
ANZSRC Field of Research 2020401199. Environmental engineering not elsewhere classified
300707. Forestry management and environment
401304. Photogrammetry and remote sensing
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Byline AffiliationsAustralian Centre for Sustainable Catchments
Monash University
Institution of OriginUniversity of Southern Queensland
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