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

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

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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.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: No evidence of copyright restrictions preventing deposit.
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information
Date Deposited: 27 Feb 2013 03:08
Last Modified: 26 Jun 2017 05:28
Uncontrolled Keywords: object-based image analysis; WorldView-2; LiDAR; decision tree; forest classification
Fields of Research : 09 Engineering > 0907 Environmental Engineering > 090702 Environmental Engineering Modelling
07 Agricultural and Veterinary Sciences > 0705 Forestry Sciences > 070504 Forestry Management and Environment
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
Socio-Economic Objective: D Environment > 96 Environment > 9605 Ecosystem Assessment and Management > 960505 Ecosystem Assessment and Management of Forest and Woodlands Environments
URI: http://eprints.usq.edu.au/id/eprint/22656

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