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Remote Sens. 2012, 4(6), 1741-1757; doi:10.3390/rs4061741
Article

Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles

*  and
Department of Earth and Space Science and Engineering, York University, 4700 Keele St., Toronto, ON M3J 1P3, Canada
* Author to whom correspondence should be addressed.
Received: 10 April 2012 / Revised: 5 June 2012 / Accepted: 5 June 2012 / Published: 11 June 2012
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Abstract

Individual tree species identification is important for urban forest inventory and ecology management. Recent advances in remote sensing technologies facilitate more detailed estimation of individual urban tree characteristics. This study presents an approach to improve the classification of individual tree species via longitudinal profiles from very high spatial resolution airborne imagery. The longitudinal profiles represent the side view tree shape, which play a very important role in individual tree species on-site identification. Decision tree classification was employed to conduct the final classification result. Using this profile approach, six major species (Maple, Ash, Birch, Oak, Spruce, Pine) of trees on the York University (Ontario, Canada) campus were successfully identified. Two decision trees were constructed, one knowledge-based and one derived from gain ratio criteria. The classification accuracy achieved were 84% and 86%, respectively.
Keywords: classification; urban; high spatial resolution; multi-spectral; longitudinal profiles; trees species; decision trees classification; urban; high spatial resolution; multi-spectral; longitudinal profiles; trees species; decision trees
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zhang, K.; Hu, B. Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles. Remote Sens. 2012, 4, 1741-1757.

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