Research Papers

Exploring high-density airborne light detection and ranging data for classification of mature coniferous and deciduous trees in complex Canadian forests

[+] Author Affiliations
Jili Li, Baoxin Hu

York University, Department of Earth and Space Science and Engineering, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada

J. Appl. Remote Sens. 6(1), 063536 (May 21, 2012). doi:10.1117/1.JRS.6.063536
History: Received March 15, 2011; Revised March 9, 2012; Accepted April 16, 2012
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Abstract.  High-density (approximately 40 points m2) airborne light detection and ranging (LiDAR) data were exploited to improve the classification of coniferous and deciduous trees in uneven aged mixed wood forests. The investigation was conducted at the individual tree level. Several features were extracted from airborne laser scanning data to characterize structural properties of individual trees, such as crown shape and foliage distribution. A decision tree algorithm was used to perform the selection of significant features and construction of a classifier. The classification was tested within various sites in Canadian forests with different species. The results demonstrated that the LiDAR features describing the foliage distribution within a tree play a significant role in discriminating coniferous and deciduous species. In the classification, 193 reference trees sampled from the entire study area were used for training, and three representative forest sites within the study area were used for assessment of accuracy. The classification accuracy was 77.3%. This study also suggested that high-density LiDAR data were effective in discriminating individual mature coniferous and deciduous trees.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Topics

LIDAR

Citation

Jili Li and Baoxin Hu
"Exploring high-density airborne light detection and ranging data for classification of mature coniferous and deciduous trees in complex Canadian forests", J. Appl. Remote Sens. 6(1), 063536 (May 21, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063536


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