Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Automated Individual Tree Detection and Crown Delineation Using High Spatial Resolution RGB Aerial Imagery

  • Park, Tae-Jin (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Jong-Yeol (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Woo-Kyun (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Kwak, Doo-Ahn (Environmental GIS/RS Centre, Korea University) ;
  • Kwak, Han-Bin (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Sang-Chul (Department of Geography, University of Maryland, College Park)
  • Received : 2011.11.12
  • Accepted : 2011.12.10
  • Published : 2011.12.30
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Abstract

Forests have been considered one of the most important ecosystems on the earth, affecting the lives and environment. The sustainable forest management requires accurate and timely information of forest and tree parameters. Appropriately interpreted remotely sensed imagery can provide quantitative data for deriving forest information temporally and spatially. Especially, analysis of individual tree detection and crown delineation is significant issue, because individual trees are basic units for forest management. Individual trees in aerial imagery have reflectance characteristics according to tree species, crown shape and hierarchical status. This study suggested a method that identified individual trees and delineated crown boundaries through adopting gradient method algorithm to amplified greenness data using red and green band of aerial imagery. The amplification of specific band value improved possibility of detecting individual trees, and gradient method algorithm was performed to apply to identify individual tree tops. Additionally, tree crown boundaries were explored using spectral intensity pattern created by geometric characteristic of tree crown shape. Finally, accuracy of result derived from this method was evaluated by comparing with the reference data about individual tree location, number and crown boundary acquired by visual interpretation. The accuracy ($\hat{K}$) of suggested method to identify individual trees was 0.89 and adequate window size for delineating crown boundaries was $19{\times}19$ window size (maximum crown size: 9.4m) with accuracy ($\hat{K}$) at 0.80.

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References

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