Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Forest Canopy Density Estimation Using Airborne Hyperspectral Data

  • Kwon, Tae-Hyub (National Academy of Agricultural Science, Rural Development Administration) ;
  • Lee, Woo-Kyun (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Kwak, Doo-Ahn (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Park, Tae-Jin (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Jong-Yoel (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Hong, Suk-Young (National Academy of Agricultural Science, Rural Development Administration) ;
  • Guishan, Cui (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Kim, So-Ra (Department of Environmental Science and Ecological Engineering, Korea University)
  • Received : 2012.05.09
  • Accepted : 2012.06.10
  • Published : 2012.06.30
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Abstract

This study was performed to estimate forest canopy density (FCD) using airborne hyperspectral data acquired in the Independence Hall of Korea in central Korea. The airborne hyperspectral data were obtained with 36 narrow spectrum ranges of visible (Red, Green, and Blue) and near infrared spectrum (NIR) scope. The FCD mapping model developed by the International Tropical Timber Organization (ITTO) uses vegetation index (VI), bare soil index (BI), shadow index (SI), and temperature index (TI) for estimating FCD. Vegetation density (VD) was calculated through the integration of VI and BI, and scaled shadow index (SSI) was extracted from SI after the detection of black soil by TI. Finally, the FCD was estimated with VD and SSI. For the estimation of FCD in this study, VI and SI were extracted from hyperspectral data. But BI and TI were not available from hyperspectral data. Hyperspectral data makes the numerous combination of each band for calculating VI and SI. Therefore, the principal component analysis (PCA) was performed to find which band combinations are explanatory. This study showed that forest canopy density can be efficiently estimated with the help of airborne hyperspectral data. Our result showed that most forest area had 60 ~ 80% canopy density. On the other hand, there was little area of 10 ~ 20% canopy density forest.

Keywords

References

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