Forest Science and Technology

Korean Society of Forest Science (한국산림과학회)
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Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data

  • Park, Taejin (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Woo-Kyun (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Lee, Jong-Yeol (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Byun, Woo-Hyuk (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Kwak, Doo-Ahn (Environmental GIS/RS Centre, Korea University) ;
  • Cui, Guishan (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Kim, Moon-Il (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Jung, Raesun (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Pujiono, Eko (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Oh, Suhyun (Department of Climate Environment, Graduate School of Life & Environmental Sciences, Korea University) ;
  • Byun, Jungyeon (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Nam, Kijun (Division of Environmental Science and Ecological Engineering, Korea University) ;
  • Cho, Hyun-Kook (Division of Forest Resources Information, Korea Forest Research Institute) ;
  • Lee, Jung-Su (Department of Forest Management, College of Forest Environmental Science, Kangwon National University) ;
  • Chung, Dong-Jun (National Forest Resource Inventory Center, National Forest Cooperatives Federation) ;
  • Kim, Sung-Ho (Division of Forest Resources Information, Korea Forest Research Institute)
  • Published : 2012.06.30
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Abstract

The importance of estimating forest volume has been emphasized by increasing interest on carbon sequestration and storage which can be converted from volume estimates. With importance of forest volume, there are growing needs for developing efficient and unbiased estimation methods for forest volume using reliable data sources such as the National Forest Inventory (NFI) and supplementary information. Therefore, this study aimed to develop a forest plot volume model using selected explanatory variables from each data type (only Forest Type Map (FTM), only airborne LiDAR and both datasets), and verify the developed models with forest plot volumes in 60 test plots with the help of the NFI dataset. In linear regression modeling, three variables (LiDAR height sum, age, and crown density class) except diameter class were selected as explanatory independent variables. These variables generated the four forest plot volume models by combining the variables of each data type. To select an optimal forest plot volume model, a statistical comparing process was performed between four models. In verification, Model no. 3 constructed by both FTM and airborne LiDAR was selected as an optimal forest plot volume model through comparing root mean square error (RMSE) and coefficient of determination $(R^2)$. The selected best performance model can predict the plot volume derived from NFI with RMSE and $R^2$ at 50.41 $(m^3)$ and 0.48, respectively.

Keywords

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