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 (R2). The selected best performance model can predict

the plot volume derived from NFI with RMSE and R2 at 50.41 (m3) and 0.48, respectively.

Keywords: airborne LiDAR; forest plot volume; Forest Type Map; linear regression analysis; National Forest

Inventory