Publications‎ > ‎international‎ > ‎

Forest plot volume estimation using National Forest Inventory, Forest Type Map and Airborne LiDAR data

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