This study explored the feasibility of height distributional metrics and intensity values

extracted from low-density airborne light detection and ranging (lidar) data to estimate

plot volumes in dense Korean pine (Pinus koraiensis) plots. Multiple linear regression

analyses were performed using lidar height and intensity distributional metrics. The

candidate variables for predicting plot volume were evaluated using three data sets:

total, canopy, and integrated lidar height and intensity metrics. All intensities of lidar

returns used were corrected by the reference distance. Regression models were developed

using each data set, and the first criterion used to select the best models was the

corrected Akaike Information Criterion (AICc). The use of three data sets was statistically

significant at R2 = 0.75 (RMSE = 52.17 m3 ha−1), R2 = 0.84 (RMSE = 45.24 m3

ha−1), and R2 = 0.91 (RMSE = 31.48 m3 ha−1) for total, canopy, and integrated lidar

distributional metrics, respectively. Among the three data sets, the integrated lidar

metrics-derived model showed the best performance for estimating plot volumes,

improving errors up to 42% when compared to the other two data sets. This is

attributed to supplementing variables weighted and biased to upper limits in dense

plots with more statistical variables that explain the lower limits. In all data sets,

intensity metrics such as skewness, kurtosis, standard deviation, minimum, and standard

error were employed as explanatory variables. The use of intensity variables

improved the accuracy of volume estimation in dense forests compared to prior

research. Correction of the intensity values contributed up to a maximum of 58%

improvement in volume estimation when compared to the use of uncorrected intensity

values (R2 = 0.78, R2 = 0.53, and R2 = 0.63 for total, canopy, and integrated lidar

distributional metrics, respectively). It is clear that the correction of intensity values is

an essential step for the estimation of forest volume.