The advancement of LiDAR technology has enabled more detailed evaluations of forest structures. The so-called “Volumetric pixel (voxel)” has emerged as a new comprehensive approach. The purpose of this study was to estimate plot-level above-ground biomass (AGB) in different plot sizes of 20 m × 20 m and 30 m × 30 m, and to develop a regression model for AGB prediction. Both point cloud-based (PCB) and voxel-based (VB) metrics were used to maximize the efficiency of low-density LiDAR data within a dense forest. Multiple regression model AGB prediction performance was found to be greatest in the 30 m × 30 m plots, with R2, adjusted R2, and standard deviation values of 0.92, 0.87, and 35.13 Mg∙ha−1, respectively. Five out of the eight selected independent variables were derived from VB metrics and the other three were derived from PCB metrics. Validation of accuracy yielded RMSE and NRMSE values of 27.8 Mg∙ha−1 and 9.2%, respectively, which is a reasonable estimate for this structurally complex intact forest that has shown high NRMSE values in previous studies. This voxel-based approach enables a greater understanding of complex forest structure and is expected to contribute to the advancement of forest carbon quantification techniques.