The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns.
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