Abstract. Mapping and monitoring forest resources require collection of spatially explicit and timely remote sensing (RS) data. Although field measurements are still important, the RS-based forest inventory helps mapping large areas to be cheaper, faster, less labor intensive, and spatially more explicit. The single-photon laser (SPL) scanning data has been exploited for different forestry applications but lacks deep examination in mapping individual trees as well as being compared with ordinary laser scanning (Linear-mode, LML) data and different individual tree detection (ITD) methods. Hence, this research focuses on applying and comparing two datasets (SPL and LML) for extracting attributes of individual trees by applying two tree crown segmentation methods (local maxima and watershed segmentation) on both datasets. The results were validated over 49 field measured plots of different species, located in southern boreal forest.The SPL yielded more accurate results for both tree density and height estimation. Watershed segmentation method yielded more accurate results for tree density and height estimation in both LML and SPL datasets. Tree density was underestimated by 4.7% (rRMSE: 32.3%) for all species. Comparing tree density estimation of different species, it was most accurate in deciduous plots (rBias: −9.5, rRMSE: 17.0%). Tree height estimation with SPL explained the variations of field-measured height very well (R2 = 0.93), and was reliably accurate, underestimated by 3.4% (rRMSE: 7.0%). The mean tree height estimation was most accurate in pine plots (rBias: 4.9%, rRMSE: 1.1%). In this research, SPL represented reliable and usable point cloud data for estimating tree height and density.