This paper looks at different datasets obtained from a recent Light Detection And Ranging (LiDAR) system acquisition and compares the reliability of two contemporary analysis approaches. Estimates of different tree variables, such as tree height, that were derived using regression analysis and a segmentation approach on data obtained via a small-footprint laser scan are contrasted with the field data for forest stand parameters, specifically volume and basal area. Plots of 2500 m2 containing plantations of Sitka spruce (Picea sitchensis Bong. Carr.) were scanned with two different point densities in years 2003 and 2004. These plots are divided into training and test regions of 625 m2 each. Regression analysis is performed using percentiles corresponding to the tree height at different vertical heights and the segmentation method is used to delineate individual tree crowns where tree metrics can be determined. Two commonly studied stand parameters are estimated with both of the above approaches and are then compared to the field data. The bias of the estimated values for the stand volume and basal area ranges from 1.21 to 6.49 m3ha-1 and - 2.69 to 1.23 m2ha-1, respectively; and the bias calculated from the segmentation ranges between - 434.76 to - 349.77 m3ha-1 for the stand volume and - 33.36 to - 42.24 m2ha-1 for the basal area. The achieved results show that the regression models estimated stand volume and basal more accurately compared with values calculated from the segmentation. Furthermore, it is shown that there was no significant difference in the estimates from the regression model when using different point densities.