Forest Stand Volume of Sitka Spruce Plantations in Britain: Can Existing Laser Scanning Methods Based on the Conventional One Provide Better Results, a Comparison of Two Approaches

Abstract

This paper looks at different datasets obtained from an airborne Light Detection And Ranging (LiDAR) system and compares the reliability of two contemporary analysis approaches. Estimates of different stand parameters, such as top tree height, were derived using regression analysis and a segmentation approach on data obtained from small-footprint laser scan were contrasted with the field measurements in 7 plots, specifically volume and basal area. Plots of 2,500m² containing plantations of Sitka spruce (Picea sitchensis Bong. Carr.) were scanned with two different point densities in years 2003 and 2004. These plots were divided into training and test regions of 625 m² each. Regression analysis was performed using percentiles corresponding to the canopy tree height at different vertical levels and a segmentation method was used to delineate individual tree crowns where tree metrics can be determined. The bias of the estimated values for the stand volume and basal area ranged from 1.21 to 6.49 m³ha^-1 (0.17 to 0.92 %) and - 2.69 to 1.23 m²ha^-1(- 3.9 to 1.7 %), respectively; and the bias calculated from the segmentation using 0.5 and 1m dataset ranged between - 349.77 to - 434.76 m³ha^-1 (- 49.7 to - 61.8 %) for the stand volume and - 33.36 to - 42.24 m²ha^-1 (- 48.5 to - 61.4 %) for the basal area. The results showed 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.