Canadian Forest Service Publications
Stability of surface LiDAR height estimates on a point and polygon basis. 2008. Wulder, M.A.; Magnussen, S.; Harding, D.; Coops, N.C.; Boudewyn, P.A.; Seemann, D. Journal of Forest Planning 13: 279-286.
Issued by: Pacific Forestry Centre
Catalog ID: 28147
Availability: PDF (download)
Airborne scanning LiDAR (Light Detection and Ranging) data has significant potential to update, audit, calibrate, and validate operational stand-level forest inventories by providing information on canopy height, vertical structure, and ground elevation. However, using LiDAR data as an operational data source in a sampling context requires repeatable and consistent attribute estimation (i.e. height), from data collected over several acquisition flight lines. We examined the consistency of LiDAR height estimates obtained from the Scanning LiDAR Imager of Canopies by Echo Recovery (SLICER) instrument over Jack pine (Pinus banksiana, var. Lamb) and black spruce (Picea mariana, var. Mill.) forest stands in central Saskatchewan, Canada. Two analyses were undertaken: first, estimated tree heights derived from pairs of LiDAR returns, acquired from multiple flight lines and within 9m of a single LiDAR footprint, were compared to assess the consistency of height estimates (point stability); secondly, height estimates from multiple flight lines within individual forest inventory polygons were compared to assess the consistency of within-polygon estimates of tree height (polygon stability). The point stability analysis indicated that over all forest classes estimates of height were consistent, with 94% of LiDAR returns (n = 15,896) having a pair-wise height difference within ± 5m. On a polygon basis, both between- and within-flight line standard deviations were considered. Results indicated that the within-polygon variability in estimated tree heights was captured by LiDAR data collected over any portion of a polygon. This result suggests that the inventory polygons are homogenous with regards to height (and related variability) and may be characterized with LiDAR, independent of actual flight path.