An application of LiDAR in a double-sample forest inventory

Abstract

Multireturn LiDAR data (2-m posting) were used in a double-sample forest inventory in central Idaho. Twenty-four 15-plot (0.2 ac) strips were established with a real-time Differential Global Positioning System. Tree dbh and height were measured on every 5th plot. Volume and basal area were computed for eight encountered species. LiDAR trees were selected with a focal max filter and height computed as the z-difference between interpolated canopy and DEM surfaces. LiDAR-derived trees/ac, height, and dbh had mean differences of -4.4 trees, -10.7 ft, and -1.01 in. from ground values. Four dbh-height models were fitted. Predicted dbh was used to compute LiDAR estimates of basal area and volume on 360 Phase 1 plots. Phase 2 LiDAR estimates on 60 plots were computed by randomly assigning heights to species classes using a 500-iteration Monte Carlo simulation. Regression estimators for Phase 2 ground and LiDAR ft3 and ft2 were computed by single and composite species. Phase 1 estimates were partitioned to obtain species volumes. The regression estimate of composite volume was partitioned by percent species distribution of trees, basal area, and volume. There was no statistical difference between individual and partitioned composite species estimates. Sampling error was ±11.5% on a mean volume estimate of 1,246 ft3/ac with standard error ±72.98 ft3/ac. © 2004 by the Society of American Foresters.