Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables

Using lidar in an area-based model-assisted approach to forest inventory has the potential to increase estimation precision for some forest inventory variables. This study documents the bias and precision of a model-assisted (regression estimation) approach to forest inventory with lidar-derived auxiliary variables relative to lidar pulse density and the number of sample plots. For managed forests on the Lewis portion of the Lewis-McChord Joint Base (35 025 ha, 23 290 forested) in western Washington state, we evaluated a regression estimator for combinations of pulse density (.05-3 pulses/m(2)) and sample size (15-105 plots) to estimate five forest yield variables: basal area, volume, biomass, number of stems, and Lorey's height. The results indicate that there is almost no loss in precision in using as few as .05 pulses/m(2) relative to 3 pulses/m(2). We demonstrate that estimation precision declined quickly for reduced sample sizes (as expected from sampling theory); but of more importance we demonstrate that sample size has a dramatic effect on the validity of inferences. Our investigations indicate that for our test dataset that central limit theorem based confidence intervals were too small on average for sample sizes smaller than 55. The results from this study can aid in identifying design components for forest inventory with lidar which satisfy users' objectives.