Abstract
Light Detection and Ranging (LiDAR) data at 0.5–2 m postings were used with double-sample, stratified procedures involving single-tree relationships in mixed, and single species stands to yield sampling errors ranging from % to %. LiDAR samples were selected with focal filter procedures and heights computed from interpolated canopy and DEM surfaces. Tree dbh and height data were obtained at various ratios of LiDAR, ground samples for DGPS located ground plots. Dbh-height and ground-LiDAR height models were used to predict dbh and compute Phase 2 estimates of basal area and volume. Phase 1 estimates were computed using the species probability distribution from ground plots in each strata. Phase 2 estimates were computed by randomly assigning LiDAR heights to species groups using a Monte Carlo simulation for each ground plot. There was no statistical difference between volume estimates from 0.5 m and 1 m LiDAR densities. Volume estimates from single-phase LiDAR procedures utilizing existing tree attributes and height bias relationships were obtained with sampling errors of 1.8% to 5.5%.
Highlights
Light detection and ranging (LiDAR) is a relatively new remote sensing tool that has the potential for use in the acquisition of measurement data for inventories of standing timber
Since LiDAR has the capability to detect individual trees and measure tree height with predictable bias when correlated with ground measurements (Persson et al 2002, Holmgren 2004), strata-level inventory estimates involving individual tree, double-sample inventory procedure have been used by researchers from Mississippi State University in conifer and mixed hardwood stands in the Northwest and Southeast (Collins 2003, Parker and Evans 2004, Parker and Glass 2004, Parker and Mitchel 2005, Parker and Evans 2006, Williams 2006)
LiDAR provides precise x, y, and z coordinate data that can be used to extract tree heights and locations; there are several sources of bias that can impact the accuracy of a per-unit area volume estimate
Summary
Light detection and ranging (LiDAR) is a relatively new remote sensing tool that has the potential for use in the acquisition of measurement data for inventories of standing timber. LiDAR inventory procedures involving average values of tree attributes such as dominant height, mean diameter, basal area, and volume have been applied to obtain unbiased stand level predictions (Naesset 2002, Naesset 2004, Popescu et al 2002). The individual tree approach to stand inventory when combined with double-sample, ground procedures permits relatively precise estimates of volume with a simple prediction function for ground-LiDAR height bias and ground-based attribute relationship functions for tree diameter and total height which can be used with any standard, standing tree volume function. Stand level approaches involving average tree attribute values for sampling units require more sophisticated prediction models than an individual tree approach and procedures that differ radically from traditional ground-based inventory methods. The objective of this paper is to summarize and discuss the procedures, models, and advantages/disadvantages of the single-tree approach to using LiDAR data in double- and single-phase forest inventory methods
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