Journal
REMOTE SENSING OF ENVIRONMENT
Volume 114, Issue 4, Pages 700-712Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2009.11.007
Keywords
Airborne laser scanner; Replication variance; Errors-in-variables; Reliability ratio; Attenuation; Calibration; Generalized least squares; Change-point
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To increase the application domain (re-use) of LiDAR-based models the random replication effects in the predictor(s) must be considered. We quantify these effects in a linear predictor (X) of four forest inventory attributes (Lorey's height HT, basal area BA, volume VOL, and stem density TPH) with LiDAR data acquired over 40 spruce-dominated large plots in southeastern Norway. A grid-based random thinning of the raw multi-echo LiDAR data, to five target densities between 0.25 m(-2) and 2.0 m(-2), generated 100 replications with each density. A DTM was estimated for each replicate and target pulse density. The four linear predictors were constructed from two indicators of canopy density and a posited average effect of a power-transform of echoes classified as canopy returns. Replication variance varied significantly among plots but the reliability ratio of X was high (>= 0.92) for HT, BA and VOL but lower for TPH, especially at low pulse densities. Reliability ratios increased with pulse density. Replication variance attenuated the linear regression coefficients by about 10% and inflated the residual variance by 3-6%. A proposed calibration was effective in reducing the impact of replication effects. A proposed bootstrap procedure can be used in practice to obtain good approximations of the replication variance. With echo-densities of approximately 1 m(-2) or higher the replication effects do not warrant the effort of a calibration. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.
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