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Open Access Open Badges Research article

Modelling variation in Pinus radiata stem volume and outerwood stress-wave velocity from LiDAR metrics

Michael S Watt1*, Thomas Adams2, Hamish Marshall3, David Pont4, John Lee4, David Crawley5 and Pete Watt6

Author Affiliations

1 Scion, PO Box 29237, Fendalton, Christchurch, New Zealand

2 MetService, 30 Salamanca Road, Kelburn, Wellington, 6021, New Zealand

3 Interpine Forestry Ltd., 99 Sala Street, Rotorua, New Zealand

4 Scion, Private Bag 3020, Rotorua, New Zealand

5 PF Olsen, PO Box 1127, Rotorua, New Zealand

6 Indufor Asia-Pacific Ltd, PO Box 105039, Auckland, New Zealand

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New Zealand Journal of Forestry Science 2013, 43:1  doi:10.1186/1179-5395-43-1

Published: 13 February 2013



Light Detection and Ranging (LiDAR) is an established technology that has been shown to provide accurate information on individual-tree and stand-level forest structure. Although LiDAR has been widely used to describe stand structural dimensions the utility of this technology to predict spatial variation in wood quality traits is largely unexplored. This study used LiDAR metrics to predict spatial variation in total stem volume (TSV) and outerwood stress-wave velocity (V) in an even-aged mature forest (25 yrs) of moderate size (stocked area of 217.8 ha). Outerwood stress-wave velocity is a good predictor of modulus of elasticity which is a key performance criterion for structural timber.


Linear and non-linear models were developed to predict TSV and V. Models of TSV were developed from the full dataset that included 163 plots while models of V were developed from a subset of 32 plots in which V had been measured.


The best statistical models that included only LiDAR data, explained 60% and 37% of the variation in TSV and V, respectively. Addition of measured stand density to both models significantly improved the R2 to, respectively, 0.76 and 0.70 for TSV and V. The root-mean square error for the final models of TSV and V were, respectively, 64.0 m3 ha-1 and 0.086 km s-1.


At the forest level LiDAR metrics were found to be useful for predicting both V and TSV. Further research should examine the link between LiDAR metrics and V across broader ranges of V to confirm these findings.

ALS; Aerial laser scanning; Modulus of elasticity; Pinus radiata; Radiata pine