Simulating the impacts of error in species and height upon tree volume derived from airborne laser scanning data

A key requirement of sustainable forest management is accurate, timely, and comprehensive information on forest resources, which is provided through forest inventories. In Canada, forest inventories are conventionally undertaken through the delineation and interpretation of forest stands using aerial photography, supported by data from permanent and temporary sample plots. In recent years, Airborne Laser Scanning (ALS) data have been shown to provide accurate estimates of a range of forest structural attributes. As a result, ALS has emerged as an increasingly common data source for enhanced forest inventory programs. Capture of species compositional information with ALS, based upon the nature of the data, is less reliable than structural variables, with species information typically derived from spectral or textural interpretation of aerial photography or very high spatial resolution digital imagery. Utilizing national allometric equations for the major species found in British Columbia, Canada, and a series of individual tree-level simulations, we analyzed (i) how incorrect species identification can influence individual tree volume prediction; (ii) which of the possible species substitutions result in higher volume errors; (iii) how the error in height that is typical for photogrammetry-based and ALS-based forest inventories impacts individual tree volume estimates; and (iv) the impact of combined errors in both species composition and height on overall individual tree volume estimates. Our results indicate that species information is important for volume calculations, and that the use of generic (i.e. all species) or cover-type allometric equations can lead to large errors in volume estimates. We also found that, even with a 50% error in species composition (whereby incorrect species-specific equations are substituted), volume estimates derived from species-specific allometric equations were more accurate than estimates derived from generic or cover-type equations. Our findings indicate that errors in species composition have less impact on individual tree volume estimates than errors in height measurement. The implications of these results are that, with very accurate estimates of height provided by ALS and knowledge of what dominant species is expected in a stand, accurate estimates of volume can be generated in the absence of more detailed species composition information. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.