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Open AccessArticle

Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling

1
Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada
2
Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Eric J. Jokela
Forests 2015, 6(8), 2608-2630; https://doi.org/10.3390/f6082608
Received: 5 June 2015 / Revised: 7 July 2015 / Accepted: 24 July 2015 / Published: 31 July 2015
Information on individual tree attributes is important for sustainable management of forest stands. Airborne Laser Scanning (ALS) point clouds are an excellent source of information for predicting a range of forest stand attributes, with plot and single tree volume being among the most important. Two approaches exist for estimating volume: area-based approach (ABA) and individual tree detection (ITD). The ABA is now routinely applied in operational forestry applications, and results in generalized plot- or stand-level attribute predictions. Alternatively, ITD-based estimates provide detailed information for individual trees, but are typically biased due to challenges associated with individual tree detection. In this study, we applied an ABA to estimate tree counts and individual tree volumes by downscaling plot-level predictions of total volume derived using ALS data in a highly productive and complex coastal temperate forest environment in British Columbia, Canada, characterized by large volumes and multi-species and multi-age stand structures. To do so, a two-parameter Weibull probability density function (PDF) was used to describe the within-plot tree volume distribution. The ABA approach was then used to model the total plot volume and the two Weibull PDF parameters. Next, the parameters were used to calculate mean tree volume and derive the number of trees and the individual tree volume distribution. Tree count estimates were minimally biased with RMSE of 149 trees·ha−1 or 24.4%. The volume distributions showed good agreement with reference data (mean Reynold’s error index = 71.7). We conclude that the approach was suitable for enriching ABA-derived forest stand attributes in the majority of the studied forest stands; however the accuracy was lower in multi-layered stands that had a multimodal individual tree volume distribution. View Full-Text
Keywords: lidar; airborne laser scanning; volume; downscaling; Weibull; individual tree distributions; tree lists; remote sensing lidar; airborne laser scanning; volume; downscaling; Weibull; individual tree distributions; tree lists; remote sensing
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MDPI and ACS Style

Tompalski, P.; Coops, N.C.; White, J.C.; Wulder, M.A. Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling. Forests 2015, 6, 2608-2630. https://doi.org/10.3390/f6082608

AMA Style

Tompalski P, Coops NC, White JC, Wulder MA. Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling. Forests. 2015; 6(8):2608-2630. https://doi.org/10.3390/f6082608

Chicago/Turabian Style

Tompalski, Piotr; Coops, Nicholas C.; White, Joanne C.; Wulder, Michael A. 2015. "Enriching ALS-Derived Area-Based Estimates of Volume through Tree-Level Downscaling" Forests 6, no. 8: 2608-2630. https://doi.org/10.3390/f6082608

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