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Forests 2015, 6(11), 4034-4054; doi:10.3390/f6114034

A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario

1
Forest Analysis Ltd., 1188 Walker Lake Dr., RR4, Huntsville, ON P1H 2J6, Canada
2
Ontario Ministry of Natural Resources and Forestry, Forest Resource Inventory Unit, 3301 Trout Lake Road, North Bay, ON P1A 4L7, Canada
3
Natural Resources Canada, Canadian Wood Fibre Centre, Canadian Forest Service, 1219 Queen Street. East, Sault Ste. Marie, ON P6A 2E5, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Joanne C. White
Received: 24 August 2015 / Revised: 29 October 2015 / Accepted: 30 October 2015 / Published: 9 November 2015
(This article belongs to the Special Issue Image-Based Point Clouds for Forest Inventory Applications)
View Full-Text   |   Download PDF [1886 KB, uploaded 9 November 2015]   |  

Abstract

Airborne Laser Scanning (ALS) metrics have been used to develop area-based forest inventories; these metrics generally include estimates of stand-level, per hectare values and mean tree attributes. Tree-based ALS inventories contain desirable information on individual tree dimensions and how much they vary within a stand. Adding size class distribution information to area-based inventories helps to bridge the gap between area- and tree-based inventories. This study examines the potential of ALS and stereo-imagery point clouds to predict size class distributions in a boreal forest. With an accurate digital terrain model, both ALS and imagery point clouds can be used to estimate size class distributions with comparable accuracy. Nonparametric imputations were generally superior to parametric imputations; this may be related to the limitation of using a unimodal Weibull function on a relatively small prediction unit (e.g., 400 m2). View Full-Text
Keywords: airborne laser scanning (ALS); LiDAR; forest inventory; image point cloud (IPC); semi-global matching (SGM); diameter distribution; parametric; nonparametric airborne laser scanning (ALS); LiDAR; forest inventory; image point cloud (IPC); semi-global matching (SGM); diameter distribution; parametric; nonparametric
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Penner, M.; Woods, M.; Pitt, D.G. A Comparison of Airborne Laser Scanning and Image Point Cloud Derived Tree Size Class Distribution Models in Boreal Ontario. Forests 2015, 6, 4034-4054.

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