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

Forests

Margaret Penner²  Murray Woods¹  Douglas G. Pitt³ 

[1] Ontario Ministry of Natural Resources and Forestry, Forest Resource Inventory Unit, 3301 Trout Lake Road, North Bay, ON P1A 4L7, Canada; E-Mail:;Forest Analysis Ltd., 1188 Walker Lake Dr., RR4, Huntsville, ON P1H 2J6, Canada;Natural Resources Canada, Canadian Wood Fibre Centre, Canadian Forest Service, 1219 Queen Street. East, Sault Ste. Marie, ON P6A 2E5, Canada; E-Mail:

关键词: airborne laser scanning (ALS);    LiDAR;    forest inventory;    image point cloud (IPC);    semi-global matching (SGM);    diameter distribution;    parametric;    nonparametric;   

DOI  :  10.3390/f6114034

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.