Canadian Forest Service Publications
A comparison of airborne laser scanning and image point cloud derived tree size class distribution models in boreal Ontario. 2015. Penner, M.; Woods, M.; Pitt, D.G. Forests 6(11):4034-4054.
Issued by: Great Lakes Forestry Centre
Catalog ID: 36509
Availability: PDF (download)
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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).
Plain Language Summary
Current Forest Resource Inventories in Ontario are designed to meet long-term (20 year) strategic management planning needs. Since harvesting operations have become more mechanized and processing facilities are increasingly optimized for particular products and sizes of raw materials, additional information on tree size assortment of stems would be useful. Such information would also enable better tactical (5-year) and operational (1-year) planning of forest operations. We investigated and compared the potential of Airborne Laser Scanning (ALS) and stereo image point clouds (IPC) measurements to predict size class distributions for a management area in a northeastern Ontario boreal forest. We found that with an accurate digital terrain model, both ALS and IPC could be used to estimate tree size class distributions with comparable accuracy.