Canadian Forest Service Publications

Forest site and type variability in ALS-based forest resource inventory attribute predictions over three Ontario forest sites. 2019. van Ewijk, K.; Treitz, P.; Woods, M.; Jones, T.; Caspersen, J. Forests 10(3): 226.

Year: 2019

Issued by: Great Lakes Forestry Centre

Catalog ID: 39821

Language: English

Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.3390/f10030226

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Plain Language Summary

Over the last decade, spatially-explicit modeling of landscape-scale forest attributes for forest inventories has greatly benefitted from airborne laser scanning (ALS) and the area-based approach (ABA) to derive wall-to-wall maps of these forest attributes. Which ALS-derived metrics to include when modeling forest inventory attributes, and how prediction accuracies vary over forest types depends largely on the structural complexity of the forest(s) being studied. Hence, the purpose of this study was to (i) examine the usefulness of adding texture and intensity metrics to height-based ALS metrics for the prediction of several forest resource inventory (FRI) attributes in one boreal and two Great Lakes, St. Lawrence (GLSL) forest region sites in Ontario and (ii) quantify and compare the site and forest type variability within the context of the FRI prediction accuracies.