Canadian Forest Service Publications

Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching. 2016. Tompalski, P.; Coops, N.C.; White, J.C.; Wulder, M.A. Forests, 7, 255.

Year: 2016

Available from: Pacific Forestry Centre

Catalog ID: 37645

Language: English

CFS Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.3390/f7110255

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Accurate information on both the current stock and future growth and yield of forest resources is critical for sustainable forest management. We demonstrate a novel approach to utilizing airborne laser scanning (ALS)-derived forest stand attributes to determine future growth and yield of six attributes at a sub-stand (25 m grid cell) level of detail: dominant height (HMAX), Lorey’s height (HL), quadratic mean diameter (QMD), basal area (BA), whole stem volume (V), and trees per hectare (TPH). The approach is designed to find the most appropriate matching yield curve and project the attributes to the age of 80 years. Comparisons to conventional plot-level projections resulted in relative mean differences of 13.4% (HMAX),

Plain Language Summary

The novel contribution of this study is in the application of growth and yield models at the cell level, combined with the use of airborne laser scanning-derived attributes to optimize yield curve selection via template matching. The benefits of the approach include improved within-stand spatial detail, optimization of yield curve selection, and the capacity to incorporate spatial uncertainty into stand-level estimates of projected attributes of interest.
We utilize an existing forest growth model to generate a comprehensive database of yield curves (templates) for all possible combinations of dominant species, site index, age, and canopy cover. Then, for each of our six attributes, we demonstrate an approach to find the most appropriate matching yield curves from all possible templates, and subsequently demonstrate the projection of these six attributes at 80 years of age. Differences were driven mostly by stand-level age and site index values that were used in the cell-based modelling, which were derived from conventional stand-level forest inventory data. Uncertainty of cell-level yield curve assignment (which decreased with increasing distance from the stand boundary) was used to refine stand-level summaries—an important consideration for stand-based forest management.

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