Canadian Forest Service Publications

Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review. 2011. van Leeuwen, M.; Hilker, T.; Coops, N.C.; Frazer, G.W.; Wulder, M.A.; Newnham, G.J.; Culvenor, D.S. Forest Ecology and Management 261(9): 1467-1478

Year: 2011

Issued by: Pacific Forestry Centre

Catalog ID: 32284

Language: English

Availability: PDF (download)

Available from the Journal's Web site.
DOI: 10.1016/j.foreco.2011.01.032

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Accurate information on the wood-quality characteristics of standing timber and logs is needed to optimize the forest production value chain and to assess the potential of forest resources to meet other services. Physical and chemical characteristics of wood vary with both tree and site characteristics. At the tree scale, crown development, stem shape and taper, branch size and branch location, knot size, type and placement, and age all influence wood properties. More broadly, at the stand level, stocking density, moisture, nutrient availability, climate, competition, disturbance, and stand age have also been identified as key determinants of wood quality. Such information is often captured in polygon based forest inventory data. Other terrain-related spatial information, such as elevation, slope and aspect, can improve assessments of site conditions and limitations upon plant growth which impact wood quality. Light Detection And Ranging (LiDAR) is an emerging technology, which directly measures the three-dimensional structure of forest canopies using ground or airborne laser instruments, and can provide highly accurate information on individual-tree and stand-level forest structure. In this paper, we explore the potential of LiDAR and other geospatial information sources to model and predict wood quality based on individual-tree and stand structural metrics. We identify a number of key wood quality attributes (i.e., basic wood density, cell perimeter, cell coarseness, fiber length, and microfibril angle) and demonstrate links between these properties and forest structure and site attributes. Finally, the potential for using LiDAR in combination with other geospatial information sources to predict wood quality in standing timber is discussed.