Canadian Forest Service Publications
Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data. Luther, J.E.; Skinner, R.; Fournier, R.A.; van Lier, O.R.; Bowers, W.W.; Côté, J.-F.; Hopkinson, C.; Moulton, T. 2013. Forestry 87: 313-326. doi: 10.1093/forestry/cpt039
Available from: Atlantic Forestry Centre
Catalog ID: 35276
CFS Availability: PDF (request by e-mail)
The objective of this study was to determine whether a suite of wood quantity and quality attributes of balsam fir and black spruce forests could be predicted using airborne laser scanner data. In situ estimates of stand structure and wood fibre attributes were derived from measurements at sample plots covering a wide range of forest conditions of insular Newfoundland. Models developed to predict field estimates explained 52–90 per cent of the variation in structure attributes, including mean and quadratic mean diameter at breast height, average and dominant height, stem density, basal area, total and merchantable volume and above-ground total biomass. Cross validated root mean square errors were ,24 per cent of mean values, with the exception of stem density, for which errors were 27–32 per cent. Models of fibre attributes explained 18–53 per cent of the variation in fibre length, wood density, radial diameter, coarseness, microfibril angle, modulus of elasticity, wall thickness and specific surface with cross-validated root mean square errors of ,14 per cent of mean values. Similar results were achieved for fibre attribute models derived using geographic, climate and vegetation variables. The results demonstrate potential for inventory of quantity and quality attributes over a large region of boreal forests in Newfoundland, Canada.
Plain Language Summary
Forest industry and forest managers require new and enhanced inventory information on which to base forest management decisions. For example, more precise information on tree- and stand-level structure is important for habitat and biodiversity studies, whereas information on attributes related to the quality of wood fiber, such as wood density and fiber length, is needed to expand the economic benefits from wood raw materials and to optimize forest value. This paper assesses the capability to predict a suite of wood quantity and quality attributes of balsam fir and black spruce forest using airborne laser scanning (ALS), also known as LiDAR (Light Detection And Ranging). The paper quantifies statistical relationships between ALS data and forest attributes measured at field plots and demonstrates the predictive capacity of ALS data to map forest attributes across landscapes. The results demonstrate the potential for inventory of quantity and quality attributes over a large region of boreal forests in Newfoundland, Canada.
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