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Determining Stand Value and Log Product Yields Using Terrestrial Lidar and Optimal Bucking: A Case Study

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Nine plots in three Douglas-fir stands of different tree sizes were scanned using terrestrial lidar systems. Tree locations in each plot and their stem profiles were automatically detected using commercially available software. Actual stem profile measurements were made on all trees after felling. Stems were optimally bucked based on log specifications and prices for two log markets: the western United States and New Zealand. Stand values and log product yields were estimated for the terrestrial lidar-derived data and compared with estimates based on the actual stem profiles. Stand and undergrowth density and tree size affected the accuracy of automated stem detection and stem profile measurements. After manual adjustments for stem quality and height, lidar-derived estimates of average stand value and log product yields were within 7% of actual estimates. Differences were noted between stand types and markets. Suggestions for future research are provided.

Keywords: Douglas-fir; inventory; manual and automated methods; terrestrial laser scanning

Document Type: Research Article

Publication date: September 1, 2008

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  • The Journal of Forestry is the most widely circulated scholarly forestry journal in the world. In print since 1902, the Journal has received several national awards for excellence. The mission of the Journal of Forestry is to advance the profession of forestry by keeping forest management professionals informed about significant developments and ideas in the many facets of forestry: economics, education and communication, entomology and pathology, fire, forest ecology, geospatial technologies, history, international forestry, measurements, policy, recreation, silviculture, social sciences, soils and hydrology, urban and community forestry, utilization and engineering, and wildlife management. The Journal is published bimonthly: January, March, May, July, September, and November.

    2015 Impact Factor: 1.476
    Ranking: 22 of 66 in forestry

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