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    Author(s): Robert C. Parker; David L. Evans
    Date: 2009
    Source: In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H., eds. Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: U.S. Department of Agriculture, Forest Service. 317-324.
    Publication Series: General Technical Report (GTR)
    Station: Washington Office
    PDF: View PDF  (1.81 MB)

    Description

    Light Detection and Ranging (LIDAR) data at 0.5- to 2-m postings were used with doublesample, stratified inventory procedures involving single-tree attribute relationships in mixed, natural, and planted species stands to yield sampling errors (one-half the confidence interval expressed as a percentage of the mean) ranging from ±2.1 percent to ±11.5 percent at α=0.05. LIDAR sample trees were selected with focal filter procedures and heights were computed as the difference between interpolated canopy and digital elevation model surfaces. Tree diameter at breast height (d.b.h.) and height data were obtained on LIDAR ground samples ranging from a 5:1 ratio on 0.08-ha rectangular strips to a 10:1 ratio on 0.02-ha circular plots established with a real-time Differential Global Positioning System. D.b.h.-height and ground-LIDAR height models were used to predict d.b.h. from adjusted LIDAR height and compute phase 2 ground and LIDAR estimates of basal area and volume. Phase 1 LIDAR estimates were computed by randomly assigning heights to species classes using the probability distribution from ground plots in each inventory strata. Phase 2 LIDAR estimates were computed by randomly assigning heights to species-product groups using a Monte Carlo simulation for each ground plot. No statistical difference was present between doublesample, mean volume estimates from 0.5-m and 1-m LIDAR posting densities with and without height bias adjustment or on smoothed and unsmoothed LIDAR canopy surfaces. Volume estimates from single-phase LIDAR inventory procedures using existing tree attribute and LIDAR-ground height bias relationships were obtained with sampling errors of 1.8 percent to 5.5 percent for full and minimized data sets to test minimum LIDAR inventory requirements.

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    Citation

    Parker, Robert C.; Evans, David L. 2009. LIDAR forest inventory with single-tree, double- and single-phase procedures. In: McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.; McWilliams, William H., eds. Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16-19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: U.S. Department of Agriculture, Forest Service. 317-324.

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