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Recovering Tree Heights from Airborne Laser Scanner Data

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Airborne laser scanner data collected over forests provide a canopy height. To obtain tree heights from airborne laser scanner data one needs a recovery model. Two such models, one (A) assuming that observations are sampled with probability proportional to displayed crown area, and the other (B) derived from the probability that a laser beam penetrates to a given canopy depth, were developed and applied to laser scanner data obtained over stands of Douglas-fir. Model estimates of recovered arithmetic mean tree heights and quantiles (75%, 85%, and 95%) were not significantly (P> 0.24) different from ground-based equivalents. An overall mean bias of-3 m in the laser canopy heights was eliminated by both methods. The median absolute difference between observed and predicted plot means and quantiles were reduced by 40 to 60%. Three alternative recovery procedures are presented for model B. For a single plot, the predictions varied significantly among the models and estimation procedures with no consistent pattern. Predictions of arithmetic mean heights were best for plots with no understory, while predictions of upper quantiles were consistent in all plots. For. Sci. 45(3):407-422.

Keywords: Canopy height; EM algorithm; PPS sampling; Weibull distribution; crown area; deconvolution; error function; extreme value distribution

Document Type: Journal Article

Affiliations: Assoc. Professor, Department of Mathematical Sciences, University of Delaware, Newark, DE 19716

Publication date: August 1, 1999

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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