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A Model-Based Approach for Airborne Laser Scanning Inventory: Application for Square Grid Spatial Pattern

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Abstract:

Height of canopy surface can be defined as the distance from the ground level to the top of the canopy. Returns collected by airborne light detection and ranging instruments (airborne laser scanning [ALS]) are essentially observations of this height. In an earlier study we showed how the probability of having the canopy surface above a given height depends on the stand density, the tree heights, the characteristics of individual crowns, and the spatial pattern of tree locations. This work provided a theory to analyze the distribution of ALS observations. This study continues our earlier work, proposing a method to estimate stand density and tree height distribution using observations of canopy height in a stand with a square grid pattern of tree locations, which is typical in plantation forests. The fitting method is based on maximum likelihood, providing means for statistical inference on the estimates. A simulation study indicated good performance potential. An example fit to a laser-scanned Norway spruce sample plot indicated that the model might be applicable also to managed stands, which do not exactly follow the assumed grid pattern. A comparison with our earlier model, based on random tree locations, is presented to demonstrate the effects of the spatial pattern on the distribution of canopy heights and on the estimated characteristics.

Keywords: airborne laser scanning; forest inventory; germ-grain model; maximum likelihood; spatial pattern

Document Type: Research Article

DOI: https://doi.org/10.5849/forsci.10-023

Publication date: 2012-04-02

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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