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    Author(s): Gavin R. Corral; Harold E. Burkhart
    Date: 2016
    Source: In: Proceedings of the 18th biennial southern silvicultural research conference. e-Gen. Tech. Rep. SRS-212. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 614 p.
    Publication Series: General Technical Report (GTR)
    Station: Southern Research Station
    PDF: Download Publication  (541.0 KB)

    Description

    We used computer simulations to test the efficacy of multivariate statistical methods to detect, quantify, and map spatial variability of forest stands. Simulated stands were developed of regularly-spaced plantations of loblolly pine (Pinus taeda L.). We assumed no affects of competition or mortality, but random variability was added to individual tree characteristics. The purpose of simulating stands without these complex interactions was to provide a controlled situation to measure the efficacy of our methods. We examined redundancy analysis, partial redundancy analysis, and spatially constrained cluster analysis for detecting spatial patterns and found that redundancy analysis and partial redundancy analysis were reliable methods to quantify and test spatial dependence, respectively. Spatially constrained cluster analysis had moderate success in mapping variability, but its application to more complex situations may be limited.

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    Citation

    Corral, Gavin R.; Burkhart, Harold E. 2016. Quantifying and mapping spatial variability in simulated forest plots. In: Proceedings of  the 18th biennial southern silvicultural research conference. e-Gen. Tech. Rep. SRS-212. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 5 p.

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