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    Author(s): William D. Smith; Barbara L. Conkling
    Date: 2004
    Source: Gen. Tech. Rep. SRS-77. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 33 p.
    Publication Series: General Technical Report (GTR)
    Station: Southern Research Station
    PDF: View PDF  (3.0 MB)

    Description

    This report focuses on the Forest Health Monitoring Program’s development and use of analytical procedures for monitoring changes in forest health and for expressing the corresponding statistical confidences. The program’s assessments of long-term status, changes, and trends in forest ecosystem health use the Santiago Declaration: “Criteria and Indicators for the Conservation and Sustainable Forest Management of Temporate and Boreal Forests” (Montreal Process) as a reporting framework. Procedures used in five aspects of data analysis are presented. The analytical procedures used are based on mixed estimation procedures. Examples using the indicators are included, along with a clear link to the analytical procedures used (1) estimating change over time within groups—estimation of growth, harvest, mortality, and crown condition; (2) testing for differences in change over time among groups—foliar transparency; (3) estimating change using covariates—impact of drought on change in foliar transparency; (4) estimating plot values for unmeasured years—comparison of observed and predicted (Best Linear Unbiased Predictions) values of foliar transparency, dieback, and total volume; and (5) estimating tree heights—examples of using estimated tree heights to estimate tree volume.

    Publication Notes

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    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    Smith, William D.; Conkling, Barbara L. 2004. Analyzing forest health data. Gen. Tech. Rep. SRS-77. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 33 p.

    Keywords

    Assessment, BLUP, change estimation, mixed models, monitoring, tree height

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