Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Ronald E. McRoberts; Erik Næsset; Terje Gobakken
    Date: 2013
    Source: Remote Sensing of Environment. 128: 268-275.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: View PDF  (362.7 KB)

    Description

    Estimates of growing stock volume are reported by the national forest inventories (NFI) of most countries and may serve as the basis for aboveground biomass and carbon estimates as required by an increasing number of international agreements. The probability-based (design-based) statistical estimators traditionally used by NFIs to calculate estimates are generally unbiased and entail only limited computational complexity. However, these estimators often do not produce sufficiently precise estimates for areas with small sample sizes. Model-based estimators may overcome this disadvantage, but they also may be biased and estimation of variances may be computationally intensive. For a minor region within Hedmark County, Norway, the study objective was to compare estimates of mean forest growing stock volume per unit area obtained using probability- and model-based estimators. Three of the estimators rely to varying degrees on maps that were constructed using a nonlinear logistic regression model, forest inventory data, and lidar data. For model-based estimators, methods for evaluating quality of fit of the models and reducing the computational intensity were also investigated. Three conclusions were drawn: the logistic regression model exhibited no serious lack of fit to the data; estimators enhanced using maps produced greater precision than estimates based on only the plot observations; and third, model-based synthetic estimators benefit from sample sizes for larger areas when applied to smaller subsets of the larger areas.

    Publication Notes

    • Check the Northern Research Station web site to request a printed copy of this publication.
    • Our on-line publications are scanned and captured using Adobe Acrobat.
    • During the capture process some typographical errors may occur.
    • Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

    Citation

    McRoberts, Ronald E.; Næsset, Erik; Gobakken, Terje. 2013. Inference for lidar-assisted estimation of forest growing stock volume. Remote Sensing of Environment. 128: 268-275.

    Cited

    Google Scholar

    Keywords

    Nonlinear logistic regression model, stratified estimator, model-assisted estimator, model-based estimator

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/46118