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): Chad Babcock; Andrew O. Finley; John B. BradfordRandy KolkaRichard BirdseyMichael G. Ryan
    Date: 2015
    Source: Remote Sensing of Environment. 169: 113-127.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: View PDF  (3.0 MB)

    Description

    Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial randomeffects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

    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

    Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall; Birdsey, Richard; Ryan, Michael G. 2015. LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients. Remote Sensing of Environment. 169: 113-127.

    Cited

    Google Scholar

    Keywords

    Bayesian hierarchical models, Markov chain Monte Carlo, Gaussian process, Geospatial, LiDAR, Forest biomass

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page