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): Andrew T. Hudak; Nicholas L. Crookston; Jeffrey S. Evans; Michael K. Falkowski; Alistair M. S. Smith; Paul E. Gessler; Penelope Morgan
    Date: 2006
    Source: Canadian Journal of Remote Sensing. 32(2): 126-­138.
    Publication Series: Scientific Journal (JRNL)
    Station: Rocky Mountain Research Station
    PDF: Download Publication  (859.5 KB)


    We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived from discrete-return lidar data (2 m post spacing), advanced land imager (ALI) multispectral (30 m) and panchromatic (10 m) data, or geographic X, Y, and Z location. In general, the lidar-derived variables had greater utility than the ALI variables for predicting the response variables, especially basal area. The variables most useful for predicting basal area were lidar height variables, followed by lidar intensity; those most useful for predicting tree density were lidar canopy cover variables, again followed by lidar intensity. The best integrated models selected via a best-subsets procedure explained ~90% of variance in both response variables. Natural-logarithm-transformed response variables were modeled. Predictions were then transformed from the natural logarithm scale back to the natural scale, corrected for transformation bias, and mapped across the two study areas. This study demonstrates that fundamental forest structure attributes can be modeled to acceptable accuracy and mapped with currently available remote sensing technologies.

    Dataset for this publication

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • 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.


    Hudak, Andrew T.; Crookston, Nicholas L.; Evans, Jeffrey S.; Falkowski, Michael K.; Smith, Alistair M. S.; Gessler, Paul E.; Morgan, Penelope. 2006. Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral data. Canadian Journal of Remote Sensing. 32(2): 126-­138.


    regression modeling and mapping, lidar and multispectral data, discrete-return light detection and ranging, multispectral satellite imagery, basal area, tree density, advanced land imager, ALI

    Related Search

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