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): Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi Kelly
    Date: 2013
    Source: Photogrammetric Engineering and Remote Sensing
    Publication Series: Scientific Journal (JRNL)
    PDF: Download Publication  (1.26 MB)


    We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and "50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management.

    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.


    Jakubowksi, Marek K.; Guo, Qinghua; Collins, Brandon; Stephens, Scott; Kelly, Maggi. 2013. Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest. Photogrammetric Engineering and Remote Sensing. 79(1): 37-49.

    Related Search

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