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): Yanjun Su; Qinghua Guo; Danny L. Fry; Brandon M. Collins; Maggi Kelly; Jacob P. Flanagan; John J. Battles
    Date: 2016
    Source: Canadian Journal of Remote Sensing
    Publication Series: Scientific Journal (JRNL)
    Station: Pacific Southwest Research Station
    PDF: Download Publication  (777.0 KB)

    Description

    Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.

    Publication Notes

    • You may send email to psw_communications@fs.fed.us 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.

    Citation

    Su, Yanjun; Guo, Qinghua; Fry, Danny L.; Collins, Brandon M.; Kelly, Maggi; Flanagan, Jacob P.; Battles, John J. 2016.A vegetation mapping strategy for conifer forests by combining airborne LiDAR data and aerial imagery. Canadian Journal of Remote Sensing. 42(1): 1-15.http://dx.doi.org/10.1080/07038992.2016.1131114

    Cited

    Google Scholar

    Keywords

    Vegetation mapping, lidar, aerial imagery, forest structure, mixed conifer forest

    Related Search


    XML: View XML
Show More
Show Fewer
Jump to Top of Page
https://www.fs.usda.gov/treesearch/pubs/54816