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): Sebastian Martinuzzi; William A. Gould; Lee A. Vierling; Andrew T. Hudak; Ross F. Nelson; Jeffrey S. Evans
    Date: 2012
    Source: Biotropica.
    Publication Series: Scientific Journal (JRNL)
    Station: International Institute of Tropical Forestry
    PDF: View PDF  (507.39 KB)

    Description

    Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world’s most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR-derived measures of three-dimensional canopy structure and subcanopy topography using classification-tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDARbased results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar-based topographic data. The accuracy of the LiDAR-based forest type classification (including native- and exotic-dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR-derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR-derived variables of vegetation structure were better predictors of forest successional status (i.e., mid-secondary, late-secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification-tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.

    Publication Notes

    • 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

    Martinuzzi, Sebastian; Gould, William A.; Vierling, Lee A.; Hudak, Andrew T.; Nelson, Ross F.; Evans, Jeffrey S. 2012. Quantifying tropical dry forest type and succession: substantial improvement with LiDAR. Biotropica. 10.1111/j.1744-7429.2012.00904.x

    Cited

    Google Scholar

    Keywords

    ALS, biodiversity, land-use legacy, secondary forests, vegetation structure

    Related Search


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