Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR. (24th July 2012)

• Record Type: Journal Article
• Authors: Martinuzzi, Sebastián; Gould, William A.; Vierling, Lee A.; Hudak, Andrew T.; Nelson, Ross F.; Evans, Jeffrey S.
• Is Part Of: Biotropica. Volume 45:Number 2(2013:Mar.)
• Page Start: 135
• Page End: 146
• Publication Date: 2012-07-24
• Subjects: Biotic communities -- Tropics -- Periodicals; Applied ecology -- Tropics -- Periodicals; Biology -- Tropics -- Periodicals; 577.80913
• DOI: 10.1111/j.1744-7429.2012.00904.x
• Languages: English
• ISSNs: 0006-3606

Abstract

<abstract abstract-type="main" xml:lang="en" id="btp904-abs-0001"> <title>Abstract</title> <p>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, <italic>i.e., </italic> 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 LiDAR‐based 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 (<italic>i.e., </italic> 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.</p> </abstract>