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Quantifying T ropical D ry F orest T ype and S uccession: S ubstantial I mprovement with LiDAR
Author(s) -
Martinuzzi Sebastián,
Gould William A.,
Vierling Lee A.,
Hudak Andrew T.,
Nelson Ross F.,
Evans Jeffrey S.
Publication year - 2013
Publication title -
biotropica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.813
H-Index - 96
eISSN - 1744-7429
pISSN - 0006-3606
DOI - 10.1111/j.1744-7429.2012.00904.x
Subject(s) - forest structure , vegetation (pathology) , canopy , tropical and subtropical dry broadleaf forests , dry forest , remote sensing , environmental science , geography , forestry , ecology , biology , medicine , pathology
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 ( L i DAR ) 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 L i DAR ‐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 G uánica B iosphere R eserve in P uerto R ico. We compared the L i DAR ‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the L i DAR ‐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 L i DAR ‐derived metrics of canopy structure and topography, and adding L andsat spectral data did not improve the classification. For the second objective, we observed that L i DAR ‐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 L i DAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of L i DAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.