Comparisons between field- and LiDAR-based measures of stand structural complexity

DOI Web Site Web Site 被引用文献3件
  • Van R. Kane
    School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA.
  • Robert J. McGaughey
    School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA.
  • Jonathan D. Bakker
    School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA.
  • Rolf F. Gersonde
    School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA.
  • James A. Lutz
    School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA.
  • Jerry F. Franklin
    School of Forest Resources, College of the Environment, University of Washington, Box 352100, Seattle, WA 98195, USA.

書誌事項

公開日
2010-04
権利情報
  • http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining
DOI
  • 10.1139/x10-024
公開者
Canadian Science Publishing

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説明

<jats:p> Forest structure, as measured by the physical arrangement of trees and their crowns, is a fundamental attribute of forest ecosystems that changes as forests progress through suc;cessional stages. We examined whether LiDAR data could be used to directly assess the successional stage of forests by determining the degree to which the LiDAR data would show the same relative ranking of structural development among sites as would traditional field measurements. We sampled 94 primary and secondary sites (19–93, 223–350, and 600 years old) from three conifer forest zones in western Washington state, USA, in the field and with small-footprint, discrete return LiDAR. Seven sets of LiDAR metrics were tested to measure canopy structure. Ordinations using the of LiDAR 95th percentile height, rumple, and canopy density metrics had the strongest correlations with ordinations using two sets of field metrics (Procrustes R = 0.72 and 0.78) and a combined set of LiDAR and field metrics (Procrustes R = 0.95). These results suggest that LiDAR can accurately characterize forest successional stage where field measurements are not available. This has important implications for enabling basic and applied studies of forest structure at stand to landscape scales. </jats:p>

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