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    Author(s): Matteo Mura; Ronald E. McRoberts; Gherardo Chirici; Marco Marchetti
    Date: 2015
    Source: Remote Sensing of Environment
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (1.0 MB)

    Description

    Among the wide array of terrestrial habitats, forest andwooded lands are the richest fromboth biological and genetic points of view because of their inherent structural and compositional complexity and diversity. Although species composition is an important biodiversity feature, forest structuremay be evenmore relevant for biodiversity assessments because a diversified structure is likely to have more niches, which in turn, host more species and contribute to a more efficient use of available resources. Structure plays a major role as a diversity indicator formanagement purposes where maps of forest structural diversity are of great utility when planning conservation strategies. Airborne laser scanning (ALS) data have been demonstrated to be a reliable and valid source of information for describing the three-dimensional structure of forests. Using ALS metrics as predictor variables, we developed regression models for predicting indices of forest structural diversity for a study area in Molise, Italy. The study had two primary objectives: (i) to estimate indices of structural diversity for the entire study area, and (ii) to construct maps depicting the spatial pattern of the structural diversity indices. Our results demonstrate the utility of simple linear models using ALS data for improving areal estimates of mean structural diversity, and the resulting maps capture the patterns of structural diversity in the study area.

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    Citation

    Mura, Matteo; McRoberts, Ronald E.; Chirici, Gherardo; Marchetti, Marco. 2015. Estimating and mapping forest structural diversity using airborne laser scanning data. Remote Sensing of Environment. 170: 133-142. https://doi.org/10.1016/j.rse.2015.09.016.

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    Keywords

    Airborne laser scanning, Forest structural diversity, GREG estimator, Model-assisted estimator

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