Modeling Mediterranean forest structure using airborne laser scanning data

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  • Francesca Bottalico
  • Gherardo Chirici
  • Raffaello Giannini
  • Salvatore Mele
  • Matteo Mura
  • Michele Puxeddu
  • Ronald E. McRobert
  • Ruben Valbuena
    University of Eastern Finland
  • Davide Travaglini
The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airbdrne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters. Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using thb echo -based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable resultS. Accuracies were greatest for dominant height (Hd) (R-2= 0.91; RMSE 8.2 and mean height weighted by basal area (R-2 = 0.83; RMSE 10.5 when using the echo -based metrics, 99th percentile of the echo height distribution And interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd Was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator With standard error of 0.10 m was considerable more precise than the SRS estimator With standard error of 0.69 m. (C) 2016 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)145-153
Number of pages9
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume57
DOIs
Publication statusPublished - 31 May 2017
Externally publishedYes
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