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Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data

  • Manuel Arias-Rodil
  • Ulises Diéguez-Aranda
  • Juan Gabriel Álvarez-González
  • César Pérez-Cruzado
  • Fernando Castedo-Dorado
  • Eduardo González-Ferreiro
Original Paper
Part of the following topical collections:
  1. SilviLaser

Abstract

Key message

We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain).

Context

The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information.

Aims

The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in Pinus radiata D. Don stands in NW Spain.

Methods

The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables.

Results

The models used to estimate average (dm) and quadratic (dg) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight.

Conclusion

The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.

Keywords

PNOA (Plan Nacional de Ortofotografía Aérea de España) project Airborne laser scanning (ALS) Remote sensing Weibull Distribution function Moment-based parameter recovery method 

Notes

Funding information

Spanish Ministry of Science and Innovation (AGL2008-02259/FOR); Eduardo González-Ferreiro was financially supported by the Plan galego de investigación, innovación e crecemento 2011-2015 (Plan I2C) (Official Journal of Galicia - DOG nº 52, 17/03/2014 p. 11343, exp: POS-A/2013/049) - Galician Government (Dirección Xeral de Ordenación e Calidade do Sistema Universitario de Galicia - Consellería de Educación e Ordenación Universitaria) and European Social Fund. Manuel Arias-Rodil was financially supported by an FPU grant (AP2012-05337) from the Spanish Ministry of Education.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

13595_2018_712_MOESM1_ESM.doc (3.5 mb)
ESM 1 (DOC 3622 kb)

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Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  • Manuel Arias-Rodil
    • 1
    • 2
  • Ulises Diéguez-Aranda
    • 1
  • Juan Gabriel Álvarez-González
    • 1
  • César Pérez-Cruzado
    • 1
  • Fernando Castedo-Dorado
    • 3
  • Eduardo González-Ferreiro
    • 1
    • 4
    • 5
  1. 1.Unidade de Xestión Forestal Sostible (UXFS) – Departamento de Enxeñería Agroforestal, Escola Politécnica SuperiorUniversidade de Santiago de CompostelaLugoSpain
  2. 2.Centro de Estudos Florestais, Instituto Superior de AgronomiaUniversidade de LisboaLisbonPortugal
  3. 3.Departamento de Ingeniería y Ciencias Agrarias, Escuela Superior y Técnica de Ingeniería AgrariaUniversidad de LeónPonferradaSpain
  4. 4.Department of Forest Engineering, Resources and Management (FERM)Oregon State UniversityCorvallisUSA
  5. 5.Laboratory of Applications of Remote Sensing in Ecology (LARSE)US Forest Service - Pacific Northwest Research StationCorvallisUSA

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