Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest

  • Ángela Blázquez-CasadoEmail author
  • Rafael Calama
  • Manuel Valbuena
  • Marta Vergarechea
  • Francisco Rodríguez
Research Paper
Part of the following topical collections:
  1. Mediterranean Pines


Key message

Using a combination of remote sensing data, Pinus pinaster Ait. and Pinus pinea L. were distinguished at individual tree level in mixed Mediterranean stands with over 95% accuracy. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products, stone pine nuts, and resin, and aiding forest managers to accurately predict this production.


The discrimination of tree species at individual level in mixed Mediterranean forest based on remote sensing is a field which has gained greater importance. In these stands, the capacity to predict the quality and quantity of non-wood forest products is particularly important due to the very different goods the two species produce.


To assess the potential of using low-density airborne LiDAR data combined with high-resolution Pleiades images to discriminate two different pine species in mixed Mediterranean forest (Pinus pinea L. and Pinus pinaster Ait.) at individual tree level.


A Random Forest model was trained using plots from the pure stand dataset, determining which LiDAR and satellite variables allow us to obtain better discrimination between groups. The model constructed was then validated by classifying individuals in an independent set of pure and mixed stands.


The model combining LiDAR and Pleiades data provided greater accuracy (83.3% and 63% in pure and mixed validation stands, respectively) than the models which only use one type of covariables.


The automatic crown delineation tool developed allows two very similar species in mixed Mediterranean conifer forest to be discriminated using continuous spatial information at the surface: Pleiades images and open source LiDAR data. This approach is easily applicable over large areas, enhancing the economic value of non-wood forest products and aiding forest managers to accurately predict production.


Crown delineation Inventory Modeling Pleiades Remote sensing Stone pine LiDAR 



The authors wish to thank the Forest Service of Valladolid province for their continuous help in plot installation, maintenance, and data collection. Similarly, the authors wish to express their gratitude to the PNT program for providing us with Pleiades images and to Dr. Fernando Pérez-Cabello for his advice with regard to interpreting the Pleiades images. Also, the authors wish to thank Dr. Iñigo Lizarralde, Dr. Rafael Alonso, and Dra. Beatriz Águeda for their general assistance and to Adam Collins for the English language advice.


Research of Ángela Blázquez-Casado was funded by a contract of Ministerio de Economía, Industria y Competitividad, Spanish Government (DI-14-06953). This study has also been financed through the project AGL2014-51964 FORMIXING (Ministerio de Economía, Industria y Competitividad, Spanish Government) and the CC16–095 PROPINEA agreement between INIA, ITACYL, and the Diputation of Valladolid.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  • Ángela Blázquez-Casado
    • 1
    • 2
    Email author
  • Rafael Calama
    • 2
    • 3
  • Manuel Valbuena
    • 4
  • Marta Vergarechea
    • 2
    • 3
  • Francisco Rodríguez
    • 1
    • 2
    • 5
  1. 1.föra forest technologiesSoriaSpain
  2. 2.iuFOR University Institute for Sustainable Forest Management INIA-UVAPalenciaSpain
  3. 3.Departamento de Selvicultura y Gestión de Sistemas ForestalesINIA-CIFORMadridSpain
  4. 4.Departamento de Educación Gobierno VascoIES Murgía BHIÁlavaSpain
  5. 5.Departamento de Producción Vegetal y Recursos Forestales, EIFABUniversidad de ValladolidSoriaSpain

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