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

Abstract

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.

Context

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.

Aims

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.

Methods

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.

Results

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.

Conclusion

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.

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Data availability

A dataset generated with the coordinates and the species of each tree measured is available in FigShare repository (Blázquez-Casado et al. 2019) at https://doi.org/10.6084/m9.figshare.7951166.v2

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Acknowledgments

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.

Funding

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.

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Correspondence to Ángela Blázquez-Casado.

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Contributions of the co-authors

Conceptualization: Francisco Rodríguez; data curation: Rafael Calama, Manuel Valbuena, and Marta Vergarechea; data analysis: Ángela Blázquez-Casado; writing (original draft): Ángela Blázquez-Casado; writing (review and editing): Rafael Calama, Ángela Blázquez-Casado, and Francisco Rodríguez; supervising the work: Francisco Rodríguez.

This article is part of the topical collection on Mediterranean pines

Handling Editor: Barry Alan Gardiner

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Blázquez-Casado, Á., Calama, R., Valbuena, M. et al. Combining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest. Annals of Forest Science 76, 57 (2019). https://doi.org/10.1007/s13595-019-0835-x

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Keywords

  • Crown delineation
  • Inventory
  • Modeling
  • Pleiades
  • Remote sensing
  • Stone pine
  • LiDAR