Methods for tree cover extraction from high resolution orthophotos and airborne LiDAR scanning in Spanish dehesas




Low-density airborne Lidar, PNOA, Tree cover, Quercus ilex


Dehesas are high value agroecosystems that benefit from the effect tree cover has on pastures. Such effect occurs when tree cover is incomplete and homogeneous. Tree cover may be characterized from field data or through visual interpretation of remote sensing data, both time-consuming tasks. An alternative is the extraction of tree cover from aerial imagery using automated methods, on spectral derivate products (i.e. NDVI) or LiDAR point clouds. This study focuses on assessing and comparing methods for tree cover estimation from high resolution orthophotos and airborne laser scanning (ALS). RGB image processing based on thresholding of the ‘Excess Green minus Excess Red’ index with the Otsu method produced acceptable results (80%), lower than that obtained by thresholding the digital canopy model obtained from the ALS data (87%) or when combining RGB and LiDAR data (87.5%). The RGB information was found to be useful for tree delineation, although very vulnerable to confusion with the grass or shrubs. The ALS based extraction suffered for less confusion as it differentiated between trees and the remaining vegetation using the height. These results show that analysis of historical orthophotographs may be successfully used to evaluate the effects of management changes while LiDAR data may provide a substantial increase in the accuracy for the latter period. Combining RGB and Lidar data did not result in significant improvements over using LIDAR data alone.


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Author Biographies

I. Borlaf-Mena, University of Alcalá

Department of Geology, Geography and Environment, Faculty of Biology, Chemistry and Environmental Sciences

PhD student

M.A. Tanase, Universidad de Alcalá

Department of Geology, Geography and Environment, Faculty of Biology, Chemistry and Environmental Sciences


A. Gómez-Sal, Universidad de Alcalá

Department of Life Sciences, Faculty of Biology, Chemistry and Environmental Sciences



Abbasi, M., Bakhtyari, H.R. 2012. Extraction of Forest Stands Parameters from Aster Data in Open Forest. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, B4.

ASPRS, American Society of Photogrammetry and remote sensing. 2013. LAS specification version 1.4 – R13. Retrieved from Last access: June 2019.

Boggs, G.S. 2010. Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas. International Journal of Applied Earth Observation and Geoinformation, 12(4), 217-224.

Brovelli, M.A., Cannata, M., Longoni, U., Reguzzoni, M., Antolin, R. 2014. v.outlier, removes outliers from vector point data [English]. Retrieved from https:// Last access: June 2019.

Brovelli, M.A., Cannata, M., Longoni, U., Reguzzoni, M., Antolin, R. 2016. GRASS GIS manual: bspline [English]. Retrieved from https://grass. Last access: June 2019.

Butler, H., Gerlek, M. 2017. PDAL Point Data Abstraction Library [English]. Retrieved from Last access: June 2019.

Carreiras, J.M., Pereira, J.M., Pereira, J.S. 2006. Estimation of tree canopy cover in evergreen oak woodlands using remote sensing. Forest Ecology and Management, 223(1-3), 45-53.

Castillejo-González, I.L., Guerrero, J.M.M., GarcíaFerrer Porras, A., F.J. Mesas-Carrascosa, M.S. de la O. 2010. Utilización de imágenes de satélite de alta resolución espacial en la determinación de la fracción de cabida cubierta en sistemas adehesados. In Ojeda, J., Pita, M.F. y Vallejo, I. (Ed.), XIV Congreso nacional de Tecnologías de la Información Geográfica. La Información Geográfica al Servicio de los Ciudadanos: de lo Global a lo Local (pp. 62- 71). Secretariado de Publicaciones de la Universidad de Sevilla.

Chen, L., Chiang, T., Teo, T. 2005. Fusion of LIDAR data and high-resolution images for forest canopy modelling. Proc. 26th Asian Conference on Remote Sensing.

De Miguel, J.M., Acosta-Gallo, B., Gómez-Sal, A. 2013. Understanding mediterranean pasture dynamics: general tree cover vs. specific effects of individual trees. Rangeland Ecology & Management, 66(2), 216- 223.

Dechesne, C., Mallet, C., Bris, A.L., Gouet, V., Hervieu, A. 2016. Forest Stand Segmentation Using Airborne Lidar Data and Very High Resolution Multispectral Imagery. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B3, 207–214. https://doi. org/10.5194/isprs-archives-xli-b3-207-2016

Evans, J.S., Hudak, A.T. 2007. A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 1029-1038.

Fernández de Ahumada, E., Martínez-Ruedas, C. 2017. El análisis de imagen como herramienta para la cuantificación del número de árboles y La fracción de cabida cubierta en sistemas agrosilvopastorales. Retrieved from

Fisher, A., Day, M., Gill, T., Roff, A., Danaher, T., & Flood, N. 2016. Large-area, high-resolution tree cover mapping with multi-temporal SPOT5 imagery, New South Wales, Australia. Remote Sensing, 8(6), 515.

Free Software Foundation. 2016. Retrieved from Last access: June 2019.

García, M. 2011. Obtención de variables forestales a partir de datos lidar (p. 16). Retrieved from Ministerio de Agricultura, Alimentación y Medio Ambiente; Red nacional de parques naturales; Tragsatec: documento-tecnico-obtencion-variables-lidar_ tcm30-68999.pdf Last access: June 2019.

Godinho, S., Guiomar, N., Gil, A. 2018. Estimating tree canopy cover percentage in a mediterranean silvopastoral systems using Sentinel-2A imagery and the stochastic gradient boosting algorithm. International Journal of Remote Sensing, 39(14), 4640-4662. .1399480

Gómez-Sal, A., Velado Alonso, E., González-García, A. 2016. Tipología y caracterización de las dehesas del proyecto LIFE+ bioDehesa para la representación de dehesas representativas. Retrieved from http:// wp-content/uploads/Informe_2_Caracterización_ fincas_RDD_II.pdf Last access: June 2019.

Guijarro, M., Pajares, G., Riomoros, I., Herrera, P. J., Burgos-Artizzu, X.P., Ribeiro, A. 2011. Automatic segmentation of relevant textures in agricultural images. Computers and Electronics in Agriculture, 75(1), 75-83. compag.2010.09.013

IGN, Instituto Geográfico Nacional. 2014. National Plan for Aerial Orthophotography. Retrieved May 21, 2019, from .

IGN, Instituto Geográfico Nacional. 2016. Plan Nacional de Ortofotografía Aérea. Especificaciones técnicas. Retrieved May 21, 2019, from http://pnoa.

IGN, Instituto Geográfico Nacional. 2019. Especificaciones Técnicas para vuelo LiDAR y procesado del MDE.

Inglada, J., Christophe, E. 2009. The Orfeo Toolbox remote sensing image processing software. 2009 IEEE International Geoscience and Remote Sensing Symposium. igarss.2009.5417481

Jennings, S., Brown, N., Sheil, D. 1999. Assessing forest canopies and understorey illumination: canopy closure, canopy cover and other measures. Forestry: An International Journal of Forest Research, 72(1), 59-74. forestry/72.1.59

Joffre, R., Lacaze, B. 1993. Estimating tree density in oak savanna-like ‘dehesa’ of southern Spain from SPOT data. International Journal of Remote Sensing, 14(4), 685-697. https://doi. org/10.1080/01431169308904368

Jones, E., Oliphant, T., Peterson, P. 2014. SciPy: Open source scientific tools for Python.

Jones, H.G., Vaughan, R.A. 2010. Remote Sensing of Vegetation. Oxford University Press.

Junta de Andalucía. (2018, February 7). Distribución de las formaciones adehesadas en Andalucía, información actualizada. Retrieved April 4, 2019, from srv/esp/

Kataoka, T., Kaneko, T., Okamoto, H., Hata, S. 2003. Crop growth estimation system using machine vision. Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003). aim.2003.1225492

Ke, Y., Quackenbush, L.J. 2011. A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. International Journal of Remote Sensing, 32(17), 4725-4747. 0.494184

Lavado, J.F., Jariego, A., Schnabel, S., Gómez, A. 2012. Análisis de la evolución histórica del arbolado de la dehesa mediante fotointerpretación y análisis OBIA. In J. Martínez Vega & P. Martín Isabel (Eds.), XV Congreso Nacional de Tecnologías de la Información Geográfica. Tecnologías de Información Geográfica en el contexto de Cambio Global (pp. 92-100). CSIC-Instituto de Economía, Geografía y Demografía (IEGD).

Lennert, M. 2016. i.segment.uspo, unsupervised segmentation parameter optimization for i.segment [English]. Retrieved from https://grass.osgeo. org/grass70/manuals/addons/i.segment.uspo.html Last access: June 2019.

McGaughey, R.J. 2016. FUSION/LDV: Software for LIDAR data analysis and visualization [English]. USDA Forest Service.

Meyer, G.E., Hindman, T.W., Laksmi, K. 1999. Machine vision detection parameters for plant species identification. In G.E.

Meyer & J.A. DeShazer (Eds.), Precision Agriculture and Biological Quality. Meyer, G.E., Neto, J.C. 2008. Verification of color vegetation indices for automated crop imaging applications. Computers and Electronics in Agriculture, 63(2), 282-293.

Moreno, G., Pulido, F.J. 2009. The Functioning, Management and Persistence of Dehesas. In A. Rigueiro-Rodríguez, J. McAdam, & M.R. Mosquera-Losada (Eds.), Agroforestry in Europe: Current Status and Future Prospects (pp. 127-160).

Morsdorf, F., Kötz, B., Meier, E., Itten, K.I., Allgöwer, B. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment, 104(1), 50-61.

Mumtaz, S.A., Mooney, K. 2008. Fusion of high resolution lidar and aerial images for object extraction. 2nd International Conference on Advances in Space Technologies.

Neteler, M., Mitasova, H. (Eds.). 2008. Open Source GIS: A GRASS GIS Approach.

Olaya, V. 2016. Sistemas de información geográfica.

Otsu, N. 1979. A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern., 9(1), 62-66.

Pu, R., Xu, B., Gong, P. 2003. Oakwood crown closure estimation by unmixing Landsat TM data. International Journal of Remote Sensing, 24(22), 4422-4445. Python Software Foundation. 2010.

Python language reference, version 2.7. Python Software Foundation.

Quantum GIS Development Team. 2017. QGIS [English]. Retrieved from site/ Last access: June 2019.

Romero de los Reyes, E., Navarro Cerrillo, R., GarcíaFerrer, A. 2007. Aplicación de ortofotos para la estimación de pérdida de individuos en dehesas de encina: (“Quercus ilex” L. subps. “ballota” (Desf.) Samp.) afectadas por procesos de decaimiento. Boletín de sanidad vegetal. Plagas., 33(1), 121-134.

Soininen, A., & TerraSolid. 2016. TerraScan Users’ Guide.

van der Walt, S., Colbert, S.C., Varoquaux, G. 2011. The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science & Engineering, 13(2), 22–30.

van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., … Yu, T. 2014. scikit-image: image processing in Python. PeerJ, 2, e453.

White, MA., Asner, G.P., Nemani, R.R., Privette, J.L., Running, S.W. 2000. Measuring Fractional Cover and Leaf Area Index in Arid Ecosystems. Remote Sensing of Environment, 74(1), 45-57.

Woebbecke, D.M., Meyer, G.E., Von Bargen, K., Mortensen, D.A. 1995. Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions. Transactions of the ASAE, 38(1), 259.

Xu, B., Gong, P., Pu, R. 2003. Crown closure estimation of oak savannah in a dry season with Landsat TM imagery: comparison of various indices through correlation analysis. International Journal of Remote Sensing, 24(9), 1811-1822.

Zimble, D.A., Evans, D.L., Carlson, G.C., Parker, R.C., Grado, S.C., Gerard, P.D. 2003. Characterizing vertical forest structure using small-footprint airborne LiDAR. Remote Sensing of Environment, 87(2-3), 171-182.






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