Analysis of the functional diversity of the herbaceous stratum in a ‘dehesa’ ecosystem using in situ hyperspectral proximal sensing


  • Carlos Gonzalo Laboratorio de Espectro-radiometría y Teledetección Ambiental (SpecLab), Consejo Superior de Investigaciones Científicas (CSIC)
  • Vicente Burchard-Levine Laboratorio de Espectro-radiometría y Teledetección Ambiental (SpecLab), Consejo Superior de Investigaciones Científicas (CSIC)
  • Víctor Rolo Grupo de Investigación Forestal, INDEHESA, Universidad de Extremadura
  • Rosario González-Cascón Departamento de Medio Ambiente y Agronomía, INIA-CSIC
  • Gerardo Moreno Grupo de Investigación Forestal, INDEHESA, Universidad de Extremadura
  • M. Pilar Martín Laboratorio de Espectro-radiometría y Teledetección Ambiental (SpecLab), Consejo Superior de Investigaciones Científicas (CSIC)



biophysical variables, vegetation indices, functional diversity, hyperspectral data, grasslands


The aim of this paper is the estimation of functional diversity (FD) of the herbaceous stratum in a ‘dehesa’ ecosystem using hyperspectral data obtained with an ASD FieldSpec® 3 (Analytical Spectral Devices Inc., Boulder, CO, EE. UU.) spectroradiometer. Optical data were correlated with biophysical variables (specific leaf area (SLA), above-ground biomass (AGB), leaf area index (LAI) and nitrogen content (N%)) and traditional diversity indices (Shannon and Evenness) and functional diversity indices (FDis) using as predictor variables: a) vegetation indices (VIs) and simple regression methods, and b) spectral bands and Partial Least Squares Regression (PLSR). Correlations, especially with biophysical variables, improved substantially when using hyperspectral information (R2 >0.6, rRMSE <0.1), which confirms the interest of the spectral dimension to estimate the functional diversity of a complex ecosystem such as semi-arid grasslands.


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How to Cite

Gonzalo C, Burchard-Levine V, Rolo V, González-Cascón R, Moreno G, Martín MP. Analysis of the functional diversity of the herbaceous stratum in a ‘dehesa’ ecosystem using in situ hyperspectral proximal sensing. CIG [Internet]. 2022 Jun. 28 [cited 2023 Jan. 28];. Available from: