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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Ali, A.M., Darvishzadeh, R., Skidmore, A.K., Duren, I. 2017. Specific leaf area estimation from leaf and canopy reflectance through optimization and validation of vegetation indices. Agricultural and Forest Meteorology, 236, 162-174.

Barrachina, M., Cristóbal, J., Tulla, A.F. 2015. Estimating above-ground biomass on mountain meadowsand pastures through remote sensing. International Journal of Applied Earth Observation and Geoinformation, 38, 184-192.

Birth, G., McVey, G. 1968. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer. Agronomy Journal, 60, 640-643.

Boschetti, M., Bocchi, S., Brivio, P.A. 2007. Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information. Agriculture, Ecosystems and Environment, 118, 267-272.

Bréda, N.J.J. 2003. Ground-based measurements of leaf area index: a review of methods, instruments, and current controversies. Journal of Experimental Botany, 54, 2403-2417.

Burchard-Levine, V., Nieto, H., Riaño, D., Migliavacca, M., El-Madany, T. S., Perez-Priego, O., et al. 2020. Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem. Remote Sensing, 12 (6), 904.

Burnett, A., Anderson, J., Davidson, K., Ely, K., Lamour, J., Li, Q., et al. 2021. A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. Journal of Experimental Botany, 72, 6175-6189.

Campos, P. 1993. Valores comerciales y ambientales de las dehesas españolas. Agricultura y sociedad, 66, 9-41.

Carmona, C. P., Rota, C., Azcárate, F.M., Peco, B., 2015. More for less: sampling strategies of plant functional traits across local environmental gradients. Functional Ecology, 29, 579-588.

Cavender-Bares, J., Gamon, J.A., Townsend, P.A. 2020. Remote Sensing of Plant Biodiversity. Springer.

Clevers, J., Van der Heijden, G., Verzakov, S., Schaepman, M.E. 2007. Estimating Grassland Biomass Using SVM Band Shaving of Hyperspectral Data. Photogrammetric Engineering & Remote Sensing, 73 (10), 1141-1148.

Cogliati, S., Rossini, M., Julitta, T., Meroni, M., Schickling, A., Burkart, A., et al. 2015. Continuous and long-term measurements of reflectance and sun-induced chlorophyll fluorescence by using novel automated field spectroscopy systems. Remote Sensing of Environment, 164, 270-281.

Córdova-Tapia, F., Zambrano, L. 2015. La diversidad funcional en la ecología de comunidades. Ecosistemas, 24 (3), 78-87.

Dechant, B., Cuntz, M., Vohland, M., Schulz, E., Doktor, D. 2017. Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism. Remote Sensing of Environment, 196, 279-292.

Doughty, C.E. Goulden, M.L. 2008. Seasonal patterns of tropical forest leaf area index and CO2 exchange. J. Geophys. Res., 113.

Dusseux, P., Gong, X., Hubert-Moy, L., Corpetti, T. 2014. Identification of grassland management practices from leaf area index time series. Journal of Applied Remote Sensing, 8 (1).

Elvidge, C.D. 1990. Visible and near infrared reflectance characteristics of dry plant materials. International Journal of Remote Sensing, 11, 1775-1795.

Escribano, J.A., Hernández, C.G., Tarquis, A.M. 2014. Selección de índices de vegetación para la estimación de la producción herbácea. Pastos, 44, 2, 6-18.

Feilhauer, H., Asner, G.P., Martin, R.E., Schmidtlein, S. 2010. Brightness-normalized Partial Least Squares Regression for hyperspectral data. Journal of Quantitative Spectroscopy and Radiative Transfer, 111 (12), 1947-1957.

Galvão, L.S., Formaggio, A.R., Tisot, D.A. 2005. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94 (4), 523-534.

Gamon, J., Serrano, L., Surfus, J. 1997. The Photochemical Reflectance Index: An Optical Indicator of Photosynthetic Radiation Use Efficiency Across Species, Functional Types and Nutrient Levels. Oecologia, 112. 492-501.

Gholizadeh, H., Gamon, J.A., Helzer, C.J., Cavender-Bares, J. 2020. Multi-temporal assessment of grassland α- and β-diversity using hyperspectral imaging. Ecological Applications, 30 (7).

Gong, Z., Kawamura, K., Ishikawa, N., Inaba, M., Alateng, D. 2015. Estimation of herbage biomass and nutritive status using band depth features with partial least squares regression in Inner Mongolia grassland, China. Grassland Science, 62 (1), 45-54.

Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Strachan., I.B. 2004. Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture. Remote Sensing of Environment, 90, 337-352.

Hanan, N., Hill, M. 2012. Savannas in a Changing Earth System. The NASA Terrestrial Ecology Tree-Grass Project, Geographic Information Science Center of Excellence (GIScCE), South Dakota State University.

Hardisky, M., Klemas, V., Smart, R. 1983. The Influences of Soil Salinity, Growth Form, and Leaf Moisture on the Spectral Reflectance of Spartina Alterniflora Canopies. Photogrammetric Engineering and Remote Sensing 49, 77-83.

Hill, M.J., Hanan, N.P., Hoffmann, W., Scholes, R., Prince, S., Ferwerda, J., et al. 2011. Remote sensing and modelling of savannas: The state of the dis-union. 34th International Symposium on Remote Sensing of Environment, Sydney.

Hollberg, J.L., Schellberg, J. 2017. Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices. Remote Sensing, 9 (1), 81.

Hurcom, S.J., Harrison, A.R. 1998. The NDVI and spectral decomposition for semi-arid vegetation abundance estimation. International Journal of Remote Sensing, 19 (16), 3109-3125.

Hunt, E.R. 1991. Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data. International Journal of Remote Sensing 12, 643-649.

Imran, H.A., Gianelle, D., Scotton, M., Rocchini, D., Dalponte, M., Macolino, S. 2021. Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery. Remote Sens., 13, 2649.

Jin, J., Wang, Q. 2019. Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance. Remote Sensing, 11 (2), 197.

Kiala, Z., Odindi, J., Mutanga, O., Peerbhay, K. 2016. Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data. Journal of Applied Remote Sensing, 10 (3).

Laliberté, E., Legendre, P. 2010. A distance-based framework for measuring functional diversity from multiple traits. Ecology, 91(1), 299-305.

Leitão, P., Schwieder, M., Suess, S., Okujeni, A., Galvão, L., Linden, S., et al. 2015. Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP. Remote Sensing, 7(10), 13098-13119.

Li, F., Jiang, L., Wang, X., Zhang, X., Zheng, J., Zhao, Q. 2013. Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China. Journal of Applied Remote Sensing, 7(1).

Liu, L., Guan, L., Peng, D., Hu, Y., Jiao, Q., Liu, L. 2012. Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories. Journal of Applied Remote Sensing, 6 (1).

Ma, X., Mahecha, M.D., Migliavacca, M., Van der Plas, F., Benavides, R., Ratcliffe, S., et al. 2019. Inferring plant functional diversity from space: the potential of Sentinel-2. Remote Sensing of Environment, 233.

Madonsela, S., Cho, M.A., Ramoelo, A., Mutanga, O. 2017. Remote sensing of species diversity using Landsat 8 spectral variables. ISPRS Journal of Photogrammetry and Remote Sensing, 133, 116-127.

Martín, M.P., Pacheco-Labrador, J., González-Cascón, R., Moreno, G., Migliavacca, García, M., et al. 2020. Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente. Revista de Teledetección, 55, 31-48.

Melendo-Vega, J.R., Martín, M.P., Vilar del Hoyo, L., Pacheco-Labrador, J., Echavarría, P., Martínez-Vega, J. 2017. Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección, 48, 13-28.

Milcu, A., Roscher, C., Gessler, A., Bachmann, D., Gockele, A., Guderle, M., et al. 2014. Functional diversity of leaf nitrogen concentrations drives grassland carbon fluxes. Ecology Letters, 17, 435-444.

Montserrat, P. 1968. La dehesa extremeña, VII Reunión científica de la Sociedad Española para el Estudio de los Pastos: Badajoz, Portugal, 1-7.

Moreno, G., Rolo, V. 2019. Agroforestry practices: silvopastorism. Agroforestry for sustainable agriculture, Burleigh Dodds Science, 119-164.

Mulder, C., Bazeley-White, E., Dimitrakopoulos, P., Scherer-Lorenzen, M., Schmid, B. 2004. Species evenness and productivity in experimental plant communities. Oikos, 107, 50-63.

Olea, L., Verdasco, M.P., Paredes, J. 1990. Características y producción de los pastos de las dehesas del S. O. de la Península Ibérica. Pastos, 131-156.

Penuelas, J., Baret, F., Filella, I. 1995. Semi-Empirical Indices to Assess Carotenoids/Chlorophyll-a Ratio from Leaf Spectral Reflectance. Photosynthetica, 31, 221-230.

Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P. et al. 2013. New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 61(3), 167.

Pellissier, P.A., Ollinger, S.V., Lepine, L.C., Palace, M.W., McDowell, W.H. 2015. Remote sensing of foliar nitrogen in cultivated grasslands of human dominated landscapes. Remote Sensing of Environment, 167, 88-97.

Psomas, A., Kneubühler, M., Huber, S., Itten, K., Zimmermann, N.E. 2010. Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. International Journal of Remote Sensing, 32 (24), 9007-9031.

Pulido, F., Picardo, A., Campos, P., Carranza, J., Coleto, J., Díaz, M. et al. 2010. Libro Verde de la Dehesa., Consejería de Medio Ambiente, Junta Castilla La Mancha.

Rinnan, Å., Berg, F., Engelsen, S.B. 2009. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal, Chem., 28, 1201-1222.

Rolo, V., Rivest, D., Lorente, M., Kattge, J., Moreno, G. 2016. Taxonomic and functional diversity in Mediterranean pastures: insights on the biodiversity-productivity trade-off. Journal of Applied Ecology 53 (5), 1575-1584.

Rossi, C., Kneubühler, M., Schütz, M., Schaepman, M.E, Haller, R.M. Risch, A.C. 2020. From local to regional: Functional diversity in differently managed alpine grasslands. Remote Sensing of Environment, 236.

Rouse, J.W., Haas, R.H., Schell, J.A, Deering, D.W. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA, 309-317.

Sakowska, K., Juszczak, R., Gianelle, D. 2016. Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission. Journal of Sensors, 1-16.

Sebastià, M.T., Llurba, R., Gouriveau, F., De Lamo, X., Ribas, A. Altimir, N. 2012 Biodiversidad y servicios ecosistémicos en pastos: distribución y respuesta al cambio global. Sociedad Española para el Estudio de los Pastos (SEEP), 51, 134-145.

Serrano, L., Peñuelas, J., Ustin, S.L. 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data. Remote Sensing of Environment, 81(2-3), 355-364.

Shannon, C., Weaver W. 1949. The mathematical theory of communication”, University of Illinois Press. Urbana, IL, EEUU, 144.

Shoko, C., Mutanga, O., Dube, T. 2016. Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space. ISPRS Journal of Photogrammetry and Remote Sensing, 120, 13-24.

Sims, D., Gamon, J. 2002. Relationships Between Leaf Pigment Content and Spectral Reflectance Across a Wide Range of Species, Leaf Structures and Developmental Stages. Remote Sensing of Environment, 81, 337-354.

Van Cleemput, E., Helsen, K., Feilhauer, H., Honnay, O., Somers, B. 2021. Spectrally defined plant functional types adequately capture multidimensional trait variation in herbaceous communities. Ecological Indicators, 120, 106970.

Vohland, M., Jarmer, T. 2008. Estimating structural and biochemical parameters for grassland from spectroradiometer data by radiative transfer modelling (PROSPECT+SAIL). International Journal of Remote Sensing, 29 (1), 191-209.

Wang, R., Gamon, J. 2019. Remote sensing of terrestrial plant biodiversity. Remote Sensing of Environment, 231, 111-218.

Wang, R., Gamon, J., Montgomery, R., Townsend, P., Zygielbaum, A., Bitan, K., et al. 2016. Seasonal Variation in the NDVI–Species Richness Relationship in a Prairie Grassland Experiment (Cedar Creek). Remote Sensing, 8 (2), 128.

Wang, R., Gamon, J., Schweiger, A.K., Cavender-Bares, J., Townsend, P.A., Zygielbaum, A.I., Kothari, S. 2018. Influence of species richness, evenness, and composition on optical diversity: A simulation study. Remote Sensing of Environment, 211, 218-228.

Wilcoxon, F. 1945. Some Uses of Statistics in Plant Pathology. Biometrics Bulletin, 1, 41-45.

Wu, C., Niu, Z., Tang, Q., Huang, W., Rivard, B., Feng, J. 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology, 149(6-7), 1015-1021.

Xiang, M., Wu, J., Wu, J., Guo, Y., Lha, D., Pan, Y., Zhang, X. 2021. Heavy Grazing Altered the Biodiversity–Productivity Relationship of Alpine Grasslands in Lhasa River Valley, Tibet. Frontiers Ecology and Evolution, 9.

Zuo, X., Zhou, X., Lv, P., Zhao, X., Zhang, J., Wang, S., et al. 2016. Testing Associations of Plant Functional Diversity with Carbon and Nitrogen Storage along a Restoration Gradient of Sandy Grassland. Frontiers in Plant Science, 7.



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 2024 May 30];49(1):89-111. Available from: