International Journal of Biometeorology

, Volume 60, Issue 6, pp 813–825 | Cite as

Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland

  • Sofia Cerasoli
  • Filipe Costa e Silva
  • João M. N. Silva
Original Paper


The application of spectral vegetation indices for the purpose of vegetation monitoring and modeling increased largely in recent years. Nonetheless, the interpretation of biophysical properties of vegetation through their spectral signature is still a challenging task. This is particularly true in Mediterranean oak forest characterized by a high spatial and temporal heterogeneity. In this study, the temporal dynamics of vegetation indices expected to be related with green biomass and photosynthetic efficiency were compared for the canopy of trees, the herbaceous layer, and two shrub species: cistus (Cistus salviifolius) and ulex (Ulex airensis). coexisting in a cork oak woodland. All indices were calculated from in situ measurements with a FieldSpec3 spectroradiometer (ASD Inc., Boulder, USA). Large differences emerged in the temporal trends and in the correlation between climate and vegetation indices. The relationship between spectral indices and temperature, radiation, and vapor pressure deficit for cork oak was opposite to that observed for the herbaceous layer and cistus. No correlation was observed between rainfall and vegetation indices in cork oak and ulex, but in the herbaceous layer and in the cistus, significant correlations were found. The analysis of spectral vegetation indices with fraction of absorbed PAR (fPAR) and quantum yield of chlorophyll fluorescence (ΔF/Fm′) evidenced strongest relationships with the indices Normalized Difference Water Index (NDWI) and Photochemical Reflectance Index (PRI)512, respectively. Our results, while confirms the ability of spectral vegetation indices to represent temporal dynamics of biophysical properties of vegetation, evidence the importance to consider ecosystem composition for a correct ecological interpretation of results when the spatial resolution of observations includes different plant functional types.


Spectral vegetation indexes In situ spectral measurements Vegetation heterogeneity Cork oak open woodlands Mediterranean forest 



Sofia Cerasoli and Filipe Silva are postdoc fellows of the Portuguese Fundação para a Ciência e Tecnologia, Ministry of Science and education (BPD/SFRH/78998/2011). João Silva is a research associate at the School of Agriculture, with funding from the FCT (Ciência 2008 program). Field work was funded by FCT (PEst-OE/AGR/UI0239/2011) in the framework of the research activities of the Forest Research Centre.


  1. Barton CVM, North PRJ (2001) Remote sensing of canopy light use efficiency using the photochemical reflectance index—model and sensitivity analysis. Remote Sens Environ 78(3):264–273. doi: 10.1016/s0034-4257(01)00224-3 CrossRefGoogle Scholar
  2. Bolton DK, Friedl MA (2013) Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agr Forest Meteorol 173:74–84. doi: 10.1016/j.agrformet.2013.01.007 CrossRefGoogle Scholar
  3. Brantley ST, Zinnert JC, Young DR (2011) Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies. Remote Sens Environ 115(2):514–523. doi: 10.1016/j.rse.2010.09.020 CrossRefGoogle Scholar
  4. Bugalho MN, Caldeira MC, Pereira JS, Aronson JA, Pausas J (2011) Mediterranean oak savannas require human use to sustain biodiversity and ecosystem services. Front Ecol Environ 5:278–286CrossRefGoogle Scholar
  5. Carvalhais N, Reichstein M, Collatz GJ, Mahecha MD, Migliavacca M, Neigh CSR, Tomelleri E, Benali AA, Papale D, Seixas J (2010) Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula. Biogeosciences 7(11):3707–3729. doi: 10.5194/bg-7-3707-2010 CrossRefGoogle Scholar
  6. Cheng Y, Gamon JA, Fuentes DA, Mao Z, Sims DA, H-l Q, Claudio H, Huete A, Rahman AF (2006) A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: a comparison of field, AVIRIS and MODIS data. Remote Sens Environ 103(3):369–378. doi: 10.1016/j.rse.2005.06.013 CrossRefGoogle Scholar
  7. Cheng Y-B, Middleton EM, Hilker T, Coops NC, Black TA, Krishnan P (2009) Dynamics of spectral bio-indicators and their correlations with light use efficiency using directional observations at a Douglas-fir forest. Meas Sci Technol 20(9):095107CrossRefGoogle Scholar
  8. Cheng Y-B, Zhang Q, Lyapustin AI, Wang Y, Middleton EM (2014) Impacts of light use efficiency and fPAR parameterization on gross primary production modeling. Agr Forest Meteorol 189–190:187–197. doi: 10.1016/j.agrformet.2014.01.006 CrossRefGoogle Scholar
  9. Chiesi M, Maselli F, Bindi M, Fibbi L, Cherubini P, Arlotta E, Tirone G, Matteucci G, Seufert G (2005) Modelling carbon budget of Mediterranean forests using ground and remote sensing measurements. Agr Forest Meteorol 135(1–4):22–34. doi: 10.1016/j.agrformet.2005.09.011 CrossRefGoogle Scholar
  10. Correia AC, Costa e Silva F, Correia AV, Hussain MZ, Rodrigues AD, David JS, Pereira JS (2013) Carbon sink strength of a Mediterranean cork oak understorey: how do semi-deciduous and evergreen shrubs face summer drought? J Veg Sci 25(2):411–426. doi: 10.1111/jvs.12102 CrossRefGoogle Scholar
  11. Costa-e-Silva F, Correia AC, Piayda A, Dubbert M, Rebmann C, Cuntz M, Werner C, David JS, Pereira JS (2015) Effects of an extremely dry winter on net ecosystem carbon exchange and tree phenology at a cork oak woodland. Agric For Meteorol 204:48–57, doi: j.agrformet.2015.01.017 CrossRefGoogle Scholar
  12. Cristiano PM, Posse G, Di Bella CM, Jaimes FR (2010) Uncertainties in fPAR estimation of grass canopies under different stress situations and differences in architecture. Int J Remote Sens 31(15):4095–4109. doi: 10.1080/01431160903229192 CrossRefGoogle Scholar
  13. David TS, Pinto CA, Nadezhdina N, Kurz-Besson C, Henriques MO, Quilhó T, Cermak J, Chaves MM, Pereira JS, David JS (2013) Root functioning, tree water use and hydraulic redistribution in Quercus suber trees: a modeling approach based on root sap flow. For Ecol Manage 307:136–146. doi: 10.1016/j.foreco.2013.07.012 CrossRefGoogle Scholar
  14. Drolet GG, Middleton EM, Huemmrich KF, Hall FG, Amiro BD, Barr AG, Black TA, McCaughey JH, Margolis HA (2008) Regional mapping of gross light-use efficiency using MODIS spectral indices. Remote Sens Environ 112(6):3064–3078. doi: 10.1016/j.rse.2008.03.002 CrossRefGoogle Scholar
  15. Filella I, Penuelas J, Llorens L, Estiarte M (2004) Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sens Environ 90(3):308–318. doi: 10.1016/j.rse.2004.01.010 CrossRefGoogle Scholar
  16. Filella I, Porcar-Castell A, Munne-Bosch S, Back J, Garbulsky MF, Penuelas J (2009) PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int J Remote Sens 30(17):4443–4455. doi: 10.1080/01431160802575661 CrossRefGoogle Scholar
  17. Fuentes DA, Gamon JA, Cheng Y, Claudio HC, H-l Q, Mao Z, Sims DA, Rahman AF, Oechel W, Luo H (2006) Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS. Remote Sens Environ 103(3):312–323CrossRefGoogle Scholar
  18. Gamon JA (2015) Reviews and Syntheses: optical sampling of the flux tower footprint. Biogeosciences 12(14):4509–4523. doi: 10.5194/bg-12-4509-2015 CrossRefGoogle Scholar
  19. Gamon JA, Bond B (2013) Effects of irradiance and photosynthetic downregulation on the photochemical reflectance index in Douglas-fir and ponderosa pine. Remote Sens Environ 135:141–149. doi: 10.1016/j.rse.2013.03.032 CrossRefGoogle Scholar
  20. Gamon JA, Penuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41(1):35–44Google Scholar
  21. Gamon JA, Field CB, Goulden ML, Griffin KL, Hartley AE, Joel G, Penuelas J, Valentini R (1995) Relationships between NDVI, canopy structure, and photosynthesis in 3 Californian vegetation types. Ecol Appl 5(1):28–41CrossRefGoogle Scholar
  22. Gamon JA, Serrano L, Surfus JS (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112(4):492–501. doi: 10.1007/s004420050337 CrossRefGoogle Scholar
  23. Gao B (1996) NDWI. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266CrossRefGoogle Scholar
  24. Gao X, Huete AR, Ni WG, Miura T (2000) Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sens Environ 74(3):609–620. doi: 10.1016/s0034-4257(00)00150-4 CrossRefGoogle Scholar
  25. Garbulsky MF, Penuelas J, Papale D, Filella I (2008) Remote estimation of carbon dioxide uptake by a Mediterranean forest. Glob Change Biol 14(12):2860–2867. doi: 10.1111/j.1365-2486.2008.01684.x CrossRefGoogle Scholar
  26. Garbulsky MF, Penuelas J, Gamon J, Inoue Y, Filella I (2011) The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sens Environ 115(2):281–297. doi: 10.1016/j.rse.2010.08.023 CrossRefGoogle Scholar
  27. Genty B, Briantais JM, Baker NR (1989) The relationship between the quantum yield of photosynthetic electron-transport and quenching of chlorophyll fluorescence. Biochimica Et Biophysica Acta 990(1):87–92CrossRefGoogle Scholar
  28. Giorgi F, Lionello P (2008) Climate change projections for the Mediterranean region. Global Planetary Change 2,3:90–104CrossRefGoogle Scholar
  29. Gitelson A, Merzlyak MN (1994) Spectral reflectance changes associated with autumn senescence of Aesculus Hippocastanum L and Acer Platanoides L. leaves - spectral features and relation to chlorophyll estimation. J Plant Physiol 143(3):286–292Google Scholar
  30. Gitelson AA, Merzlyak MN (1998) Remote sensing of chlorophyll concentration in higher plant leaves. Adv Space Res 22(5):689–692. doi: 10.1016/S0273-1177(97)01133-2
  31. Gower ST, Kucharik CJ, Norman JM (1999) Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens Environ 70(1):29–51. doi: 10.1016/S0034-4257(99)00056-5 CrossRefGoogle Scholar
  32. Harley PC, Tenhunen JD, Beyschlag W, Lange OL (1987) Seasonal changes in net photosynthesis rates and photosynthetic capacity in leaves of Cistus salvifolius, a European Mediterranean semi-deciduous shrub. Oecologia 74:380–388CrossRefGoogle Scholar
  33. Heiskanen J, Rautiainen M, Stenberg P, Mõttus M, Vesanto V-H (2013) Sensitivity of narrowband vegetation indices to boreal forest LAI, reflectance seasonality and species composition. ISPRS J Photogramm Remote Sens 78:1–14. doi: 10.1016/j.isprsjprs.2013.01.001 CrossRefGoogle Scholar
  34. Hernandez-Clemente R, Navarro-Cerrillo RM, Suarez L, Morales F, Zarco-Tejada PJ (2011) Assessing structural effects on PRI for stress detection in conifer forests. Remote Sens Environ 115(9):2360–2375. doi: 10.1016/j.rse.2011.04.036 CrossRefGoogle Scholar
  35. Hilker T, Gitelson A, Coops NC, Hall FG, Black TA (2011) Tracking plant physiological properties from multi-angular tower-based remote sensing. Oecologia 165(4):865–876. doi: 10.1007/s00442-010-1901-0 CrossRefGoogle Scholar
  36. Hmimina G, Dufrêne E, Soudani K (2014) Relationship between photochemical reflectance index and leaf ecophysiological and biochemical parameters under two different water statuses: towards a rapid and efficient correction method using real-time measurements. Plant Cell Environ 37(2):473–487. doi: 10.1111/pce.12171 CrossRefGoogle Scholar
  37. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213. doi: 10.1016/S0034-4257(02)00096-2 CrossRefGoogle Scholar
  38. Jongen M, Unger S, Fangueiro D, Cerasoli S, Silva JMN, Pereira JS (2013) Resilience of montado understorey to experimental precipitation variability fails under severe natural drought. Agr Ecosyst Environ 178:18–30. doi: 10.1016/j.agee.2013.06.014 CrossRefGoogle Scholar
  39. Kent A, Coker P (1992) Vegetation description and analysis: a practical approach. Wiley, New YorkGoogle Scholar
  40. Ma X, Huete A, Yu Q, Restrepo-Coupe N, Beringer J, Hutley LB, Kanniah KD, Cleverly J, Eamus D (2014) Parameterization of an ecosystem light-use-efficiency model for predicting savanna GPP using MODIS EVI. Remote Sens Environ 154:253–271. doi: 10.1016/j.rse.2014.08.025 CrossRefGoogle Scholar
  41. Maselli F, Moriondo M, Chiesi M, Chirici G, Puletti N, Barbati A, Corona P (2009) Evaluating the effects of environmental changes on the gross primary production of Italian forests. Remote Sens 1(4):1108–1124. doi: 10.3390/rs1041108 CrossRefGoogle Scholar
  42. Monteith JL (1972) Solar-radiation and productivity in tropical ecosystems. J Appl Ecol 9(3):747–766CrossRefGoogle Scholar
  43. Monteith JL (1977) Climate and efficiency of crop production in Britain. Philos Trans R Soc Lond Ser B-Biol Sci 281(980):277–294CrossRefGoogle Scholar
  44. Myneni RB, Williams DL (1994) On the relationship between FAPAR and NDVI. Remote Sens Environ 49(3):200–211CrossRefGoogle Scholar
  45. Ogutu BO, Dash J (2013) Assessing the capacity of three production efficiency models in simulating gross carbon uptake across multiple biomes in conterminous USA. Agr Forest Meteorol 174:158–169. doi: 10.1016/j.agrformet.2013.02.016 CrossRefGoogle Scholar
  46. Ollinger SV (2010) Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol 189(2):375–394CrossRefGoogle Scholar
  47. Penuelas J, Filella I, Gamon JA (1995) Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol 131(3):291–296. doi: 10.1111/j.1469-8137.1995.tb03064.x CrossRefGoogle Scholar
  48. Pereira JS, Chaves MM (1993) Plant water deficits in Mediterranean ecosystems. In: Smith JAC, Griffiths H (eds) Water deficits: plant responses from cell to community. BIOS Scientific, Oxford, pp 237–251Google Scholar
  49. Pereira JS, Chaves MM, Caldeira MC, Correia AV (2006) Water availability and productivity. In: Morrison JIL, Morecroft D (eds) Plant growth and climate change. Blackwell, London, pp 118–145CrossRefGoogle Scholar
  50. Piñol J, Filella I, Ogaya R, Peñuelas J (1998) Ground-based spectroradiometric estimation of live fine fuel moisture of Mediterranean plants. Agr Forest Meteorol 90(3):173–186. doi: 10.1016/S0168-1923(98)00053-7 CrossRefGoogle Scholar
  51. Pinto CA, Henriques MO, Figueiredo JP, David JS, Abreu FG, Pereira JS, Correia I, David TS (2011) Phenology and growth dynamics in Mediterranean evergreen oaks: effects of environmental conditions and water relations. For Ecol Manage 262(3):500–508. doi: 10.1016/j.foreco.2011.04.018 CrossRefGoogle Scholar
  52. Porcar-Castell A, Garcia-Plazaola JI, Nichol CJ, Kolari P, Olascoaga B, Kuusinen N, Fernandez-Marin B, Pulkkinen M, Juurola E, Nikinmaa E (2012) Physiology of the seasonal relationship between the photochemical reflectance index and photosynthetic light use efficiency. Oecologia 170(2):313–323. doi: 10.1007/s00442-012-2317-9 CrossRefGoogle Scholar
  53. Porcar-Castell A, Mac Arthur A, Rossini M, Eklundh L, Pacheco-Labrador J, Anderson K, Balzarolo M, Martín MP, Jin H, Tomelleri E, Cerasoli S, Sakowska K, Hueni A, Julitta T, Nichol CJ, Vescovo L (2015) EUROSPEC: at the interface between remote sensing and ecosystem CO2 flux measurements in Europe. Biogeosciences Discuss 12(15):13069–13121. doi: 10.5194/bgd-12-13069-2015 CrossRefGoogle Scholar
  54. Potter CS, Randerson JT, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA (1993) Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem Cycles 7(4):811–841. doi: 10.1029/93gb02725 CrossRefGoogle Scholar
  55. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126. doi: 10.1016/0034-4257(94)90134-1
  56. Ripullone F, Rivelli AR, Baraldi R, Guarini R, Guerrieri R, Magnani F, Peñuelas J, Raddi S, Borghetti M (2011) Effectiveness of the photochemical reflectance index to track photosynthetic activity over a range of forest tree species and plant water statuses. Funct Plant Biol 38(3):177–186. doi: 10.1071/FP10078 CrossRefGoogle Scholar
  57. Rondeaux G, Steven M, Baret F (1996) Optimization of soil-adjusted vegetation indices. Remote Sens Environ 55(2):95–107. doi: 10.1016/0034-4257(95)00186-7 CrossRefGoogle Scholar
  58. Rouse J, Haas R, Schell J, Deering D (1974) Monitoring vegetation systems in the great plains with ERTS. In: SP-351 N (ed) Third ERTS Symposium. NASA, Washington, DC, USA, pp 309–317Google Scholar
  59. Running SW, Nemani RR, Heinsch FA, Zhao MS, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54(6):547–560. doi: 10.1641/0006-3568(2004)054[0547:acsmog];2 CrossRefGoogle Scholar
  60. Schimel D, Pavlick R, Fisher JB, Asner GP, Saatchi S, Townsend P, Miller C, Frankenberg C, Hibbard K, Cox P (2015) Observing terrestrial ecosystems and the carbon cycle from space. Glob Change Biol 21(5):1762–1776. doi: 10.1111/gcb.12822 CrossRefGoogle Scholar
  61. Sims DA, Luo H, Hastings S, Oechel WC, Rahman AF, Gamon JA (2006) Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sens Environ 103(3):289–303CrossRefGoogle Scholar
  62. Soudani K, Hmimina G, Dufrêne E, Berveiller D, Delpierre N, Ourcival J-M, Rambal S, Joffre R (2014) Relationships between photochemical reflectance index and light-use efficiency in deciduous and evergreen broadleaf forests. Remote Sens Environ 144:73–84. doi: 10.1016/j.rse.2014.01.017 CrossRefGoogle Scholar
  63. Tagesson T, Mastepanov M, Tamstorf MP, Eklundh L, Schubert P, Ekberg A, Sigsgaard C, Christensen TR, Strom L (2012) High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem 1992–2008. Int J Appl Earth Obs Geoinf 18:407–416. doi: 10.1016/j.jag.2012.03.016 CrossRefGoogle Scholar
  64. Ustin SL, Gamon JA (2010) Remote sensing of plant functional types. New Phytol 186(4):795–816. doi: 10.1111/j.1469-8137.2010.03284.x CrossRefGoogle Scholar
  65. Werner C, Correia O, Beyschlag W (1999) Two different strategies of Mediterranean macchia plants to avoid photoinhibitory damage by excessive radiation levels during summer drought. Acta Oecologica-International Journal of Ecology 20(1):15–23. doi: 10.1016/s1146-609x(99)80011-3 CrossRefGoogle Scholar
  66. Yuan W, Cai W, Xia J, Chen J, Liu S, Dong W, Merbold L, Law B, Arain A, Beringer J, Bernhofer C, Black A, Blanken PD, Cescatti A, Chen Y, Francois L, Gianelle D, Janssens IA, Jung M, Kato T, Kiely G, Liu D, Marcolla B, Montagnani L, Raschi A, Roupsard O, Varlagin A, Wohlfahrt G (2014) Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agr Forest Meteorol 192–193:108–120. doi: 10.1016/j.agrformet.2014.03.007 CrossRefGoogle Scholar

Copyright information

© ISB 2015

Authors and Affiliations

  • Sofia Cerasoli
    • 1
  • Filipe Costa e Silva
    • 1
  • João M. N. Silva
    • 1
  1. 1.Centro de Estudos Florestais, Instituto Superior de AgronomiaUniversidade de LisboaLisboaPortugal

Personalised recommendations