Community Ecology

, Volume 18, Issue 1, pp 56–62 | Cite as

Comparing the accuracy of three non-destructive methods in estimating aboveground plant biomass

  • G. ÓnodiEmail author
  • Gy. Kröel-Dulay
  • E. Kovács-Láng
  • P. Ódor
  • Z. Botta-Dukat
  • B. Lhotsky
  • S. Barabás
  • J. Garadnai
  • M. Kertész
Open Access


Aboveground plant biomass is one of the most important features of ecosystems, and it is widely used in ecosystem research. Non-destructive biomass estimation methods provide an important toolkit, because the destructive harvesting method is in many cases not feasible. However, only few studies have compared the accuracy of these methods in grassland communities to date. We studied the accuracy of three widely used methods for estimation of aboveground biomass: the visual cover estimation method, the point intercept method, and field spectroscopy. We applied them in three independent series of field samplings in semi-arid sand grasslands in Central Hungary. For each sampling method, we applied linear regression to assess the strength of the relationship between biomass proxies and actual aboveground biomass, and used coefficient of determination to evaluate accuracy. We found no evidence that the visual cover estimation, which is generally considered as a subjective method, was less accurate than point intercept method or field spectroscopy in estimating biomass. Based on our three datasets, we found that accuracy was lower for the point intercept method compared to the other two methods, while field spectroscopy and visual cover estimation were similar to each other in the semi-arid sand grassland community. We conclude that visual cover estimation can be as accurate for estimating aboveground biomass as other approaches, thus the choice amongst the methods should be based on additional pros and cons associated with each of the method and related to the specific research objective.


Biomass proxies Coefficient of determination Field experiment Field spectroscopy Point intercept method Semi-arid grassland Visual cover estimation 



Aboveground Net Primary Productivity


Normalized Differential Vegetation Index


The Plant List (2010) 


  1. Asrar, G., Kanemasu, E.T., Miller, G.P. and Weiser, R.L. 1986. Light interception and leaf area estimates from measurements of grass canopy reflectance. IEEE Trans. Geosci. Remote Sens. GE-24: 76–82.CrossRefGoogle Scholar
  2. Bråthen, K.A. and Hagberg, O. 2004. More efficient estimation of plant biomass. J. Veg. Sci. 15: 653–660.CrossRefGoogle Scholar
  3. Braun-Blanquet, J. 1932. Plant Sociology. The Study of Plant Communities. McGraw-Hill, New York.Google Scholar
  4. Byrne, K.M., Lauenroth, W.K. and Adler, P.B. 2013. Contrasting effects of precipitation manipulations on production in two sites within the Central Grassland Region, USA. Ecosystems 16: 1039–1051.CrossRefGoogle Scholar
  5. Byrne, K.M., Lauenroth, W.K., Adler, P.B. and Byrne, C.M., 2011. Estimating aboveground net primary production in grasslands: A comparison of nondestructive methods. Rangel. Ecol. Manag. 64: 498–505.CrossRefGoogle Scholar
  6. Canfield, R.H. 1941. Application of the line interception method in sampling range vegetation. J. For. 39: 388–394.Google Scholar
  7. Catchpole, W.R. and Wheeler, C.J. 1992. Estimating plant biomass: A review of techniques. Aust. J. Ecol. 17: 121–131.CrossRefGoogle Scholar
  8. Damgaard, C., Merlin, A., Mesléard, F. and Bonis, A. 2011. The demography of space occupancy: measuring plant colonization and survival probabilities using repeated pin-point measurements. Methods Ecol. Evol. 2: 110–115.CrossRefGoogle Scholar
  9. Döbert, T.F., Webber, B.L., Sugau, J.B., Dickinson, K.J.M. and Didham, R.K. 2015. Can leaf area index and biomass be estimated from Braun-Blanquet cover scores in tropical forests? J. Veg. Sci. 26: 1043–1053.CrossRefGoogle Scholar
  10. Faraway, J.J. 2005. Linear Models with R. CRC Press, Boca Raton.Google Scholar
  11. Fay, P.A., Blair, J.M., Smith, M.D., Nippert, J.B., Carlisle, J.D. and Knapp, A.K. 2011. Relative effects of precipitation variability and warming on tallgrass prairie ecosystem function. Biogeosciences 8: 3053–3068.CrossRefGoogle Scholar
  12. Filella, I., Peñuelas, J., Llorens, L. and 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: 308–318.CrossRefGoogle Scholar
  13. Frank, D.A. and McNaughton, S.J. 1990. Aboveground biomass estimation with the canopy intercept method: A plant growth form caveat. Oikos 57: 57–60.CrossRefGoogle Scholar
  14. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Peñuelas, J. and Valentini, R. 1995. Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 5: 28–41.CrossRefGoogle Scholar
  15. Gilgen, A.K. and Buchmann, N. 2009. Response of temperate grasslands at different altitudes to simulated summer drought differed but scaled with annual precipitation. Biogeosciences 6: 2525– 2539.CrossRefGoogle Scholar
  16. Godínez-Alvarez, H., Herrick, J.E., Mattocks, M., Toledo, D. and Van Zee, J. 2009. Comparison of three vegetation monitoring methods: Their relative utility for ecological assessment and monitoring. Ecol. Indic. 9: 1001–1008.CrossRefGoogle Scholar
  17. Goodall, D. 1952. Some considerations in the use of point quadrats for the analysis of vegetation. Aust. J. Biol. Sci. 5: 1–41.CrossRefGoogle Scholar
  18. Greig-Smith, P. 1983. Quantitative Plant Ecology. University of California Press, Berkeley.Google Scholar
  19. Grime, J.P., Fridley, J.D., Askew, A.P., Thompson, K., Hodgson, J.G. and Bennett, C.R. 2008. Long-term resistance to simulated climate change in an infertile grassland. Proc. Natl. Acad. Sci. 105: 10028–10032.CrossRefPubMedPubMedCentralGoogle Scholar
  20. Gu, Y., Wylie, B.K., Howard, D.M., Phuyal, K.P. and Ji, L. 2013. NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin, USA. Ecol. Indic. 30: 1–6.CrossRefGoogle Scholar
  21. Hahn, I. and Scheuring, I. 2003. The effect of measurement scales on estimating vegetation cover: a computer-assisted experiment. Community Ecol. 4: 29–33.Google Scholar
  22. Jobbágy, E.G., Sala, O.E. and Paruelo, J.M. 2002. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83: 307–319.Google Scholar
  23. Jonasson, S. 1988. Evaluation of the point intercept method for the estimation of plant biomass. Oikos 52: 101–106.CrossRefGoogle Scholar
  24. Klimeš, L. 2003. Scale-dependent variation in visual estimates of grassland plant cover. J. Veg. Sci. 14: 815–821.CrossRefGoogle Scholar
  25. Knapp, A., Carroll, C.W., Denton, E., La Pierre, K., Collins, S. and Smith, M. 2015. Differential sensitivity to regional-scale drought in six central US grasslands. Oecologia 177: 949–957.CrossRefGoogle Scholar
  26. Kongstad, J., Schmidt, I.K., Riis-Nielsen, T., Arndal, M.F., Mikkelsen, T.N. and Beier, C. 2012. High resilience in heathland plants to changes in temperature, drought, and CO2 in combination: Results from the CLIMAITE experiment. Ecosystems 15: 269–283.CrossRefGoogle Scholar
  27. Kovács-Láng, E., Kröel-Dulay, G., Kertész, M., Fekete, G., Mika, J., Dobi-Wantuch, I., Rédei, T., Rajkai, K., Hahn, I. and Bartha, S. 2000. Changes in the composition of sand grasslands along a climatic gradient in Hungary and implications for climate change. Phytocoenologia 30: 385–408.CrossRefGoogle Scholar
  28. Kovács-Láng, E., Molnár, E., Kröel-Dulay, G. and Barabás, S. 2008. The KISKUN LTER: Long-term ecological research in the Kiskunság, Hungary. Institute of Ecology and Botany, Hungarian Academy of Sciences, Vácrátót.Google Scholar
  29. Kröel-Dulay, G., Ransijn, J., Schmidt, I.K., Beier, C., De Angelis, P., de Dato, G., Dukes, J.S., Emmett, B., Estiarte, M., Garadnai, J., Kongstad, J., Kovács-Láng, E., Larsen, K.S., Liberati, D., Ogaya, R., Riis-Nielsen, T., Smith, A.R., Sowerby, A., Tietema, A. and Penuelas, J. 2015. Increased sensitivity to climate change in disturbed ecosystems. Nat. Commun. 6: 6682 doi:10.1038/ ncomms7682.CrossRefGoogle Scholar
  30. Milton, E.J., Schaepman, M.E., Anderson, K., Kneubühler, M. and Fox, N. 2009. Progress in field spectroscopy. Remote Sens. Environ., Imaging Spectroscopy Special Issue 113, Supplement 1: S92–S109.CrossRefGoogle Scholar
  31. Molnár, Z. 2003. Sanddunes in Hungary (Kiskunság). TermészetBÚVÁR Alapítvány Kiadó, Budapest.Google Scholar
  32. Nemani, R.R., Keeling, C.D., Hashimoto, H., Jolly, W.M., Piper, S.C., Tucker, C.J., Myneni, R.B. and Running, S.W. 2003. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300: 1560–1563.CrossRefGoogle Scholar
  33. Paruelo, J.M., Epstein, H.E., Lauenroth, W.K. and Burke, I.C. 1997. ANPP estimates from NDVI for the Central Grassland Region of the United States. Ecology 78: 953–958.CrossRefGoogle Scholar
  34. Pearson, R.L., Miller, L.D. and Tucker, C.J. 1976. Hand-held spectral radiometer to estimate gramineous biomass. Appl. Opt. 15: 416–418.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Peet, R.K., Wentworth, T.R. and White, P.S. 1998. A flexible, multipurpose method for recording vegetation composition and structure. Castanea 63: 262–274.Google Scholar
  36. Peñuelas, J., Prieto, P., Beier, C., Cesaraccio, C., De ANGELIS, P., De DATO, G., Emmett, B.A., Estiarte, M., Garadnai, J., Gorissen, A., Láng, E.K., Kröel-Dulay, G., Llorens, L., Pellizzaro, G., Riis-Nielsen, T., Schmidt, I.K., Sirca, C., Sowerby, A., Spano, D. and Tietema, A. 2007. Response of plant species richness and primary productivity in shrublands along a north–south gradient in Europe to seven years of experimental warming and drought: reductions in primary productivity in the heat and drought year of 2003. Glob. Change Biol. 13: 2563–2581.CrossRefGoogle Scholar
  37. R Core Team, 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL
  38. Redjadj, C., Duparc, A., Lavorel, S., Grigulis, K., Bonenfant, C., Maillard, D., Saïd, S. and Loison, A. 2012. Estimating herbaceous plant biomass in mountain grasslands: a comparative study using three different methods. Alp. Bot. 122: 57–63.CrossRefGoogle Scholar
  39. Röttgermann, M., Steinlein, T., Beyschlag, W. and Dietz, H. 2000. Linear relationships between aboveground biomass and plant cover in low open herbaceous vegetation. J. Veg. Sci. 11: 145– 148.CrossRefGoogle Scholar
  40. Roujean, J.-L. and Breon, F.-M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens. Environ. 51: 375–384.CrossRefGoogle Scholar
  41. Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A. and Harlan, J.C. 1974. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA/GSFCT Type III Final Report, Greenbelt, MD, USA.Google Scholar
  42. Sala, O.E. and Austin, A.T. 2000. Methods of estimating above-ground net primary productivity. In: Sala, O.E., Jackson, R.B., Mooney, H.A. and Howarth, R.W. (eds.), Methods in Ecosystem Science. Springer New York, pp. 31–43.CrossRefGoogle Scholar
  43. Sala, O.E., Parton, W.J., Joyce, L.A. and Lauenroth, W.K. 1988. Primary production of the Central Grassland Region of the United States. Ecology 69: 40–45.CrossRefGoogle Scholar
  44. Scurlock, J.M.O., Johnson, K. and Olson, R.J. 2002. Estimating net primary productivity from grassland biomass dynamics measurements. Glob. Change Biol. 8: 736–753.CrossRefGoogle Scholar
  45. Sykes, J.M., Horrill, A.D. and Mountford, M.D. 1983. Use of visual cover assessments as quantitative estimators of some British woodland taxa. J. Ecol. 71: 437–450.CrossRefGoogle Scholar
  46. The Plant List, 2010. Version 1. Published on the Internet; (accessed 1st January).
  47. Tucker, C.J. and Sellers, P.J. 1986. Satellite remote sensing of primary production. Int. J. Remote Sens. 7: 1395–1416.CrossRefGoogle Scholar
  48. Whitbeck, M. and Grace, J.B. 2006. Evaluation of non-destructive methods for estimating biomass in marshes of the upper Texas, USA coast. Wetlands 26: 278–282.CrossRefGoogle Scholar
  49. Wilson, J.B. 2011. Cover plus: ways of measuring plant canopies and the terms used for them. J. Veg. Sci. 22: 197–206.CrossRefGoogle Scholar
  50. Wintle, B.C., Fidler, F., Vesk, P.A., L. Moore, J. 2013. Improving visual estimation through active feedback. Methods Ecol. Evol. 4: 53–62.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2017

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • G. Ónodi
    • 1
    Email author
  • Gy. Kröel-Dulay
    • 1
    • 2
  • E. Kovács-Láng
    • 1
  • P. Ódor
    • 1
  • Z. Botta-Dukat
    • 1
  • B. Lhotsky
    • 1
  • S. Barabás
    • 3
  • J. Garadnai
    • 1
  • M. Kertész
    • 1
  1. 1.MTA Centre for Ecological ResearchInstitute of Ecology and BotanyVácrátótHungary
  2. 2.MTA Centre for Ecological ResearchGINOP Sustainable Ecosystems GroupTihanyHungary
  3. 3.Corvinus University of Budapest, Department of BotanyBudapestHungary

Personalised recommendations