Aboveground Biomass of Grassland



Biomass is an important component of grassland ecosystems and plays a critical role in the sustainable use of grassland resources and the global carbon cycle. Satellite remote sensing provides an important approach for estimating aboveground biomass (AGB) at large spatial scales while biomass harvesting offers reliable and site-specific biomass magnitude and is only way to give indispensable ground truth for satellite remote sensing. In this study, estimate models for grassland AGB for the Lhasa area located at the central Tibetan Plateau (TP) are developed based on the relationships between the field measurements and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (NDVI, EVI), and the models are validated against independent field measurements. The result shows that exponential relationships exist between AGB and MODIS vegetation indices. MODIS NDVI is more effective to estimate grassland AGB in the study area with R2 = 0.63 than EVI with R2 = 0.50 and is an optimal regression model for AGB estimation. For green AGB estimation, the performance of NDVI (R2 = 0.69) is also better than EVI (R2 = 0.59). In the study area, AGB spatially presents decreases from east to west, with great regional differences due to inhomogeneous grassland types and impact of various environmental and climatic factors. AGB is above 100 g/m2 in some eastern regions whereas it is lower than 20 g/m2 in the west.


Aboveground biomass Remote sensing Field measurement MODIS Central Tibetan Plateau 


  1. Agricultural and Pastoral Bureau of Lhasa Municipality. 1991. Land Resources in Lhasa Area, 181–213. Beijing: China Agricultural Science and Technology Press.Google Scholar
  2. Bai, Y.F., J.G. Wu, Q. Xing, et al. 2008. Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau. Ecology 89: 2140–2153.CrossRefGoogle Scholar
  3. Barrachina, M., J. Cristobal, and A.F. Tulla. 2015. Estimating above-ground biomass on mountain meadows and pastures through remote sensing. International Journal of Applied Earth Observation and Geoinformation 38: 184–192.CrossRefGoogle Scholar
  4. Butterfield, H.S., and C.M. Malmstrom. 2009. The effects of phenology on indirect measures of aboveground biomass in annual grasses. International Journal of Remote Sensing 30: 3133–3146.CrossRefGoogle Scholar
  5. Chen, B., X. Zhang, J. Tao, et al. 2014. The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology 189: 11–18.CrossRefGoogle Scholar
  6. Chu, D., L. Lu, and T. Zhang. 2007. Sensitivity of Normalized Difference Vegetation Index (NDVI) to Seasonal and Interannual Climate Conditions in the Lhasa Area, Tibetan Plateau, China. Arctic, Antarctic, and Alpine Research 39 (4): 635–641.CrossRefGoogle Scholar
  7. Costanza, R., R. d’Arge, R. de Groot, S. Farber, M. Grasso, et al. 1997. The value of the world’s ecosystem services and natural capital. Nature 387: 253–260.CrossRefGoogle Scholar
  8. Dusseux, P., L. Hubert-Moy, T. Corpetti, and F. Vertès. 2015. Evaluation of SPOT imagery for the estimation of grassland biomass. International Journal of Applied Earth Observation and Geoinformation 38: 72–77.CrossRefGoogle Scholar
  9. Eisfelder, C., I. Klein, A. Bekkuliyeva, C. Kuenzer, M.F. Buchroithner, and S. Dech. 2017. Above-ground biomass estimation based on NPP time-series –A novel approach for biomass estimation in semi-arid Kazakhstan. Ecological Indicators 72: 13–22.CrossRefGoogle Scholar
  10. Gaitán, J.J., D. Bran, G. Oliva, et al. 2013. Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in Patagonian steppes. Ecological Indicators 34: 181–191.CrossRefGoogle Scholar
  11. Gao, Q., Y. Li, E. Lin, et al. 2006. Temporal and spatial distribution of grassland degradation in Northern Tibet. Acta Geographica Sinica 60 (6): 965–973.Google Scholar
  12. Gao, Q., Y. Li, Y. Wan, X. Qin, W. Jiangcun, and Y. Liu. 2009. Dynamics of alpine grassland NPP and its response to climate change in Northern Tibet. Climatic Change 97: 515–528.CrossRefGoogle Scholar
  13. Gao, Y., X. Liu, C. Min, et al. 2013. Estimation of the North-South Transect of eastern China forest biomass using remote sensing and forest inventory data. International Journal of Remote Sensing 34 (15): 5598–5610.CrossRefGoogle Scholar
  14. Hagen, S.C., P. Heilman, R. Marsett, N. Torbick, W. Salas, J. van Ravensway, and J. Qi. 2012. Mapping total vegetation cover across western rangelands with moderate-resolution imaging spectroradiometer data. Rangel and Ecology and Management 65: 456–467.CrossRefGoogle Scholar
  15. Han, Z., W. Song, X. Deng, and X. Xu. 2018. Grassland ecosystem responses to climate change and human activities within the Three-River Headwaters region of China. Scientific Reports 8 (1): 9079.CrossRefGoogle Scholar
  16. Harris, R.B. 2010. Rangeland degradation on the Qinghai-Tibetan plateau: A review of the evidence of its magnitude and causes. Journal of Arid Environments 74 (1): 1–12.CrossRefGoogle Scholar
  17. Heute, A.R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25: 53–70.CrossRefGoogle Scholar
  18. Houghton, R., D. Skole, C.A. Nobre, J. Hackler, K. Lawrence, and W.H. Chomentowski. 2000. Annual fluxes of carbon from deforestation and regrowth in the BrazilianAmazon. Nature 403 (6767): 301–304.CrossRefGoogle Scholar
  19. Huete, A., C. Justice, and H. Liu. 1994. Development of vegetation and soil indices for MODIS-EOS. Remote Sensing of Environment 49 (3): 224–234.CrossRefGoogle Scholar
  20. Ji, Q., Q. Robeto, and L. Calos. 2008. Dry matter availability assessment in Tibetan grasslands using ground-level remotely-sensed data. Acta Agrestia Sinica 16 (1): 34–38.Google Scholar
  21. Jin, Y., X. Yang, J. Qiu, et al. 2014. Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland, Northern China. Remote Sensing 6 (2): 1496–1513.CrossRefGoogle Scholar
  22. Jobbagy, E.G., and O.E. Sala. 2000. Controls of grass and shrub aboveground production in the patagonian steppe. Ecological Applications 10 (2): 541–549.CrossRefGoogle Scholar
  23. Lauenroth, W.K., H.W. Hunt, D.M. Swift, and J.S. Singh. 1986. Estimating aboveground net primary production in grasslands−A simulation approach. Ecological Modelling 33: 297–314.CrossRefGoogle Scholar
  24. Liang, T., S. Yang, Q. Feng, et al. 2016. Multi-factor modeling of above-ground biomass in alpine grassland: A case study in the three-river headwaters region, China. Remote Sensing of Environment 186: 164–172.CrossRefGoogle Scholar
  25. Liu, S., L. Zhou, C. Qiu, et al. 1999. Grassland Degradation and Desertification in Naqu Prefecture of Tibet. Lhasa: Tibet People’s Press.Google Scholar
  26. Liu, S., X. Su, S. Dong, et al. 2015. Modeling aboveground biomass of an alpine desert grassland with SPOT-VGT NDVI. GIScience and Remote Sensing 52 (6): 680–699.CrossRefGoogle Scholar
  27. Liu, S., F. Cheng, S. Dong, H. Zhao, X. Hou, and X. Wu. 2017. Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet plateau based on validated MODIS NDVI. Scientific Reports 7 (1): 4182.CrossRefGoogle Scholar
  28. Lobell, D.B., and C.B. Field. 2007. Global scale climate-crop yield relationships and the impacts of recent warming. Environmental Research Letters 2 (1): 014002.CrossRefGoogle Scholar
  29. Lu, D. 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing 27 (7): 1297–1328.CrossRefGoogle Scholar
  30. Meng, B., J. Ge, T. Liang, et al. 2017. Evaluation of remote sensing inversion error for the above-ground biomass of alpine meadow grassland based on multi-source satellite data. Remote Sensing 9 (4): 372.CrossRefGoogle Scholar
  31. Ministry of Agriculture of China. 1996. Grassland Resources in China. Beijing: Chinese Sciences and Technology Press.Google Scholar
  32. Nakchu Bureau of Animal Husbandry. 1992. Land resources in Nakchu prefecture of Tibet. Chinese Agricultural Sciences and TechnologyGoogle Scholar
  33. North, P.R.J. 2002. Estimation of f APAR, LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment 80 (1): 114–121.CrossRefGoogle Scholar
  34. Salomonson, V.V., and I. Appel. 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote Sensing of Environment 89 (3): 351–360.CrossRefGoogle Scholar
  35. Segoli, M., E.D. Ungar, and M. Shachak. 2008. Shrubs enhance resilience of a semi-arid ecosystem by engineering and regrowth. Ecohydrology 1 (4): 330–339.CrossRefGoogle Scholar
  36. Shen, M., Y. Tang, J. Klein, et al. 2008. Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau. Journal of Plant Ecology 1 (4): 247–257.CrossRefGoogle Scholar
  37. Shen, M., G. Zhang, N. Cong, S. Wang, W. Kong, and S. Piao. 2014. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agricultural and Forest Meteorology 189: 71–80.CrossRefGoogle Scholar
  38. Tibet Land Management Bureau, and Tibet Animal Husbandry Bureau. 1994. Grassland Resources in Tibet Autonomous Region. Beijing: Science Press.Google Scholar
  39. Tsalyuk, M., M. Kelly, K. Koy, et al. 2015. Monitoring the impact of grazing on rangeland conservation easements using MODIS vegetation indices. Rangeland Ecology & Management 68 (2): 173–185.CrossRefGoogle Scholar
  40. Wang, J., X. Zhang, B. Chen, and P. Shi. 2013. Causes and restoration of degraded alpine grassland in northern Tibet. Journal of Resources and Ecology 4 (1): 43–49.CrossRefGoogle Scholar
  41. White, R., and S.M.R. Murray. 2000. Pilot analysis of global ecosystems: Grassland ecosystems. World Resources Institute.Google Scholar
  42. Yan, F., B. Wu, and Y. Wang. 2015. Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us Sandy Land, China. Agricultural and Forest Meteorology 200: 119–128.CrossRefGoogle Scholar
  43. Yang, Y.H., J.Y. Fang, Y.D. Pan, and C.J. Ji. 2009. Aboveground biomass in Tibetan grasslands. Journal of Arid Environments 73: 91–95.CrossRefGoogle Scholar
  44. Yuan, X., L. Li, X. Tian, G. Luo, and X. Chen. 2016. Estimation of above-ground biomass using MODIS satellite imagery of multiple land-cover types in China. Remote Sensing Letters 7 (12): 1141–1149.CrossRefGoogle Scholar
  45. Zhao, F., B. Xu, X. Yang, et al. 2014. Remote sensing estimates of grassland aboveground biomass based on MODIS net primary productivity (NPP): A case study in the Xilingol grassland of Northern China. Remote Sensing 6 (6): 5368–5386.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Duo Chu
    • 1
  1. 1.Tibet Institute of Plateau Atmospheric and Environmental SciencesTibet Meteorological BureauLhasaChina

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