Tracking of Vegetation Carbon Dynamics from 2001 to 2016 by MODIS GPP in HKH Region

  • Zhenhua ChaoEmail author
  • Mingliang Che
  • Zhanhuan Shang
  • A. Allan Degen


Carbon dynamics, a key index to evaluate ecosystems, are very complex in the Hindu Kush Himalayan (HKH) region due to the topography, diverse regional climate, and different land cover types. MODIS GPP was used to evaluate carbon sequestration in the HKH region from 2001 to 2016. In general, the spatio-temporal variation of the average daily gross primary productivity (GPP) was very heterogeneous due to the changing terrain, diverse regional climate, and different land cover types in the region. Many factors should be considered for GPP measurements, including satellite, airplane, ground-based and modelling data. We concluded that it is necessary to determine the driving forces of GPP in the future in order to establish scientific policies and development programs for the HKH region.


Hindu Kush Himalayan region Carbon dynamics MODIS GPP Spatio-temporal variation Qinghai-Tibet Plateau 


  1. Almeida, C.T.D., R.C. Delgado, L.S. Galvão, et al. 2018. Improvements of the MODIS Gross Primary Productivity model based on a comprehensive uncertainty assessment over the Brazilian Amazonia. ISPRS Journal of Photogrammetry and Remote Sensing 145: 268–283.Google Scholar
  2. Anav, A., P. Friedlingstein, C. Beer, et al. 2015. Spatiotemporal patterns of terrestrial gross primary production: A review. Reviews of Geophysics 53: 785–818.Google Scholar
  3. Cao, R., M. Shen, J. Zhou, et al. 2018. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation. Agricultural and Forest Meteorology 249: 176–186.Google Scholar
  4. Chao, Z., P. Zhang, and X. Wang. 2018. Impacts of urbanization on the net primary productivity and cultivated land change in Shandong province, China. Journal of the Indian Society of Remote Sensing 46: 809–819.Google 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 189-190: 11–18.Google Scholar
  6. Chettri, N., B. Shakya, R. Thapa, et al. 2008. Status of a protected area system in the Hindu Kush-Himalayas: An analysis of PA coverage. The International Journal of Biodiversity Science & Management 4: 164–178.Google Scholar
  7. Cox, P.M., D. Pearson, B.B. Booth, et al. 2013. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494: 341–344.Google Scholar
  8. Dong, T., J. Liu, B. Qian, et al. 2017. Deriving maximum light use efficiency from crop growth model and satellite data to improve crop biomass estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10: 104–117.Google Scholar
  9. Elalem, S., and I. Pal. 2015. Mapping the vulnerability hotspots over Hindu-Kush Himalaya region to flooding disasters. Weather and Climate Extremes 8: 46–58.Google Scholar
  10. Frazier, A.E., C.S. Renschler, and S.B. Miles. 2013. Evaluating post-disaster ecosystem resilience using MODIS GPP data. International Journal of Applied Earth Observation and Geoinformation 21: 43–52.Google Scholar
  11. Guan, K., J.A. Berry, Y. Zhang, et al. 2016. Improving the monitoring of crop productivity using spaceborne solar-induced fluorescence. Global Change Biology 22: 716–726.Google Scholar
  12. Guanter, L., Y. Zhang, M. Jung, et al. 2014. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proceedings of the National Academy of Sciences of the United States of America 111: E1327–E1333.Google Scholar
  13. Han, Z., W. Song, X. Deng, et al. 2018. Grassland ecosystem responses to climate change and human activities within the Three-River Headwaters region of China. Scientific Reports 8: 9079. Scholar
  14. Hilker, T., N.C. Coops, M.A. Wulder, et al. 2008. The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements. Science of the Total Environment 404: 411–423.Google Scholar
  15. Hua, T., and X. Wang. 2018. Temporal and spatial variations in the climate controls of vegetation dynamics on the Tibetan Plateau during 1982–2011. Advances in Atmospheric Sciences 35: 1337–1346.Google Scholar
  16. Justice, C.O., J.R.G. Townshend, E.F. Vermote, E. Masuoka, et al. 2002. An overview of MODIS Land data processing and product status. Remote Sensing of Environment 83: 3–15.Google Scholar
  17. Keenan, T.F., I. Baker, A. Barr, et al. 2012. Terrestrial biosphere model performance for inter-annual variability of land-atmosphere CO2 exchange. Global Change Biology 18: 1971–1987.Google Scholar
  18. Li, Q., C. Zhang, Y. Shen, et al. 2016. Quantitative assessment of the relative roles of climate change and human activities in desertification processes on the Qinghai-Tibet Plateau based on net primary productivity. Catena 147: 789–796.Google Scholar
  19. Lin, S., J. Li, Q. Liu, et al. 2018. Effects of forest canopy vertical stratification on the estimation of gross primary production by remote sensing. Remote Sensing 10: 1329. Scholar
  20. Liu, D., Y. Li, T. Wang, et al. 2018. Contrasting responses of grassland water and carbon exchanges to climate change between Tibetan Plateau and Inner Mongolia. Agricultural and Forest Meteorology 249: 163–175.Google Scholar
  21. Miao, F., Z. Guo, R. Xue, X. Wang, Y. Shen, C. Cooper, 2015. Effects of Grazing and Precipitation on Herbage Biomass, Herbage Nutritive Value, and Yak Performance in an Alpine Meadow on the Qinghai–Tibetan Plateau. PLOS ONE 10(6):e0127275Google Scholar
  22. Monteith, J.L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9: 747–766.Google Scholar
  23. Morel, J., A. Bégué, P. Todoroff, et al. 2014. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. European Journal of Agronomy 61: 60–68.Google Scholar
  24. Peng, J., Z. Liu, Y. Liu, et al. 2012. Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent. Ecological Indicators 14: 28–39.Google Scholar
  25. Propastin, P., and M. Kappas. 2009. Modeling net ecosystem exchange for grassland in central Kazakhstan by combining remote sensing and field data. Remote Sensing 1: 159–183.Google Scholar
  26. Ren, G., and A.B. Shrestha. 2017. Climate change in the Hindu Kush Himalaya. Advances in Climate Change Research 8: 137–140.Google Scholar
  27. Revadekar, J.V., S. Hameed, D. Collins, et al. 2013. Impact of altitude and latitude on changes in temperature extremes over South Asia during 1971-2000. International Journal of Climatology 33: 199–209.Google Scholar
  28. Running, S., Q. Mu, M. Zhao, 2015. MOD17A2H MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. doi: 10.5067/MODIS/MOD17A2H.006Google Scholar
  29. Sánchez, M.L., N. Pardo, I.A. Pérez, et al. 2015. GPP and maximum light use efficiency estimates using different approaches over a rotating biodiesel crop. Agricultural and Forest Meteorology 214-215: 444–455.Google Scholar
  30. Schubert, P., F. Lagergren, M. Aurela, et al. 2012. Modeling GPP in the Nordic forest landscape with MODIS time series data—Comparison with the MODIS GPP product. Remote Sensing of Environment 126: 136–147.Google Scholar
  31. Sharma, E., D. Molden, P. Wester, et al. 2016. The Hindu Kush Himalayan monitoring and assessment programme: Action to sustain a global asset. Mountain Research and Development 36: 236–239.Google Scholar
  32. Shen, M., S. Piao, X. Chen, et al. 2016. Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Global Change Biology 22: 3057–3066.Google Scholar
  33. Shi, F., X. Wu, X. Li, et al. 2018. Weakening relationship between vegetation growth over the Tibetan Plateau and large-scale climate variability. Journal of Geophysical Research: Biogeosciences 123: 1247–1259.Google Scholar
  34. Shim, C., J. Hong, J. Hong, et al. 2014. Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea. Advances in Space Research 54: 2296–2308.Google Scholar
  35. Sun, X., G. Ren, A.B. Shrestha, et al. 2017. Changes in extreme temperature events over the Hindu Kush Himalaya during 1961–2015. Advances in Climate Change Research 8: 157–165.Google Scholar
  36. Sun, Y., C. Frankenberg, M. Jung, et al. 2018. Overview of Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sensing of Environment 209: 808–823.Google Scholar
  37. Tiwari, P.C., and B. Joshi. 2015. Local and regional institutions and environmental governance in Hindu Kush Himalaya. Environmental Science & Policy 49: 66–74.Google Scholar
  38. Turner, D.P., W.D. Ritts, W.B. Cohen, et al. 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment 102: 282–292.Google Scholar
  39. United Nations, 2017. World Population Prospects: The 2017 Revision. Scholar
  40. Vitousek, P.M., H.A. Mooney, J. Lubchenco, et al. 1997. Human domination of earth’s ecosystems. Science 277: 6.Google Scholar
  41. Wu, Z., P. Dijkstra, G.W. Koch, J. Peñuelas, B.A. Hungate, 2011. Responses of terrestrial ecosystems to temperature and precipitation change: a meta-analysis of experimental manipulation. Global Change Biology 17(2):927–942.Google Scholar
  42. Xin, Q., M. Broich, A.E. Suyker, et al. 2015. Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States. Agricultural and Forest Meteorology 201: 111–119.Google Scholar
  43. Xu, J., R.E. Grumbine, A. Shrestha, et al. 2009. The melting Himalayas: Cascading effects of climate change on water, biodiversity, and livelihoods. Conservation Biology 23: 520–530.Google Scholar
  44. You, Q., G. Ren, Y. Zhang, et al. 2017. An overview of studies of observed climate change in the Hindu Kush Himalayan (HKH) region. Advances in Climate Change Research 8: 141–147.Google Scholar
  45. Zeng, Y., X. Chen, and W. Jin. 2014. Land use/cover change and its impact on soil carbon in eastern part of Qinghai Plateau in near 10 years. Transactions of the Chinese Society of Agricultural Engineering 30: 275–282. (in Chinese).Google Scholar
  46. Zhang, C.L., Q. Li, Y.P. Shen, et al. 2018. Monitoring of aeolian desertification on the Qinghai-Tibet Plateau from the 1970s to 2015 using Landsat images. Science of the Total Environment 619-620: 1648–1659.Google Scholar
  47. Zhang, J., C. Liu, H. Hao, et al. 2015. Spatial-temporal change of carbon storage and carbon sink of grassland ecosystem in the Three-River Headwaters region based on MODIS GPP/NPP data. Ecology and Environmental Sciences 24: 8–13.Google Scholar
  48. Zhang, Q., J.M. Chen, W. Ju, et al. 2017. Improving the ability of the photochemical reflectance index to track canopy light use efficiency through differentiating sunlit and shaded leaves. Remote Sensing of Environment 194: 1–15.Google Scholar
  49. Zhao, M., F.A. Heinsch, R.R. Nemani, et al. 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 95: 164–176.Google Scholar
  50. Zhao, M., and S.W. Running. 2010. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329: 940–943.Google Scholar
  51. Zheng, Y., L. Zhang, J. Xiao, et al. 2018. Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agricultural and Forest Meteorology 263: 242–257.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhenhua Chao
    • 1
    Email author
  • Mingliang Che
    • 1
  • Zhanhuan Shang
    • 2
  • A. Allan Degen
    • 3
  1. 1.School of Geographic ScienceNantong UniversityNantongChina
  2. 2.School of Life Sciences, State Key Laboratory of Grassland Agro-EcosystemsLanzhou UniversityLanzhouChina
  3. 3.Desert Animal Adaptations and Husbandry, Wyler Department of Dryland AgricultureBlaustein Institutes for Desert Research, Ben-Gurion University of the NegevBeer ShevaIsrael

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