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Journal of Mountain Science

, Volume 16, Issue 2, pp 323–336 | Cite as

Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model

  • Hui Ye
  • Xiao-tao Huang
  • Ge-ping LuoEmail author
  • Jun-bang Wang
  • Miao Zhang
  • Xin-xin Wang
Article

Abstract

Remote sensing (RS) technologies provide robust techniques for quantifying net primary productivity (NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model (DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC·m-2yr-1 over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC·m-2yr-1, the annual NPP with an increasing trend was observed at a rate of 1.04 gC·m-2yr-1 between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.

Keywords

Remote sensing Defoliation formulation model Net primary production Grazed land Spatial-temporal patterns Xinjiang 

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Notes

Acknowledgements

This work was supported by the international Partnership Program of the Chinese Academy of Science (Grant No. 131965KYSB20160004), the National Natural Science Foundation of China (Grant No. U1803243), the Network Plan of the Science and Technology Service, Chinese Academy of Sciences (STS Plan) and Qinghai innovation platform construction project (2017-ZJ-Y20).

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References

  1. Digital elevation data set of China. Available online at: http://www.wdc.cn/wdcdrre (Accessed on 2018-03-21) Global Livestock Information System (GLIS). Available online at: http://www.fao.org/docrep/010/a1259e/a1259e00.htm (Accessed on 2018-03-06) The standard of Chinese Ministry of Agriculture. Available online at: http://www.chinaforage.com/standard/zaixuliang.htm (Accessed on 2018-04-11) Xinjiang Yearbook. Available online at: http://www.xjtj.gov.cn/stats data/50years/(Accessed on 2018-04-16
  2. Abdalla K, Chivenge P, Everson C, et al. (2016) Long-term annual burning of grassland increases CO2 emissions from soils. Geoderma 282: 80–86.  https://doi.org/10.1016/j.geoderma.2016.07.009 Google Scholar
  3. Ali I, Cawkwell F, Dwyer E, et al. (2016) Satellite remote sensing of grasslands: from observation to management. Journal of Plant Ecology 9: 649–671.  https://doi.org/10.1093/jpe/rtw005 Google Scholar
  4. An YZ, Gao W, Gao ZQ, et al. (2013) Assessment of Desertification in the Agro-Pastoral Transitional Zone in Northern China (1982- 2006) Using GIMMS NDVI Data. In: Remote Sensing and Modeling of Ecosystems for Sustainability X.  https://doi.org/10.1117/12.2021857 Google Scholar
  5. Anderson G, Hanson J, Haas R (1993) Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment, 45(2): 165–175.  https://doi.org/10.1016/0034-4257(93)90040-5 Google Scholar
  6. Banks T, Doman S (2001) Kazakh nomads, rangeland policy and the environment in Altay: insights from new range ecology. In: Second International Convention of Asia Scholars, Free University, Berlin.Google Scholar
  7. Bouwman A, Lee D, Asman W, et al. (1997) A global highresolution emission inventory for ammonia. Global Biogeochemical Cycles 11(4): 561–587.  https://doi.org/10.1029/97GB02266 Google Scholar
  8. Cao M, Prince SD, Small J, et al. (2004) Remotely sensed interannual variations and trends in terrestrial net primary productivity 1981–2000. Ecosystems 7: 233–242.  https://doi.org/10.1007/s10021-003-0189-x Google Scholar
  9. Chen J, Shen M, Kato T (2009) Diurnal and seasonal variations in light-use efficiency in an alpine meadow ecosystem: causes and implications for remote sensing. Journal of Plant Ecology 2(4): 173–185.  https://doi.org/10.1093/jpe/rtp020 Google Scholar
  10. Chen Y, Lee G, Lee P, et al. (2007) Model analysis of grazing effect on above-ground biomass and above-ground net primary production of a Mongolian grassland ecosystem. Journal of Hydrology 333(1): 155–164.  https://doi.org/10.1016/j.jhydrol.2006.07.019 Google Scholar
  11. Council NR (1985) Nutrient requirements of sheep, National Academies.Google Scholar
  12. Dangal SR, Tian H, Lu C, et al. (2016) Synergistic effects of climate change and grazing on net primary production of Mongolian grasslands. Ecosphere 7(5): e01274.  https://doi.org/10.1002/ecs2.1274 Google Scholar
  13. Dangal SR (2017) Interactive Effects of Climate Change and Grazing on Ecosystem Productivity and Greenhouse Gas Balance at Multiple Scales from Landscape to Global. Auburn University, Alabama, The Unite StatesGoogle Scholar
  14. Davis SC, Burkle LA, Cross WF, et al. (2014) The effects of timing of grazing on plant and arthropod communities in high-elevation grasslands. Plos One 9(10): e110460.  https://doi.org/10.1371/journal.pone.0110460 Google Scholar
  15. Dong YQ, Sun ZJ, An SH, et al. (2017) Natural Restoration of Degraded Grassland on the Northern Xinjiang, China: The Restoration Difference between Lightly and Moderately Degraded Deserts under Grazing Exclusion. Fresenius Environmental Bulletin 26(6): 3845–3855.Google Scholar
  16. Eichelmann E, Wagner-Riddle C, Warland J, et al. (2016) Evapotranspiration, water use efficiency, and energy partitioning of a mature switchgrass stand. Agricultural and Forest Meteorology 217: 108–119.  https://doi.org/10.1016/j.agrformet.2015.11.008 Google Scholar
  17. Feng X, Zhao Y (2011) Grazing intensity monitoring in Northern China steppe: Integrating CENTURY model and MODIS data. Ecological Indicators 11(1): 175–182.  https://doi.org/10.1016/j.ecolind.2009.07.002 Google Scholar
  18. Feng YX, Luo GP, Zhou DC, et al. (2011) Effects of land use change on landscape pattern of the Manas River watershed in Xinjiang, China. Environmental Earth Sciences 64(8): 2067–2077.  https://doi.org/10.1007/s12665-011-1029-5 Google Scholar
  19. Fetzel T, Havlik P, Herrero M, et al. (2017) Quantification of uncertainties in global grazing systems assessment. Global Biogeochemical Cycles, 31: 1089–1102.  https://doi.org/10.1002/2016GB005601 Google Scholar
  20. Flynn ES, Dougherty CT, Wendroth O (2008) Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors. Agronomy Journal, 100(1): 114–121.  https://doi.org/10.2134/agrojnl2006.0363 Google Scholar
  21. Gao J (2006) Quantification of grassland properties: how it can benefit from geoinformatic technologies? International Journal of Remote Sensing, 27(7): 1351–1365.  https://doi.org/10.1080/01431160500474357 Google Scholar
  22. Gill R, Kelly R, Parton W, et al. (2002) Using simple environmental variables to estimate below‐ground productivity in grasslands. Global ecology and biogeography 11(1): 79–86.  https://doi.org/10.1046/j.1466-822X.2001.00267.x Google Scholar
  23. Gomez-Casanovas N, Delucia NJ, Bernacchi CJ, et al. (2018) Grazing alters net ecosystem C fluxes and the global warming potential of a subtropical pasture. Ecological Applications 28(2): 557–572.  https://doi.org/10.1002/eap.1670 Google Scholar
  24. Gu A, Fan Y, Wu H, et al. (2010) Relationship between the number of three main microorganisms and the soil environment of degraded grassland on the north slope of the Tianshan Mountains. Acta Prataculturae Sinica 19(2): 116–123.  https://doi.org/10.11686/cyxb20100217(In Chinese)Google Scholar
  25. Han QF, Luo GP, Li CF, et al. (2014) Modeling the grazing effect on dry grassland carbon cycling with Biome-BGC model. Ecological Complexity 17: 149–157.  https://doi.org/10.1016/j.ecocom.2013.12.002 Google Scholar
  26. Han QF, Luo GP, Li CF, et al. (2016) Simulated grazing effects on carbon emission in Central Asia. Agricultural and Forest Meteorology 216: 203–214.  https://doi.org/10.1016/j.agrformet.2015.10.007 Google Scholar
  27. Herrero M, Thornton PK (2013) Livestock and global change: emerging issues for sustainable food systems. PNAS 110 (52): 20878–20881.  https://doi.org/10.1073/pnas.1321844111 Google Scholar
  28. Herrero M, Thornton PK(2013) Livestock and global change: emerging issues for sustainable food systems. Proceedings of the National Academy of Sciences: 110, 20878–20881.Google Scholar
  29. Huang XT, Luo GP, Lv NN (2017) Spatio-temporal patterns of grassland evapotranspiration and water use efficiency in arid areas. Ecological Research: 1–13.  https://doi.org/10.1007/s11284-017-1463-2 Google Scholar
  30. Hutchinson G, Mcintosh P (2000) A case study of integrated risk assessment mapping in the Southland region of New Zealand. Environmental toxicology and chemistry, 19(4): 1143–1147.  https://doi.org/10.1002/etc.5620190446 Google Scholar
  31. Kawamura K, Akiyama T, Yokota HO, et al. (2005) Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China. Agriculture, Ecosystems & Environment 107(1): 83–93.  https://doi.org/10.1016/j.agee.2004.09.008 Google Scholar
  32. Keeling R, Piper S, Bollenbacher A, et al. (2009) Atmospheric carbon dioxide record from Mauna Loa. Oak Ridge National Laboratory (ORNL), United States.Google Scholar
  33. Li A, Wu J, Huang J (2012) Distinguishing between human-induced and climate-driven vegetation changes: a critical application of RESTREND in inner Mongolia. Landscape ecology 27(7): 969–982.  https://doi.org/10.1007/s10980-012-9751-2 Google Scholar
  34. Li CF, Zhang C, Luo GP, et al. (2015) Carbon stock and its responses to climate change in Central Asia. Global Change Biology 21(5): 1951–1967.  https://doi.org/10.1111/gcb.12846 Google Scholar
  35. Li SG, Asanuma J, Kotani A, et al. (2007) Evapotranspiration from a Mongolian steppe under grazing and its environmental constraints. Journal of Hydrology 333(1): 133–143.  https://doi.org/10.1016/j.jhydrol.2006.07.021 Google Scholar
  36. Liang S, Yi Q, Liu J (2015) Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator. Ecological Indicators 58: 64–76.  https://doi.org/10.1016/j.ecolind.2015.05.036 Google Scholar
  37. Liu XY, Long RJ, Shang ZH (2011) Evaluation method of ecological services function and their value for grassland ecosystems. Acta Prataculturae Sinica 1(20): 167–174.  https://doi.org/10.11686/cyxb20110124(In Chinese)Google Scholar
  38. Luo GP, Han QF, Zhou DC, et al. (2012) Moderate grazing can promote aboveground primary production of grassland under water stress. Ecological Complexity 11: 126–136.  https://doi.org/10.1016/j.ecocom.2012.04.004 Google Scholar
  39. Petz K, Alkemade R, Bakkenes M, et al. (2014) Mapping and modelling trade-offs and synergies between grazing intensity and ecosystem services in rangelands using global-scale datasets and models. Global Environmental Change 29: 223–234.  https://doi.org/10.1016/j.gloenvcha.2014.08.007 Google Scholar
  40. Potter CS, Randerson JT, Field CB, et al. (1993) Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles 7(4): 811–841.  https://doi.org/10.1029/93GB02725 Google Scholar
  41. Prince SD, Colstoun BE, Kravitz LL (1998) Evidence from rain‐use efficiencies does not indicate extensive Sahelian desertification. Global Change Biology, 4(4): 359–374.  https://doi.org/10.1046/j.1365-2486.1998.00158.x Google Scholar
  42. Psomas A, Kneubühler M, Huber S, et al. (2011) 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.  https://doi.org/10.1080/01431161.2010.532172 Google Scholar
  43. Osem Y, Perevolotsky A, Kigel J (2002) Grazing effect on diversity of annual plant communities in a semi‐arid rangeland: interactions with small ‐ scale spatial and temporal variation in primary productivity. Journal of Ecology 90(6): 936–946.  https://doi.org/10.1046/j.1365-2745.2002.00730.x Google Scholar
  44. Quaife T, Quegan S, Disney M, et al. (2008) Impact of land cover uncertainties on estimates of biospheric carbon fluxes. Global Biogeochemical Cycles 22(4).  https://doi.org/10.1029/2007GB003097.
  45. Ren X, Zheng JH, Mu C, et al. (2017) Correlation analysis of the spatial-temporal variation of grassland net primary productivity and climate factors in Xinjiang in the past 15 years. Ecological Science 36(3): 43–51. (In Chinese)Google Scholar
  46. Seligman NG, Cavagnaro JB, Horno ME (1992) Simulation of defoliation effects on primary production of a warm-season, semiarid perennial-species grassland. Ecological Modelling, 60(1): 45–61.  https://doi.org/10.1016/0304-3800(92)90012-4 Google Scholar
  47. Su R, Cheng J, Chen D, et al. (2017) Effects of grazing on spatiotemporal variations in community structure and ecosystem function on the grasslands of Inner Mongolia, China. Scientific Reports 7(1): 40.  https://doi.org/10.1038/s41598-017-00105-y Google Scholar
  48. Steinfeld H, Gerber P, Wassenaar T, et al. (2006) Livestock’s long shadow: environmental issues and options, Food & Agriculture Org.Google Scholar
  49. Sternberg T, Tsolmon R, Middleton N, et al. (2011) Tracking desertification on the Mongolian steppe through NDVI and fieldsurvey data. International Journal of Digital Earth 4(1): 50–64.  https://doi.org/10.1080/17538940903506006 Google Scholar
  50. Tjoelker MG, Oleksyn J, Reich PB (2001) Modelling respiration of vegetation: evidence for a general temperature-dependent Q10. Global Change Biology 7(2): 223–230.  https://doi.org/10.1046/j.1365-2486.2001.00397.x Google Scholar
  51. Trepekli A, Loupa G, Rapsomanikis S (2016) Seasonal evapotranspiration, energy fluxes and turbulence variance characteristics of a Mediterranean coastal grassland. Agricultural and Forest Meteorology, 226: 13–27.  https://doi.org/10.1016/j.agrformet.2016.05.006 Google Scholar
  52. Vitousek PM, Ehrlich PR, Ehrlich AH, et al. (1986) Human appropriation of the products of photosynthesis. BioScience 36(6): 368–373.  https://doi.org/10.2307/1310258 Google Scholar
  53. Wang JB, Liu JY, Shao QQ, et al. (2009) Spatial-temporal patterns of net primary productivity for 1988–2004 based on GLOPEMCEVSA model in the" Three-River Headwaters" region of Qinghai Province, China. Journal of Plant Ecology 33(2): 254–269. (In Chinese)  https://doi.org/10.3773/j.issn.1005-264x.2009.02.003 Google Scholar
  54. Wang JB, Liu JY, Cao MK, et al. (2011) Modelling carbon fluxes of different forests by coupling a remote-sensing model with an ecosystem process model. International Journal of Remote Sensing, 32: 6539–6567.  https://doi.org/10.1080/01431161.2010.512933 Google Scholar
  55. Wang JB, Dong JY, Liu JY, et al. (2014) Comparison of Gross Primary Productivity Derived from GIMMS NDVI3g, GIMMS, and MODIS in Southeast Asia. Remote Sensing 6: 2108–2133.  https://doi.org/10.3390/rs6032108 Google Scholar
  56. Wang JB, Dong JW, Yi Y, et al. (2017) Decreasing net primary production due to drought and slight decreases in solar radiation in China from 2000 to 2012. Journal of Geophysical Research: Biogeosciences 122(1): 261–278.  https://doi.org/10.1002/2016JG003417 Google Scholar
  57. Wang S, Tian H, Liu J, et al. (2003) Pattern and change of soil organic carbon storage in China: 1960s–1980s. Tellus Series BChemical and Physical Meteorology 55(2): 416–427.  https://doi.org/10.3402/tellusb.v55i2.16715 Google Scholar
  58. Wessels KJ, Prince SD, Frost PE, et al. (2004) Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sensing of Environment 91(1): 47–67.  https://doi.org/10.1016/j.rse.2004.02.005 Google Scholar
  59. Wylie B, Harrington Jr J, Prince S, et al. (1991) Satellite and groundbased pasture production assessment in Niger: 1986–1988. International Journal of Remote Sensing 12(6): 1281–1300.  https://doi.org/10.1080/01431169108929726 Google Scholar
  60. Xu B, Yang X, Tao W, et al. (2008) MODIS‐based remote sensing monitoring of grass production in China. International Journal of Remote Sensing 29(17-18): 5313–5327.  https://doi.org/10.1080/01431160802036276 Google Scholar
  61. Yan L, Zhou G, Zhang F (2013) Effects of different grazing intensities on grassland production in China: a meta-analysis. Plos One, 8(12): e81466.  https://doi.org/10.1371/journal.pone.0081466 Google Scholar
  62. Yang HF, Gang CC, Mu SJ, et al. (2014) Analysis of the spatiotemporal variation in net primary productivity of grassland during the past 10 years in Xinjiang. Acta Pratacultuae Sinica 23(3): 39–50.  https://doi.org/10.11686/cyxb20140305(In Chinese)Google Scholar
  63. Yang X, Guo X, Fitzsimmons M (2012) Assessing light to moderate grazing effects on grassland production using satellite imagery. International Journal of Remote Sensing 33(16): 5087–5104.  https://doi.org/10.1080/01431161.2012.657372 Google Scholar
  64. Ye H, Wang JB, Huang M, et al. (2012) Spatial pattern of vegetation precipitation use efficiency and its response to precipitation and temperature on the Qinghai-Xizang Plateau of China. Chinese Journal of Plant Ecology 36(12): 1237–1247.  https://doi.org/10.3724/SP.J.1258.2012.01237 Google Scholar
  65. Yuan XL, Li LH, Chen X, et al. (2015) Effects of Precipitation Intensity and Temperature on NDVI-Based Grass Change over Northern China during the Period from 1982 to 2011. Remote Sensing 7: 10164–10183.  https://doi.org/10.3390/rs70810164 Google Scholar
  66. Zeng FW, Collatz GJ, Pinzon JE, et al. (2013) Evaluating and quantifying the climate-driven interannual variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at global scales. Remote Sensing 5(8): 3918–3950.  https://doi.org/10.3390/rs5083918 Google Scholar
  67. Zhang C, Tian HQ, Liu J, et al. (2005) Pools and distributions of soil phosphorus in China. Global Biogeochemical Cycles 19(1).  https://doi.org/10.1029/2004GB002296
  68. Zhang M, Luo GP, De Maeyer P, et al. (2017) Improved Atmospheric Modelling of the Oasis-Desert System in Central Asia Using WRF with Actual Satellite Products. Remote Sensing 9(12): 1273.  https://doi.org/10.3390/rs9121273 Google Scholar
  69. Zhang Y, Xiao X, Wu X, et al. (2017) A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Scientific data 4: 170165.  https://doi.org/10.1038/sdata.2017.165 Google Scholar
  70. Zhao M, Running SW (2010) Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009. Science 329(5994): 940–943.  https://doi.org/10.1126/science.1192666 Google Scholar
  71. Zhou DC, Luo GP, Han QF, et al. (2012) Impacts of grazing and climate change on the aboveground net primary productivity of mountainous grassland ecosystems along altitudinal gradients over the Northern Tianshan Mountains, China. Acta Ecologica Sinica 32(1): 81–92. (In Chinese)  https://doi.org/10.5846/stxb201010141445 Google Scholar
  72. Zhu Z, Bi J, Pan Y, et al. (2013) Global data sets of vegetation leaf area index (LAI) 3g and Fraction of Photosynthetically Active Radiation (FPAR) 3g derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the period 1981 to 2011. Remote Sensing 5(2): 927–948.  https://doi.org/10.3390/rs5020927 Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  2. 2.Key Laboratory of Restoration Ecology for Cold Regions in Qinghai, Northwest Institute of Plateau BiologyChinese Academy of SciencesXiningChina
  3. 3.Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  4. 4.Northwest Land and Resources Research CenterShaanxi Normal UniversityXi’anChina
  5. 5.Ministry of Education Key Laboratory of Biodiversity Science and Ecological Engineering, Institute of Biodiversity ScienceFudan UniversityShanghaiChina
  6. 6.China University of Chinese Academy of SciencesBeijingChina

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