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


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.


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


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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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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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