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Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

Author

Listed:
  • Qingqing Ma

    (State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China)

  • Linrong Chai

    (State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China)

  • Fujiang Hou

    (State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China)

  • Shenghua Chang

    (State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China)

  • Yushou Ma

    (The Institute of Rangeland Research, Academy of Animal and Veterinary Sciences, Qinghai University, Xining 810016, China)

  • Atsushi Tsunekawa

    (Arid Land Research Center, Tottori University, 1390 Hamasaka, Tottori 680-0001, Japan)

  • Yunxiang Cheng

    (State Key Laboratory of Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
    School of Ecology and Environment, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China)

Abstract

Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity ( p < 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

Suggested Citation

  • Qingqing Ma & Linrong Chai & Fujiang Hou & Shenghua Chang & Yushou Ma & Atsushi Tsunekawa & Yunxiang Cheng, 2019. "Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau," Sustainability, MDPI, vol. 11(2), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:417-:d:197787
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    References listed on IDEAS

    as
    1. Liang Yan & Guangsheng Zhou & Feng Zhang, 2013. "Effects of Different Grazing Intensities on Grassland Production in China: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-9, December.
    2. Fu, Lintao & Bo, Tianli & Du, Guozhen & Zheng, Xiaojing, 2012. "Modeling the responses of grassland vegetation coverage to grazing disturbance in an alpine meadow," Ecological Modelling, Elsevier, vol. 247(C), pages 221-232.
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    Cited by:

    1. Zhiyuan Song & Ziyi Gao & Xianming Yang & Yuejing Ge, 2022. "Distinguishing the Impacts of Human Activities and Climate Change on the Livelihood Environment of Pastoralists in the Qinghai Lake Basin," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    2. Hua Cheng & Baocheng Jin & Kai Luo & Jiuying Pei & Xueli Zhang & Yonghong Zhang & Jiaqi Tang & Qin Yang & Guojun Sun, 2021. "Vegetation Response to Goats Grazing Intensity in Semiarid Hilly Grassland of the Loess Plateau, Lanzhou, China," Sustainability, MDPI, vol. 13(6), pages 1-16, March.
    3. Hui Liu & Xiaoyu Song & Lin Qin & Wang Wen & Xiaodi Liu & Zhiqiang Hu & Yu Liu, 2020. "Improvement and Application of Key Pasture Theory for the Evaluation of Forage–Livestock Balance in the Seasonal Grazing Regions of China’s Alpine Desert Grasslands," Sustainability, MDPI, vol. 12(17), pages 1-12, August.
    4. Suizi Wang & Jiangwen Fan & Yuzhe Li & Lin Huang, 2019. "Effects of Grazing Exclusion on Biomass Growth and Species Diversity among Various Grassland Types of the Tibetan Plateau," Sustainability, MDPI, vol. 11(6), pages 1-13, March.
    5. Jérôme Théau & Étienne Lauzier-Hudon & Lydiane Aubé & Nicolas Devillers, 2021. "Estimation of forage biomass and vegetation cover in grasslands using UAV imagery," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.

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