IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i2p417-d197787.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/2/417/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/2/417/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaohuan Liu & Zhonghe Zhao, 2018. "Analysis of Carbon Storage and Its Contributing Factors—A Case Study in the Loess Plateau (China)," Energies, MDPI, vol. 11(6), pages 1-18, June.
    2. Yang, Fanzheng & Hou, Lingling & Xia, Fang, 2024. "Intergenerational altruism, pessimism bias on tenure insecurity, and sustainable land use: Evidence from household grassland management in China," Ecological Economics, Elsevier, vol. 215(C).
    3. Hu, Yuanning & Huang, Jikun & Hou, Lingling, 2019. "Impacts of the Grassland Ecological Compensation Policy on Household Livestock Production in China: An Empirical Study in Inner Mongolia," Ecological Economics, Elsevier, vol. 161(C), pages 248-256.
    4. Byrne, Anne T. & Hadrich, Joleen C. & Robinson, Brian E. & Han, Guodong, 2020. "A factor-income approach to estimating grassland protection subsidy payments to livestock herders in Inner Mongolia, China," Land Use Policy, Elsevier, vol. 91(C).
    5. 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.
    6. Yunqing Hao & Zhengwei He, 2019. "Effects of grazing patterns on grassland biomass and soil environments in China: A meta-analysis," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-15, April.
    7. Xuemin Gong & Yijia Wang & Tianyu Zhan & Chenxu Wang & Changjia Li & Yanxu Liu, 2023. "Advances in Meta-Analysis of the Effects of Grazing on Grassland Ecosystems in China," Agriculture, MDPI, vol. 13(5), pages 1-16, May.
    8. Leah L. Bremer & Neil Nathan & Clay Trauernicht & Puaʻala Pascua & Nicholas Krueger & Jordan Jokiel & Jayme Barton & Gretchen C. Daily, 2021. "Maintaining the Many Societal Benefits of Rangelands: The Case of Hawaiʻi," Land, MDPI, vol. 10(7), pages 1-30, July.
    9. Azimatjan Mamattursun & Han Yang & Kamila Ablikim & Nurbiya Obulhasan, 2022. "Spatiotemporal Evolution and Driving Forces of Vegetation Cover in the Urumqi River Basin," IJERPH, MDPI, vol. 19(22), pages 1-25, November.
    10. Gang Li & Zhi Zhang & Linlu Shi & Yan Zhou & Meng Yang & Jiaxi Cao & Shuhong Wu & Guangchun Lei, 2018. "Effects of Different Grazing Intensities on Soil C, N, and P in an Alpine Meadow on the Qinghai—Tibetan Plateau, China," IJERPH, MDPI, vol. 15(11), pages 1-16, November.
    11. Li, Dongqing & Hou, Lingling & Zuo, Alec, 2021. "Informal institutions and grassland protection: Empirical evidence from pastoral regions in China," Ecological Economics, Elsevier, vol. 188(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:417-:d:197787. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.