IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i22p14884-d969311.html
   My bibliography  Save this article

Spatiotemporal Dynamics of Aboveground Biomass and Its Influencing Factors in Xinjiang’s Desert Grasslands

Author

Listed:
  • Gongxin Wang

    (College of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Grassland Restoration and Ecology, Ministry of Education Key Laboratory of Grassland Resources and Ecology of Western Arid Region, Urumqi 830052, China)

  • Changqing Jing

    (College of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Grassland Restoration and Ecology, Ministry of Education Key Laboratory of Grassland Resources and Ecology of Western Arid Region, Urumqi 830052, China)

  • Ping Dong

    (College of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Grassland Restoration and Ecology, Ministry of Education Key Laboratory of Grassland Resources and Ecology of Western Arid Region, Urumqi 830052, China)

  • Baoya Qin

    (College of Resources and Environment, Xinjiang Agricultural University, Urumqi 830052, China)

  • Yang Cheng

    (College of Grassland Science, Xinjiang Agricultural University, Urumqi 830052, China
    Xinjiang Key Laboratory of Grassland Restoration and Ecology, Ministry of Education Key Laboratory of Grassland Resources and Ecology of Western Arid Region, Urumqi 830052, China)

Abstract

Grassland biomass is a significant parameter for measuring grassland productivity and the ability to sequester carbon. Estimating desert grassland biomass using the best remote sensing inversion model is essential for understanding grassland carbon stocks in arid and semi-arid regions. The present study constructed an optimal inversion model of desert grassland biomass based on actual biomass measurement data and various remote-sensing product data. This model was used to analyze the spatiotemporal variation in desert grassland biomass and climate factor correlation in Xinjiang from 2000 to 2019. The results showed that (1) among the established inversion models of desert grasslands aboveground biomass (AGB), the exponential function model with the normalized differential vegetation index (NDVI) as the independent variable was the best. Furthermore, (2) the NDVI of desert grasslands in Xinjiang showed a highly significant increasing trend from 2000 to 2019 with a spatially concentrated distribution in the north and a more dispersed distribution in the south. In addition, (3) the average AGB value was 52.35 g·m −2 in Xinjiang from 2000 to 2019 and showed a spatial distribution with low values in the southeast and high values in the northwest. Moreover, (4) the low fluctuation in the coefficient of desert grassland variation accounted for 65.26% of overall AGB fluctuation (<0.10) from 2000 to 2019. Desert grassland AGB in most areas (88.65%) showed a significant increase over the last 20 years. Lastly, (5) the correlation between desert grassland precipitation and AGB was stronger than that between temperature and AGB from 2000 to 2019. This study provides a scientific basis and technical support for grassland livestock management and carbon storage assessments in Xinjiang.

Suggested Citation

  • Gongxin Wang & Changqing Jing & Ping Dong & Baoya Qin & Yang Cheng, 2022. "Spatiotemporal Dynamics of Aboveground Biomass and Its Influencing Factors in Xinjiang’s Desert Grasslands," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:22:p:14884-:d:969311
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/22/14884/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/22/14884/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiumei Wang & Jianjun Dong & Taogetao Baoyin & Yuhai Bao, 2019. "Estimation and Climate Factor Contribution of Aboveground Biomass in Inner Mongolia’s Typical/Desert Steppes," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    2. R. D. Evans & A. Koyama & D. L. Sonderegger & T. N. Charlet & B. A. Newingham & L. F. Fenstermaker & B. Harlow & V. L. Jin & K. Ogle & S. D. Smith & R. S. Nowak, 2014. "Greater ecosystem carbon in the Mojave Desert after ten years exposure to elevated CO2," Nature Climate Change, Nature, vol. 4(5), pages 394-397, May.
    3. Carly Golodets & Marcelo Sternberg & Jaime Kigel & Bertrand Boeken & Zalmen Henkin & No’am Seligman & Eugene Ungar, 2013. "From desert to Mediterranean rangelands: will increasing drought and inter-annual rainfall variability affect herbaceous annual primary productivity?," Climatic Change, Springer, vol. 119(3), pages 785-798, August.
    Full references (including those not matched with items on IDEAS)

    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. Martínez-Valderrama, J. & Ibáñez, J. & Ibáñez, M.A. & Alcalá, F.J. & Sanjuán, M.E. & Ruiz, A. & del Barrio, G., 2021. "Assessing the sensitivity of a Mediterranean commercial rangeland to droughts under climate change scenarios by means of a multidisciplinary integrated model," Agricultural Systems, Elsevier, vol. 187(C).
    2. Lehrer, David & Becker, Nir & Bar, Pua, 2019. "The drivers behind nature conservation cost," Land Use Policy, Elsevier, vol. 89(C).
    3. Ungar, Eugene David, 2019. "Perspectives on the concept of rangeland carrying capacity, and their exploration by means of Noy-Meir's two-function model," Agricultural Systems, Elsevier, vol. 173(C), pages 403-413.
    4. Fust, Pascal & Schlecht, Eva, 2022. "Importance of timing: Vulnerability of semi-arid rangeland systems to increased variability in temporal distribution of rainfall events as predicted by future climate change," Ecological Modelling, Elsevier, vol. 468(C).
    5. Anandkumar Naorem & Somasundaram Jayaraman & Ram C. Dalal & Ashok Patra & Cherukumalli Srinivasa Rao & Rattan Lal, 2022. "Soil Inorganic Carbon as a Potential Sink in Carbon Storage in Dryland Soils—A Review," Agriculture, MDPI, vol. 12(8), pages 1-20, August.
    6. Udi Segev & Jaime Kigel & Yael Lubin & Katja Tielbörger, 2015. "Ant Abundance along a Productivity Gradient: Addressing Two Conflicting Hypotheses," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.

    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:14:y:2022:i:22:p:14884-:d:969311. 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.