IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i10p3539-d173304.html
   My bibliography  Save this article

A Regionalized Study on the Spatial-Temporal Changes of Grassland Cover in the Three-River Headwaters Region from 2000 to 2016

Author

Listed:
  • Naijing Liu

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)

  • Yaping Yang

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

  • Ling Yao

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)

  • Xiafang Yue

    (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China)

Abstract

The Three-River Headwaters Region (TRHR) is located in the interior of the Qinghai-Tibetan Plateau, which is a typical research area in East Asia and is of fragile environment. This paper studied the characteristics of grassland cover changes in the TRHR between 2000 and 2016 using methods of area division (AD) based on natural conditions and tabulate area (TA) dependent on Moderate-resolution Imaging Spectroradiometer (MODIS) 44B product. Further investigations were conducted on some of the typical areas to determine the characteristics of the changes and discuss the driving factors behind these changes. Classification and Regression Trees (CART), Random Forest (RF), Bayesian (BAYE), and Support Vector Machine (SVM) Machine Learning (ML) methods were employed to evaluate the correlation between grassland cover changes and corresponding variables. The overall trend for grassland cover in the TRHR towards recovery that rose 0.91% during the 17-year study period. The results showed that: (1) The change in grassland cover was more divisive in similar elevation and temperature conditions when the precipitation was stronger. The higher the temperature was, the more significant the rise of grassland cover was in comparable elevation and precipitation conditions. (2) There was a distinct decline and high change standard deviation of grassland cover in some divided areas, and strong correlations were found between grassland cover change and aspect, slope, or elevation in these areas. (3) The study methods of AD and TA achieved enhancing performance in interpretation of grassland cover changes in the broad and high elevation variation areas. (4) RF and CART methods showed higher stability and accuracy in application of grassland cover change study in TRHR among the four ML methods utilized in this study.

Suggested Citation

  • Naijing Liu & Yaping Yang & Ling Yao & Xiafang Yue, 2018. "A Regionalized Study on the Spatial-Temporal Changes of Grassland Cover in the Three-River Headwaters Region from 2000 to 2016," Sustainability, MDPI, vol. 10(10), pages 1-24, October.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3539-:d:173304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/10/3539/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/10/10/3539/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Yaowen Kou & Quanzhi Yuan & Xiangshou Dong & Shujun Li & Wei Deng & Ping Ren, 2023. "Dynamic Response and Adaptation of Grassland Ecosystems in the Three-River Headwaters Region under Changing Environment: A Review," IJERPH, MDPI, vol. 20(5), pages 1-30, February.

    More about this item

    Keywords

    MOD44B; grassland cover; regionalization; Machine Learning;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    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:10:y:2018:i:10:p:3539-:d:173304. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.