IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i21p9154-d1503925.html
   My bibliography  Save this article

Exploring the Spatial-Temporal Patterns, Drivers, and Response Strategies of Desertification in the Mu Us Desert from Multiple Regional Perspectives

Author

Listed:
  • Jiaying Li

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China
    These authors contributed equally to this work.)

  • Yu Li

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China
    These authors contributed equally to this work.)

  • Xuhui Wang

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

  • Zhongxu Ma

    (College of Landscape Architecture and Arts, Northwest A&F University, Yangling 712100, China)

Abstract

Desertification poses a serious threat to the global ecological environment and challenges the achievement of an ecological civilization. Understanding the spatial and temporal evolution of desertification in the Mu Us Desert, a key area in northern China, is crucial for predicting regional trends and analyzing causes. This study employs quantitative methods, including remote sensing data from Landsat satellites (2000–2020), combined with multi-scale analysis and statistical models, to systematically analyze desertification trends. The analysis reveals that desertification improved significantly after 2005 due to effective human intervention and governance efforts. In particular, the eastern regions (Shaanxi Province and Inner Mongolia) showed marked improvement, while the western regions exhibited limited change. The greatest progress was seen in the reduction in high-desertification areas to moderate levels. Quantitatively, human activities contributed to a 17.3% reduction in desertification ( p < 0.05), while meteorological factors were responsible for a 45.8% reduction ( p < 0.05). Conversely, desertification in Ningxia worsened by 41.8% due to unsustainable land use. Additionally, spatial correlation analysis highlighted that those areas of severe desertification became more uniformly distributed over time. The key drivers influencing desertification were agricultural development, urbanization, climate warming, and vegetation coverage, with human activities playing a substantial role. Initially, agricultural factors had the strongest correlation with desertification, but over time, population growth, rising temperatures, and vegetation cover (NDVI) became more prominent. These findings offer scientific support for desertification control in the Mu Us Desert and provide methodological insights for other severely desertified regions, contributing to sustainable ecological development.

Suggested Citation

  • Jiaying Li & Yu Li & Xuhui Wang & Zhongxu Ma, 2024. "Exploring the Spatial-Temporal Patterns, Drivers, and Response Strategies of Desertification in the Mu Us Desert from Multiple Regional Perspectives," Sustainability, MDPI, vol. 16(21), pages 1-28, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9154-:d:1503925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/21/9154/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/21/9154/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinghu Pan & Tianyu Li, 2013. "Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo-Vegetation feature space," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 915-927, September.
    2. Lanying Han & Zhengcai Zhang & Qiang Zhang & Xin Wan, 2015. "Desertification assessments in the Hexi corridor of northern China’s Gansu Province by remote sensing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(3), pages 2715-2731, February.
    3. Xinyang Ji & Jinzhong Yang & Jianyu Liu & Xiaomin Du & Wenkai Zhang & Jiafeng Liu & Guangwei Li & Jingkai Guo, 2023. "Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    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. Yingshuang Li & Hengkai Li, 2023. "Dynamic monitoring of desertification based on multi-features in the red soil region, Southern China: a case in Dongjiang source area," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 619-640, January.
    2. Qingfu Liu & Yanyun Zhao & Xuefeng Zhang & Alexander Buyantuev & Jianming Niu & Xiaojiang Wang, 2018. "Spatiotemporal Patterns of Desertification Dynamics and Desertification Effects on Ecosystem Services in the Mu Us Desert in China," Sustainability, MDPI, vol. 10(3), pages 1-19, February.
    3. Samia S. Hasan & Omar A. Alharbi & Abdullah F. Alqurashi & Amr S. Fahil, 2024. "Assessment of Desertification Dynamics in Arid Coastal Areas by Integrating Remote Sensing Data and Statistical Techniques," Sustainability, MDPI, vol. 16(11), pages 1-19, May.
    4. Changfu Tong & Hongfei Hou & Hexiang Zheng & Ying Wang & Jin Liu, 2024. "A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm," Land, MDPI, vol. 13(11), pages 1-22, October.
    5. Weiyi Lu & Geer Teni & Huishi Du, 2024. "Vegetation–Lake–Sand Landscape of Northeast China Sandy Land between 1980 and 2022: Pattern, Evolution, and Driving Forces," Sustainability, MDPI, vol. 16(8), pages 1-18, April.
    6. Abdelrahim Salih, 2023. "Spectral Mixture Analysis (SMA) Model for Extracting Urban Fractions from Landsat and Sentinel-2A Images in the Al-Ahsa Oasis, Eastern Region of Saudi Arabia," Land, MDPI, vol. 12(10), pages 1-17, September.

    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:16:y:2024:i:21:p:9154-:d:1503925. 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.