IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p5619-d1104958.html
   My bibliography  Save this article

Optimal Scale Selection and an Object-Oriented Method Used for Measuring and Monitoring the Extent of Land Desertification

Author

Listed:
  • Junliang Han

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Liusheng Han

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
    Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China)

  • Guangwei Sun

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Haoxiang Mu

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Zhiyi Zhang

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Xiangyu Wang

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

  • Shengshuai Wang

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China)

Abstract

Desertification has become a major problem in the field, affecting both the global ecological environment and economy. The effective monitoring of desertified land is an important prerequisite for land desertification protection and governance. With the aim of addressing the problems of spectral confusion as well as the salt and pepper phenomenon concerning the successful extraction of desertification information by utilizing the pixel-based method in the studies, Landsat remote sensing images obtained from the year 2001 to 2021 were selected in this study as the data source, and then, the object-oriented random forest classification method was improved by using different optimal segmentation scale selection techniques and combining multi-thematic index characteristics for measuring the extent of land desertification. Finally, the improved method was applied to study the dynamic changes in desertification in the Mu Us Sandy Land Ecological Function Reserve. The results show that the optimal scale determined by different optimal segmentation scale selection methods is not entirely consistent, and a minor scale should be selected as the optimal scale. Compared with the pixel-based classification method, the overall accuracy of object-oriented classification based on the optimal segmentation scale was improved by 8.06%, the Kappa coefficient increased by 0.1114, and the salt and pepper phenomenon was significantly reduced. From 2001 to 2021, the area of desertified land decreased by 587.12 km 2 and the area of severely desertified land decreased by 4115.92 km 2 , indicating that the control effect was remarkable. This study can provide effective decision-making evidence and support for the successful governance of desertification.

Suggested Citation

  • Junliang Han & Liusheng Han & Guangwei Sun & Haoxiang Mu & Zhiyi Zhang & Xiangyu Wang & Shengshuai Wang, 2023. "Optimal Scale Selection and an Object-Oriented Method Used for Measuring and Monitoring the Extent of Land Desertification," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:5619-:d:1104958
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/5619/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/5619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hongchun Zhu & Lijie Cai & Haiying Liu & Wei Huang, 2016. "Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    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. Abhilash Bandam & Eedris Busari & Chloi Syranidou & Jochen Linssen & Detlef Stolten, 2022. "Classification of Building Types in Germany: A Data-Driven Modeling Approach," Data, MDPI, vol. 7(4), pages 1-23, April.

    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:15:y:2023:i:7:p:5619-:d:1104958. 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.