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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
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    References listed on IDEAS

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    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.
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