IDEAS home Printed from https://ideas.repec.org/a/ids/ijsusd/v27y2024i3p280-297.html
   My bibliography  Save this article

Intelligent monitoring method for agricultural nonpoint source pollution based on remote sensing image changes

Author

Listed:
  • Tiebo Sun
  • Meng Li
  • Weibing Wang
  • Chunyue Liu

Abstract

To address issues related to insufficient monitoring coverage, inaccurate measurement of pollution concentration, and inadequate load monitoring using traditional methods, an intelligent monitoring method for agricultural non-point source pollution based on remote sensing image changes is proposed. Utilising Landsat ETM+ remote sensing imagery, a multi-layer perceptron algorithm is employed to detect and identify agricultural non-point source pollution areas. The Min-Cut algorithm is used for remote sensing image segmentation of agricultural non-point source pollution targets. Remote sensing image changes are detected based on the segmentation results and difference diagram. Intelligent monitoring of agricultural non-point source pollution is achieved by combining the detection results of remote sensing image changes with relevant monitoring indicators. Experimental findings demonstrate that the proposed method achieves a monitoring coverage rate exceeding 96.3%, the mean accuracy of pollution concentration monitoring is 97.1%, the maximum accuracy of pollution load monitoring is 99.3%.

Suggested Citation

  • Tiebo Sun & Meng Li & Weibing Wang & Chunyue Liu, 2024. "Intelligent monitoring method for agricultural nonpoint source pollution based on remote sensing image changes," International Journal of Sustainable Development, Inderscience Enterprises Ltd, vol. 27(3), pages 280-297.
  • Handle: RePEc:ids:ijsusd:v:27:y:2024:i:3:p:280-297
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=140009
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijsusd:v:27:y:2024:i:3:p:280-297. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=25 .

    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.