Article: Intelligent monitoring method for agricultural nonpoint source pollution based on remote sensing image changes Journal: International Journal of Sustainable Development (IJSD) 2024 Vol.27 No.3 pp.280 - 297 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%. Inderscience Publishers - linking academia, business and industry through research

You can view the full text of this article for free using the link below.

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

Authors: Tiebo Sun; Meng Li; Weibing Wang; Chunyue Liu

Addresses: School of Intelligent Manufacturing, Jiangsu Food & Pharmaceutical Science College, Huai'an City, Jiangsu Province, 223003, China ' School of Intelligent Manufacturing, Jiangsu Food & Pharmaceutical Science College, Huai'an City, Jiangsu Province, 223003, China ' School of Intelligent Manufacturing, Jiangsu Food & Pharmaceutical Science College, Huai'an City, Jiangsu Province, 223003, China ' College of Automotive Engineering, Jiangsu Vocational College of Electronics and Information, Huai'an City, Jiangsu Province, 223003, China

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%.

Keywords: remote sensing image changes; agricultural non-point source pollution; intelligent monitoring; Landsat ETM+ remote sensing images; min-cut algorithm; difference diagram; monitoring indicators.

DOI: 10.1504/IJSD.2024.140009

International Journal of Sustainable Development, 2024 Vol.27 No.3, pp.280 - 297

Received: 11 Sep 2023
Accepted: 09 Jan 2024
Published online: 15 Jul 2024 *

Full-text access for editors Full-text access for subscribers Free access Comment on this article

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

Keep up-to-date