Method for automatically identifying plumes of large point sources of emissions around the world based on satellite data

By employing an automatic identification method based on satellite data and utilizing high-resolution satellite remote sensing inversion and image processing technology, the problem of identifying large-scale pollution point source emission plumes worldwide has been solved, enabling efficient, real-time monitoring and environmental assessment on a global scale.

CN117036972BActive Publication Date: 2025-12-12ANHUI UNIV
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Patent Information

Application Number
CN202310710598.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-06-15
Publication Date
2025-12-12
Estimated Expiration
2043-06-15

AI Technical Summary

Technical Problem

Traditional ground-based remote sensing monitoring and ground chemical sampling analysis methods cannot achieve continuous and effective observation over large areas of the globe, while satellite remote sensing observation is affected by cloud interference, making it difficult to accurately identify emission plumes from large global pollution point sources.

Method used

An automatic discrimination method based on satellite data was adopted, which utilizes high-resolution satellite remote sensing to invert column concentration data, combined with median filter and Otsu algorithm-max inter-class variance method to identify and eliminate false positive plumes, and finally determine the global large point source pollutant gas emission plumes.

Benefits of technology

It enables efficient, real-time monitoring and identification of large emission point sources of pollution plumes on a global scale, overcoming the limitations of terrain and geographical environment, and providing comprehensive environmental assessment and emission source characterization studies.

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Abstract

The application provides a method for automatically identifying smoke plumes of global large point sources based on satellite data, which is based on the column concentration data retrieved by high-resolution satellite remote sensing, selects the region around the global large point source as the research region, and obtains the local concentration enhancement data by deducting the background concentration from the original column concentration. The best global segmentation threshold in the research region is obtained, and the median of the foreground pixels is further taken as the final threshold. The pixels greater than the final threshold in the concentration enhancement image are recorded as the final pixel group, and the information of the final pixel group is traversed to screen out the initial screening plume pixels that meet the conditions, so as to screen out the plume pixels that meet the conditions. The retrieved global large point source emission plume is artificially identified, and the false positive plume is removed, so that the global large point source pollution gas emission smoke plume is finally obtained. The application can automatically identify the emission plume of the preset global emission point source during satellite observation.
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