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A Novel Approach to Automatically Identify Open-Pit Coal Mining Dynamics Based on Temporal Satellite Images.

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Title: A Novel Approach to Automatically Identify Open-Pit Coal Mining Dynamics Based on Temporal Satellite Images.
Authors: Li, Zhibin1 (AUTHOR), Zhao, Yanling1,2 (AUTHOR) ylzhao@cumtb.edu.cn, Ren, He2,3 (AUTHOR), He, Tingting1,3 (AUTHOR), Sun, Yueming1,2 (AUTHOR)
Source: Remote Sensing. Mar2025, Vol. 17 Issue 6, p1029. 22p.
Subjects: Strip mining, Coal mining, Resource exploitation, Remote-sensing images, Restoration ecology
Abstract: Open-pit coal mining drives socioeconomic development but imposes significant environmental impacts. The timely monitoring of mining dynamics is essential for sustainable resource exploitation and ecological restoration. However, existing studies often rely on predefined mining boundaries, limiting their applicability in unknown regions. This study proposes an innovative approach that leverages the intra-annual coal frequency index (ACFI) to identify potential open-pit mining areas, and integrates the Rays method to monitor their temporal changes. By applying a predefined discriminative rule, this approach effectively distinguishes open-pit coal mines from other disturbances and enables spatiotemporal monitoring without the need for prior knowledge of their locations. Applied to the Chenbarhu Banner coalfield, Inner Mongolia, the method achieved 92% accuracy and a kappa coefficient of 0.84 in identifying mining areas. It effectively distinguished active and closed mines, detecting key temporal features with 94% accuracy (kappa = 0.86). The study also identified mining directions and extents, such as 4–13° for the Baorixile mine and 69–141° for the Dongming mine, while excluding non-mining areas with high precision. A strong correlation (r = 0.929, p < 0.01) between annual mining area and coal production further validated the approach. This method provides accurate, scalable tools for monitoring mining dynamics and supports decision-making in regulatory and ecological management processes. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Novel Approach to Automatically Identify Open-Pit Coal Mining Dynamics Based on Temporal Satellite Images.
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Remote+Sensing%22&quot;&gt;Remote Sensing&lt;/searchLink&gt;. Mar2025, Vol. 17 Issue 6, p1029. 22p.
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Strip+mining%22&quot;&gt;Strip mining&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Coal+mining%22&quot;&gt;Coal mining&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Resource+exploitation%22&quot;&gt;Resource exploitation&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Remote-sensing+images%22&quot;&gt;Remote-sensing images&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Restoration+ecology%22&quot;&gt;Restoration ecology&lt;/searchLink&gt;
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Open-pit coal mining drives socioeconomic development but imposes significant environmental impacts. The timely monitoring of mining dynamics is essential for sustainable resource exploitation and ecological restoration. However, existing studies often rely on predefined mining boundaries, limiting their applicability in unknown regions. This study proposes an innovative approach that leverages the intra-annual coal frequency index (ACFI) to identify potential open-pit mining areas, and integrates the Rays method to monitor their temporal changes. By applying a predefined discriminative rule, this approach effectively distinguishes open-pit coal mines from other disturbances and enables spatiotemporal monitoring without the need for prior knowledge of their locations. Applied to the Chenbarhu Banner coalfield, Inner Mongolia, the method achieved 92% accuracy and a kappa coefficient of 0.84 in identifying mining areas. It effectively distinguished active and closed mines, detecting key temporal features with 94% accuracy (kappa = 0.86). The study also identified mining directions and extents, such as 4–13&#176; for the Baorixile mine and 69–141&#176; for the Dongming mine, while excluding non-mining areas with high precision. A strong correlation (r = 0.929, p &lt; 0.01) between annual mining area and coal production further validated the approach. This method provides accurate, scalable tools for monitoring mining dynamics and supports decision-making in regulatory and ecological management processes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: &lt;i&gt;Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17061029
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 1029
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      – SubjectFull: Strip mining
        Type: general
      – SubjectFull: Coal mining
        Type: general
      – SubjectFull: Resource exploitation
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: Restoration ecology
        Type: general
    Titles:
      – TitleFull: A Novel Approach to Automatically Identify Open-Pit Coal Mining Dynamics Based on Temporal Satellite Images.
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            NameFull: Li, Zhibin
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            NameFull: Zhao, Yanling
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            NameFull: Ren, He
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            NameFull: He, Tingting
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              M: 03
              Text: Mar2025
              Type: published
              Y: 2025
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