ACMI: An index for exposed coal mapping using Landsat imagery
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909134668955648 |
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| author | Yang, Zhen Zhang, Jianyong Zhao, Yanchuang |
| author_facet | Yang, Zhen Zhang, Jianyong Zhao, Yanchuang |
| contents | Remotely sensing the spatial distribution of exposed coal (EC) is significant for understanding the footprints of mining activities. However, widely applicable methods for the identification of EC surfaces remain inadequate because the choices of recent methods confront the diverse EC types and backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index (ACMI) which was empirically formulated by an iterative process of identifying parameters that maximize the separability of EC and non-EC surfaces. The performance of ACMI was tested in six study areas worldwide with different landscape types and coal types. Based on the visual inspection, ACMI was more effective in highlighting EC surfaces and suppressing non-EC surfaces than the existing methods. Compared with the sample points obtained through direct interpretation, ACMI obtained better EC mapping results than previous methods with the F1 score and overall accuracy (OA) no less than 0.91 and 93.20% across all the selected Landsat images of the study areas, respectively. In addition, ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its default threshold. The default threshold makes EC mapping using ACMI an automated process. The new index has the potential to support a variety of mining-activity-related studies, such as the identification of mining disturbances and illegal mining detection at multi-spatial-temporal scales. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_07220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ACMI: An index for exposed coal mapping using Landsat imagery Yang, Zhen Zhang, Jianyong Zhao, Yanchuang Image and Video Processing Remotely sensing the spatial distribution of exposed coal (EC) is significant for understanding the footprints of mining activities. However, widely applicable methods for the identification of EC surfaces remain inadequate because the choices of recent methods confront the diverse EC types and backgrounds. Therefore, this study proposed a new Automated Coal Mapping Index (ACMI) which was empirically formulated by an iterative process of identifying parameters that maximize the separability of EC and non-EC surfaces. The performance of ACMI was tested in six study areas worldwide with different landscape types and coal types. Based on the visual inspection, ACMI was more effective in highlighting EC surfaces and suppressing non-EC surfaces than the existing methods. Compared with the sample points obtained through direct interpretation, ACMI obtained better EC mapping results than previous methods with the F1 score and overall accuracy (OA) no less than 0.91 and 93.20% across all the selected Landsat images of the study areas, respectively. In addition, ACMI was demonstrated to have a stable optimal threshold and 0 can serve as its default threshold. The default threshold makes EC mapping using ACMI an automated process. The new index has the potential to support a variety of mining-activity-related studies, such as the identification of mining disturbances and illegal mining detection at multi-spatial-temporal scales. |
| title | ACMI: An index for exposed coal mapping using Landsat imagery |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2403.07220 |