A new method to extract coal-covered area in open-pit mine based on remote sensing

ABSTRACT

Coal is a prominent energy resource in China. Therefore, it is crucial to accurately extract the coal-covered areas. Taking Xinjiang’s Zhundong mining region as the study area, Landsat 8 OLI data were employed to assess the spectral curves of four typical ground objects, including vegetation, bare land, water body and bare coal. The Normalized Difference Bare Coal Index (NDBCI) and the Normalized Difference Coal Index (NDCI) were developed to extract coal-covered area. Higher-resolution Sentinel-2B data for the same period were used for verification, with extraction accuracy evaluated by five metrics including Kappa coefficient, Overall accuracy, Checking accuracy, Checking completeness and F1-score. The results of extracting coal-covered areas showed that (1) the NDBCI showed ‘internal fragmentation’ and the NDCI demonstrated ‘pixel overflow’ during the extraction process. Therefore, we determined the optimal thresholds −0.03 for NDBCI and 0.04 for NDCI. (2) NDBCI distinguished pixels with lower grey-scale values, such as water body, road and gangue. However, some dump zones and shed patches were misclassified. (3) NDCI generated clear boundaries and more complete interiors, and the dump zone and shed could be distinguished. However, some water body parts were misclassified as coal-covered areas. (4) Combined application of NDBCI and NDCI generated a ‘complementary’ effect better than both individual modes. Kappa coefficient, Overall accuracy and F1-score reached 0.95, 98.76% and 0.75, respectively. This study successfully extracted coal-covered areas by developing a remote sensing index based on spectral traits and a priori knowledge of the study area. The proposed combined extraction mode achieved high accuracy for rapid and reliable identification of coal-covered areas.

Highlights

1. Two new remote sensing indexes for coal-covered area extraction were proposed in this paper.

2. Complementary advantages were found between NDBCI and NDCI in the extraction.

3. Accuracy assessment was performed based on manually deciphered high-precision raster data.

KEYWORDS:

• Spectral characteristic
• NDCI
• NDBCI
• Coal-covered area
• Remote sensing extraction

Acknowledgements

We appreciate the editor’s and reviewers’ for their comments and suggestions on our manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Authors contribution statement

Xin He: Data curation, Methodology, Implementation, Formal analysis, Writing original draft. Fei Zhang: Conceptualization, Funding acquisition. Chi Yung Jim and Ngai Weng Chan: Writing-Reviewing and Editing. Mou Leong Tan and Jingchao Shi: Supervision.

Additional information

Funding

The work was supported by the Key Research and Development Program of the Xinjiang Uygur Autonomous Region (2022B01012) and the Tianshan Talent Project (Phase III) of the Xinjiang Uygur Autonomous Region.