{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T15:18:56Z","timestamp":1783005536770,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["420701250"],"award-info":[{"award-number":["420701250"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Open-pit mining offers significant benefits, such as enhanced safety conditions and high efficiency, making it a crucial method for use in the modern coal industry. Nevertheless, the comprehensive process of \u201cstripping-mining-discharge-reclamation\u201d inevitably leads to ecological disturbances in the mine and surrounding areas. Consequently, dynamic monitoring and supervision of open-pit mining activities are imperative. Unfortunately, current methods are inadequate for accurately identifying and continuously monitoring bare coal identification using medium spatial resolution satellite images (e.g., Landsat). This is due to the complex environmental conditions around mining areas and the need for specific image acquisition times, which pose significant challenges for large-scale bare coal area mapping. To address these issues, the paper proposes a novel bare coal index (BCI) based on Landsat OLI imagery. This index is derived from the spectral analysis, sensitivity assessment, and separability study of bare coal. The effectiveness and recognition capability of the proposed BCI are rigorously validated. Our findings demonstrate that the BCI can rapidly and accurately identify bare coal, overcoming limitations related to image acquisition timing, thus enabling year-round image availability. Compared to existing identification methods, the BCI exhibits superior resistance to interference in complex environments. The application of the BCI in the Chenqi Coalfield, Shengli Coalfield, and Dongsheng Coalfield in Inner Mongolia, China, yielded an average overall accuracy of 97% and a kappa coefficient of 0.87. Additionally, the BCI was also applied for bare coal area identification across the entire Inner Mongolia region, with a correct classification accuracy of 90.56%. These results confirm that the proposed index is highly effective for bare coal identification and can facilitate digital mapping of extensive bare coal (BC) coverage in open-pit mining areas.<\/jats:p>","DOI":"10.3390\/rs16244648","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:52:49Z","timestamp":1733975569000},"page":"4648","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhibin","family":"Li","sequence":"first","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6701-754X","authenticated-orcid":false,"given":"Yanling","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Ren","sequence":"additional","affiliation":[{"name":"Academy of Eco-Civilization Development for Jing-Jin-Ji Megalopolis, Tianjin Normal University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueming","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"ref_1","unstructured":"(2023, June 03). 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