A Novel Exposed Coal Index Combining Flat Spectral Shape and Low Reflectance

Coal, as a traditional energy source, has made remarkable contributions to global economic development. However, surface coal mining brings a series of eco-environmental problems. Therefore, it is crucial to obtain the distribution information of coal mines. Due to the diverse appearance of coal mines and complex background environments, it is very challenging to identify coal mines at a large scale. Exposed coal is an important indicator of coal mining. Spectral indices based on satellite images possess the advantages of simplicity and high efficiency. In this study, the Exposed Coal Index (ECI) was proposed. It enables the accurate identification of exposed coal at a large scale. The effectiveness of the ECI was investigated in four typical surface coal mine distribution regions across the world. Through spectral analysis, two key characteristics of coal spectra were discovered (i.e., the flat spectral shape in the visible to near-infrared range and the low reflectance in the near-infrared band). The ECI utilized these two features to successfully differentiate coal from various background land cover types in all study cases. The results showed that the ECI was effective in visual evaluation, separability analysis, and coal mapping, with superior performance than the three previously proposed indices. The ECI can also be perfectly applied to Landsat 8 images, demonstrating its excellent generalization capability. In addition, compared with three global mining datasets, ECI provided more comprehensive information on coal mine distribution. The proposed ECI is simple, robust, and expected to provide strong support for regional resource management and sustainable development.