# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 1 Introduction
Gradually, remote sensing identification is replacing manual interpretation as the primary technical method due to its shorter monitoring cycle and broader monitoring range [26]. Bare coal identification using remote sensing is essentially a classification problem. Various scholars have achieved accurate identification of bare coal areas through remote sensing images, employing methods such as object-based classification, machine learning, and deep learning. For example, [PERSON] et al. utilized an object-oriented classification approach with Pleiades data to develop an accurate classification rule for the Panxie coal mine in eastern China. This rule, when applied to Landsat satellite imagery, has yielded high classification accuracy for feature identification in the region [27]. [PERSON] et al. used a deep learning approach with Sentinel-2 data to investigate the widespread presence of unregistered mines and tailing dams in Brazil. Their research provided practical solutions, particularly for addressing the issue of illegal mining activities [28]. However, these methods require a large number of training samples. The subjectivity in selecting training samples and the significant impact of sample quality on recognition accuracy can exacerbate the problem of result stability. Achieving the desired accuracy often necessitates multiple iterations. To address these issues, some scholars have proposed utilizing the unique spectral characteristics of features by constructing a system of indexes for the detection of specific features. Bare coal areas often exhibit significant spectral differences from other land classes due to variations in color and texture. Consequently, [PERSON] et al. developed a normalized difference coal index (NDCI) using Landsat's SWIR1 and NIR bands, achieving satisfactory results in two coal mining areas in China [29]. [PERSON] al. conducted an in-depth analysis of the spectral values associated with bare coal compared to various land cover classes. They introduced a new opencast coal mine index (OCMI) and critically evaluated its adaptability to seasonal variations, achieving an average accuracy of 86.24% [30]. [PERSON] et al. proposed an exposed coal index (ECI) based on the blue, green, and near-infrared bands, which was validated in four typical open-pit mines worldwide [31]. However, while current methods can determine whether bare coal is present within a pixel, the substantial ambiguity in pixel information inherent to medium spatial resolution satellite imagery often results in a significant number of omissions and commission patches, causing serious interference in the subsequent process of locating the mining area. Given the spatial information limitations of medium spatial resolution satellite imagery, incorporating a bare coal fraction could enhance the accuracy of bare coal identification. Nonetheless, constructing a bare coal index using medium spatial resolution satellite images still encounters several challenges: (1) the presence of various types of land cover within the same pixel complicates bare coal detection [32; 33], presenting a significant challenge in accurately detecting bare coal areas; and (2) many existing methods propose utilizing fixed months or a specific number of months for identification, severely limiting the advantages of the short revisit period of satellite imagery and rendering these methods unsuitable for long-term continuous monitoring [34]. To address these issues, this study focuses on three typical coalfields in Inner Mongolia, China: Chenqi Coalfield, Shengli Coalfield, and Dongsheng Coalfield. Based on Landsat data, we propose a new method for large-scale, long-term, and continuous monitoring of open-pit bare coal mining areas, successfully achieving identification across the entire Inner Mongolia region. The study aims to provide technical support to governmental departments for the supervision and verification of open-pit mining activities and the ecological restoration of mines.