# A Novel Index for Detecting Bare Coal in Open-Pit Mining Areas Based on Landsat Imagery
## 1 Introduction  
Coal as a resource of energy plays a pivotal role in the global market as a crucial fossil fuel. Currently, coal-fried power plants generate 37% of the world's electricity, making coal indispensable for global power generation and ensuring its continued prominence in the foreseeable future. Additionally, coal is crucial for the energy supply of key industries such as steel, cement, and aluminum. Simultaneously, as a primary feedstock for hydrogen production, coal plays a vital role in promoting the transition to clean energy and supporting economic development [1]. Driven by energy and economic demands, China's coal production has been increasing in recent years. However, this growth presents a significant challenge in addressing the resultant land and ecosystem damage [2, 3]. Open-pit mining is one of the two primary methods of coal extraction, offering advantages such as good safety conditions, high efficiency, and low cost [4, 5]. It accounts for 40% of global coal production, with even higher percentages in some countries [6]. However,the \"stripping-mining-discharge-reclamation\" process associated with surface coal mining inevitably results in significant ecological and environmental challenges [7; 8; 9; 10; 11; 12]. To mitigate the environmental impacts of open-pit mining, the concept of concurrent mining and reclamation has gradually been introduced into mine restoration practices [13; 14]. Implementing concurrent mining and reclamation requires the timely monitoring of mining progress and conditions. However, mining maps provided by mining companies are often outdated and do not reflect the current mining dynamics in a timely manner. As a result, the need for continuous monitoring and evaluation of open-pit coal mining activities has become increasingly urgent and important. Rapid and accurate identification of coal mining areas offers government regulators an effective technical tool to facilitate efficient regulatory verification. At the same time, it plays a crucial role in regulating mining activities, promoting the healthy development of the coal industry, and protecting the ecological environment. Traditional methods for monitoring open-pit mining typically rely on a combination of ground surveys and statistical analyses to gather information, a process that is costly, time-consuming, and challenging to apply to large-scale monitoring [15; 16]. In contrast, remote sensing technology offers a viable alternative for identifying open-pit mining areas, owing to its high efficiency and wide monitoring range [17; 18]. Currently, the identification of open-pit mining areas predominantly utilizes satellite remote sensing images. Commonly used images in existing studies include World View [19], Quickbird [20], GEOye [21], and GF-2 [22]. While high-resolution satellite imagery provides significantly more detailed information, it comes with higher acquisition costs and demands several to dozens of times more storage capacity than Landsat images. Additionally, high-resolution imagery often requires extensive pre-processing, including radiometric and geometric corrections, which increases both the time and computational resources needed for image processing and subsequent analysis. Accelerating this process typically necessitates more advanced computing configurations. Conversely, a free medium-spatial-resolution satellite imagery system, such as Landsat, would provide significant benefits for large-scale, long-term monitoring of the entire lifecycle of open-pit coal mines due to its ease of data acquisition and straightforward processing [23; 24; 25]. Early bare coal identification from satellite images primarily has relied on manual visual interpretation, demanding the expertise of researchers with extensive practical experience. However, this approach is greatly influenced by individual subjective factors, limiting its ability to meet the requirements of a rapid response, particularly in large study areas.