A Novel Approach to Automatically Identify Open-Pit Coal Mining Dynamics Based on Temporal Satellite Images

Open-pit coal mining drives socioeconomic development but imposes significant environmental impacts. The timely monitoring of mining dynamics is essential for sustainable resource exploitation and ecological restoration. However, existing studies often rely on predefined mining boundaries, limiting their applicability in unknown regions. This study proposes an innovative approach that leverages the intra-annual coal frequency index (ACFI) to identify potential open-pit mining areas, and integrates the Rays method to monitor their temporal changes. By applying a predefined discriminative rule, this approach effectively distinguishes open-pit coal mines from other disturbances and enables spatiotemporal monitoring without the need for prior knowledge of their locations. Applied to the Chenbarhu Banner coalfield, Inner Mongolia, the method achieved 92% accuracy and a kappa coefficient of 0.84 in identifying mining areas. It effectively distinguished active and closed mines, detecting key temporal features with 94% accuracy (kappa = 0.86). The study also identified mining directions and extents, such as 4–13° for the Baorixile mine and 69–141° for the Dongming mine, while excluding non-mining areas with high precision. A strong correlation (r = 0.929, p < 0.01) between annual mining area and coal production further validated the approach. This method provides accurate, scalable tools for monitoring mining dynamics and supports decision-making in regulatory and ecological management processes.