Identification, Spatiotemporal Evolution, and Risk Assessment of Underground Coal Fires Based on Time-Series Satellite Thermal Anomalies: A Case Study in Midong, China

Underground coal fires (UCFs) represent a persistent global hazard, causing resource loss, land subsidence, toxic emissions, ecological degradation, and severe threats to mining safety. Unlike surface wildfires, the concealed and protracted nature of UCFs makes accurate risk assessment and dynamic monitoring exceptionally challenging. This study presented a robust remote sensing framework to characterise the spatiotemporal evolution of UCFs and assesed the effectiveness of suppression efforts in the Midong coalfield, Xinjiang, China, utilising time-series Landsat-8 Thermal Infrared (TIRS) imagery from 2013 to 2020.

For the coal fires identification and delineation, land surface temperature (LST) was retrieved from TIRS data using the Radiative Transfer Equation model. The retrieved LST effectively distinguishes fire areas from their surroundings, with significantly higher temperatures observed—up to 7.4°C higher in summer and 5.8°C in cold seasons. A comparative analysis of four thermal thresholding algorithms (Mean+2SD, Hotspot analysis, EDA, and Fractal model) was conducted. Due to the strong spatial dependence of UCF distribution, the Hotspot Analysis (HSA) model was identified as optimal for delineating fire boundaries, achieving a 65% area overlap accuracy and 70% location precision for fire spot identification. To further mitigate false alarms caused by solar radiation and surface heterogeneity, a Hotspot Sequential Frequency Extraction (HSFE) method was developed. This technique filters transient noise by identifying pixels with a high recurrence frequency (>75%) as high-probability fire risks.

Regarding the spatiotemporal analysis of coal fire evolution, the thermal severity and distribution assessed by the Coal-fire Thermal-island Intensity Ratio (CTIR) remain consistent with UCF development. The analysis captures the initially rapid fire growth, marked by a CTIR increase of 0.024 a^-1 and a total areal expansion rate of 1.29×10⁵ m²·a^-1. However, the application of this risk evaluation successfully quantified the effectiveness of fire interventions: following suppression measures, the CTIR shifted to a decrease of 0.005–0.006 a^-1. Similarly, Sequence Overlap Dynamic Analysis (SODA) reveals significant reductions of up to 74% in specific sections. Furthermore, the Thermal Anomaly Density Centre (TADC) concept was introduced to track migration, revealing that fire centroid movement is not simply unidirectional expansion but exhibits multi-directional, bilateral, and round-trip propagation.

This research demonstrates that integrating advanced spatiotemporal algorithms with satellite thermal data can effectively reconstruct the coal fire life cycle. The study also elucidates the complex coupling mechanism of anthropogenic and natural factors on UCF evolution, specifically characterizing their joint impacts on heat release, spatial distribution, and migration trajectories. The firedynamic behaviours reveal a strong "zoning effect", where thermal anomalies cluster along geological stratigraphic strikes and fracture zones. While geological fractures and faults fundamentally dictate fire initiation and propagation, frequent mining activities act as primary catalysts accelerating spread. Conversely, the implementation of targeted fire control and mining restrictions leads to the rapid disintegration and decline of large-scale fire zones. Ultimately, this framework not only offers critical data support for mining fire detection and spontaneous combustion safety management, but also demonstrates scalable, broad applicability for monitoring peat fires and other smoldering wildfires, providing generalized solution for integrated environmental management and dynamic fire risk mitigation.