Remote Sensing Identification and Extraction Algorithms for Coal Fire Risk Areas: A Case Study of the Xingsheng Open-Pit Coal Mine in Xinjiang, China

Identifying coal fire risk areas is essential for safe production in coal mines. Land Surface Temperature (LST) retrieval and high-temperature anomaly extraction are core techniques for coal fire risk detection. To address the insufficient evaluation of the accuracy of relevant algorithms for arid open-pit mines, this study takes the Xingsheng Open-Pit Coal Mine in Yiwu County, Xinjiang as the research object. Based on Landsat imagery and UAV thermal infrared data, we systematically compared five mainstream LST retrieval algorithms and six high-temperature anomaly extraction algorithms and determined the optimal combination for long-term monitoring. The results indicate that all five algorithms can effectively depict LST spatial distribution under normal temperature conditions. The Jiménez-Muñoz split-window algorithm performs best for small-scale coal fire identification, with a mean absolute error of 3.25 °C and a relative error of 5.53%, and its fitting slope of 0.82 proves superior stability. For high-temperature anomaly extraction methods, the gradient threshold method achieves a 100% overlap rate with actual anomalies and no omission, which is ideal for large-scale surveys; the cluster analysis method balances detection accuracy and economic benefits for pit-scale investigations. Using 52 valid Landsat images from 2013 to 2025, long-term monitoring reveals that high-temperature anomalies are most active in summer, with an average patch area of 5.65 × 105 m2, and weaken sharply in winter. According to the observed spatiotemporal evolution patterns, the dynamic changes in thermal anomalies are inferred to be mainly associated with human mining activities, with coal seam conditions as the secondary influencing factor. This study provides reliable technical references for coal mine safety management and coal fire disaster prevention.