Long-Term Monitoring of Persistent Coal-Fire Thermal Anomalies Using STL Decomposition of Landsat LST Time Series

Long-term trends, seasonal fluctuations, and short-term disturbances are superimposed in land surface temperature (LST) time series, complicating the identification of persistent coal-fire thermal anomalies. Based on 1188 Landsat scenes of the Midong mining area, Xinjiang, China, acquired during 1990–2025, this study applied Seasonal-trend decomposition using LOESS (STL) to extract long-term temperature trends. The contemporaneous spatial mean of the trend components in non-mining areas was used as the background reference to construct the background-corrected temperature difference ΔLST. Thermal anomalies were identified using year-specific thresholds, and their spatiotemporal evolution and relationship with mining-land changes were analyzed in conjunction with land-use/land-cover (LULC) records. In 2020 and 2024, the matching rates with field-measured high-temperature points were 75.0% and 83.3%, respectively, for Z > 2, compared with 47.2% and 41.7% for Z > 3. To focus on thermal anomalies with larger standardized temperature deviations and to examine their long-term evolution, subsequent analyses used the Z > 3 results. Thermal-anomaly area peaked at approximately 1.7 km2 in 2016 and subsequently declined overall, but anomalies were repeatedly detected at some locations within open-pit mining areas during 2016–2025. From 2014 onward, thermal anomalies in these mining areas accounted for more than 80% of the total identified anomaly area. The mining-land area ratio and the thermal-anomaly area ratio were positively correlated (Spearman’s r = 0.376, p = 0.0245), but mining-land area ratios were generally lower in 2016–2025 than in 2005–2015, while thermal-anomaly area ratios remained relatively high. At comparable mining-land area ratios, thermal-anomaly area ratios were generally higher in the later period. Long-term records of thermal anomalies and LULC reveal patterns of localized anomaly recurrence that are difficult to capture using annual area statistics or the current extent of mining land, providing a basis for determining long-term monitoring priorities in mining areas.