煤火作为一种全球性环境灾害，具有持续时间长、治理难度大的特点，对生态环境、生物健康和能源安全造成了极大威胁。煤火的演化是一个时空连续的过程，地表温度（Land Surface Temperature，LST）是反映其演化规律的关键指标。随着多源遥感数据的不断积累，时间序列方法逐渐成为探测煤火的重要手段，通过地表热异常确定煤火位置对煤田灭火工程有重要的现实意义。地表温度数据是随机性的复杂时间序列，给长时间序列煤火监测带来了挑战。本研究以新疆三道坝煤田火区为研究对象，构建了一种基于STL（Seasonal-Trend decomposition procedure based on Loess）时序分解的煤火监测方法。基于Landsat卫星影像和谷歌地球引擎（Google Earth Engine，GEE）云平台，构建了研究区1998-2023年期间的地表温度长期时间序列，对地表温度序列进行STL时序分解，分析其时空变化趋势，并利用趋势分量、随机抽样一致性（Random Sample Consensus，RANSAC）算法来判断煤火区域及演化周期。结果表明：STL时序分解后可以有效分离地表温度长时间序列数据中的季节性和随机波动影响，分解后的趋势项更能精准反映出地表温度长时间尺度下的演变趋势；2016年现场20个实测发火点中，16个位于趋势项分量平均值、极差值的高值区域；RANSAC算法分析1998-2023年煤火演化过程，结果与实地调查基本一致，验证了STL时序分解方法在煤火监测中的有效性和可靠性。本研究构建的方法提高了监测精度，增强了对复杂时空变化的适应性，识别的煤火燃烧时空特征与现场调研基本一致，完成了长期、大范围的地表温度分析，明确了研究区煤火的时空演化特征，为后续的煤火监测和治理提供参考。

Coal fires represent a major global environmental hazard characterized by long combustion cycles, strong concealment, and considerable difficulty in mitigation. They pose severe risks to ecological security, human health, and energy resources. As coal-fire evolution is a continuous spatiotemporal process, Land Surface Temperature (LST) serves as a key indicator for identifying thermal anomalies and tracking fire development. With the rapid accumulation of multi-source remote sensing data, time-series analysis has become a powerful tool for long-term coal-fire monitoring. However, LST data often exhibit strong randomness and complex variability, which introduces challenges for long-term sequence analysis. This study aims to develop a robust coal-fire monitoring method capable of accurately characterizing long-term LST variations and identifying coal-fire evolution patterns. The Sandaoba coalfield in Xinjiang, China, was selected as the study area. Using Landsat imagery and the Google Earth Engine (GEE) platform, a continuous LST dataset from 1998 to 2023 was constructed. The Seasonal-Trend decomposition procedure based on Loess (STL) was applied to separate the LST time series into trend, seasonal, and residual components, allowing the removal of strong seasonal fluctuations and stochastic disturbances. Spatiotemporal patterns of LST were analyzed using the extracted trend component. To further identify coal-fire development stages and delineate active fire zones, the Random Sample Consensus (RANSAC) algorithm was used to fit long-term temperature trends at the pixel scale. Field survey data collected in 2016 were employed to validate the identification results. we obtained the following results: The STL decomposition effectively separated the long-term LST series into stable trend components and variable seasonal and residual signals. The extracted trend item revealed long-term warming trajectories associated with coal-fire activity more accurately compared with the raw LST series. Among the 20 coal-fire points detected in the 2016 field survey, 16 fell within high-value areas of the mean and range of the trend component, indicating strong spatial consistency. The RANSAC based trend fitting captured spatiotemporal LST evolution from 1998 to 2023 and demonstrated high agreement with field observations, successfully identifying the initiation, expansion, and gradual stabilization stages of coal fires. These results validate the robustness and reliability of combining STL decomposition with RANSAC for long-term coal-fire monitoring. The proposed STL-based method significantly improves the capability to extract meaningful long-term LST trends and enhances the adaptability of coal-fire monitoring to complex spatiotemporal conditions. The method accurately identifies thermal anomalies associated with coal-fire development and shows strong consistency with field investigation results. This framework enables long-term, large-scale LST analysis and provides an effective tool for capturing the spatiotemporal characteristics of coal-fire evolution. The findings offer valuable technical support for future coal-fire surveillance, early warning, and mitigation planning.