The study aimed to evaluate the applicability of environmental indices in the monitoring of smouldering coal-waste dumps. A dump located in the Upper Silesian Coal Basin served as the research site for a multi-method analysis combining remote sensing and field-based data. Two UAV survey campaigns were conducted, capturing RGB, infrared, and multispectral imagery. These were supplemented with direct ground measurements of subsurface temperature and detailed vegetation mapping. Additionally, publicly available satellite data from the Landsat and Sentinel missions were analysed. A range of vegetation and fire-related indices (NDVI, SAVI, EVI, BAI, among others) were calculated to identify thermally active zones and assess vegetation conditions within these degraded areas. The results revealed strong seasonal variability in vegetation indices on thermally active sites, with evidence of disrupted vegetation cycles, including winter greening in moderately heated root zones - a pattern indicative of stress and degradation processes. While satellite data proved useful in reconstructing the fire history of the dump, their spatial resolution was insufficient for detailed monitoring of small-scale thermal anomalies. The study highlights the diagnostic potential of UAV-based remote sensing in post-industrial environments undergoing land degradation but emphasises the importance of field validation for accurate environmental assessment.

翻译：本研究旨在评估环境指数在阴燃煤矸石堆监测中的适用性。研究选取上西里西亚煤盆地的一处煤矸石堆作为场地，采用遥感与实地数据相结合的多方法分析。通过两次无人机航测，获取了RGB、红外及多光谱影像，并辅以地下温度的实地直接测量与详细植被制图。此外，还分析了来自Landsat和Sentinel任务的公开卫星数据。通过计算一系列植被与火灾相关指数（包括NDVI、SAVI、EVI、BAI等），以识别热活跃区并评估这些退化区域内的植被状况。结果显示，热活跃点上的植被指数具有强烈的季节性变化，并存在植被周期紊乱的证据，例如在中等加热的根区出现冬季返青现象——这是胁迫与退化过程的典型模式。虽然卫星数据在重建煤矸石堆火灾历史方面具有价值，但其空间分辨率不足以详细监测小尺度热异常。本研究凸显了基于无人机的遥感技术在经历土地退化的后工业环境中具有诊断潜力，但强调实地验证对于准确环境评估的重要性。