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煤矸石自燃堆场监测中的适用性。研究选取上西里西亚煤盆地内一处堆场作为多方法分析的研究场地，结合遥感与实地观测数据。通过两次无人机航测，获取了RGB、红外及多光谱影像，并辅以地下温度的直接地面测量与详细的植被分布制图。此外，还分析了来自Landsat和Sentinel任务的公开卫星数据。研究计算了一系列植被与火灾相关指数（如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。结果表明，热活跃点上的植被指数具有显著的季节性变化，其植被周期受到干扰，包括在中等加热的根区出现冬季返青现象——这一模式指示了胁迫与退化过程。虽然卫星数据在重建堆场火灾历史方面具有实用价值，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的诊断潜力，同时强调了实地验证对于准确环境评估的重要性。