Abstract This study aims to improving long-term post-fire environment assessment. It proposes a method for monitoring fire impact using Sentinel-2 satellite data by combining spectral and textural features of land cover types inside a post-fire study sites. Specific objectives were to 1) test stability of the burnt area index for Sentinel-2 (BAIS2) for identification of burn in study sites, 2) investigate the optimal feature combination for mapping land covers inside study sites, and 3) assess and analyse dynamic in land covers of study sites. BAIS2 was shown independent on date acquisition of satellite images to distinguish forest burn from other land covers over the analysed May–September vegetation period. Texture of study site improved the classification results. The most accurate classification method for identification of study sites land covers (with 0.84 Kappa coefficient and 0.86 overall accuracy) was based on combination of Sentinel-2 bands, BAIS2, and texture by Fourier transform. Analysis of vegetation recovery within the study sites demonstrated different recovery rates. Natural regeneration of pine was not observed, during three to six years of observations following fire events. The proposed method and findings can support planning of forest management measures needed to effectively restore forest cover.

中文翻译：

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基于 Sentinel-2 卫星数据监测塞尔维亚火灾后森林疤痕

摘要 本研究旨在改进长期火灾后环境评估。它提出了一种使用 Sentinel-2 卫星数据通过结合火灾后研究地点内土地覆盖类型的光谱和纹理特征来监测火灾影响的方法。具体目标是 1) 测试 Sentinel-2 (BAIS2) 燃烧面积指数的稳定性，以识别研究地点的燃烧，2) 调查绘制研究地点内土地覆盖的最佳特征组合，以及 3) 评估和分析动态在研究地点的土地覆盖中。BAIS2 显示独立于卫星图像的日期采集，以在分析的 5 月至 9 月植被期间区分森林燃烧与其他土地覆盖。研究部位的纹理改善了分类结果。用于识别研究地点土地覆盖的最准确分类方法（具有 0.84 Kappa 系数和 0.86 整体准确度）是基于通过傅立叶变换结合 Sentinel-2 波段、BAIS2 和纹理。对研究地点内植被恢复的分析显示了不同的恢复率。在火灾事件发生后的三到六年观察中，没有观察到松树的自然再生。建议的方法和结果可以支持有效恢复森林覆盖所需的森林管理措施的规划。在火灾事件发生后的三到六年观察中。建议的方法和结果可以支持有效恢复森林覆盖所需的森林管理措施的规划。在火灾事件发生后的三到六年观察中。建议的方法和结果可以支持有效恢复森林覆盖所需的森林管理措施的规划。