Accurate and consistent broad-scale mapping of fire severity is an important resource for fire management as well as fire-related ecological and climate change research. Remote sensing and machine learning approaches present an opportunity to enhance accuracy and efficiency of current practices. Quantitative biophysical models of photosynthetic, non-photosynthetic and bare cover fractions have not been widely applied to fire severity studies but may provide greater consistency in comparisons of different fires across the landscape compared to reflectance-based indices. We systematically tested and compared reflectance and fractional cover candidate severity indices derived from Sentinel 2 satellite imagery using a random forest (RF) machine learning framework. Assessment of predictive power (cross-validation) was undertaken to quantify the accuracy of mapping severity of new fires. The effect of environmental variables on the accuracy of the RF predicted severity classification was examined to assess the stability of the mapping across the landscape. The results indicate that fire severity can be mapped with very high accuracy using Sentinel 2 imagery and RF supervised classification. The mean accuracy was >95% for the unburnt and extreme severity class (complete crown consumption), >85% for high severity class (full crown scorch), >80% for low severity (burnt understory, unburnt canopy) and >70% for the moderate severity class (partial canopy scorch). Higher canopy cover and higher topographic complexity was associated with a higher rate of under-prediction, due to the limitations of optical sensors in viewing the burnt understorey of low severity classes under these conditions. Further research is aimed at improving classification accuracy of low and moderate severity classes and applying the RF algorithm to hazard reduction fires.

中文翻译：

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使用哨兵 2 和随机森林绘制澳大利亚东南部火灾严重程度的遥感方法

准确一致的大尺度火灾严重程度图是火灾管理以及与火灾相关的生态和气候变化研究的重要资源。遥感和机器学习方法为提高当前实践的准确性和效率提供了机会。光合作用、非光合作用和裸覆盖部分的定量生物物理模型尚未广泛应用于火灾严重程度研究，但与基于反射率的指数相比，可以在整个景观中不同火灾的比较中提供更大的一致性。我们使用随机森林 (RF) 机器学习框架系统地测试和比较了源自 Sentinel 2 卫星图像的反射率和部分覆盖候选严重性指数。进行了预测能力评估（交叉验证）以量化绘制新火灾严重程度的准确性。检查环境变量对 RF 预测严重性分类准确性的影响，以评估整个景观映射的稳定性。结果表明，可以使用 Sentinel 2 图像和 RF 监督分类以非常高的准确度映射火灾严重程度。未烧毁和极端严重等级（完全烧毁冠部）的平均准确度>95%，高严重等级（全冠焦烧）>85%，低严重等级（下层被烧，树冠未烧毁）>80%，>70%对于中等严重程度（部分冠层焦化）。更高的冠层覆盖和更高的地形复杂性与更高的预测不足率有关，由于光学传感器在这些条件下观察低严重度等级的燃烧下层的局限性。进一步的研究旨在提高低度和中度严重性等级的分类准确性，并将 RF 算法应用于减少灾害的火灾。