A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest
A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest
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DOI：
10.1016/j.rse.2020.111702
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发表时间：
2020-04-01
影响因子：
13.5
通讯作者：
Collins, Luke
中科院分区：
文献类型：
--
作者：
Gibson, Rebecca;Danaher, Tim;Collins, Luke
准确和一致的大尺度火灾严重程度地图是火灾管理以及与火灾有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做法的准确性和效率提供了机会。定量生物物理模型的光合作用，非光合作用和裸露的覆盖部分还没有被广泛应用于火灾的严重性研究，但可以提供更大的一致性比较不同的火灾在整个景观相比，反射率为基础的指数。我们使用随机森林（RF）机器学习框架系统地测试和比较了从Sentinel 2卫星图像中获得的反射率和覆盖率候选严重指数。评估预测能力（交叉验证）进行量化的准确性映射新的火灾的严重性。研究了环境变量对RF预测严重程度分类准确性的影响，以评估整个景观的映射稳定性。结果表明，火灾的严重程度可以映射非常高的精度使用哨兵2图像和RF监督分类。对于未燃烧和极端严重性等级（完全树冠消耗）的平均准确度>95%，对于高严重性等级（完全树冠烧焦）的平均准确度>85%，对于低严重性等级（燃烧的林下植物、未燃烧的冠层）的平均准确度>80%，以及对于中等严重性等级（部分冠层烧焦）的平均准确度>70%。较高的树冠覆盖率和较高的地形复杂性与较高的预测不足率，由于光学传感器的限制，在这些条件下，在查看燃烧的下层低严重程度的类。进一步的研究旨在提高低和中等严重程度类别的分类精度，并将RF算法应用于减少火灾危害。
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.