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The utility of Random Forests for wildfire severity mapping
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2018-10-01 , DOI: 10.1016/j.rse.2018.07.005
L. Collins , P. Griffioen , G. Newell , A. Mellor

Abstract Reliable fire severity mapping is a vital resource for fire scientists and land management agencies globally. Satellite derived pre- and post-fire differenced severity indices (∆FSI), such as the differenced Normalised Burn Ratio (∆NBR), are widely used to map the severity of large wildfires. Fire severity classification is commonly undertaken through the identification of severity class thresholds in ∆FSI. However, several shortcomings have been identified with severity classifications using ∆FSI, including poor delineation of low fire severity classes, and unsatisfactory performance when ∆FSI classification thresholds are applied to new landscapes. Our study assesses the performance of the Random Forest classifier (RF) for improving the accuracy of satellite based wildfire severity mapping across heterogeneous landscapes using Landsat imagery. We collected point based fire severity training data (n = 10,855) from sixteen large wildfires occurring across south-eastern Australia between 2006 and 2016. The predictive accuracy of fire severity classification using ∆NBR and the RF incorporating numerous spectral indices, was assessed using bootstrapping and cross validation. Image acquisition and index calculation for each fire was undertaken in Google Earth Engine (GEE). Results of the bootstrapping validation show that the RF classifier had very high classification accuracy (>95%) for unburnt (UB), crown scorch (CS) and crown consumption (CC) severity classes, and high classification accuracy (>74%) for low severity classes (crown unburnt, CU; partial crown scorch, PCS). The RF classification outperformed the ∆NBR classification for all severity classes, increasing classification accuracy by between 6%–21%. Cross validation using independent fires produced similar median classification accuracy as the bootstrapping validation, though the RF classification of CU was substantially reduced to ~55%. ∆NBR, ∆NDWI and ∆NDVI were the three most important variables in the RF model. The Landsat satellite platform used to derive the indices had little effect on classification accuracy. Maps produced using the RF classifier in GEE had similar spatial patterns in fire severity classes as maps produced using time-consuming hand digitisation of aerial images. GEE was found to be a highly efficient platform for image acquisition, processing and production of severity maps. Our study shows that fire severity mapping using RF classifiers provides a robust method for broad scale mapping of fire severity across heterogeneous landscapes. Furthermore, the GEE-based classification framework supports the operational application of this approach in a land management agency context for the rapid production of fire severity maps.

中文翻译:

随机森林用于野火严重程度映射的效用

摘要 可靠的火灾严重程度图是全球火灾科学家和土地管理机构的重要资源。卫星衍生的火灾前后差异严重程度指数 (ΔFSI),例如差异归一化燃烧比 (ΔNBR),被广泛用于绘制大型野火的严重程度。火灾严重性分类通常通过确定 ∆FSI 中的严重性等级阈值来进行。然而,使用 ∆FSI 进行严重性分类时已经发现了一些缺点,包括对低火灾严重性等级的描述不佳,以及将 ∆FSI 分类阈值应用于新景观时的性能不令人满意。我们的研究评估了随机森林分类器 (RF) 的性能,以使用 Landsat 图像提高基于卫星的野火严重性映射跨异构景观的准确性。我们从 2006 年至 2016 年间发生在澳大利亚东南部的 16 场大型野火中收集了基于点的火灾严重性训练数据(n = 10,855)。 使用 ∆NBR 和包含众多光谱指数的 RF 对火灾严重性分类的预测准确性,使用自举法和交叉验证。每次火灾的图像采集和指标计算均在 Google Earth Engine (GEE) 中进行。自举验证的结果表明,RF 分类器对于未燃烧 (UB)、冠部焦烧 (CS) 和冠部消耗 (CC) 严重等级具有非常高的分类准确度 (>95%),对于低严重性等级(冠部未燃烧,CU;部分冠部焦化,PCS)具有高分类准确度 (>74%)。RF 分类在所有严重性等级上都优于 ∆NBR 分类,将分类准确度提高了 6%–21%。使用独立火​​灾的交叉验证产生了与引导验证相似的中值分类准确度,尽管 CU 的 RF 分类大幅降低到约 55%。∆NBR、∆NDWI 和 ∆NDVI 是 RF 模型中三个最重要的变量。用于推导指数的 Landsat 卫星平台对分类精度影响不大。使用 GEE 中的 RF 分类器生成的地图在火灾严重性等级中具有与使用耗时的航拍图像手动数字化生成的地图相似的空间模式。GEE 被发现是用于图像采集、处理和生成严重度图的高效平台。我们的研究表明,使用 RF 分类器的火灾严重性映射为跨异构景观的火灾严重性的大规模映射提供了一种稳健的方法。此外,基于 GEE 的分类框架支持该方法在土地管理机构环境中的操作应用,以快速生成火灾严重程度图。
更新日期:2018-10-01
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