Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential
Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential
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DOI：
10.3390/rs10060879
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发表时间：
2018-06
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通讯作者：
S. Parks;Lisa M. Holsinger;M. Voss;R. Loehman;Nathaniel P. Robinson
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作者：
S. Parks;Lisa M. Holsinger;M. Voss;R. Loehman;Nathaniel P. Robinson
基于陆地卫星的火灾严重程度数据集是监测和研究目的的宝贵资源。这些网格火灾严重程度数据集通常使用火灾前和火灾后图像生成，以估计火灾引起的生态变化的程度。在这里，我们介绍使用 Google Earth Engine (GEE) 平台生成三种基于 Landsat 的火灾严重程度指标的方法：Delta 标准化燃烧比 (dNBR)、相对化 Delta 标准化燃烧比 (RdNBR) 和相对化燃烧比 (RBR)。我们的方法不依赖于耗时的先验场景选择，而是使用平均合成方法，其中将预先指定的日期范围（火灾前和火灾后）内的所有有效像素（例如，无云）堆叠起来，并使用每个堆叠上每个像素的平均值来生成最终的火灾严重程度数据集。该方法表明，与标准方法相比，可以相对容易和快速地生成火灾严重程度数据集，在标准方法中，明智地识别一个火灾前场景和一个火灾后场景并用于生成火灾严重程度数据集。我们还使用美国西部 18 场火灾的现场火灾严重程度图来验证 GEE 得出的火灾严重程度指标。这些验证与仅使用一个火灾前和火灾后场景生成的基于陆地卫星的火灾严重程度数据集进行了比较，后者自诞生以来一直是生成此类数据集的标准方法。结果表明，与仅使用一个火灾前场景和一个火灾后场景的并行版本相比，GEE 衍生的火灾严重程度数据集通常显示出改进的验证统计数据，尽管某些验证中的一些改进或多或少可以忽略不计。我们提供代码和示例地理空间火灾历史图层，为我们评估的 18 场火灾生成 dNBR、RdNBR 和 RBR。尽管我们的方法要求在应用我们的方法之前独立生成地理空间火灾历史图层（即火灾周界），但我们建议我们的 GEE 方法可以合理地应用于数百到数千起火灾，从而增加全球火灾严重程度监测和研究的机会。
Landsat-based fire severity datasets are an invaluable resource for monitoring and research purposes. These gridded fire severity datasets are generally produced with pre- and post-fire imagery to estimate the degree of fire-induced ecological change. Here, we introduce methods to produce three Landsat-based fire severity metrics using the Google Earth Engine (GEE) platform: The delta normalized burn ratio (dNBR), the relativized delta normalized burn ratio (RdNBR), and the relativized burn ratio (RBR). Our methods do not rely on time-consuming a priori scene selection but instead use a mean compositing approach in which all valid pixels (e.g., cloud-free) over a pre-specified date range (pre- and post-fire) are stacked and the mean value for each pixel over each stack is used to produce the resulting fire severity datasets. This approach demonstrates that fire severity datasets can be produced with relative ease and speed compared to the standard approach in which one pre-fire and one post-fire scene are judiciously identified and used to produce fire severity datasets. We also validate the GEE-derived fire severity metrics using field-based fire severity plots for 18 fires in the western United States. These validations are compared to Landsat-based fire severity datasets produced using only one pre- and post-fire scene, which has been the standard approach in producing such datasets since their inception. Results indicate that the GEE-derived fire severity datasets generally show improved validation statistics compared to parallel versions in which only one pre-fire and one post-fire scene are used, though some of the improvements in some validations are more or less negligible. We provide code and a sample geospatial fire history layer to produce dNBR, RdNBR, and RBR for the 18 fires we evaluated. Although our approach requires that a geospatial fire history layer (i.e., fire perimeters) be produced independently and prior to applying our methods, we suggest that our GEE methodology can reasonably be implemented on hundreds to thousands of fires, thereby increasing opportunities for fire severity monitoring and research across the globe.