Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests
Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests
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DOI：
10.3390/rs10111680
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发表时间：
2018-11-01
期刊：
影响因子：
5
通讯作者：
Aponte, Cristina
中科院分区：
文献类型：
--
作者：
Bang Nguyen Tran;Tanase, Mihai A.;Aponte, Cristina
从光学遥感数据得到的光谱指数已被广泛用于从局部到全球尺度的森林火灾严重程度分类。然而，对不同森林类型的多种指数进行比较分析的情况很少。这代表了澳大利亚东南部温带地区火灾管理机构的信息差距，其特点是天然林的多样性，结构各异，以及主要树木的火灾再生策略。我们评估了10个光谱指数在1998年，2006年，2007年和2009年在澳大利亚东南部野火烧毁的8个地区。这些野火地区包括13种森林类型，占该地区790万公顷森林面积的86%。森林类型被聚集成6个森林组的基础上，他们的火再生策略（种子，resprouters）和结构（树高和冠层覆盖）。指数性能进行了评估，每个森林类型和森林组通过检查其敏感性四个火灾严重程度等级（未烧毁，低，中，高），使用三个独立的方法（方差分析，可分性，和最优性）。对于表现最好的指数，我们计算了指数特定的阈值（森林类型和组），以区分四个严重程度类别，并评估了独立样本的火灾严重程度分类的准确性。我们的研究结果表明，最好的性能指标的火灾严重程度不同的森林类型和组。表现最好的指数的总体准确度范围从0.50到0.78，kappa值范围从0.33（公平协议）到0.77（实质性协议），这取决于森林组和指数。火灾的严重程度在重新发芽的疏林和林地最准确地映射使用三角洲归一化烧毁比（dNBR）。相比之下，dNDVI（三角洲归一化差异植被指数）表现最好的开放式森林与混合火的反应（resprouters和seeders），和dNDWI（三角洲归一化差异水指数）是最准确的专性播种封闭的森林。我们的分析强调了所有指标对热带雨林火灾影响的敏感性较低。我们的结论是，最佳的光谱指数量化火灾的严重程度不同的森林类型，但有范围组森林结构和火再生策略，以简化火灾的严重程度分类在异质性森林景观。
Spectral indices derived from optical remote sensing data have been widely used for fire-severity classification in forests from local to global scales. However, comparative analyses of multiple indices across diverse forest types are few. This represents an information gap for fire management agencies in areas like temperate south-eastern Australia, which is characterised by a diversity of natural forests that vary in structure, and in the fire-regeneration strategies of the dominant trees. We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in south-eastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 7.9M ha region's forest area. Forest types were aggregated into six forest groups based on their fire-regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index performance was evaluated for each forest type and forest group by examining its sensitivity to four fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, separability, and optimality). For the best-performing indices, we calculated index-specific thresholds (by forest types and groups) to separate between the four severity classes, and evaluated the accuracy of fire-severity classification on independent samples. Our results indicated that the best-performing indices of fire severity varied with forest type and group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values ranged from 0.33 (fair agreement) to 0.77 (substantial agreement), depending on the forest group and index. Fire severity in resprouter open forests and woodlands was most accurately mapped using the delta Normalised Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference vegetation index) performed best for open forests with mixed fire responses (resprouters and seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate seeder closed forests. Our analysis highlighted the low sensitivity of all indices to fire impacts in Rainforest. We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes.