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首页> 外文期刊>International journal of remote sensing >Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images
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Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images

机译:使用源自LANDSAT TM / ETM图像的NBR(归一化燃烧比)和NDVI(归一化植被指数)进行火灾严重性评估

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In this work, the capacity of NBR and NDVI indices derived from LANDSAT TM/ETM images has been analysed for fire severity assessment. For this purpose, three fires occurring in southern Spain were studied. Firstly, the displacements of burned and unburned pixels in the pre-/post-fire NIR-MIR and NIR-R bi-spectral spaces were analysed with the aim of establishing which of the two indices was the most sensitive for discriminating severity levels. Then, the capacity of the two indices, both from a uni-temporal (post-fire) and bi-temporal perspective (pre and post-fire), to discriminate three severity levels was studied. Based on the results, it was decided that the most suitable way to assess wildfire severity by index segmentation was to discriminate between unburned and burned pixels according to their NBR pre-/post-fire difference values (dNBR), and, subsequently, to distinguish between pixels with an extreme and moderate severity based on the NBR post-fire values. The thresholds calculated for these indices permitted fire severity mapping with an accuracy of 86.42% ( ± 4.31%). These thresholds could be extrapolated to other fires with similar characteristics although a calculation of their own specific thresholds could improve the accuracy of the fire severity map obtained.
机译:在这项工作中,分析了从LANDSAT TM / ETM图像得出的NBR和NDVI指数的能力,以进行火灾严重性评估。为此,对西班牙南部发生的三场大火进行了研究。首先,分析了射击前/后NIR-MIR和NIR-R双光谱空间中燃烧和未燃烧像素的位移,目的是确定两个指数中哪个对区分严重性水平最敏感。然后,研究了从单时态(射击后)和双时态(射击前和射击后)两个指标区分三个严重性级别的能力。根据结果​​,决定通过索引分段评估野火严重性的最合适方法是根据未燃烧像素和已燃烧像素的NBR火灾前/火灾后差异值(dNBR)进行区分,然后进行区分基于NBR发射后值在极端和中等严重程度的像素之间的距离。为这些指数计算的阈值允许火灾严重性映射,准确性为86.42%(±4.31%)。这些阈值可以外推到具有类似特征的其他火灾,尽管计算它们自己的特定阈值可以提高获得的火灾严重性图的准确性。

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