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首页> 外文期刊>Photogrammetric Engineering & Remote Sensing: Journal of the American Society of Photogrammetry >Testing the sensitivity of a MODIS-like daytime active fire detection model in Alaska using NOAA/AVHRR infrared data
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Testing the sensitivity of a MODIS-like daytime active fire detection model in Alaska using NOAA/AVHRR infrared data

机译：使用NOAA / AVHRR红外数据测试阿拉斯加类似MODIS的白天主动火情探测模型的灵敏度

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摘要

A MODIS-like daytime active fire detection model was tested in Alaskan biomes using NOAA-AVHRR infrared data, and its performance was assessed across a range of channel 3 (3.8 #mu#m) brightness temperature and contextual standard deviation thresholds.Absolute thresholding of channel 3 (T3) and the channel 3/4 difference (T34) was more effective than contextual analysis in minimizing false detections, although detection sensitivity to actual fire pixels was lower. The contextual analysis became more effective in terms of fire detections as the T3 and standard deviation thresholds were loosened. However, enhanced fire detection capabilities were achieved at the expense of increased false detections associated primarily with cloud edges. False detections increased exponentially and detections of active fires increased linearly as thresholds were loosened. Furthermore, T3 and standard deviation thresholds suggested for the MODIS global fire detection product appear too high for Alaska. An optimal T3 threshold between 314 and 315K and a standard deviation threshold between 2.5 and 3.5 are proposed. These results suggest that each biome or region may require different thresholds to optimize algorithm performance, recognizing that optimization of the model depends upon user goals. Effective cloud removal is clearly the most significant issue facing this type of fire detection method.
机译：使用NOAA-AVHRR红外数据在阿拉斯加生物群系中测试了类似于MODIS的白天主动火灾探测模型，并在3通道（3.8＃mu＃m）亮度温度和上下文标准偏差阈值范围内评估了其性能。通道3（T3）和通道3/4差异（T34）在最小化错误检测方面比上下文分析更有效，尽管对实际火灾像素的检测灵敏度较低。随着T3和标准偏差阈值的放宽，上下文分析在火灾检测方面变得更加有效。然而，以主要与云边缘相关的增加的错误检测为代价，实现了增强的火灾检测能力。随着阈值的放宽，虚假检测呈指数增加，主动火灾检测呈线性增加。此外，对于阿拉斯加，MODIS全球火灾探测产品建议的T3和标准偏差阈值似乎过高。提出了在314和315K之间的最佳T3阈值和在2.5和3.5之间的标准偏差阈值。这些结果表明，认识到模型的优化取决于用户目标，每个生物群落或区域可能需要不同的阈值来优化算法性能。显然，有效的除云是此类火灾探测方法面临的最重要问题。

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