资源简介

摘要： MODIS火灾产品的火点检测算法主要以4和11 μm通道亮温数据来识别火点,在应用于不同地区和不同季节时有一定局限性。在分析MODIS现有火点检测算法的基础上,对算法相关阈值及参数进行适当调整,同时考虑火灾前后NDVI的变化,以及林火燃烧过程中伴生烟羽使火点下风方气溶胶光学厚度明显增加的特点,构建了基于亮温—植被指数—气溶胶光学厚度的火点识别算法,并应用多次火灾个例对本算法进行验证。结果表明:算法提高了对高温热点和低温焖烧火点的识别能力,有效降低了高温热点的误报率和低温火点的漏报率,使火点检测算法在不同环境的适应性有所增强。 关键词： 森林火灾;  算法;  气溶胶光学厚度;  归一化植被指数     Abstract: The traditional Moderate Resolution Imaging Spectroradiometer(MODIS) fire detection algorithm relies primarily on hot spot detection using brightness temperature data derived from the 4 and 11 channels.There are limitations to the effectiveness of this algorithm when it is applied to monitor forest fires in different regions and four seasons.In response to these problems,a detailed description of an improved algorithm based on brightness temperature,vegetation index and Aerosol Optical Depth (AOD) is offered by adjusting the corresponding potential fire thresholds and contextual thresholds in the traditional algorithm,by exploring the differences in the post\|fire and pre\|fire values of the Normalized Difference Vegetation Index(NDVI),and by analysing the obvious increase in the AOD on the leeward side caused by the spread of a smoke plume.This approach is confirmed by several fire events in China.The study reveals that the improved algorithm achieves significantly lower false alarm rates and is more sensitive to cool fires.Then the adaptability of this algorithm in all environment is also enhanced.

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