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    In the southeastern United States, most wildland fires are of low intensity. A
    substantial number of these fires cannot be detected by the MODIS contextual algorithm. To
    improve the accuracy of fire detection for this region, the remote-sensed characteristics of
    these fires have to be systematically analyzed. Using an adjusted algorithm, this study
    collected a database including 6596 remote-sensed fire pixels in 72 MODIS granules, of
    which 3809 fire pixels are missed by the MODIS contextual algorithm. The statistical
    distributions of the sensor~observed fire reflectance and brightness temperature at relevant
    spectral channels are analyzed. The study explains the reasons that the detection of low
    intensity fires by the MODIS contextual algorithm is significantly influenced by view angles,
    especially when view angles are greater than 40 degrees. This paper discusses and suggests
    several aspects which could improve regional detection of low intensity fires. The results
    indicate that I) the R2 threshold R2 < 0.3 is still valid for detecting low intensity fires omitted
    by the MODIS contextual algorithm; 2) the threshold T~ > 310 K is too high, and a lower
    threshold of T, > 293 K should be adopted instead; 3) the threshold 1>T> 10 K is also too
    high, and both algorithms that use it risk omitting small fires because of this threshold.

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    Wang, W.; Qu, J.J.; Hao, X.; Liu, Y. 2009. Analysis of the moderate resolution imaging spectroradiometer contextual algorithm for small fire detection. Journal of Applied Remote Sensing Vol.3.


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    algorithm, MODIS, regional fire detection, small fire

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