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Title: Analysis of the moderate resolution imaging spectroradiometer contextual algorithm for small fire detection, Journal of Applied Remote Sensing Vol.3

Author: Wang, W.; Qu, J.J.; Hao, X.; Liu, Y.;

Date: 2009

Source: Journal of Applied Remote Sensing Vol.3.

Publication Series: Scientific Journal (JRNL)

Description:

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 70 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 1) the R2 threshold R2 < 0.3 is still valid for detecting low intensity fires omitted by the MODIS contextual algorithm; 2) the threshold T4 > 310 K is too high, and a lower threshold of T4 > 293 K should be adopted instead; 3) the threshold ΔT > 10 K is also too high, and both algorithms that use it risk omitting small fires because of this threshold.

Keywords: algorithm, MODIS, regional fire detection, small fire

Publication Notes:

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.

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Citation:


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. DOI: 10.1117/1.3078426

 


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