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Journal of Applied Remote Sensing

Analysis of the moderate resolution imaging spectroradiometer contextual algorithm for small fire detection
Author(s): Wanting Wang; John J. Qu; Xianjun Hao; Yongqiang Liu
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Paper Abstract

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 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 Δ T > 10 K is also too high, and both algorithms that use it risk omitting small fires because of this threshold.

Paper Details

Date Published: 1 January 2009
PDF: 9 pages
J. Appl. Remote Sens. 3(1) 031502 doi: 10.1117/1.3078426
Published in: Journal of Applied Remote Sensing Volume 3, Issue 1
Show Author Affiliations
Wanting Wang, George Mason Univ. (United States)
John J. Qu, George Mason Univ. (United States)
Xianjun Hao, George Mason Univ. (United States)
Yongqiang Liu, U.S.D.A. Forest Service (United States)

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