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Title: An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States
Author: Wang, Wanting; Qu, John J.; Xianjun Hao, ; Liu, Yongqiang; Sommers, William T.
Source: Remote Sensing of Environment. 108: 189-197.
Publication Series: Not categorized
Description: Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United States, where most fires are small and relatively cool, the MODIS version 4 contextual algorithm can be adjusted and improved for more accurate regional fire detection. Based on the MODIS version 4 contextual algorithm and a smoke detection algorithm, an improved algorithm using four TIR channels and seven solar reflectance channels is described. This approach is presented with fire events in the southeastern United States. The study reveals that the T22 of most small, cool fires undetected by the MODIS version 4 contextual algorithm is lower than 310 K. The improved algorithm is more sensitive to small, cool fires in the southeast especially for fires detected at large scan angles.
Keywords: Algorithm, Remote Sensing, MODIS, Regional fire detection, Small, cool fires
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Wang, Wanting; Qu, John J.; Xianjun Hao; Liu, Yongqiang; Sommers, William T. 2006. An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States. Remote Sensing of Environment. 108: 189-197.
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