An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States |
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Authors: | Wanting Wang John J Qu Xianjun Hao Yongqiang Liu William T Sommers |
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Affiliation: | a EastFIRE Lab, ESGS, College of Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA b NASA Goddard Space Flight Center (GSFC), Code 614.4, Greenbelt, MD 20771, USA c USDA Forest Service, Forestry Sciences Laboratory, Athens, GA 30602, USA |
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Abstract: | 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. |
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Keywords: | Algorithm Remote Sensing MODIS Regional fire detection Small cool fires |
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