Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data  

作　　者：Bryan Hally Luke Wallace Karin Reinke Simon Jones Andrew Skidmore 

机构地区：[1]School of Science,RMIT University,Melbourne,Australia [2]Bushfire and Natural Hazards Cooperative Research Centre,Melbourne,Australia [3]Faculty for Geo-Information Science and Earth Observation(ITC),University of Twente,Enschede,Netherlands [4]Department of Environmental Science,Macquarie University,Sydney,Australia

出　　处：《International Journal of Digital Earth》2019年第9期1030-1045,共16页国际数字地球学报（英文）

摘　　要：A vital component of fire detection from remote sensors is the accurateestimation of the background temperature of an area in fire’s absence,assisting in identification and attribution of fire activity. Newgeostationary sensors increase the data available to describebackground temperature in the temporal domain. Broad area methodsto extract the expected diurnal cycle of a pixel using this temporally richdata have shown potential for use in fire detection. This paper describesan application of a method for priming diurnal temperature fitting ofimagery from the Advanced Himawari Imager. The BAT method is usedto provide training data for temperature fitting of target pixels, to whichthresholds are applied to detect thermal anomalies in 4 μm imageryover part of Australia. Results show the method detects positive thermalanomalies with respect to the diurnal model in up to 99% of caseswhere fires are also detected by Low Earth Orbiting (LEO) satellite activefire products. In absence of LEO active fire detection, but where aburned area product recorded fire-induced change, this method alsodetected anomalous activity in up to 75% of cases. Potentialimprovements in detection time of up to 6 h over LEO products are alsodemonstrated.

关 键 词：Fire detection diurnal variation geostationary sensors broad area training advanced himawari imager 

分 类 号：TN9[电子电信—信息与通信工程]