MODIS Brightness Temperature Change-Based Forest Fire Monitoring
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Forests, as one of the most important natural resources in the world, are facing several challenges due to human activities and climate changes. The timely detection of forest fires plays an essential role in managing this wealth. Despite several well-developed fire detection methods, the detection of fires in early hours is still challenging. In this paper, we developed a new near-real-time hybrid method for fire detection that has high sensitivity to small and cold forest fires as well as less rate of false alarms. This method is based on the detection of both spatial and temporal changes. The change detection technique was used, and the identification of fire pixels was performed in the area with a significant change compared to the previous time. Since most of the false fire pixels had almost the same temperature in the images, they were masked. This mask allowed us to reduce the fire thresholds that lead to detect small fires. Furthermore, the omission and commission errors were minimized. The proposed algorithm was applied to forty case studies in the north of Iran. The identified fire pixels were validated with ground observations collected by the Forests, Range and Watershed Management Organization of Iran. Results show that the proposed algorithm was able to detect small and cool-case fires efficiently.
KeywordsChange detection Environment management Fire detection MODIS Remote sensing
We thank Forests, Range and Watershed Management Organization (FRWMO) of Iran for trooping ground observations and Earth Observing System Data Gateway, Land Processes Distributed Active Archive Centre (DAAC), for providing MODIS data.
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