Partition dynamic threshold monitoring technology of wildfires near overhead transmission lines by satellite
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Wildfires are a major natural disaster that can threaten the safe and stable operation of overhead transmission lines. Compared with large-area forest fires, transmission-line wildfires usually cover a small area and spread rapidly, making monitoring accuracy and real-time requirements of high priority. Wildfire monitoring based on satellite remote sensing has advantages in terms of monitoring-range width and the capacity for real-time monitoring; however, the detection threshold changes dynamically due to the influences of climate, geography, weather, and other factors that affect monitoring accuracy. To focus on small-area wildfires near overhead transmission lines, we developed a partition dynamic threshold calculation method based on time-series prediction. Basic thresholds are obtained based on a large number of historical values, followed by partitioning one of these values according to digital elevation model data and subsequent correction. Compared with conventional constant-threshold monitoring methods, our proposed method significantly reduced missed and false detection rates. Additionally, to improve fire-spot localization to the overhead transmission-line towers, we developed a tower-location algorithm based on block searching. Compared with the traditional traversal algorithm, our algorithm enabled a 15,000-fold increase in operation speed. These improvements will significantly enhance the monitoring of transmission-line wildfires, which are highly reliant upon alarm speed.
KeywordsWildfire Satellite monitoring Partition dynamic threshold Time-series prediction Tower location Block searching
This work was supported by the State Grid Major S&T Project (No. 5216A015001M). The authors would like to thank the Editor and the reviewers whose comments and suggestions have been very helpful in improving the quality of this study.
Study conception and design were the work of JL, data collection was performed by GZ, data analysis was performed by YL, algorithm performance testing was performed by BL, and the case study was performed by Li fu He. The manuscript was written by YL and JL.
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Conflict of interests
The authors declare that they have no conflicts of interest regarding the publication of this manuscript.
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