2022
DOI: 10.3390/f13060963
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Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

Abstract: As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorit… Show more

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Cited by 6 publications
(6 citation statements)
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“…Among the most widely used tools for the development of susceptibility maps, coverage models, analysis of large volumes of data, and localization of forest fires, several metaheuristics have been identified, including hybrid evolutionary algorithms for evaluating and mapping wildfire susceptibility [8]; biogeography-based optimization (BBO) for fire detection using a system based on the distribution of fire/flame color pixels [11]; a multi-objective programming model for wildfire suppression that considers rescue priority, utilizing the gravitational search algorithm (GSA) [12]; and ant colony optimization (ACO) for predicting temperature distribution in tunnel fires [13] concerning fire duration [14]. ACO is also employed for evacuation route planning [15], and genetic algorithm (GA) is used for data-driven wildfire spread prediction [4,16]. A wildfire early warning system has been developed via particle swarm optimization (PSO) [5], and PSO is applied to identify fire sources in utility tunnels [17].…”
Section: Introductionmentioning
confidence: 99%
“…Among the most widely used tools for the development of susceptibility maps, coverage models, analysis of large volumes of data, and localization of forest fires, several metaheuristics have been identified, including hybrid evolutionary algorithms for evaluating and mapping wildfire susceptibility [8]; biogeography-based optimization (BBO) for fire detection using a system based on the distribution of fire/flame color pixels [11]; a multi-objective programming model for wildfire suppression that considers rescue priority, utilizing the gravitational search algorithm (GSA) [12]; and ant colony optimization (ACO) for predicting temperature distribution in tunnel fires [13] concerning fire duration [14]. ACO is also employed for evacuation route planning [15], and genetic algorithm (GA) is used for data-driven wildfire spread prediction [4,16]. A wildfire early warning system has been developed via particle swarm optimization (PSO) [5], and PSO is applied to identify fire sources in utility tunnels [17].…”
Section: Introductionmentioning
confidence: 99%
“…Another method using an ensemble classification method based on AdaBoost for prediction of forest fire occurrences was proposed in Rosadi and Andriyani (2021). Finally, a method combining the genetic algorithm and brightness temperature change detection is proposed for forest fire detection for the data of FengYun-3D Chinese satellite in Dong et al (2022).…”
Section: Introductionmentioning
confidence: 99%
“…With newer generation satellites such as JAXA's Himawari 8 and 9 satellites and the GOES-16 and GOES-17 satellites in the western hemisphere, a significant increase in temporal resolution is now available from sensors of this type, with AHI-8/9 measuring the full disk at 10 min intervals. In the fire detection space, a number of studies have focused on adapting existing algorithms, predominantly using background brightness temperature derived from the image context [11][12][13][14][15]. The work of [15,16] utilised this extra temporal resolution to provide single-pixel fitting of the diurnal temperature cycle in an extension of the method developed by [17]; however, this method continues to present shortcomings with regard to cloud masking techniques, which had not been refined for the sensor in question at the time.…”
Section: Introductionmentioning
confidence: 99%