A burned area mapping method for the FY-3D MERSI based on the single-temporal L1 data and multi-temporal daily active fire products

Shan, Tianchan; Zheng, Wei; Chen, Jie; Tang, Shihao

doi:10.1080/01431161.2020.1826064

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…where T 7 and T 7bg represent the mid-infrared brightness temperature of the identified pixel and the background pixel, T 7_14 and T 7_14bg denote the brightness difference between the mid-infrared and far-infrared channels in the identified pixel and background pixel, and T TH1 and T TH2 indicate the dynamic thresholds calculated by Equations ( 9) and (10).…”

Section: Confirmation Of Fire Spot Pixelsmentioning

confidence: 99%

“…T TH2 = n 2 × δT 7_14bg (10) where δT 7bg represents the standard deviation of the brightness temperature of the midinfrared channel at effective background pixel, δT 7_14bg the standard deviation of the brightness temperature difference between the mid-infrared and far-infrared channels at the effective background pixel, n 1 the correction coefficients of T TH1 , and n 2 the correction coefficients of T TH2 . Note that identification thresholds are obtained based on numerous historical real fire spot samples, which vary with observation time and areas.…”

Section: Confirmation Of Fire Spot Pixelsmentioning

confidence: 99%

“…With the development of remote sensing technology, the application skill of polar-orbiting meteorological satellites in forest and grassland fire detection has been well developed [5][6][7][8]. In China, Fengyun series satellites have been used to monitor forest and grassland fires in the Greater Hinggan Mountains, Inner Mongolia, Sichuan Province and other places for more than 30 years [9][10][11]. Numerous cases have shown that algorithms are also used in the Moderate Resolution Imaging Spectro-radiometer, National Oceanic and Atmospheric Administration series and other satellites to monitor fires [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite

Chen

Zheng

Wu³

et al. 2022

Remote Sensing

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Geo-Kompsat-2A (GK-2A) is the third new-generation geostationary meteorological satellite that orbits Asia and monitors China and its surrounding areas, following the Himawari-8 and Fengyun-4A satellites. The nadir point positioning and satellite channel parameters of the GK-2A are better than those of the Himawari-8 and FY-4A, which are more conducive to fire monitoring in China. In this study, a new fire detection algorithm is proposed based on GK-2A satellite data. That is, considering the large solar zenith angle correction for reflectance and the proportion information of background pixels in the existing spatial threshold method, fires under the different underlying surface types and solar radiation states can be automatically identified. Moreover, the accuracy of the Himawari-8 fire monitoring algorithm and the present algorithm of GK-2A is compared and analyzed through the ground truth fire spot data. The results show that compared with the original fire monitoring algorithm with fixed parameter thresholds, the brightness temperature difference of this algorithm is reduced by 0.55 K, and the correction coefficient is reduced by 0.6 times, the fire can be found earlier, and the monitoring sensitivity is improved. According to the practical fire case, the present fire monitoring algorithm of GK-2A has better monitoring accuracy than the fire monitoring algorithm of Himawari-8. The present fire monitoring algorithm of GK-2A can meet the fire monitoring requirements under different sun angles, different cloud cover ratios and vegetation ratios with good versatility.

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Section: Confirmation Of Fire Spot Pixelsmentioning

confidence: 99%

Section: Confirmation Of Fire Spot Pixelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite

Chen

Zheng

Wu³

et al. 2022

Remote Sensing

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“…This paper presents a burned area mapping method through fire location clustering and VI for FY3D/MERSI-II. 5 This method can accurately extract burned area, laying a foundation for subsequent research.…”

Section: Introductionmentioning

confidence: 99%

A burned area mapping method through fire location clustering and vegetation index for medium resolution spectral imager

Sun,

He,

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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Extracting the burned area can evaluate the loss of economy and vegetation. Remote sensing technology can be used in the mapping of burned areas. Most previous methods extracting burned areas based on the changes of Vegetation Index (VI), but these changes may not completely be caused by fire. This paper introduces an accurate burned area mapping method through fire location clustering and VI for FY3D Medium Resolution Spectral Imager-II (MERSI-Ⅱ) data. Firstly, select cumulative map of fire location obtained from MERSI-Ⅱ mid-infrared and far-infrared brightness temperature, the cumulative results are filtered and clustered as initial samples. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster initial samples into burn samples. Besides, we can calculate the coverage of the burned area through multiple spectral data by using the reflectance and the temperature. We chose Normalized Burn Ration (NBR) based on Near Infrared (NIR) and short-wave infrared channels, and Vegetation Index Thermal (VIT) based on NIR and long wave infrared channels. Finally, NBR and VIT are used to expand burned samples to extract burned area by region growth algorithm. We compare the proposed method with the true color map after fire and the Collection 6 MODIS burn area products (MCD64A1) for verification. The results show that the proposed method has a high correspondence with true color map. The commission errors are lower than MCD64A1, with 0.1703 and 0.4671 respectively. The kappa coefficient is significantly higher than that of MCD64A1, with the two being 0.8429 and 0.6272 respectively.

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“…Compared to the previous Fengyun series, the detection performance of FY-3E is significantly enhanced by the installation of Medium Resolution Spectral Imager-LL (MERSI-LL) on FY-3E, providing a novel way for monitoring wildfire occurrence at dusk. The National Satellite Meteorological Centre has conducted extensive research on wildfire identification algorithms based on the satellite data of Fengyun-3 series (Shengli and Cheng, 2010;Shan et al, 2021;Chen et al, 2022a). However, differences in spectrometer and monitoring time prevented traditional wildfire identification algorithms from being applied to the FY-3E.…”

Section: Introductionmentioning

confidence: 99%

Wildfire monitoring technologies of transmission line corridors based on Fengyun-3E satellite imaging

Li,

Chen,

et al. 2023

Front. Energy Res.

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Facing towards the Carbon Neutrality goal by 2060, renewable energy sources are boosted in China in the past decade. Either these renewables or power systems are sensitive and vulnerable to natural disasters, such as wildfire and typhoon. In this paper, a fire spot identification algorithm is proposed for wildfire disaster occurred in transmission line corridors based on Fengyun-3E (FY-3E). It mitigates the effect of large observation of solar zenith angle, as well as changes of satellite views, on the infrared channel detection. And cloud information under complex atmospheric conditions is analyzed to extract the cloud pixel fire spot, which decreases false ratio of fire-spot alerts. According to the analysis of hybrid pixel linear spectrums, the fire-spot detection sensitivity of FY-3E is four times as that of geostationary meteorological satellites. The effectiveness of the proposed algorithm was confirmed by an application in the Shanxi province power grid of State Grid in China. It shows that and the FY-3E has a superiority in wildfire monitoring for power system in terms of fire detection sensitivity, spatial range accuracy, and positioning accuracy. Compared to geostationary meteorological satellites, the fire locating accuracy of FY-3E is increased by more than one-fold. This research can assist the operation and maintenance departments of transmission lines to rapidly detect wildfire, thereby reducing the damage of wildfires on the power grid.

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A burned area mapping method for the FY-3D MERSI based on the single-temporal L1 data and multi-temporal daily active fire products

Cited by 6 publications

References 42 publications

Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite

Fire Monitoring Algorithm and Its Application on the Geo-Kompsat-2A Geostationary Meteorological Satellite

A burned area mapping method through fire location clustering and vegetation index for medium resolution spectral imager

Wildfire monitoring technologies of transmission line corridors based on Fengyun-3E satellite imaging

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