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Early Fire Detection with Higher Sensitivity and Timeliness: Porting the RST-FIRES Algorithm to Rapid Scan Geostationary Data.

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Title: Early Fire Detection with Higher Sensitivity and Timeliness: Porting the RST-FIRES Algorithm to Rapid Scan Geostationary Data.
Authors: Falconieri, Alfredo1 (AUTHOR), Colonna, Roberto2 (AUTHOR), Di Leo, Vita Elena1,2 (AUTHOR), Filizzola, Carolina1,2 (AUTHOR), Mazzeo, Giuseppe1 (AUTHOR), Pergola, Nicola1 (AUTHOR) nicola.pergola@cnr.it, Pietrapertosa, Carla1 (AUTHOR), Tramutoli, Valerio2 (AUTHOR)
Source: Remote Sensing. Jun2026, Vol. 18 Issue 11, p1861. 19p.
Subjects: Satellite-based remote sensing, Fire detectors, Geostationary satellites, Forest fire management
Geographic Terms: Calabria (Italy)
Abstract: Highlights: What are the main findings? Porting the RST-FIRES algorithm to MSG-RSS data (5-min resolution) increases fire detection sensitivity by 145% compared to the standard 15-min mode. The high-frequency observations significantly improve detection timeliness, providing an average lead time of 65 min before official fire reports, nearly doubling the performance of the standard configuration. What are the implications of the main findings? The proposed methodology enables the immediate operational use of RST algorithm on new satellite sensors without waiting for years of historical archives. These results ensure methodological continuity for the Meteosat Third Generation (MTG) mission, allowing for enhanced near-real-time monitoring and early warning systems for wildfires. In this work, the portability of the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES) has been preliminary experimented on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite in Rapid Scan Service (RSS) mode. Such a configuration offers 5 min of revisit time as compared with 15 min in the standard mode (0-degree). The impact in early fire detection has been assessed and quantified, also in comparison with the results of the RST-FIRES implemented on MSG/SEVIRI 0-degree data, using the official fire bulletins of the Calabria Region (Southern Italy) for the events occurred during July 2022, for which the official regional fire catalogue was available. The results obtained suggest that SEVIRI-RSS data could allow for a rather systematic earlier detection and a better sensitivity than SEVIRI 0-degree because of the improved temporal (and spatial) resolutions. These findings are remarkable in view of the next implementation of RST-FIRES on Meteosat Third Generation/Flexible Combined Imager (MTG/FCI) data, to exploit the improved spatial (2–1 km) and temporal (10–2.5 min) resolutions offered by such a new-generation geostationary mission, together with a more suitable dynamic range in the MIR spectral region (saturation at ~500 K @3.8 micron). The use of synthetic background reference fields would allow, in fact, for a straightforward RST-FIRES application to MTGI/FCI data allowing for a more effective fire early warning system. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Early Fire Detection with Higher Sensitivity and Timeliness: Porting the RST-FIRES Algorithm to Rapid Scan Geostationary Data.
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  Data: <searchLink fieldCode="DE" term="%22Calabria+%28Italy%29%22">Calabria (Italy)</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Porting the RST-FIRES algorithm to MSG-RSS data (5-min resolution) increases fire detection sensitivity by 145% compared to the standard 15-min mode. The high-frequency observations significantly improve detection timeliness, providing an average lead time of 65 min before official fire reports, nearly doubling the performance of the standard configuration. What are the implications of the main findings? The proposed methodology enables the immediate operational use of RST algorithm on new satellite sensors without waiting for years of historical archives. These results ensure methodological continuity for the Meteosat Third Generation (MTG) mission, allowing for enhanced near-real-time monitoring and early warning systems for wildfires. In this work, the portability of the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES) has been preliminary experimented on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite in Rapid Scan Service (RSS) mode. Such a configuration offers 5 min of revisit time as compared with 15 min in the standard mode (0-degree). The impact in early fire detection has been assessed and quantified, also in comparison with the results of the RST-FIRES implemented on MSG/SEVIRI 0-degree data, using the official fire bulletins of the Calabria Region (Southern Italy) for the events occurred during July 2022, for which the official regional fire catalogue was available. The results obtained suggest that SEVIRI-RSS data could allow for a rather systematic earlier detection and a better sensitivity than SEVIRI 0-degree because of the improved temporal (and spatial) resolutions. These findings are remarkable in view of the next implementation of RST-FIRES on Meteosat Third Generation/Flexible Combined Imager (MTG/FCI) data, to exploit the improved spatial (2–1 km) and temporal (10–2.5 min) resolutions offered by such a new-generation geostationary mission, together with a more suitable dynamic range in the MIR spectral region (saturation at ~500 K @3.8 micron). The use of synthetic background reference fields would allow, in fact, for a straightforward RST-FIRES application to MTGI/FCI data allowing for a more effective fire early warning system. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18111861
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1861
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      – SubjectFull: Satellite-based remote sensing
        Type: general
      – SubjectFull: Fire detectors
        Type: general
      – SubjectFull: Geostationary satellites
        Type: general
      – SubjectFull: Forest fire management
        Type: general
      – SubjectFull: Calabria (Italy)
        Type: general
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      – TitleFull: Early Fire Detection with Higher Sensitivity and Timeliness: Porting the RST-FIRES Algorithm to Rapid Scan Geostationary Data.
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              M: 06
              Text: Jun2026
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              Y: 2026
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