Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2
Burn severity has been widely studied. Typical approaches use spectral differencing indices from remotely sensed data to extrapolate in-situ severity assessments. Next generation geostationary data offer near-continuous fire behaviour information, which has been used for fire detection and monitorin...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322400027X |
| _version_ | 1827347509856436224 |
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| author | Konstantinos Chatzopoulos-Vouzoglanis Karin J. Reinke Mariela Soto-Berelov Simon D. Jones |
| author_facet | Konstantinos Chatzopoulos-Vouzoglanis Karin J. Reinke Mariela Soto-Berelov Simon D. Jones |
| author_sort | Konstantinos Chatzopoulos-Vouzoglanis |
| collection | DOAJ |
| description | Burn severity has been widely studied. Typical approaches use spectral differencing indices from remotely sensed data to extrapolate in-situ severity assessments. Next generation geostationary data offer near-continuous fire behaviour information, which has been used for fire detection and monitoring but remains underutilized for fire impact estimation. Here, we explore the association between remotely sensed fire intensity metrics and spectral differencing severity indices to understand whether and where they describe similar wildfire effects. The commonly used Differenced Normalised Burn Ratio (dNBR) severity index was calculated for Advanced Himawari Imager (AHI − 2 km) and Sentinel-2 (20 m) data and compared to different Fire Radiative Power (FRP) metrics derived from fire hotspot detections from AHI data across Australia. The comparison was implemented through different stratifications based on biogeographical region, land cover, fire type, and percentage of AHI pixel burned (fire fractional cover). The results indicate that FRP and dNBR metrics do not correlate in most scenarios, noting correlations being marginally stronger for hotter fires. However, correlations become significantly stronger when data are grouped using fire type information and fire fractional cover, with correlations peaking (R = 0.75) for large fires that burned 41–60 % of an AHI pixel. In conclusion, remotely sensed fire intensity and severity proxies capture different aspects of wildfire impact, that only correlate with each other after using auxiliary data. Spectral differencing severity metrics have been used extensively during the past decades, however high-frequency fire intensity estimations have the potential to augment the existing information and reveal new ways of characterizing wildfire impact over large areas. |
| first_indexed | 2024-03-07T23:51:40Z |
| format | Article |
| id | doaj.art-0d6b6aa9dc734c1f8c4697469dc08316 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-07T23:51:40Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-0d6b6aa9dc734c1f8c4697469dc083162024-02-19T04:13:13ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-03-01127103673Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2Konstantinos Chatzopoulos-Vouzoglanis0Karin J. Reinke1Mariela Soto-Berelov2Simon D. Jones3Corresponding author at: Geospatial Science, School of Science, STEM College, RMIT University, Melbourne, VIC 3000, Australia.; Geospatial Science, School of Science, STEM College, RMIT University, Melbourne, VIC 3000, Australia; SmartSat Cooperative Research Centre, Eleanor Harrald Building, Lot Fourteen, Frome Road, Adelaide, SA 5000, AustraliaGeospatial Science, School of Science, STEM College, RMIT University, Melbourne, VIC 3000, Australia; SmartSat Cooperative Research Centre, Eleanor Harrald Building, Lot Fourteen, Frome Road, Adelaide, SA 5000, AustraliaGeospatial Science, School of Science, STEM College, RMIT University, Melbourne, VIC 3000, Australia; SmartSat Cooperative Research Centre, Eleanor Harrald Building, Lot Fourteen, Frome Road, Adelaide, SA 5000, AustraliaGeospatial Science, School of Science, STEM College, RMIT University, Melbourne, VIC 3000, Australia; SmartSat Cooperative Research Centre, Eleanor Harrald Building, Lot Fourteen, Frome Road, Adelaide, SA 5000, AustraliaBurn severity has been widely studied. Typical approaches use spectral differencing indices from remotely sensed data to extrapolate in-situ severity assessments. Next generation geostationary data offer near-continuous fire behaviour information, which has been used for fire detection and monitoring but remains underutilized for fire impact estimation. Here, we explore the association between remotely sensed fire intensity metrics and spectral differencing severity indices to understand whether and where they describe similar wildfire effects. The commonly used Differenced Normalised Burn Ratio (dNBR) severity index was calculated for Advanced Himawari Imager (AHI − 2 km) and Sentinel-2 (20 m) data and compared to different Fire Radiative Power (FRP) metrics derived from fire hotspot detections from AHI data across Australia. The comparison was implemented through different stratifications based on biogeographical region, land cover, fire type, and percentage of AHI pixel burned (fire fractional cover). The results indicate that FRP and dNBR metrics do not correlate in most scenarios, noting correlations being marginally stronger for hotter fires. However, correlations become significantly stronger when data are grouped using fire type information and fire fractional cover, with correlations peaking (R = 0.75) for large fires that burned 41–60 % of an AHI pixel. In conclusion, remotely sensed fire intensity and severity proxies capture different aspects of wildfire impact, that only correlate with each other after using auxiliary data. Spectral differencing severity metrics have been used extensively during the past decades, however high-frequency fire intensity estimations have the potential to augment the existing information and reveal new ways of characterizing wildfire impact over large areas.http://www.sciencedirect.com/science/article/pii/S156984322400027XFire radiative powerGeostationaryFire intensityBurn severityHimawariSentinel-2 |
| spellingShingle | Konstantinos Chatzopoulos-Vouzoglanis Karin J. Reinke Mariela Soto-Berelov Simon D. Jones Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2 International Journal of Applied Earth Observations and Geoinformation Fire radiative power Geostationary Fire intensity Burn severity Himawari Sentinel-2 |
| title | Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2 |
| title_full | Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2 |
| title_fullStr | Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2 |
| title_full_unstemmed | Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2 |
| title_short | Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2 |
| title_sort | are fire intensity and burn severity associated advancing our understanding of frp and nbr metrics from himawari 8 9 and sentinel 2 |
| topic | Fire radiative power Geostationary Fire intensity Burn severity Himawari Sentinel-2 |
| url | http://www.sciencedirect.com/science/article/pii/S156984322400027X |
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