Zone adaptive fuel mapping for high resolution wildfire spread forecasting
Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurat...
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| Vydané v: | Scientific reports Ročník 15; číslo 1; s. 22254 - 16 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
Nature Publishing Group UK
01.07.2025
Nature Portfolio |
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the
F2-score
metric, reveals that ZAFM achieves the highest
F2-score
of approximately 0.90, while the
F2-scores
for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher
F2-scores
in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map. |
|---|---|
| AbstractList | Abstract Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map. Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map. Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map.Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map. Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation and mitigation due to their increasing occurrence. Wildfire spread simulators are crucial for reducing damage, but they rely heavily on accurate fuel maps, which are often outdated, have low resolution, or are unavailable in many regions. While land cover maps are more up-to-date, high-resolution globally, and widely available, there is no universally accepted method to convert land cover maps into fuel maps. In this work, an automatic methodology for generating high-resolution fuel maps from land cover maps called zone-adaptive fuel mapping (ZAFM) is proposed. ZAFM is a consistent local approach that makes use of public resources to create a fuel map. The proposed methodology has been tested using, as a study case, an EWE that occurred in the north-east of Spain during the summer of 2022. To assess the accuracy of the proposed fuel mapping method, we compared the fire spread forecast using the ZAFM fuel map with fire evolutions based on different fuel maps derived from the land cover map of the study area. The accuracy assessment, based on the F2-score metric, reveals that ZAFM achieves the highest F2-score of approximately 0.90, while the F2-scores for the other fuel maps range from 0.78 to 0.89, with no individual simulation reaching 0.90. ZAFM was also evaluated against other publicly available fuel maps covering Catalonia, and once again achieved higher F2-scores in the case study simulations. These results highlight the superior predictive performance of ZAFM and underscore the importance of using up-to-date, high-resolution data to improve wildfire spread forecasts. Furthermore, since ZAFM relies on open-access data maps, it can be applied worldwide with any available high-resolution land cover map. |
| ArticleNumber | 22254 |
| Author | Suppi, Remo Cortés, Ana Margalef, Tomàs Sánchez, Paula Carrillo, Carlos González, Irene |
| Author_xml | – sequence: 1 givenname: Paula surname: Sánchez fullname: Sánchez, Paula email: paula.sanchez.gayet@uab.cat organization: Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona – sequence: 2 givenname: Irene surname: González fullname: González, Irene organization: Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona – sequence: 3 givenname: Carlos surname: Carrillo fullname: Carrillo, Carlos email: carles.carrillo@uab.cat organization: Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona – sequence: 4 givenname: Ana surname: Cortés fullname: Cortés, Ana organization: Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona – sequence: 5 givenname: Remo surname: Suppi fullname: Suppi, Remo organization: Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona – sequence: 6 givenname: Tomàs surname: Margalef fullname: Margalef, Tomàs organization: Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40596365$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s11069-017-2934-z 10.3390/rs15164112 10.1016/j.scitotenv.2021.147057 10.1109/TGRS.2020.3037160 10.3390/fire6020037 10.1016/j.geomat.2024.100036 10.1016/j.rse.2007.05.005 10.1007/s11069-023-05896-0 10.1071/WF01028 10.3390/fire4030059 10.1016/j.trd.2022.103190 10.1029/2006JG000230 10.3390/rs12213660 10.1016/j.rse.2018.08.018 10.3390/fire6100395 10.14195/978-989-26-0884-6_189 10.1038/s41559-016-0058 10.21950/YABYCN 10.5194/bg-13-2061-2016 10.3390/rs17030415 10.1038/s41612-025-00906-3 10.1016/j.mex.2023.102218 10.1016/B978-0-12-815721-3.00001-1 10.1071/WF23140 10.3390/f11080789 10.3390/su16103900 10.3390/rs14051264 10.1016/j.rse.2011.01.017 10.1071/wf12137 10.5194/isprs-annals-X-1-W1-2023-881-2023 10.5281/zenodo.7254221 10.2737/int-gtr-122 10.1016/j.rse.2010.03.008 10.1016/j.srs.2024.100185 10.3390/fire6050215 10.1139/x02-052 10.3390/f12081011 10.1016/j.earscirev.2025.105064 10.1016/j.rsase.2022.100810 10.5194/nhess-18-847-2018 10.1016/j.jag.2018.08.020 10.1016/j.jag.2025.104436 10.1016/j.foreco.2016.06.037 10.5194/nhess-10-2515-2010 10.1016/j.ecolind.2022.108726 10.1093/forestscience/35.2.319 10.1175/WCAS-D-22-0043.1 10.3390/rs16183536 10.5194/essd-15-1287-2023 10.1007/s11069-020-04197-0 10.2737/rmrs-gtr-153 10.2737/RMRS-RP-4 |
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| Keywords | High-resolution fuel mapping Land cover map Forest fires |
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| References | A Abdollahi (6402_CR54) 2025 A Ferrer Palomino (6402_CR55) 2021 6402_CR20 6402_CR22 6402_CR23 P Palaiologou (6402_CR9) 2020 A Carbone (6402_CR40) 2023 6402_CR28 M-N Tuanmu (6402_CR37) 2010; 114 B Granda (6402_CR14) 2023 6402_CR26 6402_CR27 ML Pettinari (6402_CR30) 2014; 23 M García (6402_CR53) 2011; 115 S Grajdura (6402_CR12) 2022; 104 I Mitsopoulos (6402_CR16) 2017 A Sá (6402_CR24) 2023; 10 J Ruffault (6402_CR6) 2018; 18 Ö Akyürek (6402_CR56) 2023; 117 E Aragoneses (6402_CR32) 2021; 4 R Keane (6402_CR18) 2001; 10 C Ardohain (6402_CR45) 2025; 11 6402_CR33 6402_CR34 AA Ager (6402_CR11) 2021; 784 6402_CR39 6402_CR35 M Huesca (6402_CR50) 2019; 74 I Rahimi (6402_CR36) 2024 R Hoffrén (6402_CR49) 2024 J Franke (6402_CR21) 2018; 217 M Senande-Rivera (6402_CR8) 2025 E Chuvieco (6402_CR15) 2023 E Chuvieco (6402_CR48) 2007 D Domingo (6402_CR52) 2020 6402_CR5 J Hollis (6402_CR19) 2024; 33 E Aragoneses (6402_CR25) 2023; 15 RU Shaik (6402_CR43) 2022 E Kutchartt (6402_CR29) 2024; 76 D Bowman (6402_CR3) 2017; 1 S Schulze (6402_CR2) 2020 6402_CR42 6402_CR44 M Mutlu (6402_CR51) 2008; 112 6402_CR46 RU Shaik (6402_CR17) 2025; 262 J Garner (6402_CR7) 2022 F Tedim (6402_CR1) 2020 AA Ager (6402_CR10) 2010; 10 M López-De-Castro (6402_CR41) 2022; 27 QE Barber (6402_CR38) 2016; 377 D Strauss (6402_CR4) 1989; 35 6402_CR13 6402_CR57 ML Pettinari (6402_CR31) 2016; 13 6402_CR58 6402_CR59 B Janga (6402_CR47) 2023 |
| References_xml | – year: 2017 ident: 6402_CR16 publication-title: Nat. Hazard. doi: 10.1007/s11069-017-2934-z – year: 2023 ident: 6402_CR47 publication-title: Remote Sens. doi: 10.3390/rs15164112 – ident: 6402_CR5 – ident: 6402_CR22 – ident: 6402_CR26 – volume: 784 year: 2021 ident: 6402_CR11 publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2021.147057 – ident: 6402_CR42 doi: 10.1109/TGRS.2020.3037160 – year: 2023 ident: 6402_CR14 publication-title: Fire doi: 10.3390/fire6020037 – volume: 76 year: 2024 ident: 6402_CR29 publication-title: Geomatica doi: 10.1016/j.geomat.2024.100036 – volume: 112 start-page: 274 year: 2008 ident: 6402_CR51 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2007.05.005 – volume: 117 start-page: 1105 year: 2023 ident: 6402_CR56 publication-title: Nat. Hazard. doi: 10.1007/s11069-023-05896-0 – volume: 10 start-page: 301 year: 2001 ident: 6402_CR18 publication-title: Int. J. Wildland Fire doi: 10.1071/WF01028 – volume: 4 start-page: 15 year: 2021 ident: 6402_CR32 publication-title: Fire doi: 10.3390/fire4030059 – volume: 104 year: 2022 ident: 6402_CR12 publication-title: Transp. Res. Part D Transp. Environ. doi: 10.1016/j.trd.2022.103190 – year: 2007 ident: 6402_CR48 publication-title: J. Geophys. Res. doi: 10.1029/2006JG000230 – year: 2020 ident: 6402_CR52 publication-title: Remote Sens. doi: 10.3390/rs12213660 – volume: 217 start-page: 221 year: 2018 ident: 6402_CR21 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.08.018 – year: 2023 ident: 6402_CR40 publication-title: Fire doi: 10.3390/fire6100395 – ident: 6402_CR23 doi: 10.14195/978-989-26-0884-6_189 – volume: 1 start-page: 0058 year: 2017 ident: 6402_CR3 publication-title: Nat. Ecol. Evol. doi: 10.1038/s41559-016-0058 – ident: 6402_CR27 doi: 10.21950/YABYCN – volume: 13 start-page: 2061 year: 2016 ident: 6402_CR31 publication-title: Biogeosciences doi: 10.5194/bg-13-2061-2016 – year: 2025 ident: 6402_CR54 publication-title: Remote Sens. doi: 10.3390/rs17030415 – year: 2025 ident: 6402_CR8 publication-title: NPJ Clim. Atmos. Sci. doi: 10.1038/s41612-025-00906-3 – ident: 6402_CR58 – volume: 10 year: 2023 ident: 6402_CR24 publication-title: MethodsX doi: 10.1016/j.mex.2023.102218 – ident: 6402_CR33 – start-page: 3 volume-title: Extreme Wildfire Events and Disasters year: 2020 ident: 6402_CR1 doi: 10.1016/B978-0-12-815721-3.00001-1 – volume: 33 start-page: 1 year: 2024 ident: 6402_CR19 publication-title: Int. J. Wildland Fire doi: 10.1071/WF23140 – year: 2020 ident: 6402_CR9 publication-title: Forests doi: 10.3390/f11080789 – year: 2024 ident: 6402_CR36 publication-title: Sustainability doi: 10.3390/su16103900 – year: 2022 ident: 6402_CR43 publication-title: Remote Sens. doi: 10.3390/rs14051264 – volume: 115 start-page: 1369 year: 2011 ident: 6402_CR53 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2011.01.017 – ident: 6402_CR28 – volume: 23 start-page: 643 year: 2014 ident: 6402_CR30 publication-title: Int. J. Wildland Fire doi: 10.1071/wf12137 – ident: 6402_CR46 doi: 10.5194/isprs-annals-X-1-W1-2023-881-2023 – ident: 6402_CR34 doi: 10.5281/zenodo.7254221 – ident: 6402_CR35 doi: 10.2737/int-gtr-122 – volume: 114 start-page: 1833 year: 2010 ident: 6402_CR37 publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2010.03.008 – volume: 11 year: 2025 ident: 6402_CR45 publication-title: Sci. Remote Sens. doi: 10.1016/j.srs.2024.100185 – year: 2023 ident: 6402_CR15 publication-title: Fire doi: 10.3390/fire6050215 – ident: 6402_CR57 – ident: 6402_CR39 doi: 10.1139/x02-052 – year: 2021 ident: 6402_CR55 publication-title: Forests doi: 10.3390/f12081011 – volume: 262 year: 2025 ident: 6402_CR17 publication-title: Earth Sci. Rev. doi: 10.1016/j.earscirev.2025.105064 – volume: 27 start-page: 100810 year: 2022 ident: 6402_CR41 publication-title: Remote Sens. Appl. Soc. Environ. doi: 10.1016/j.rsase.2022.100810 – volume: 18 start-page: 847 year: 2018 ident: 6402_CR6 publication-title: Nat. Hazard. doi: 10.5194/nhess-18-847-2018 – volume: 74 start-page: 159 year: 2019 ident: 6402_CR50 publication-title: Int. J. Appl. Earth Obs. Geoinf. doi: 10.1016/j.jag.2018.08.020 – ident: 6402_CR44 doi: 10.1016/j.jag.2025.104436 – volume: 377 start-page: 46 year: 2016 ident: 6402_CR38 publication-title: For. Ecol. Manag. doi: 10.1016/j.foreco.2016.06.037 – volume: 10 start-page: 2515 year: 2010 ident: 6402_CR10 publication-title: Nat. Hazard. doi: 10.5194/nhess-10-2515-2010 – ident: 6402_CR13 doi: 10.1016/j.ecolind.2022.108726 – volume: 35 start-page: 319 year: 1989 ident: 6402_CR4 publication-title: For. Sci. doi: 10.1093/forestscience/35.2.319 – year: 2022 ident: 6402_CR7 publication-title: Weather Clim. Soc. doi: 10.1175/WCAS-D-22-0043.1 – year: 2024 ident: 6402_CR49 publication-title: Remote Sens. doi: 10.3390/rs16183536 – volume: 15 start-page: 1287 year: 2023 ident: 6402_CR25 publication-title: Earth Syst. Sci. Data doi: 10.5194/essd-15-1287-2023 – year: 2020 ident: 6402_CR2 publication-title: Nat. Hazards doi: 10.1007/s11069-020-04197-0 – ident: 6402_CR20 doi: 10.2737/rmrs-gtr-153 – ident: 6402_CR59 doi: 10.2737/RMRS-RP-4 |
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| Snippet | Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive anticipation... Abstract Extreme wildfire events (EWE), although a rare natural hazard, account for a substantial portion of global wildfire damage, requiring proactive... |
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| SubjectTerms | 639/705 704/172 704/4111 Forest fires High-resolution fuel mapping Humanities and Social Sciences Land cover map multidisciplinary Science Science (multidisciplinary) |
| Title | Zone adaptive fuel mapping for high resolution wildfire spread forecasting |
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