Pyrogeography: An Alternative Zonation for Europe
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Résumé
first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing: Column Width: Background: Open AccessAbstract Pyrogeography: An Alternative Zonation for Europe † by Luiz Felipe Galizia 1,2,*, Renaud Barbero 1, Marcos Rodrigues 3,4 and Thomas Curt 1 1 RECOVER, INRAE, 13182 Aix-en-Provence, France 2 Doctoral School Environmental Sciences, Aix-Marseille University, 13007 Marseille, France 3 Department of Geography, University of Zaragoza, 50009 Zaragoza, Spain 4 Department of Agriculture and Forest Engineering, University of Lleida, 25003 Lleida, Spain * Author to whom correspondence should be addressed. † Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022. Environ. Sci. Proc. 2022, 17(1), 80; https://doi.org/10.3390/environsciproc2022017080 Published: 16 August 2022 (This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk) Download Download PDF Download XML Download Epub Browse Figure Versions Notes Studies dealing with wildland fire at global or continental scales normally use coarse-resolution spatial units, within which fire-regime components are aggregated for statistical purposes. Here, we developed the first European pyrogeography based on different fire-regime components to better capture the spatial heterogeneity of fire regimes. Pyroregions were delineated through the identification of similar distributions of fire-regime components computed from a remote sensing dataset over the period 2001–2018. We identified four large-scale pyroregions with different patterns of fire activity across the continent. The spatial mismatch between the pyrogeography and ecoregions suggests that other factors, besides vegetation-based classification systems, are driving fire regimes in Europe. Comparisons of interannual climate–fire relationships at different spatial aggregations presented stronger relationships (R2 = 0.65) at the pyroregion level (Figure 1). Overall, the developed pyrogeography provides a level of generalization that aids in understanding fire regimes and contributes to improving the performance of statistical models that predict future fire regimes. Therefore, pyroregions can also be understood as a tool for effective fire risk management and planning. Author ContributionsConceptualization, L.F.G., T.C., R.B. and M.R.; formal analysis, L.F.G.; writing—original draft preparation, L.F.G.; writing—review and editing, L.F.G., T.C., R.B. and M.R. All authors have read and agreed to the published version of the manuscript.FundingThis research was funded by the project MED-Star, supported by the European Union under the Operational Program Italy/France Maritime (project No. CUP E88H19000120007). Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementAll the data that support this study are open access and can be accessed using websites or data repositories described below. Remotely sensed fire dataset is available at https://doi.pangaea.de/10.1594/PANGAEA.895835 (accessed on 16 March 2021). The ERA5 high-resolution reanalysis of the Canadian FWI System indices are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview (accessed on 2 February 2021).Conflicts of InterestThe authors declare no conflict of interest. Figure 1. Comparisons of the interannual relationships between burned area and fire-weather index (FWI) at different spatial aggregations from the period 2001–2018. The color code in the maps represents the different spatial units for each type of aggregation. Scatterplots presented the interannual correlation (Pearson) between annual burned area and FWI at different aggregations. Figure 1. Comparisons of the interannual relationships between burned area and fire-weather index (FWI) at different spatial aggregations from the period 2001–2018. The color code in the maps represents the different spatial units for each type of aggregation. Scatterplots presented the interannual correlation (Pearson) between annual burned area and FWI at different aggregations. Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Galizia, L.F.; Barbero, R.; Rodrigues, M.; Curt, T. Pyrogeography: An Alternative Zonation for Europe. Environ. Sci. Proc. 2022, 17, 80. https://doi.org/10.3390/environsciproc2022017080 AMA Style Galizia LF, Barbero R, Rodrigues M, Curt T. Pyrogeography: An Alternative Zonation for Europe. Environmental Sciences Proceedings. 2022; 17(1):80. https://doi.org/10.3390/environsciproc2022017080 Chicago/Turabian Style Galizia, Luiz Felipe, Renaud Barbero, Marcos Rodrigues, and Thomas Curt. 2022. "Pyrogeography: An Alternative Zonation for Europe" Environmental Sciences Proceedings 17, no. 1: 80. https://doi.org/10.3390/environsciproc2022017080 Find Other Styles Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. 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Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».