Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain

Models of human-caused ignition probability are typically developed from static or structural points of view. This research analyzes the intra-annual dimension of fire occurrence and fire-triggering factors in NE Spain and moves forward towards more accurate predictions. Applying the Maximum Entropy...

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Detalhes bibliográficos
Autores: Martín, Yago, Zúñiga Antón, María, Rodrigues Mimbrero, Marcos
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Recursos:Universitat de Lleida (UdL)
Repositorio:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/65970
Acesso em linha:https://doi.org/10.1080/19475705.2018.1526219
http://hdl.handle.net/10459.1/65970
Access Level:acceso abierto
Palavra-chave:Wildfire
Ignition danger
Human drivers
Temporal dimension
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spelling Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast SpainMartín, YagoZúñiga Antón, MaríaRodrigues Mimbrero, MarcosWildfireIgnition dangerHuman driversTemporal dimensionModels of human-caused ignition probability are typically developed from static or structural points of view. This research analyzes the intra-annual dimension of fire occurrence and fire-triggering factors in NE Spain and moves forward towards more accurate predictions. Applying the Maximum Entropy algorithm (MaxEnt) and using wildfire data (2008–2011) and GIS and remote sensing data for the explanatory variables, we construct eight occurrence data scenarios by splitting wildfire records into the four seasons and then separating each season into working and non-working days. We assess model accuracy using a cross-validation k-fold procedure and an operational validation with 2012 data. Results report a substantial contribution of accessibility across models, often coupled with Land Surface Temperature. In addition, we observe great temporal variability, with WAI strongly influencing winter models, whereas distance to roads stands out during working days. Model performances stand consistently above 0.8 AUC in all temporal scenarios, with outstanding predictive effectiveness during summer months. The comparison among static-to-dynamic approaches reveals superior performance of simulations considering temporal scenarios, with AUC values from 0.7 to 0.85. Overall, we believe our approach is reliable enough to derive dynamic predictions of human-caused fire occurrence.This research was funded jointly from a predoctoral Fulbright-Iberdrola grant, a ‘Juan de la Cierva’ postdoctoral fellowship grant (FJCI-2016-31090) at the Univesity of Lleida, and the research group GEOT, from the University of Zaragoza.Taylor & Francis2018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.1080/19475705.2018.1526219http://hdl.handle.net/10459.1/65970reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésReproducció del document publicat a: https://doi.org/10.1080/19475705.2018.1526219Geomatics, natural hazards & risk, 2018, vol. 10, núm. 1, p. 385-411cc-by, (c) Martín et al., 2018info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/659702026-06-24T12:42:17Z
dc.title.none.fl_str_mv Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
title Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
spellingShingle Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
Martín, Yago
Wildfire
Ignition danger
Human drivers
Temporal dimension
title_short Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
title_full Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
title_fullStr Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
title_full_unstemmed Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
title_sort Modelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
dc.creator.none.fl_str_mv Martín, Yago
Zúñiga Antón, María
Rodrigues Mimbrero, Marcos
author Martín, Yago
author_facet Martín, Yago
Zúñiga Antón, María
Rodrigues Mimbrero, Marcos
author_role author
author2 Zúñiga Antón, María
Rodrigues Mimbrero, Marcos
author2_role author
author
dc.subject.none.fl_str_mv Wildfire
Ignition danger
Human drivers
Temporal dimension
topic Wildfire
Ignition danger
Human drivers
Temporal dimension
description Models of human-caused ignition probability are typically developed from static or structural points of view. This research analyzes the intra-annual dimension of fire occurrence and fire-triggering factors in NE Spain and moves forward towards more accurate predictions. Applying the Maximum Entropy algorithm (MaxEnt) and using wildfire data (2008–2011) and GIS and remote sensing data for the explanatory variables, we construct eight occurrence data scenarios by splitting wildfire records into the four seasons and then separating each season into working and non-working days. We assess model accuracy using a cross-validation k-fold procedure and an operational validation with 2012 data. Results report a substantial contribution of accessibility across models, often coupled with Land Surface Temperature. In addition, we observe great temporal variability, with WAI strongly influencing winter models, whereas distance to roads stands out during working days. Model performances stand consistently above 0.8 AUC in all temporal scenarios, with outstanding predictive effectiveness during summer months. The comparison among static-to-dynamic approaches reveals superior performance of simulations considering temporal scenarios, with AUC values from 0.7 to 0.85. Overall, we believe our approach is reliable enough to derive dynamic predictions of human-caused fire occurrence.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1080/19475705.2018.1526219
http://hdl.handle.net/10459.1/65970
url https://doi.org/10.1080/19475705.2018.1526219
http://hdl.handle.net/10459.1/65970
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1080/19475705.2018.1526219
Geomatics, natural hazards & risk, 2018, vol. 10, núm. 1, p. 385-411
dc.rights.none.fl_str_mv cc-by, (c) Martín et al., 2018
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by, (c) Martín et al., 2018
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812455

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