Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)

Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were in...

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Autores: Jiménez-Ruano, Adrián, Jolly, William M., Freeborn, Patrick H., Vega-Nieva, Daniel José, Monjarás-Vega, Norma Angélica, Briones-Herrera, Carlos Iván, Rodrigues Mimbrero, Marcos
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2022
País:España
Recursos:Universitat de Lleida (UdL)
Repositório:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/84385
Acesso em linha:https://doi.org/10.3390/f13081200
http://hdl.handle.net/10459.1/84385
Access Level:Acceso aberto
Palavra-chave:Wildfire occurrence
Ignition location
GLM
GAM
NDVI
Talussos (Mecànica dels sòls)
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spelling Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)Jiménez-Ruano, AdriánJolly, William M.Freeborn, Patrick H.Vega-Nieva, Daniel JoséMonjarás-Vega, Norma AngélicaBriones-Herrera, Carlos IvánRodrigues Mimbrero, MarcosWildfire occurrenceIgnition locationGLMGAMNDVITalussos (Mecànica dels sòls)Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr−1 km−1) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis.This research funded in part by Project FIREPATHS (PID2020-116556RA-I00), Spanish Ministry of Science and Innovation.MDPI2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/f13081200http://hdl.handle.net/10459.1/84385reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)Inglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116556RA-I00Reproducció del document publicat a https://doi.org/10.3390/f13081200Forests, 2022, vol.13, núm.8, p.1-16cc-by (c) Jiménez-Ruano et al., 2022info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:repositori.udl.cat:10459.1/843852026-06-24T12:42:17Z
dc.title.none.fl_str_mv Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
title Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
spellingShingle Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
Jiménez-Ruano, Adrián
Wildfire occurrence
Ignition location
GLM
GAM
NDVI
Talussos (Mecànica dels sòls)
title_short Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
title_full Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
title_fullStr Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
title_full_unstemmed Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
title_sort Spatial predictions of human and natural-caused wildfire likelihood across Montana (USA)
dc.creator.none.fl_str_mv Jiménez-Ruano, Adrián
Jolly, William M.
Freeborn, Patrick H.
Vega-Nieva, Daniel José
Monjarás-Vega, Norma Angélica
Briones-Herrera, Carlos Iván
Rodrigues Mimbrero, Marcos
author Jiménez-Ruano, Adrián
author_facet Jiménez-Ruano, Adrián
Jolly, William M.
Freeborn, Patrick H.
Vega-Nieva, Daniel José
Monjarás-Vega, Norma Angélica
Briones-Herrera, Carlos Iván
Rodrigues Mimbrero, Marcos
author_role author
author2 Jolly, William M.
Freeborn, Patrick H.
Vega-Nieva, Daniel José
Monjarás-Vega, Norma Angélica
Briones-Herrera, Carlos Iván
Rodrigues Mimbrero, Marcos
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Wildfire occurrence
Ignition location
GLM
GAM
NDVI
Talussos (Mecànica dels sòls)
topic Wildfire occurrence
Ignition location
GLM
GAM
NDVI
Talussos (Mecànica dels sòls)
description Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr−1 km−1) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis.
publishDate 2022
dc.date.none.fl_str_mv 2022
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/10.3390/f13081200
http://hdl.handle.net/10459.1/84385
url https://doi.org/10.3390/f13081200
http://hdl.handle.net/10459.1/84385
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116556RA-I00
Reproducció del document publicat a https://doi.org/10.3390/f13081200
Forests, 2022, vol.13, núm.8, p.1-16
dc.rights.none.fl_str_mv cc-by (c) Jiménez-Ruano et al., 2022
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Jiménez-Ruano et al., 2022
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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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