Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models

[EN] Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire....

Descripción completa

Detalles Bibliográficos
Autores: Quintano Pastor, Carmen, Fernández Manso, Alfonso, Fernández Guisuraga, José Manuel, Roberts, Dar A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/20718
Acceso en línea:https://www.mdpi.com/2072-4292/16/2/361
https://hdl.handle.net/10612/20718
Access Level:acceso abierto
Palabra clave:Ingeniería forestal
Ecología. Medio ambiente
Evapotranspiration
EeMETRIC
SSEBop
Fire severity
Mediterranean
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
2417.13 Ecología vegetal
id ES_830b4e3eb7140e0446621782f2902342
oai_identifier_str oai:buleria.unileon.es:10612/20718
network_acronym_str ES
network_name_str España
repository_id_str
spelling Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration ModelsQuintano Pastor, CarmenFernández Manso, AlfonsoFernández Guisuraga, José ManuelRoberts, Dar A.Ingeniería forestalEcología. Medio ambienteEvapotranspirationEeMETRICSSEBopFire severityMediterranean2506.16 Teledetección (Geología)3106.01 Conservación3106.06 Protección3106.99 Otras (Incendios forestales)2417.13 Ecología vegetal[EN] Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.SIMDPIIngenieria AgroforestalEscuela de Ingeniería Agraria y Forestal2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://www.mdpi.com/2072-4292/16/2/361https://hdl.handle.net/10612/20718reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónInglésinfo:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2022-139156OB-C21info:eu-repo/grantAgreement/Junta de Castilla y León//LE005P20info:eu-repo/grantAgreement/Portuguese Foundation for Science and Technology//UIDBhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/207182026-06-24T12:43:27Z
dc.title.none.fl_str_mv Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
title Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
spellingShingle Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
Quintano Pastor, Carmen
Ingeniería forestal
Ecología. Medio ambiente
Evapotranspiration
EeMETRIC
SSEBop
Fire severity
Mediterranean
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
2417.13 Ecología vegetal
title_short Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
title_full Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
title_fullStr Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
title_full_unstemmed Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
title_sort Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
dc.creator.none.fl_str_mv Quintano Pastor, Carmen
Fernández Manso, Alfonso
Fernández Guisuraga, José Manuel
Roberts, Dar A.
author Quintano Pastor, Carmen
author_facet Quintano Pastor, Carmen
Fernández Manso, Alfonso
Fernández Guisuraga, José Manuel
Roberts, Dar A.
author_role author
author2 Fernández Manso, Alfonso
Fernández Guisuraga, José Manuel
Roberts, Dar A.
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingenieria Agroforestal
Escuela de Ingeniería Agraria y Forestal
dc.subject.none.fl_str_mv Ingeniería forestal
Ecología. Medio ambiente
Evapotranspiration
EeMETRIC
SSEBop
Fire severity
Mediterranean
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
2417.13 Ecología vegetal
topic Ingeniería forestal
Ecología. Medio ambiente
Evapotranspiration
EeMETRIC
SSEBop
Fire severity
Mediterranean
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
2417.13 Ecología vegetal
description [EN] Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.
publishDate 2024
dc.date.none.fl_str_mv 2024
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://www.mdpi.com/2072-4292/16/2/361
https://hdl.handle.net/10612/20718
url https://www.mdpi.com/2072-4292/16/2/361
https://hdl.handle.net/10612/20718
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2022-139156OB-C21
info:eu-repo/grantAgreement/Junta de Castilla y León//LE005P20
info:eu-repo/grantAgreement/Portuguese Foundation for Science and Technology//UIDB
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/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:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412105065594880
score 15,301629

Similares en LA Referencia