Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery

The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’ssusceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forest...

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Autores: Peña Molina, Esther, Moya Navarro, Daniel, Marino del Amo, Eva, Tomé Morán, Jose Luis, Fajardo Cantos, Álvaro, González Romero, Javier, Lucas Borja, Manuel Esteban, Heras Ibáñez, Jorge Antonio de las
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/37418
Acceso en línea:https://doi.org/10.3390/rs16101718
https://www.mdpi.com/2072-4292/16/10/1718
https://hdl.handle.net/10578/37418
Access Level:acceso abierto
Palabra clave:Cloud computing
Forest management
Google Earth Engine
Remote sensing
Wildfires
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spelling Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite ImageryPeña Molina, EstherMoya Navarro, DanielMarino del Amo, EvaTomé Morán, Jose LuisFajardo Cantos, ÁlvaroGonzález Romero, JavierLucas Borja, Manuel EstebanHeras Ibáñez, Jorge Antonio de lasCloud computingForest managementGoogle Earth EngineRemote sensingWildfiresThe modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’ssusceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest firesthat occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively.Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused onexamining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using theGoogle Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed toassess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVIand NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggestaccelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE)index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire.MDPI202420242024info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.3390/rs16101718https://www.mdpi.com/2072-4292/16/10/1718https://hdl.handle.net/10578/37418reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglés2020-PREDUCLM-16032RTA2017-0042-C05-05PRESFIRE: SBPLY/19/180501/000130/1MCIN/AEI/10.13039/501-100011033ENFIRES: PID2020-116494RR-C43info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/374182026-05-27T07:36:41Z
dc.title.none.fl_str_mv Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
title Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
spellingShingle Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
Peña Molina, Esther
Cloud computing
Forest management
Google Earth Engine
Remote sensing
Wildfires
title_short Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
title_full Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
title_fullStr Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
title_full_unstemmed Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
title_sort Fire Vulnerability, Resilience, and Recovery Rates of Mediterranean Pine Forests Using a 33-Year Time Series of Satellite Imagery
dc.creator.none.fl_str_mv Peña Molina, Esther
Moya Navarro, Daniel
Marino del Amo, Eva
Tomé Morán, Jose Luis
Fajardo Cantos, Álvaro
González Romero, Javier
Lucas Borja, Manuel Esteban
Heras Ibáñez, Jorge Antonio de las
author Peña Molina, Esther
author_facet Peña Molina, Esther
Moya Navarro, Daniel
Marino del Amo, Eva
Tomé Morán, Jose Luis
Fajardo Cantos, Álvaro
González Romero, Javier
Lucas Borja, Manuel Esteban
Heras Ibáñez, Jorge Antonio de las
author_role author
author2 Moya Navarro, Daniel
Marino del Amo, Eva
Tomé Morán, Jose Luis
Fajardo Cantos, Álvaro
González Romero, Javier
Lucas Borja, Manuel Esteban
Heras Ibáñez, Jorge Antonio de las
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Cloud computing
Forest management
Google Earth Engine
Remote sensing
Wildfires
topic Cloud computing
Forest management
Google Earth Engine
Remote sensing
Wildfires
description The modification of fire regimes and their impact on vegetation recovery, soil properties, and fuel structure are current key research areas that attempt to identify the thresholds of vegetation’ssusceptibility to wildfires. This study aimed to evaluate the vulnerability of Mediterranean pine forests (Pinus halepensis Mill. and Pinus pinaster Aiton) to wildfires, analyzing two major forest firesthat occurred in Yeste (Spain) in 1994 and 2017, affecting over 14,000 and 3200 hectares, respectively.Four recovery regions were identified based on fire severity—calculated using the delta Normalized Burn Ratio (dNBR) index—and recurrence: areas with high severity in 2017 but not in 1994 (UB94-HS17), areas with high severity in 1994 but not in 2017 (HS94-UB17), areas with high severity in both fires (HS94-HS17), and areas unaffected by either fire (UB94-UB17). The analysis focused onexamining the recovery patterns of three spectral indices—the Normalized Difference Vegetation Index (NDVI), Normalized Moisture Index (NDMI), and Normalized Burn Ratio (NBR)—using theGoogle Earth Engine platform from 1990 to 2023. Additionally, the Relative Recovery Indicator (RRI), the Ratio of Eighty Percent (R80P), and the Year-on-Year average (YrYr) metrics were computed toassess the spectral recovery rates by region. These three spectral indices showed similar dynamic responses to fire. However, the Mann–Kendall and unit root statistical tests revealed that the NDVIand NDMI exhibited distinct trends, particularly in areas with recurrence (HS94-HS17). The NDVI outperformed the NBR and NDMI in distinguishing variations among regions. These results suggestaccelerated vegetation spectral regrowth in the short term. The Vegetation Recovery Capacity After Fire (VRAF) index showed values from low to moderate, while the Vulnerability to Fire (V2FIRE)index exhibited values from medium to high across all recovery regions. These findings enhance our understanding of how vegetation recovers from fire and how vulnerable it is to fire.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.3390/rs16101718
https://www.mdpi.com/2072-4292/16/10/1718
https://hdl.handle.net/10578/37418
url https://doi.org/10.3390/rs16101718
https://www.mdpi.com/2072-4292/16/10/1718
https://hdl.handle.net/10578/37418
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 2020-PREDUCLM-16032
RTA2017-0042-C05-05
PRESFIRE: SBPLY/19/180501/000130/1
MCIN/AEI/10.13039/501-100011033
ENFIRES: PID2020-116494RR-C43
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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