Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study

[EN] The benefits of upscaling methods applied to multi-source remotely sensed products aimed at enhancing the reliability of fire severity assessments across extensive burned landscapes have not been explored to date. In this context, light detection and ranging (LiDAR) scans procured by unmanned a...

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Autores: Monzón González, Andrea, Calvo Galván, María Leonor, Fernández García, Víctor, Fernández Guisuraga, José Manuel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/25606
Acceso en línea:https://www-tandfonline-com.unileon.idm.oclc.org/doi/full/10.1080/15481603.2025.2555626
https://hdl.handle.net/10612/25606
Access Level:acceso abierto
Palabra clave:Ecología. Medio ambiente
Ingeniería forestal
Fire severity
Relativized burn ratio
Sentinel-2
Unmanned aerial vehicles
Upscaling
2417.13 Ecología Vegetal
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
id ES_4bca0a7845cbb6cddc4897fd601ecaea
oai_identifier_str oai:buleria.unileon.es:10612/25606
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
title Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
spellingShingle Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
Monzón González, Andrea
Ecología. Medio ambiente
Ingeniería forestal
Fire severity
Relativized burn ratio
Sentinel-2
Unmanned aerial vehicles
Upscaling
2417.13 Ecología Vegetal
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
title_short Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
title_full Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
title_fullStr Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
title_full_unstemmed Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
title_sort Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case study
dc.creator.none.fl_str_mv Monzón González, Andrea
Calvo Galván, María Leonor
Fernández García, Víctor
Fernández Guisuraga, José Manuel
author Monzón González, Andrea
author_facet Monzón González, Andrea
Calvo Galván, María Leonor
Fernández García, Víctor
Fernández Guisuraga, José Manuel
author_role author
author2 Calvo Galván, María Leonor
Fernández García, Víctor
Fernández Guisuraga, José Manuel
author2_role author
author
author
dc.contributor.none.fl_str_mv Ecologia
Facultad de Ciencias Biologicas y Ambientales
dc.subject.none.fl_str_mv Ecología. Medio ambiente
Ingeniería forestal
Fire severity
Relativized burn ratio
Sentinel-2
Unmanned aerial vehicles
Upscaling
2417.13 Ecología Vegetal
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
topic Ecología. Medio ambiente
Ingeniería forestal
Fire severity
Relativized burn ratio
Sentinel-2
Unmanned aerial vehicles
Upscaling
2417.13 Ecología Vegetal
2506.16 Teledetección (Geología)
3106.01 Conservación
3106.06 Protección
3106.99 Otras (Incendios forestales)
description [EN] The benefits of upscaling methods applied to multi-source remotely sensed products aimed at enhancing the reliability of fire severity assessments across extensive burned landscapes have not been explored to date. In this context, light detection and ranging (LiDAR) scans procured by unmanned aerial vehicles (UAVs) may be suitable as a bridge tool for improving fire severity estimates outside areas not sampled by field inventories. This proof-of-concept study explores the spatial extrapolation of fire severity estimates at the wildfire scale by leveraging post-fire UAV-LiDAR metrics (local scale matching the extent of the UAV survey) as an intermediate step in the upscaling process to bridge field inventories (plot scale) with bi-temporal Sentinel-2 spectral indices. We considered total vegetation consumption, measured in calibration (n = 20) and validation (n = 15) field plots, as an individual fire severity indicator within a case-study wildfire in the western Mediterranean Basin. Ten-times repeated fivefold cross-validation resampling of ordinary least squares model showed that the vertical complexity index (VCI) derived from UAV-LiDAR scans was an accurate proxy for total vegetation consumption in the calibration field plots (R2 = 0.84; RMSE = 11.70), outperforming conventional Sentinel-2 spectral indices such as the Relativized Burn Ratio (RBR) (R2 = 0.61; RMSE = 18.16). The UAV-LiDAR VCI metric was used to generate a wall-to-wall product representing total vegetation consumption at the local scale, which served as an intermediate reference dataset (n = 128) in the upscaling procedure bridging field calibration plots with Sentinel-2 optical data. The use of this product significantly improved the extrapolation of total vegetation consumption estimates at the wildfire scale derived from the RBR index, as assessed in independent validation field plots (R2 = 0.76; RMSE = 15.21). The upscaling method not only outperformed the traditional wildfire-scale extrapolation of RBR estimates calibrated using a limited number of field inventories without the UAV-LiDAR bridge (R2 = 0.53; RMSE = 23.31), but also minimized the underestimation of high fire severity. The upscaling method using UAV-LiDAR data offers a practical approach to reducing field sampling effort while enhancing the generalizability of fire severity estimates. Future research should evaluate the applicability of this method across multiple wildfire events in Mediterranean regions and in other biomes
publishDate 2025
dc.date.none.fl_str_mv 2025
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-tandfonline-com.unileon.idm.oclc.org/doi/full/10.1080/15481603.2025.2555626
https://hdl.handle.net/10612/25606
url https://www-tandfonline-com.unileon.idm.oclc.org/doi/full/10.1080/15481603.2025.2555626
https://hdl.handle.net/10612/25606
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//LE081P23
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Taylor and Francis
Taylor and Francis Group
publisher.none.fl_str_mv Taylor and Francis
Taylor and Francis Group
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
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spelling Upscaling wildfire consumption using UAV-LiDAR and Sentinel-2 data: a Mediterranean case studyMonzón González, AndreaCalvo Galván, María LeonorFernández García, VíctorFernández Guisuraga, José ManuelEcología. Medio ambienteIngeniería forestalFire severityRelativized burn ratioSentinel-2Unmanned aerial vehiclesUpscaling2417.13 Ecología Vegetal2506.16 Teledetección (Geología)3106.01 Conservación3106.06 Protección3106.99 Otras (Incendios forestales)[EN] The benefits of upscaling methods applied to multi-source remotely sensed products aimed at enhancing the reliability of fire severity assessments across extensive burned landscapes have not been explored to date. In this context, light detection and ranging (LiDAR) scans procured by unmanned aerial vehicles (UAVs) may be suitable as a bridge tool for improving fire severity estimates outside areas not sampled by field inventories. This proof-of-concept study explores the spatial extrapolation of fire severity estimates at the wildfire scale by leveraging post-fire UAV-LiDAR metrics (local scale matching the extent of the UAV survey) as an intermediate step in the upscaling process to bridge field inventories (plot scale) with bi-temporal Sentinel-2 spectral indices. We considered total vegetation consumption, measured in calibration (n = 20) and validation (n = 15) field plots, as an individual fire severity indicator within a case-study wildfire in the western Mediterranean Basin. Ten-times repeated fivefold cross-validation resampling of ordinary least squares model showed that the vertical complexity index (VCI) derived from UAV-LiDAR scans was an accurate proxy for total vegetation consumption in the calibration field plots (R2 = 0.84; RMSE = 11.70), outperforming conventional Sentinel-2 spectral indices such as the Relativized Burn Ratio (RBR) (R2 = 0.61; RMSE = 18.16). The UAV-LiDAR VCI metric was used to generate a wall-to-wall product representing total vegetation consumption at the local scale, which served as an intermediate reference dataset (n = 128) in the upscaling procedure bridging field calibration plots with Sentinel-2 optical data. The use of this product significantly improved the extrapolation of total vegetation consumption estimates at the wildfire scale derived from the RBR index, as assessed in independent validation field plots (R2 = 0.76; RMSE = 15.21). The upscaling method not only outperformed the traditional wildfire-scale extrapolation of RBR estimates calibrated using a limited number of field inventories without the UAV-LiDAR bridge (R2 = 0.53; RMSE = 23.31), but also minimized the underestimation of high fire severity. The upscaling method using UAV-LiDAR data offers a practical approach to reducing field sampling effort while enhancing the generalizability of fire severity estimates. Future research should evaluate the applicability of this method across multiple wildfire events in Mediterranean regions and in other biomesSIThis study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022-139156OB-C21) within the National Program for the Promotion of Scientific-Technical Research (2021-2023); and by the Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23). Andrea Monzón-Gonzalez was supported by a pre-doctoral fellowship from the University of LeónTaylor and FrancisTaylor and Francis GroupEcologiaFacultad de Ciencias Biologicas y Ambientales2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://www-tandfonline-com.unileon.idm.oclc.org/doi/full/10.1080/15481603.2025.2555626https://hdl.handle.net/10612/25606reponame: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//LE081P23http://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/256062026-06-24T12:43:27Z
score 15,812429

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