Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Producción Científica

Detalles Bibliográficos
Autores: Pérez Rodríguez, Luis A., Quintano Pastor, María del Carmen, Marcos Porras, Elena María, Suárez Seoane, Susana, Calvo, Leonor, Fernández Manso, Alfonso
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Valladolid
Repositorio:UVaDOC. Repositorio Documental de la Universidad de Valladolid
OAI Identifier:oai:uvadoc.uva.es:10324/52336
Acceso en línea:https://doi.org/10.3390/rs12081295
https://uvadoc.uva.es/handle/10324/52336
Access Level:acceso abierto
Palabra clave:Unmanned aerial vehicles
Vehículos aéreos no tripulados
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spelling Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning AlgorithmsPérez Rodríguez, Luis A.Quintano Pastor, María del CarmenMarcos Porras, Elena MaríaSuárez Seoane, SusanaCalvo, LeonorFernández Manso, AlfonsoUnmanned aerial vehiclesVehículos aéreos no tripuladosProducción CientíficaPrescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17)MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/rs12081295https://uvadoc.uva.es/handle/10324/52336reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolidinstname:Universidad de ValladolidIngléshttps://www.mdpi.com/2072-4292/12/8/1295info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:uvadoc.uva.es:10324/523362026-06-13T12:44:47Z
dc.title.none.fl_str_mv Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
title Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
spellingShingle Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
Pérez Rodríguez, Luis A.
Unmanned aerial vehicles
Vehículos aéreos no tripulados
title_short Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
title_full Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
title_fullStr Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
title_full_unstemmed Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
title_sort Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
dc.creator.none.fl_str_mv Pérez Rodríguez, Luis A.
Quintano Pastor, María del Carmen
Marcos Porras, Elena María
Suárez Seoane, Susana
Calvo, Leonor
Fernández Manso, Alfonso
author Pérez Rodríguez, Luis A.
author_facet Pérez Rodríguez, Luis A.
Quintano Pastor, María del Carmen
Marcos Porras, Elena María
Suárez Seoane, Susana
Calvo, Leonor
Fernández Manso, Alfonso
author_role author
author2 Quintano Pastor, María del Carmen
Marcos Porras, Elena María
Suárez Seoane, Susana
Calvo, Leonor
Fernández Manso, Alfonso
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Unmanned aerial vehicles
Vehículos aéreos no tripulados
topic Unmanned aerial vehicles
Vehículos aéreos no tripulados
description Producción Científica
publishDate 2020
dc.date.none.fl_str_mv 2020
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/rs12081295
https://uvadoc.uva.es/handle/10324/52336
url https://doi.org/10.3390/rs12081295
https://uvadoc.uva.es/handle/10324/52336
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2072-4292/12/8/1295
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:UVaDOC. Repositorio Documental de la Universidad de Valladolid
instname:Universidad de Valladolid
instname_str Universidad de Valladolid
reponame_str UVaDOC. Repositorio Documental de la Universidad de Valladolid
collection UVaDOC. Repositorio Documental de la Universidad de Valladolid
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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