Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data

This work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computat...

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Detalles Bibliográficos
Autores: López Rebollo, Jorge, González Aguilera, Diego, Hernández López, David, Moreno Hidalgo, Miguel Ángel
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/40409
Acceso en línea:https://doi.org/10.3390/f14040662
https://hdl.handle.net/10578/40409
Access Level:acceso abierto
Palabra clave:Electrical fires
Forest fires
LiDAR
Power lines
Risk analysis
Software Development
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spelling Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR DataLópez Rebollo, JorgeGonzález Aguilera, DiegoHernández López, DavidMoreno Hidalgo, Miguel ÁngelElectrical firesForest firesLiDARPower linesRisk analysisSoftware DevelopmentThis work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computation of the catenary of the wires using different calculation methods, the estimation of the vegetation growth and the identification of the risk areas. To this end, a coarse-to-fine approach is proposed, so that a preliminary analysis is performed with public airborne LiDAR data, and then a more detailed inspection is provided with drone LiDAR data over those areas classified as high risk. The tool and the methodology developed were validated along a high-voltage power line of 53 km in a real forest area. The results show that although the preliminary analysis based on public airborne LiDAR data is more conservative, it is very useful for selecting those areas of higher risk for further analysis with drone LiDAR dataMDPI202520252023info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.3390/f14040662https://hdl.handle.net/10578/40409reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésREF: 101036926.Project H2020 TREEADSinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/404092026-05-27T07:36:41Z
dc.title.none.fl_str_mv Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
title Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
spellingShingle Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
López Rebollo, Jorge
Electrical fires
Forest fires
LiDAR
Power lines
Risk analysis
Software Development
title_short Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
title_full Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
title_fullStr Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
title_full_unstemmed Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
title_sort Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data
dc.creator.none.fl_str_mv López Rebollo, Jorge
González Aguilera, Diego
Hernández López, David
Moreno Hidalgo, Miguel Ángel
author López Rebollo, Jorge
author_facet López Rebollo, Jorge
González Aguilera, Diego
Hernández López, David
Moreno Hidalgo, Miguel Ángel
author_role author
author2 González Aguilera, Diego
Hernández López, David
Moreno Hidalgo, Miguel Ángel
author2_role author
author
author
dc.subject.none.fl_str_mv Electrical fires
Forest fires
LiDAR
Power lines
Risk analysis
Software Development
topic Electrical fires
Forest fires
LiDAR
Power lines
Risk analysis
Software Development
description This work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computation of the catenary of the wires using different calculation methods, the estimation of the vegetation growth and the identification of the risk areas. To this end, a coarse-to-fine approach is proposed, so that a preliminary analysis is performed with public airborne LiDAR data, and then a more detailed inspection is provided with drone LiDAR data over those areas classified as high risk. The tool and the methodology developed were validated along a high-voltage power line of 53 km in a real forest area. The results show that although the preliminary analysis based on public airborne LiDAR data is more conservative, it is very useful for selecting those areas of higher risk for further analysis with drone LiDAR data
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.3390/f14040662
https://hdl.handle.net/10578/40409
url https://doi.org/10.3390/f14040662
https://hdl.handle.net/10578/40409
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv REF: 101036926.
Project H2020 TREEADS
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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score 15,81155

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