Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain

[EN] While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide acce...

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Autores: Prendes Pérez, Covadonga, Buján Seoane, Sandra, Ordóñez Galán, Celestino, Canga, Elena
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/18658
Acceso en línea:https://iforest.sisef.org/abstract/?id=ifor2989-012
https://hdl.handle.net/10612/18658
Access Level:acceso abierto
Palabra clave:Ingeniería forestal
GIS
Pixel-based Classification
OBIA
Quality Measures
Forest Roads Network
Accuracy Assessment
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spelling Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern SpainPrendes Pérez, CovadongaBuján Seoane, SandraOrdóñez Galán, CelestinoCanga, ElenaIngeniería forestalGISPixel-based ClassificationOBIAQuality MeasuresForest Roads NetworkAccuracy Assessment[EN] While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-auto-matic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.SIThis study was funded by the SCALyFOR project (R&D Projects “Research Challenges”, Spanish Ministry of Economy and CompetitivenessViterbo SISEFIngeniería Cartografica, Geodesica y FotogrametriaEscuela Superior y Tecnica de Ingenieros de Minas2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://iforest.sisef.org/abstract/?id=ifor2989-012https://hdl.handle.net/10612/18658reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad Rey Juan CarlosInglésinfo:eu-repo/grantAgreement/MinistryofEconomyandCompetitiveness/SCALyFOR/http://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/186582026-06-24T12:43:27Z
dc.title.none.fl_str_mv Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
title Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
spellingShingle Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
Prendes Pérez, Covadonga
Ingeniería forestal
GIS
Pixel-based Classification
OBIA
Quality Measures
Forest Roads Network
Accuracy Assessment
title_short Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
title_full Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
title_fullStr Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
title_full_unstemmed Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
title_sort Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain
dc.creator.none.fl_str_mv Prendes Pérez, Covadonga
Buján Seoane, Sandra
Ordóñez Galán, Celestino
Canga, Elena
author Prendes Pérez, Covadonga
author_facet Prendes Pérez, Covadonga
Buján Seoane, Sandra
Ordóñez Galán, Celestino
Canga, Elena
author_role author
author2 Buján Seoane, Sandra
Ordóñez Galán, Celestino
Canga, Elena
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingeniería Cartografica, Geodesica y Fotogrametria
Escuela Superior y Tecnica de Ingenieros de Minas
dc.subject.none.fl_str_mv Ingeniería forestal
GIS
Pixel-based Classification
OBIA
Quality Measures
Forest Roads Network
Accuracy Assessment
topic Ingeniería forestal
GIS
Pixel-based Classification
OBIA
Quality Measures
Forest Roads Network
Accuracy Assessment
description [EN] While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-auto-matic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.
publishDate 2019
dc.date.none.fl_str_mv 2019
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://iforest.sisef.org/abstract/?id=ifor2989-012
https://hdl.handle.net/10612/18658
url https://iforest.sisef.org/abstract/?id=ifor2989-012
https://hdl.handle.net/10612/18658
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MinistryofEconomyandCompetitiveness/SCALyFOR/
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Viterbo SISEF
publisher.none.fl_str_mv Viterbo SISEF
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
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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score 15,301603

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