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dc.contributor | Escuela Superior y Tecnica de Ingenieros de Minas | es_ES |
dc.contributor.author | Buján Seoane, Sandra | |
dc.contributor.author | Guerra Hernández, Juan | |
dc.contributor.author | González Ferreiro, Eduardo Manuel | |
dc.contributor.author | Miranda Barros, David | |
dc.contributor.other | Ingeniería Cartografica, Geodesica y Fotogrametria | es_ES |
dc.date | 2021 | |
dc.date.accessioned | 2024-03-07T07:06:49Z | |
dc.date.available | 2024-03-07T07:06:49Z | |
dc.identifier.citation | Buján, S., Guerra-Hernández, J., González-Ferreiro, E., & Miranda, D. (2021). Forest road detection using LiDAR data and hybrid classification. Remote Sensing, 13(3), 1-36. https://doi.org/10.3390/RS13030393 | es_ES |
dc.identifier.other | https://www.mdpi.com/2072-4292/13/3/393 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/18654 | |
dc.description.abstract | [EN] Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bareearth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0-97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7-89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2 | es_ES |
dc.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Attribution-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nd/4.0/ | * |
dc.subject | Ingeniería forestal | es_ES |
dc.subject.other | Forest network extraction | es_ES |
dc.subject.other | Object/pixel based classification | es_ES |
dc.subject.other | Random forest | es_ES |
dc.subject.other | Importance of variables | es_ES |
dc.subject.other | Quality measures | es_ES |
dc.subject.other | Sensitivity analysis | es_ES |
dc.title | Forest Road Detection Using LiDAR Data and Hybrid Classification | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/RS13030393 | |
dc.description.peerreviewed | SI | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MinistryofEconomyIndustryandCompetitiveness/PID2019-111154RB-I00/CLIMAPLAN | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 2072-4292 | |
dc.journal.title | Remote Sensing | es_ES |
dc.volume.number | 13 | es_ES |
dc.issue.number | 3 | es_ES |
dc.page.initial | 393 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.project | This research was supported by: (1) the Project “Sistema de ayuda a la decisión para la adaptación al cambio climático a través de la planificación territorial y la gestión de riesgos (CLIMAPLAN) (PID2019-111154RB-I00): Proyectos de I+D+i - RTI”; and (2) “National Programme for the Promotion of Talent and Its Employability” of the Ministry of Economy, Industry, and Competitiveness (Torres-Quevedo program) via a postdoctoral grant (PTQ2018-010043) to Juan Guerra Hernández | es_ES |
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