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Título
Forest Road Detection Using LiDAR Data and Hybrid Classification
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Remote Sensing
Número de la revista
3
Datos de la obra
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
Editor
MDPI
Fecha
2021
Résumé
[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
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/MinistryofEconomyIndustryandCompetitiveness/PID2019-111154RB-I00/CLIMAPLAN
URI
DOI
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