RT Journal Article
T1 Forest Road Detection Using LiDAR Data and Hybrid Classification
A1 Buján Seoane, Sandra
A1 Guerra Hernández, Juan
A1 González Ferreiro, Eduardo
A1 Miranda Barrós, David
K1 Forest network extraction
K1 Object/pixel based classification
K1 Random forest
K1 Importance of variables
K1 Quality measures
K1 Sensitivity analysis
AB 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. Bare-earth 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
PB MDPI
YR 2021
FD 2021
LK http://hdl.handle.net/10347/24457
UL http://hdl.handle.net/10347/24457
LA eng
NO Remote Sens. 2021, 13(3), 393; https://doi.org/10.3390/rs13030393
NO 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
DS Minerva
RD 28 abr 2026