RT info:eu-repo/semantics/article
T1 Forest Road Detection Using LiDAR Data and Hybrid Classification
A1 Buján Seoane, Sandra
A1 Guerra Hernández, Juan
A1 González Ferreiro, Eduardo Manuel
A1 Miranda Barros, David
A2 Ingeniería CartograficaGeodesica y Fotogrametria
K1 Ingeniería forestal
K1 Forest network extraction
K1 Object/pixel based classification
K1 Random forest
K1 Importance of variables
K1 Quality measures
K1 Sensitivity analysis
AB [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
PB MDPI
LK https://hdl.handle.net/10612/18654
UL https://hdl.handle.net/10612/18654
NO 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
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 21-may-2024