NDLI: Forest Road Detection Using LiDAR Data and Hybrid Classification

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Forest Road Detection Using LiDAR Data and Hybrid Classification

Content Provider	MDPI
Author	Sandra, Buján Juan, Guerra-Hernández Eduardo, González-Ferreiro Miranda, David
Copyright Year	2021
Description	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/m $2$ . 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/m $2$ .
Starting Page	393
e-ISSN	20724292
DOI	10.3390/rs13030393
Journal	Remote Sensing
Issue Number	3
Volume Number	13
Language	English
Publisher	MDPI
Publisher Date	2021-01-23
Access Restriction	Open
Subject Keyword	Remote Sensing Forest Network Extraction Object/pixel Based Classification Random Forest Importance of Variables Quality Measures Sensitivity Analysis
Content Type	Text
Resource Type	Article

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