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Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR.
- Source :
- Remote Sensing; May2014, Vol. 6 Issue 5, p4043-4060, 18p
- Publication Year :
- 2014
-
Abstract
- LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer's accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads. [ABSTRACT FROM AUTHOR]
- Subjects :
- FOREST canopies
LIDAR
LASER based sensors
LOGGING
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 6
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 96222530
- Full Text :
- https://doi.org/10.3390/rs6054043