Object-based classification of abandoned logging roads under heavy canopy using LiDAR

typical abandoned logging road
Sherba J, Blesius L, Davis JD
Journal Title or Book Publisher
Remote Sensing
Publication type

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.


Sherba, J., L. Blesius, J. Davis (2014).  Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR.  Remote Sensing 2014, 6(5), 4043-4060; doi:10.3390/rs6054043.