Object-based Classification of Abandoned Logging Roads Under Heavy Canopy Using LiDAR

Abstract

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach to abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin, 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.