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Remote Sens. 2014, 6(5), 4043-4060; doi:10.3390/rs6054043
Article

Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

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Received: 7 February 2014 / Revised: 15 April 2014 / Accepted: 24 April 2014 / Published: 2 May 2014
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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.
Keywords: LiDAR; object-based classification; logging roads; forest roads LiDAR; object-based classification; logging roads; forest roads
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Sherba, J.; Blesius, L.; Davis, J. Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR. Remote Sens. 2014, 6, 4043-4060.

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