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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

* ,
 and
Department of Geography and Environment, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA
* Author to whom correspondence should be addressed.
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 (CC BY 3.0).
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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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