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

Remote Sensing

Jason Sherba¹  Leonhard Blesius² 

[1] Department of Geography and Environment, San Francisco State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA;

关键词: LiDAR;    object-based classification;    logging roads;    forest roads;   

DOI  :  10.3390/rs6054043

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland