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Automated Extraction of Forest Road Network Geometry from Aerial LiDAR


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Created: Apr 01, 2018 at 7:28 p.m.
Last updated: Apr 09, 2018 at 5:34 p.m.
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Abstract

We developed an algorithm that was designed to create a spatial database of a forested transportation network using aerial LiDAR. The algorithm uses two main attributes, LiDAR intensity values and ground return density. The road extraction process was developed using aerial LiDAR from McDonald-Dunn Research Forest near Corvallis, Oregon, U.S.A. The road extraction process requires X, Y, Z coordinates, intensity values, canopy type, and the maximum road grade. To compare the results of the process, nine road segments were field surveyed with terrestrial LiDAR. The result of the road extraction process resulted in 80% true positives, 34% false positives, 20% false negatives, and 38% true negatives in identifying forest roads. The average absolute value difference in the road width between the two data sets were 1.1m, while the cut/fill slope differences were minimal (> 4%) and the difference in road cross slope was two percent. These results were comparable with other published studies that examined differences between LiDAR measurements and field measurements.

Raw project data is available by contacting ctemps@unr.edu

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How to Cite

Storm, J. C. (2018). Automated Extraction of Forest Road Network Geometry from Aerial LiDAR, HydroShare, http://www.hydroshare.org/resource/04830201cb704fa3955680c8d004f71d

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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