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Year 2015, Volume: 1 Issue: 1, 21 - 33, 01.06.2015

Abstract

References

  • White, R. A., Dietterick, B.C., Mastin, T., Strohman, R., 2010. Forest Roads Mapped Using LiDAR in Steep Forested Terrain. Remote Sensing, 2(4):1120-1141.
  • Watershed Sciences., 2008. LiDAR Remote Sensing Data Collection: McDonald-Dunn Research Forest. Corvallis, OR, USA.
  • Soler, T., Michalak, P., Weston, N.D., Snay, R. A., Foote, R. H. 2006. Accuracy of OPUS solutions for 1-h to 4-h observing sessions. GPS solutions, 10(1), 45-55.
  • Rieger, W., Kerschner, M., Reiter, T., Rottensteiner, F., 1999. Roads and buildings from laser scanner data within a forest enterprise. International Archives of Photogrammetry and Remote Sensing, 32(3), W14.
  • Olsen, M.J., Young, A.P., Ashford, S.A., 2012. TopCAT- Topographical Compartment Analysis Tool to Anlyze seacliff and beach change in GIS. Computers & Geosciences, 284-292.
  • Olsen, M., 2011. Bin ‘n’ grid: A simple program for statistical filtering of point cloud data.
  • Jenson, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective. (J. Howard, D. Kaveney, K. Schiaparelli, & E. Thomas, Eds.) Upper Saddle River, NJ, USA.
  • Craven, M., Wing, M., Sessions, J., Wimer, J., 2011. Assessment of Airborne Light Detection and Ranging (LiDAR) for use in Common Forest Engineering Geomatic Applications. M.S. Thesis, Oregon State Universtiy, College of Forestry, Corvallis. Retrieved from Oregon State University, OR, USA.
  • Bourke, P., 1989. Efficient Triangulation Algorithm Suitable for Terrain Modelling. Pan Pacific Computer Conference, Beijing, China.
  • Beck, S., 2014. The Use of LiDAR to Identify Forest Transportation Networks. M.S. Thesis, Oregon State University, College of Forestry, Corvallis. Retrieved from Oregon State University, OR, USA.

Automated Extraction of Forest Road Network Geometry from Aerial LiDAR

Year 2015, Volume: 1 Issue: 1, 21 - 33, 01.06.2015

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

References

  • White, R. A., Dietterick, B.C., Mastin, T., Strohman, R., 2010. Forest Roads Mapped Using LiDAR in Steep Forested Terrain. Remote Sensing, 2(4):1120-1141.
  • Watershed Sciences., 2008. LiDAR Remote Sensing Data Collection: McDonald-Dunn Research Forest. Corvallis, OR, USA.
  • Soler, T., Michalak, P., Weston, N.D., Snay, R. A., Foote, R. H. 2006. Accuracy of OPUS solutions for 1-h to 4-h observing sessions. GPS solutions, 10(1), 45-55.
  • Rieger, W., Kerschner, M., Reiter, T., Rottensteiner, F., 1999. Roads and buildings from laser scanner data within a forest enterprise. International Archives of Photogrammetry and Remote Sensing, 32(3), W14.
  • Olsen, M.J., Young, A.P., Ashford, S.A., 2012. TopCAT- Topographical Compartment Analysis Tool to Anlyze seacliff and beach change in GIS. Computers & Geosciences, 284-292.
  • Olsen, M., 2011. Bin ‘n’ grid: A simple program for statistical filtering of point cloud data.
  • Jenson, J.R., 2007. Remote Sensing of the Environment: An Earth Resource Perspective. (J. Howard, D. Kaveney, K. Schiaparelli, & E. Thomas, Eds.) Upper Saddle River, NJ, USA.
  • Craven, M., Wing, M., Sessions, J., Wimer, J., 2011. Assessment of Airborne Light Detection and Ranging (LiDAR) for use in Common Forest Engineering Geomatic Applications. M.S. Thesis, Oregon State Universtiy, College of Forestry, Corvallis. Retrieved from Oregon State University, OR, USA.
  • Bourke, P., 1989. Efficient Triangulation Algorithm Suitable for Terrain Modelling. Pan Pacific Computer Conference, Beijing, China.
  • Beck, S., 2014. The Use of LiDAR to Identify Forest Transportation Networks. M.S. Thesis, Oregon State University, College of Forestry, Corvallis. Retrieved from Oregon State University, OR, USA.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Storm J.c. BECK This is me

Michael J. OLSEN This is me

John SESSİONS This is me

Michael G. WİNG

Publication Date June 1, 2015
Published in Issue Year 2015 Volume: 1 Issue: 1

Cite

APA BECK, S. J., OLSEN, M. J., SESSİONS, J., WİNG, M. G. (2015). Automated Extraction of Forest Road Network Geometry from Aerial LiDAR. European Journal of Forest Engineering, 1(1), 21-33.

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The works published in European Journal of Forest Engineering (EJFE) are licensed under a  Creative Commons Attribution-NonCommercial 4.0 International License.