Next Article in Journal
Extraction of Vertical Walls from Mobile Laser Scanning Data for Solar Potential Assessment
Previous Article in Journal
Airborne Remote Sensing of a Biological Hot Spot in the Southeastern Bering Sea
Article Menu

Export Article

Open AccessLetter
Remote Sens. 2011, 3(3), 638-649; doi:10.3390/rs3030638

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

Department of Forest Ecology and Biogeosciences, College of Natural Resources, University of Idaho, 975 W. 6th St., Moscow, ID 83844, USA
Spatial Information Research Center, Fuzhou University, Fuzhou, Fujian 350002, China
Boise Center Aerospace Laboratory, Department of Geosciences, Idaho State University, Boise, ID 83702, USA
School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA
Rocky Mountain Research Station, Forest Service, US Department of Agriculture, 1221 S. Main St., Moscow, ID 83843, USA
Northwest Watershed Research Center, Agricultural Research Service, US Department of Agriculture, Boise, ID 83712, USA
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 20 January 2011 / Revised: 15 February 2011 / Accepted: 9 March 2011 / Published: 22 March 2011
View Full-Text   |   Download PDF [371 KB, uploaded 19 June 2014]   |  


With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy of each algorithm was assessed via comparison with >7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.
Keywords: LiDAR; algorithm; filtering; DTM; MCC; BCAL LiDAR; algorithm; filtering; DTM; MCC; BCAL
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Tinkham, W.T.; Huang, H.; Smith, A.M.S.; Shrestha, R.; Falkowski, M.J.; Hudak, A.T.; Link, T.E.; Glenn, N.F.; Marks, D.G. A Comparison of Two Open Source LiDAR Surface Classification Algorithms. Remote Sens. 2011, 3, 638-649.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top