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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
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2011, 3(3), 638-649;
Received: 20 January 2011 / Revised: 15 February 2011 / Accepted: 9 March 2011 / Published: 22 March 2011
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. View Full-Text
Keywords: LiDAR; algorithm; filtering; DTM; MCC; BCAL LiDAR; algorithm; filtering; DTM; MCC; BCAL

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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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.

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