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

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

1 Department of Forest Ecology and Biogeosciences, College of Natural Resources, University of Idaho, 975 W. 6th St., Moscow, ID 83844, USA 2 Spatial Information Research Center, Fuzhou University, Fuzhou, Fujian 350002, China 3 Boise Center Aerospace Laboratory, Department of Geosciences, Idaho State University, Boise, ID 83702, USA 4 School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA 5 Rocky Mountain Research Station, Forest Service, US Department of Agriculture, 1221 S. Main St., Moscow, ID 83843, USA 6 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; in revised form: 15 February 2011 / Accepted: 9 March 2011 / Published: 22 March 2011
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Abstract: 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

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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.

AMA Style

Tinkham WT, Huang H, Smith AMS, Shrestha R, Falkowski MJ, Hudak AT, Link TE, Glenn NF, Marks DG. A Comparison of Two Open Source LiDAR Surface Classification Algorithms. Remote Sensing. 2011; 3(3):638-649.

Chicago/Turabian Style

Tinkham, Wade T.; Huang, Hongyu; Smith, Alistair M. S.; Shrestha, Rupesh; Falkowski, Michael J.; Hudak, Andrew T.; Link, Timothy E.; Glenn, Nancy F.; Marks, Danny G. 2011. "A Comparison of Two Open Source LiDAR Surface Classification Algorithms." Remote Sens. 3, no. 3: 638-649.

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