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United States Department of Agriculture

Agricultural Research Service

Research Project: SNOW AND HYDROLOGIC PROCESSES IN THE INTERMOUNTAIN WEST Title: A comparison of two open source LiDAR surface classification algorithms

Authors
item Tinkham, Wade -
item Huang, Hongyu -
item Smith, Alistar -
item Shrestha, Rupesh -
item Falkowski, Michael -
item Hudak, Andrew -
item Link, Timothy -
item Glenn, Nancy -
item Marks, Daniel

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 9, 2011
Publication Date: March 22, 2011
Citation: Tinkham, W.T., Huang, H., Smith, A.M., Shrestha, R., Falkowski, M.J., Hudak, A.T., Link, T.E., Glenn, N.F., Marks, D.G. 2011. A comparison of two open source LiDAR surface classification algorithms. Remote Sensing. 3:638-649.

Interpretive Summary: 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.

Technical 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.

Last Modified: 11/24/2014
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