Document Type
Article
Publication Date
3-22-2011
Department
College of Forest Resources and Environmental Science
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.
Publication Title
Remote Sensing
Recommended Citation
Tinkham, W. T.,
Huang, H.,
Smith, A. M.,
Shrestha, R.,
Falkowski, M. J.,
Hudak, A. T.,
&
et al.
(2011).
A comparison of two open source LiDAR surface classification algorithms.
Remote Sensing,
3(3), 638-649.
http://doi.org/10.3390/rs3030638
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/1992
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Version
Publisher's PDF
Publisher's Statement
© 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). Publisher’s version of record: https://doi.org/10.3390/rs3030638