Rocky Mountain Research Station Publications
RMRS Online Publication - Journal
Articles, External Publications, and Special Reports
A multiscale curvature algorithm for classifying
discrete return LiDAR in forested environments
Evans, Jeffrey S.; Hudak, Andrew T. 2007. A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. Geoscience and Remote Sensing. 45(4): 1029-1038.
One prerequisite to the use of light detection and ranging (LiDAR) across disciplines is differentiating ground from nonground returns. The objective was to automatically and objectively classify points within unclassified LiDAR point clouds, with few model parameters and minimal postprocessing. Presented is an automated method for classifying LiDAR returns as ground or nonground in forested environments occurring in complex terrains. Multiscale curvature classification (MCC) is an iterative multiscale algorithm for classifying LiDAR returns that exceed positive surface curvature thresholds, resulting in all the LiDAR measurements being classified as ground or nonground. The MCC algorithm yields a solution of classified returns that support bare-earth surface interpolation at a resolution commensurate with the sampling frequency of the LiDAR survey. Errors in classified ground returns were assessed using 204 independent validation points consisting of 165 field plot global positioning system locations and 39 National Oceanic and Atmospheric AdministrationNational Geodetic Survey monuments. Jackknife validation and Monte Carlo simulation were used to assess the quality and error of a bare-earth digital elevation model interpolated from the classified returns. A local indicator of spatial association statistic was used to test for commission errors in the classified ground returns. Results demonstrate that the MCC model minimizes commission errors while retaining a high proportion of ground returns and provides high confidence in the derived ground surface.
Keywords: classification, curvature, digital elevation model (DEM), filtering, forestry, interpolation, light detection and ranging (LiDAR), thin-plate spline, vegetation
About PDFs: For best results, do not open the PDF in your Web browser. Right-click on the PDF link to download the PDF file directly to your computer. Click here for more PDF help.
Download Article
http://www.fs.fed.us/rm/pubs_other/rmrs_2007_evans_j001.pdf
PDF File Size: 900 K
Title: RMRS Other
Publications: A multiscale curvature algorithm for classifying
discrete return LiDAR in forested environments
Electronic Publish Date: November 26, 2007
Last Update: November
26, 2007