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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #327354
Title: Digital elevation modeling via curvature interpolation for lidar data

Author
item KIM, HWAMOG - Mississippi State University
item Willers, Jeffrey
item KIM, SEONGJAI - Mississippi State University

Submitted to: ELECTRONIC JOURNAL OF DIFFERENTIAL EQUATIONS
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/1/2016
Publication Date: 3/21/2016
Publication URL: http://handle.nal.usda.gov/10113/62580
Citation: Kim, H., Willers, J.L., Kim, S. 2016. Digital elevation modeling via curvature interpolation for lidar data. ELECTRONIC JOURNAL OF DIFFERENTIAL EQUATIONS. 23:47-57.

Interpretive Summary: A mathematical approach (based upon partial differential equations (PDE)) was examined as a technique to resolve Moiré’ effects in LIDAR (LIght Detection And Ranging) point clouds while generating a digital surface elevation model. This effect can be a source that generates artifacts in the final digital surface whenever changes in the way the LIDAR returns are mixed with different scanning densities over the target area with variable rates of change in the slope of the target area. Comparisons of results obtained by non-PDE based surface construction methods and the PDE surface construction method are discussed, and we conclude the PDE-approach performs best.

Technical Abstract: Digital elevation model (DEM) is a three-dimensional (3D) representation of a terrain's surface - for a planet (including Earth), moon, or asteroid - created from point cloud data which measure terrain elevation. Its modeling requires surface reconstruction for the scattered data, which is an ill-posed problem and most computational algorithms become overly expensive as the number of sample points increases. This article studies an effective partials differential equation (PDE) based algorithm, called curature infomration, estimated from an intermediate surface, in order to construct a reliable image surface that utilizes information from all the data points. The CIM is applied for DEM for point cloud data acquired by light detection and ranging (LiDAR) technology. IT converges to a piece-wise smooth image, requiring O (N) operations independently of the number of sample points, where N is the number of grid points.