A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments

TLDR

Differentiating ground from nonground returns is a prerequisite for using LiDAR across disciplines. The study aimed to automatically and objectively classify points in unclassified LiDAR point clouds with few model parameters and minimal postprocessing. The authors created an automated multiscale curvature classification (MCC) algorithm that iteratively assigns LiDAR returns as ground or nonground based on curvature thresholds, and validated its accuracy with 204 independent points, jackknife resampling, Monte Carlo simulation, and spatial association statistics. The MCC algorithm produces classified returns that support bare‑earth surface interpolation at LiDAR‑sampling resolution, minimizes commission errors, retains a high proportion of ground points, and yields high‑confidence ground surfaces.

Abstract

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 Administration-National 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