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

By Jeffrey Scott Evans, Andrew Thomas Hudak. IEEE Transactions on Geoscience and Remote Sensing, 2007

363 citations recorded by the source corpus.

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

Subfields: Environmental Engineering, Geology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/6f67290972a0b69a6a0300b97926bb54e9a69a94

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Citations in the corpus

The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo (2016, methodology): “To identify ground points in forested areas, points tagged as “last return” are classified using the Multiscale Curvature Classification (MCC) algorithm (Evans and Hudak, 2007).”
Airborne LiDAR for DEM generation: some critical issues (2008, methodology): “Silván-Cárdenas and Wang (2006) proposed a multiscale Hermite transform method, employing the scale-space theory to represent the added to the method presented by Evans and Hudak (2007), called to as multiscale curvature algorithm.”
High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, methodology): “…runs with different algorithm parameters, a form of optimization that is a common and recommended practice with other ﬁ ltering algorithm packages (Evans & Hudak, 2007; Sithole & Vosselman, 2004; Tinkham et al., 2012; Zhang et al., 2003), and has previously been used across a range of different…”
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors (2010, background): “…stand/plot-scaled prediction of forest attributes in coniferous stands of Scandinavia (e.g. Næsset, 2002; Maltamo et al., 2006; Packalén and Maltamo, 2006; Packalén and Maltamo, 2007; Breidenbach et al., 2010) and United States (e.g. Hudak et al., 2006; Evans and Hudak, 2007; Hudak et al., 2008).”
Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data (2008, background): “Evans and Hudak (2007) developed the Multiscale Curvature Classification algorithm to classify the returns as either ground or non-ground.”
Analyzing High Resolution Topography for Advancing the Understanding of Mass and Energy Transfer Through Landscapes: A Review (2015, background): “Other vegetation classification approaches have been proposed by Streutker and Glenn (2006), specifically for arid landscapes, and by Evans and Hudak (2007) (the multi-870 scale curvature algorithm MCC designed for high biomass areas).”