Assessing the performance of two non-commercial bare earth classification algorithms for LiDAR data sets within the southern Appalachians

Abstract

Two non-commercial LiDAR bare earth classification algorithms were assessed for their suitability to accurately filter ground points from non-ground points in a forested mountainous area within the southern Appalachians. These algorithms were the multiscale curvature classification algorithm (MCC) and the adaptive triangular irregular network algorithm (ATIN). The filtered ground points were used to create three meter resolution experimental surfaces that were compared to three meter resolution control surfaces created from commercial LiDAR filtering software. Suitability for use as an alternative to commercial filtering algorithms was determined for the MCC and ATIN algorithms. The areas of the experimental surfaces that represented elevation deviation from that of the control surfaces were examined for the likelihood that the landscape characteristics of the study are were influencing vertical error sustained during classification implementation.