A new method for generating canopy height models from discrete-return LiDAR point clouds

Abstract

Tree height underestimation and occurrence of visible data pits are two major problems in light detection and ranging (LiDAR)-derived digital surface models and canopy height models (CHMs) in forested areas. To address the two major problems, a new method is proposed for generating CHMs from discrete-return LiDAR point clouds using a selecting and sorting mechanism based on a circle centred at the target point, followed by spatial interpolation. Test results from simulated and real LiDAR point clouds show that the new method outperformed three other methods in terms of treetop approximation, crown surface representation and data pit removal.

Acknowledgements

The authors would like to thank the NSF OpenTopography Facility for providing LiDAR data for this research and anonymous reviewers for their comments on the manuscript.