A new method for generating canopy height models from discrete-return LiDAR point clouds
- 3 June 2014
- journal article
- research article
- Published by Taylor & Francis Ltd in Remote Sensing Letters
- Vol. 5 (6), 575-582
- https://doi.org/10.1080/2150704x.2014.938180
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.Keywords
This publication has 8 references indexed in Scilit:
- Sensitivity of LiDAR-derived three-dimensional shape signatures for individual tree crowns: a simulation studyRemote Sensing Letters, 2010
- Development of a pit filling algorithm for LiDAR canopy height modelsComputers & Geosciences, 2009
- Illuminating Northern California's Active FaultsEos, 2009
- Seeing the Trees in the ForestPhotogrammetric Engineering & Remote Sensing, 2004
- Quantifying canopy height underestimation by laser pulse penetration in small-footprint airborne laser scanning dataCanadian Journal of Remote Sensing, 2003
- Combined high-density lidar and multispectral imagery for individual tree crown analysisCanadian Journal of Remote Sensing, 2003
- Automated analysis and classification of landforms using high-resolution digital elevation data: applications and issuesCanadian Journal of Remote Sensing, 2003
- Airborne laser scanning: basic relations and formulasISPRS Journal of Photogrammetry and Remote Sensing, 1999