Leafless roughness of complex tree morphology using terrestrial LiDAR

A. S. Antonarakis, Keith S. Richards, Mike Bithell, James Brasington

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)
182 Downloads (Pure)

Abstract

Strategies for extracting roughness parameters from riparian forests need to address the issue that the trees are more than just stems and that in large rivers flow can rise into the canopy. Remote sensing information with 3-D capabilities such as lidar can be used to extract information on trees. However, first and last pulse airborne lidar data are insufficient to characterize the complex vertical structure of vegetation because by definition, there are few data at intermediate levels. Terrestrial laser scanning (TLS) is used in this study to define complex structures at a millimetric scanning resolution for the purpose of extracting canopy parameters relevant for the parameterization of the flow resistance equations. We will mainly be concerned with the projected area of leafless trees, estimating the total tree dimensions using several different methods. These include manipulating mass point cloud data obtained from TLS to create stage-dependent projected areas through complex meshing techniques and voxelization. Stage-dependent projected areas were defined for natural and planted poplar forests in the riparian zone of the Garonne and Allier rivers in southern and central France, respectively. Roughness values for planted poplar forests dominant in many western European river floodplains range from Manning's n = 0.037–0.094 and n = 0.140–0.330 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively. Roughness values for natural poplar forests ranged from n = 0.066–0.210 and n = 0.202–0.720 for below-canopy flow (2 m) and extreme in-canopy flow (8 m), respectively.
Original languageEnglish
JournalWater Resources Research
DOIs
Publication statusPublished - 01 Oct 2009

Fingerprint

Dive into the research topics of 'Leafless roughness of complex tree morphology using terrestrial LiDAR'. Together they form a unique fingerprint.

Cite this