Small-footprint, waveform-resolving lidar estimation of submerged and sub-canopy topography in coastal environments

INTERNATIONAL JOURNAL OF REMOTE SENSING(2009)

Cited 49|Views1
No score
Abstract
The experimental advanced airborne research lidar (EAARL) is an airborne lidar instrument designed to map near-shore submerged topography and adjacent land elevations simultaneously. This study evaluated data acquired by the EAARL system in February 2003 and March 2004 along the margins of Tampa Bay, Florida, USA, to map bare-earth elevations under a variety of vegetation types and submerged topography in shallow, turbid water conditions. A spatial filtering algorithm, known as the iterative random consensus filter (IRCF), was used to extract ground elevations from a point cloud of processed last-surface EAARL returns. Filtered data were compared with acoustic and field measurements acquired in shallow submerged (0-2.5 m water depth) and sub-canopy environments. Root mean square elevation errors (RMSEs) ranged from 10-14 cm for submerged topography to 16-20 cm for sub-canopy topography under a variety of vegetation communities. The effect of lidar sampling angles and global positioning system (GPS) satellite configuration on accuracy was investigated. Results show high RMSEs for data acquired during periods of poor satellite configuration and at large sampling angles along the edges of the lidar scan. The results presented in this study confirm the cross-environment capability of a green-wavelength, waveform-resolving lidar system, making it an ideal tool for mapping coastal environments.
More
Translated text
Key words
lidar sampling angle,lidar estimation,experimental advanced airborne research,airborne lidar instrument,coastal environment,global positioning system,near-shore submerged topography,submerged topography,eaarl system,lidar system,sub-canopy topography,processed last-surface eaarl return,filtering,estimation,lidar,point cloud,algorithms,topography,turbidity,root mean square,turbidite,submersion,testing,consensus,spatial filtering,instrumentation,waveforms
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined