|Ursic, Michael - Mick|
|MOMM, HENRIQUE - Middle Tennessee State University|
Submitted to: Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 8/13/2012
Publication Date: 8/13/2012
Citation: Ursic, M., Langendoen, E. J., Momm, H., Wren, D. G., and Kuhnle, R. A. Using terrestrial LIDAR to characterize morphology and texture of a sand and gravel bed in a laboratory flume. Hydraulic Measurements and Experimental Methods Conference, Snowbird, Utah. 6 pp. 2012. CD-ROM.
Interpretive Summary: Terrestrial laser scanners provide an efficient method of gathering large quantities of topographic information at high resolution in relatively short times. However, limitations related to scanner optics reduce applicability where high precision at close range is required. Scientists of the Watershed Physical Processes Research Unit at the USDA-ARS-National Sedimentation Laboratory have developed new procedures to reduce single point error to under 2 mm as well as automatically classify sand and gravel particles composing the ground surface.
Technical Abstract: High resolution scanning by terrestrial LiDAR provides an efficient method of gathering large quantities of topographic information. However, artifacts resulting from inherent scanner limitations reduce applicability where high precision at close range is required. Research at the USDA-ARS-National Sedimentation Laboratory necessitated reduction of single point error to ± 2 mm as well as classification of particle types from overlapping scans of a sand and gravel bed in a 35 cm wide flume at a range of approximately 1.1 m. Error reduction was accomplished by optimization of target locations used for geo-referencing procedures as well as three- dimensional polynomial fitting of resulting point clouds. Classification of points as either sand or gravel was accomplished by use of return intensities. Return intensities were altered by increasing moisture content of the sand portion of the bed, which reduced reflectivity. Reduction of sand reflectivity and thus return intensity made it possible to distinguish gravel particles. Results of return intensity and the influence of range, moisture content, and incidence angle will be presented as well as methods used for reducing single point error.