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Proceedings Paper

Acquisition of underwater topography in a mountain channel using terrestrial laser scanning
Author(s): N. Miura; Y. Asano
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Paper Abstract

For better risk management, detailed and quantitative measurement of channel and stream-bed structure is required to assimilate and forecast how the water and sediment flow in mountain channels. Our previous research demonstrated good performance of green-wavelength TLS for measurement of submerged stream-bed in a steep mountain channel. The results also showed that each of water depth and flow velocity alone does not affect the accuracy of TLS measurement. Instead, it was indicated that the specification of data acquisition may have an impact on the accuracy of derived Digital Terrain Models (DTMs). Therefore, this paper examines how the acquisition protocol of TLS affects the accuracy of data collected in the mountain channel. First, it is tested whether different scanner height, that is, incident angle affects the data acquisition in terms of point density and accuracy. Then, the difference in minimum point spacing is examined to find how much impact it has on derived DTM. It is also analyzed whether a combination of multiple TLS data acquired from different direction improves data accuracy, compared to the data acquired by single measurement. All the acquired underwater data by TLS are water refraction corrected and validated using field surveyed data. The results of these tests showed that the accuracy of derived DTM was improved when the scanner height was raised or data was acquired from multiple directions, however, acquiring denser point cloud with minimum point spacing of 1 mm did not improve the accuracy of the data.

Paper Details

Date Published: 21 October 2014
PDF: 9 pages
Proc. SPIE 9239, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI, 92391D (21 October 2014); doi: 10.1117/12.2067143
Show Author Affiliations
N. Miura, The Univ. of Tokyo (Japan)
Y. Asano, The Univ. of Tokyo (Japan)

Published in SPIE Proceedings Vol. 9239:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVI
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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