Subsidence Monitoring Using Lidar and Morton Code Indexing
Publication: Journal of Surveying Engineering
Volume 142, Issue 1
Abstract
Point-based data-acquisition technology in traditional survey engineering does not provide complete data about mining-induced subsidence basins. To overcome these shortcomings, this research applied light detection and ranging (LIDAR) data, obtained with a terrestrial laser scanner (TLS) for monitoring the surface deformation of mining areas, to acquire full data about mining-induced subsidence basins. First, to improve the organization efficiency of LIDAR data, the decimal Morton code–based indexing method was proposed for discrete-grid indexing to organize LIDAR data according to original point coordinates, to avoid the generation of grids without data, and to build a topological relationship among scattered points. Thus, this approach enabled highly efficient access of LIDAR data and restoration of coordinates for each point. In the end, the processed data were applied in engineering practice. The subsidence curves of two sections of the subsidence basin, in both strike and dip directions measured with a high-grade control survey, were compared with the subsidence curves extracted from the LIDAR data. The good coincidence effect suggests that using LIDAR data to build the subsidence basin model can result not only in richer surface-deformation information than the traditional methods but also in higher monitoring precision.
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Acknowledgments
This research has been supported by the National Natural Science Foundation of China (Grant 41361078) and the Ph.D. research startup foundation of Jiangxi University of Science and Technology (Grant jxxjbs15005).
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Published In
Journal of Surveying Engineering
Volume 142 • Issue 1 • February 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 29, 2013
Accepted: Oct 24, 2015
Published online: Dec 16, 2015
Published in print: Feb 1, 2016
Discussion open until: May 16, 2016
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