Liu, Xiaoye and Zhang, Zhenyu (2011) Effects of LiDAR data reduction and breaklines on the accuracy of digital elevation model. Survey Review, 43 (323). pp. 614-628. ISSN 0039-6265
Abstract
This paper explores the effects of LiDAR data reduction on the accuracies of produced TINs and gridded DEMs. It examined to what extent a set of LiDAR (light detection and ranging) data can be reduced without sacrificing the accuracy of produced terrain model. A primary focus was on the integration of breaklines to the reduction process to assess the contribution of breaklines to improving
the accuracy of terrain models in data reduction. A series of TINs and gridded DEMs were produced and assessed at reduced data density levels with and without breaklines respectively. The results showed that LiDAR data can be reduced to a certain level without significantly decreasing the accuracy of produced terrain models. When incorporating breaklines into terrain modelling, the accuracy of produced TINs and gridded DEMs decreased only slightly as data density decreased, indicating that breaklines made a significant contribution to improving the accuracy of terrain models in data reduction.
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | © 2011 Survey Review Ltd. Permanent restricted access to published version due to publisher copyright policy. Scopus error 2-s2.0-80053366294 (Sep 2011) |
| Faculty / Department / School: | Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information |
| Date Deposited: | 14 Oct 2011 03:15 |
| Last Modified: | 17 Dec 2014 01:38 |
| Uncontrolled Keywords: | LiDAR; DEM; digital elevation model; data reduction; breaklines |
| Fields of Research : | 04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040699 Physical Geography and Environmental Geoscience not elsewhere classified 09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling |
| Socio-Economic Objective: | D Environment > 96 Environment > 9606 Environmental and Natural Resource Evaluation > 960604 Environmental Management Systems |
| Identification Number or DOI: | 10.1179/003962611X13117748892317 |
| URI: | http://eprints.usq.edu.au/id/eprint/19714 |
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